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Yesterday Fred Giasson announced the release of code associated with Structured Dynamics‘ open source semantics components (also called sComponents). A semantic component is an ontology-driven component, or widget, based on Flex. Such a component takes record descriptions, ontologies and target attributes/types as inputs and then outputs some (possibly interactive) visualizations of the records.
Though not all layers are by any means complete, from an architectural standpoint the release of these semantic components provides the last and missing layer to complete our open semantic framework. Completing this layer now also enables Structured Dynamics to rationalize its open source Web sites and various groups and mailing lists associated with them.
We first announced the open semantic framework — or OSF — a couple of weeks back. Refer to that original post for more description of the general design [1]. However, we can show this framework with the semantic components layer as illustrated by what some have called the “semantic muffin”:
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The OSF stack consists of these layers, moving from existing assets upward through increasing semantics and usability:
Not all of these layers are required in a given deployment and their adoption need not be sequential or absolutely depend on prior layers. Nonetheless, they do layer and interact with one another in the general manner shown.
Current semantic components, or widgets, include: filter; tabular templates (similar to infoboxes); maps; bar, pie or linear charts; relationship (concept) browser; story and text annotator and viewer; workbench for creating structured views; and dashboard for presenting pre-defined views and component arrangements. These are generic tools that respond to the structures and data fed to them, adaptable to any domain without modification.
Though Fred’s post goes into more detail — with subsequent posts to get into the technical nuances of the semantic components — the main idea of these components is shown by the diagram below.
These various semantic components get embedded in a layout canvas for the Web page. By interacting with the various components, new queries are generated (most often as SPARQL queries) to the various structWSF Web services endpoints. The result of these requests is to generate a structured results set, which includes various types and attributes.
An internal ontology that embodies the desired behavior and display options (SCO, the Semantic Component Ontology) is matched with these types and attributes to generate the formal instructions to the semantic components. These instructions are presented via the sControl component, that determines which widgets (individual components, with multiples possible depending on the inputs) need to be invoked and displayed on the layout canvas. Here is a picture of the general workflow:
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New interactions with the resulting displays and components cause the iteration path to be generated anew, again starting a new cycle of queries and results sets. As these pathways and associated display components get created, they can be named and made persistent for later re-use or within dashboard invocations.
As the release of the semantic components drew near, it was apparent that releases of previous layers had led to some fragmentation of Web sites and mailing lists. The umbrella nature of the open semantic framework enabled us to consolidate and rationalize these resources.
Our first change was to consolidate all OSF-related material under the existing OpenStructs.org Web site. It already contained the links and background material to structWSF and irON. To that, we added the conStruct and OSF material as well. This consolidation also allowed us to retire the previous conStruct Web site as well, which now re-directs to OpenStructs.
We also had fragmentation in user groups and mailing lists. Besides shared materials, these had many shared members. The Google groups for irON, structWSF and conStruct were thus archived and re-directed to the new Open Semantic Framework Google group and mailing list. Personal notices of the change and invites have been issued to all members of the earlier groups. For those interested in development work and interchange with other developers on any of these OSF layers, please now direct your membership and attention to the OSF group.
There has also been a revigoration of the developers’ community Web site at http://community.openstructs.org/. It remains the location for all central developer resources, including bug and issue tracking and links to SVNs.
Actual code SVN repositories are unchanged. These code repositories may be found at:
We hope you find these consolidations helpful. And, of course, we welcome new participants and contributors!
Structured Dynamics has been in a fervent — and, we believe, fruitful — design phase for the past 18 months. All of the working parts related to how to embrace becoming a semantic enterprise have now been defined and designed. Actual tools and components accompany many of these parts and have been deployed.
Recently, I have been speaking and blogging much about rationale, process, mindset and approach for how to bring semantics into the organization. But, prior to now, we have not spoken much about the overall design behind our approach. Today, as we complete our design phase and introduce our first exemplar instance of it — Citizen Dan [1] — we are finally in a position to describe this overall approach.
We term our approach the open semantic framework, also OSF. The open semantic framework is a combination of a layered architecture and modular software. The open semantic framework represents the software component of the four-component total open solution, recently described in a three part series. I return to this topic in the conclusion of this post.
Over the past nine months, I have been focusing my writing largely on the semantic enterprise, with more specificity regarding our Open SEAS (Semantic Enterprise Adoption and Solutions) initiative. In bits and pieces, these writings have tended to reflect a number of objectives:
To date, the result of these design objectives is perhaps best captured in my Seven Pillars of the Open Semantic Enterprise posting, as well as our general discussions regarding adaptive ontologies. Yet, still, these writings have been somewhat piecemeal. What this document attempts to do is to place all of these perspectives into a single, coherent whole.
Structured Dynamics has been a strong advocate for layered architectures, with clear APIs between layers as appropriate. But these layers are not “laminates” that completely cover the layer below, nor are they all needed or necessary. Depending on the circumstance, some layers are unneeded or superfluous. Layers may be added or not incrementally.
In this manner, then, the open semantic framework is perhaps more akin to a pearl, than to a laminate or cocoon. Each subsequent layer does not “embed” the layer prior to it, and some layers actually may inter-operate with multiple layers below or above it (this is notably true for the “ontologies” layer, which has interactions up and down the stack).
Nonetheless, we can envision this pearl of the open semantic framework and its layers as follows:
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Others have termed this the “semantic muffin” or even “semantic muppet” or “semantic blob”. Whatever (hehe). The real idea is that layers may accrete (as in the growth of a pearl) and occur over time and be uneven. Each layer, though, does have a role to play (though it may not be needed in a given deployment), and does act to augment existing information assets in the transition to a semantic framework. Beginning at the core, each of these layers — with external references as appropriate for more details — is described below.
The open semantic framework is premised on leveraging existing information assets. Sure, once the framework is in place, new information can be brought into it in a more direct, semantic manner. But, the real thrust and benefit of this framework is to provide an incremental pathway for finally inter-operating and federating prior decades of data, structure and information assets.
These information assets may reside inside or outside the enterprise. They may (and DO!) exist in many formats and are described by many schema. They may come from internal transaction systems or warehouses, or may exist external on the Web or at supplier or partner sites. These information assets may span from conventional databases and relational data systems to XML interchange standards, Web pages and standard internal text or documents. In short, there is NO information asset that is not amenable to be included in this framework.
The information transformation layer provides either: 1) extraction of concepts and entities as structured metadata from source text or documents; or 2) conversion of existing data assets to interoperable form. As implemented by Structured Dynamics, the extractions are conducted by either scones (Subject Concept or Named EntitieS) or third-party utilities, and the conversions occur via irON (instance record Object Notation) or third-party “RDFizers“.
Depending on the source, the net result of the transformation is to produce interoperable data and information that can be ingested and used by other layers in the framework.
Though not strictly analogous, this layer bears some resemblance to the ETL (extract, transfer, load) utilities used in many enterprise information integration applications. Unlike those conventional systems, this information transformation layer also may capture and represent some of the source schema.
In all cases, however, these transformations are relatively simple and get parsed against the available structure (the ontologies, schema and entity reference lists) in the system to generate the semantic metadata (tags).
At this point, the extracted structure is generally at the level of instance records, or the ABox, with simple assertions of attribute-value pairs for specific records [2]. Little schema transformation or mapping occurs at this layer (if such is needed, that occurs at the structWSF layer; see next). Actual federation or interoperation occurs at later layers based on the TBox structures [2].
This modular portion of the framework is explicitly designed with APIs to allow third-party tools to be plugged in and substituted.
The major workhorse of the open semantic framework is the structWSF (Web services framework) layer. structWSF is the most complicated of the OSF layers and has many supporting software packages and capabilities. The structWSF layer provides the standard, common interface (”canonical”) layer by which existing information assets get represented and presented to the outside world and to other layers in the OSF stack.
structWSF is a platform-independent Web services framework for accessing and exposing structured RDF data. Its central organizing perspective is that of the dataset. These datasets contain instance records, with the structural relationships amongst the data and their attributes and concepts defined via ontologies (schema with accompanying vocabularies; see below).
The structWSF middleware framework is generally RESTful in design and is based on HTTP and Web protocols and open standards. The current structWSF framework comes packaged with a baseline set of about twenty Web services in CRUD, browse, search and export and import. All Web services are exposed via APIs and SPARQL endpoints. Each request to an individual Web service returns an HTTP status and optionally a document of resultsets. Each results document can be serialized in many ways, and may be expressed as either RDF or pure XML. An internal representation, structXML [3], is used for internal communications across all structWSF Web services and with other layers.
structWSF has a central service that governs access rights and permissions. These rights occur at the level of the dataset, which gives immense flexibility to how data may be accessed, read, modified, created or deleted (or not). Datasets within a given structWSF instance may be accessed directly via API or via SPARQL queries to the instance’s endpoint. Depending on rights and query, results sets may be returned from a given structWSF instance in an infinite variety of ways.
This latter capability is the essential interface for subsequent layers in the open semantic framework stack. Depending on those subsequent components, pre-staged data and results sets may be returned for an essentially limitless variety of purposes.
Each structWSF instance also has a unique Web address that enables one or a multitude of instances to communicate and share with one another. This simple, but elegant, method enables structWSF instances to participate or not in potentially global or restricted local networks and collaboration environments. This is currently the largest untapped potential of structWSF with respect to its existing deployments.
The newest layer in the stack is the semantic components layer. This layer takes results sets — most often generated by a specific query or data slice request — from one or more structWSF instances and then presents that information via a variety of data visualization or data presentation widgets (what we specifically call ‘semantic components‘ due to their design [4]). The operation and sensitivity of these display components are themselves driven by a presentation and data analysis (including statistics) ontology.
Current display widgets include: filter; tabular templates (similar to infoboxes); maps; bar, pie or linear charts; relationship (concept) browser; story and text annotator and viewer; workbench for creating structured views; and dashboard for presenting pre-defined views and component arrangements. These are generic tools that respond to the structures and data fed to them, adaptable without modification to any domain.
As presently implemented by Structured Dynamics, this layer consists either of Flex data visualization components or structured data display templates based on Smarty. The inherent design allows for updates to other bases (such as HTML5). The layer may also be swapped out or substituted with third-party capabilities.
The strength and power of this system is governed by its own ontology, the Semantic Component Ontology (SCO) (see next).
This is an extremely flexible layer in the open semantic framework stack. Expect an ongoing series of explanatory blog posts and online resources in the upcoming weeks to explain this innovative capability.
The ontologies layer actually refers to all structured assets driving the system. As such, this layer might be considered the “brain” (though rather simply specified!) of the open semantic framework.
At a true schema or TBox level [2], the ontologies layer represents the concept and relationships of the domain at hand. This layer also hosts the specific local entities and prominent things (people, places, events, etc.) useful for extracting local and domain-specific relevance. However, those views are also supplemented with some administrative ontologies (two examples are SCO and irON) that guide how the user interfaces or widgets in the system should behave.
The concept level represents the “world view” of the specific instantiation of the open semantic framework at hand. This conceptual (TBox) view provides the structural organization of information, inferencing capabilities, and navigation, faceting and explorer structure. The entity (ABox) view provides tagging for prominent individuals and instances important to the domain at hand, and guides the structure behind data visualizations of attribute or indicator data.
The administrative level uses simple roles and relationships for attributes and indicators to inform the framework as to how and with what widget to display information. For example, a “type” of information that is geographically related can be instructed to use the map component as an option for display. Whether some information is used for totals, comparison purposes, or other specifications useful to data visualization and graphing may also be specified.
The language and relationships (predicates or properties) of these administrative ontologies are simple and straightforward. It is, for example, relatively easy to define data display functions at the broad dataset and attributes level. Simple determinations drive how results sets and their associated results types may be displayed, no matter what datasets or slices may be generated as a result of the queries or requests fed to the system.
The structure in these layers can be replaced by other structures for other instantiations and circumstances. Indeed, all other layers in the open semantic framework can remain relatively fixed while tailoring the instance to new domains solely via this layer. The ontologies layer is what gives any given instantiation of OSF — such as Citizen Dan — its unique focus and scope.
The thinnest layer (that is, least substantial with respect to this framework) is the content management system (CMS) layer. In its current form, the open semantic framework uses the Drupal CMS via our conStruct plug-in modules. The design of the framework, however, has explicitly accommodated the possibility that other CMSs may substitute for this role.
The CMS layer is optional if structWSF endpoints are sufficient or if simple Web pages hosting semantic components are deemed as adequate. Very small organizations or deployments may reasonably choose to have no CMS layer at all.
However, for most sites or portals with more than a few active users, it is desirable to have broad flexibility in theming (”skinning”), user rights and permissions, or other functionality. These are the roles of the CMS layer. Drupal, for example, is presently supported by more than 4500 third-party modules in every conceivable function, from polling to blogs and rating systems and bulletin boards.
For such generalized portals or collaboration environments, it makes sense to adopt and install a flexible CMS system, such as Drupal. Much of the user experience and functional environment can be provided through such means.
The open semantic framework is thus designed to reside easily in a CMS while also providing the hooks to take advantage of the generalized user rights and functionality of the CMS. In this manner, the open semantic framework is able to stay focused on its structured data and interoperability purposes, while still gaining the advantages of rich-featured content management systems.
With its inherent open-world orientation [5] and distributed and collaborative potential, the open semantic framework was designed from the outset to be Web-capable and Web-oriented:
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A Web-oriented architecture (WOA) has a number of understood requirements, to which the open semantic framework adheres. Specifically, these design considerations support the framework as being part of WOA:
Citizen Dan is our first exemplar instance of this open semantic framework. The details page for the project goes into some of Citizen Dan’s functionality and capabilities.
Citizen Dan is specifically geared to local governments and localities, with an emphasis on community indicator systems (CIS). CIS have become a popular way of measuring and tracking measures of local economic and social well-being; they are closely related to sustainability and how to measure it as used in many economic and environmental domains.
However, in the context of this post, what is really interesting about Citizen Dan is that its semantic framework is a completely open and generic one. The same set of tools and capabilities described on its details page can be applied to any domain that needs to manage and understand information in its own domain. This includes from unstructured text or documents to conventional structured databases.
What changes from domain to domain are the data structures (the ontologies, schema and entity reference lists; see above) that are fed to this open semantic framework. By swapping out new structures, what can be called Citizen Dan in one instance can morph to become Curriculum Carla in say, the education instance or Doctor Doolittle in the veterinary science instance [6].
We can illustrate these multiple instances as follows:
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What this figure illustrates is that even a branded expression of the framework — such as Citizen Dan — is merely an instance of that framework. And, actually, when expressed in such a packaged manner, we can more accurately call the standard and bundled suite of generic functions and accompanying structure of Citizen Dan as an instantiation of the open semantic framework:
in·stan·ti·a·tion \in-‘stan(t)-shē-ā-shən\ (noun) [7]
By replacing the structure bases, and by tailoring the function suite appropriate to a given market and use, we can create many instantiations of the open semantic framework for different domains and markets. In this manner, Citizen Dan can be seen as an early exemplar of the framework, but not as a definer and limiter to it.
So far, this discussion has focused solely on considerations of software and architecture. While we see the power of the open semantic framework, highly useful in itself, this is inadequate alone to achieve acceptance and success in the enterprise (as we noted in our most recent posts). The very forces that are compelling enterprises to look at new options, are also the same ones that pose difficult hurdle rates for acceptance of open source.
To address this issue, we have developed a four-legged foundation to what we termed the total open solution. The solution involves software, structure, documentation and methods (or best practices). Each of these connect and relate to the other foundations.
The open semantic framework is clearly the software (and architecture) leg to this foundation. Again, however, what is interesting is that the mere swapping out of the structure can also make the system relatively ready for other domains.
We see these relationships in the following diagram, that also shows that the DocWiki portions of the solution embody the documentation (aside from code-level comments) and methods legs of the foundation:
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Differences between domains may also lead to differences as to which components are included or not in that domain’s desired instantiation.
The hugely important implied point, however, from the diagram above, is to show how nearly universal the content and methods in the DocWiki may be to other domains. Because the deltas between domains largely result from structure and what specific functional components are included or not, it becomes clear that most documentation and practices shared with the DocWiki will be applicable across domains. Sure, the use cases and some of the specific terminology may change, but we can also now see a high degree of re-usability of documentation and knowledge base across markets. This realization makes the usefulness and leverage of the DocWiki even higher.
Developing “common language” by which to describe and convey things — especially new things like semantics that also have strong technical aspects — is tough, very tough. We are only now beginning on this process; we look to many in the community and elsewhere to help define informative and evocative terminology.
Per the original design objectives above, Structured Dynamics has approached the challenge of the semantic enterprise in what we think is both a pragmatic and a new way. The insistence on preserving and respecting existing information assets, matched with the opportunities and different mindsets arising from an open-world approach [5], have necessitated thinking through new designs and developing new concepts. Any time such new thinking and concepts occurs, new language and new metaphors must accompany it.
While certainly there are components and various software packages that populate and comprise an open semantic framework, the framework is also just as importantly a world view or way to think about information, information development, and its architecture. For example, a pivotal concept is that an open semantic framework is built around generic tools responsive to the information structures fed to them. This realization shifts the locus of emphasis from software development per se to creating, managing and adapting data and information structures. While this democratizes the information development process and is more inclusive of all knowledge workers, it also imposes needs for new toolsets and business processes. We are only at the nascent stages of understanding and learning about these differences.
Similarly, a development approach that is inherently incremental and leverages (rather than replaces or displaces) existing information assets means IT projects need to be considered in a new light. Small projects with more emphasis on tangible and demonstrable benefits will alter budgets, lower risks, and place a need for quicker turnaround. Like the architecture of the open semantic framework itself, projects based on OSF are also more distributed, decentralized and modular.
With such decentralization also comes the need for mechanisms and systems to overcome vendor “lock-in” and proprietary systems. A key thrust in support of what we have called the total open solution and its mixture of documentation and methods to accompany software and structure is specifically targeted at this issue. Tools and means for collaboration and concurrent contributions are another possible answer. Prior software practices in agile development and version control will see extensions to all manner of information development across the enterprise.
We are proud of our design work and proof-testing with clients over the past 18 months. We believe the open semantic framework and its implications to be a fundamental shift in how organizations need to think about their information development, existing information assets, and IT budgets and processes. We know widescale adoption is not yet at hand — enterprises are justifiably conservative when it comes to new thinking. But, given global competition and tight pocketbooks, the open semantic framework is a formulation to which enterprises and governments should pay very close attention.
Ten years ago the message was the end of obscene rents from proprietary enterprise software licenses. Five years ago the message was the arrival and fast maturing of open source. Today, the message is the open world and semantics.
These forces are conspiring to change much within enterprise IT. And, this change will undoubtedly be for the good — for the enterprise. But these forces are not necessarily good news within conventional IT departments and definitely not for traditional vendors unwilling to transform their business models.
I have been beating the tom-tom on this topic for a few months, specifically in regards to the semantic enterprise. But I have by no means been alone nor unique. The last two weeks have seen an interesting confluence of reports and commentaries by others that richen the story of the changing information technology landscape. I’ll be drawing on the observations of Thomas Wailgum (CIO magazine) [1], John Blossom [2] and Andy Mulholland, CTO of Capgemini [3].
Wailgum describes the “New Normal” and how it might kill IT [1]. He picks up on the viewpoint that ties the recent meltdowns in the financial sector as a seismic force for changes in information technology. While he acknowledges many past challenges to IT from PCs and servers and Y2K and software becoming a commodity, he puts the global recession’s impact on business — the “New Normal”– into an entirely different category.
His basic thesis is that these financial shocks are forcing companies to scrutinize IT as never before, in particular “unfavorable licensing agreements and much-too-much shelfware; ill-conceived purchasing and integration strategies; and questionable software married to entrenched business processes.”
Yet, he also argues that IT and its systems are too ingrained into the core business processes of the enterprise to be allowed to fail. IT systems are now thoroughly intertwined with:
But top management is disappointed and disaffected. IT systems gobble up too many limited resources. They are inflexible. They are old and require still more limited resources to modernize. They are complex. They create and impose delays. And all of these negatives lead to huge losses in opportunity costs. Wailgum notes Gartner, for instance, as saying that by 2012 perhaps 20 percent of businesses will own no IT assets at all in their desire to outsource this headache.
I think this devastating diagnosis is largely correct, though perhaps incomplete in that no mention is made of the flipside: what IT has failed to deliver. I think this flipside is equally damning.
Despite decades of trying, IT still has not broken down the data stovepipes in the enterprise. Rather, they have proliferated like rabbits. And, IT has failed to unlock the data in the 80% of enterprise information contained within documents (unstructured data).
Unfortunately, after largely zeroing in and mostly diagnosing the situation, Wailgum’s remedy comes off sounding like a tired 12-step program. He argues for new mindsets, better communications, getting in touch with customers, being willing to take risks, and being nimble. Well, duh.
So, over the decades of IT failures there has been accompanying decades of criticism, hand-wringing, and hackneyed solutions. Without some more insightful thinking, this analysis can make our understanding of the New Normal look pretty old.
John Blossom [2] picks up on these arguments and looks at the issues from the vendor’s perspective. Blossom characterizes Wailgum’s piece as “outlining the enormous value gap that’s been arising in enterprise information technologies.” And, while clearly new approaches are needed and farming them out may become more prevalent, Blossom cautions this is not necessarily good news for vendors.
As Blossom puts it, “what seems to be happening is that many of the business processes through which these enterprises survived and thrived over the past several decades are shooting blanks. . . . many of the fundamental concepts of IT that have been promoted for the past few decades no longer give businesses operational advantages but they have to keep spending on them anyway.”
As he has been arguing for quite some time, one fundamental change agent has been the Web itself. “The Web has accelerated the flow of information and services that can lead to effective decision-making far more rapidly than enterprise IT managers have been able to accommodate.”
Web search engines and social media tools can begin to replace some of the dedicated expenditures and systems within the enterprise. Moreover, the extent, growth and value of external data and content is readily apparent. Without outreach and accommodation of external data — even if it can solve its own internal data federation challenges — the individual enterprise is at risk of itself becoming a stovepipe.
Prior focuses on strategy and capturing workflows are perhaps being supplanted by the need for operational flexibility and on-the-fly aggregation and rapid service development tools. In an increasingly interconnected and rapidly changing world with massive information growth, being able to control workflows and to depend on central IT platforms may become last decade’s “Old Normal.” Floating on top of these massive forces and riding with their tides is a better survival tactic than digging fixed emplacements in the face of the tsunami.
These factors of Web, open source, agnosticism as to platform or software applications, and the need to mash up innovations from anywhere are not the traditional vendor game. Just as businesses and their IT departments must get leaner, so must the expectation of vendors to extort exorbitant rents from their clients. “Fasten your seatbelts, it’s going to be a bumpy night!” [4]
So, Blossom agrees with the Wailgum diagnosis, but also helps us begin to understand parts of the cure. Blossom argues the importance of:
Much, if not all of this, can be provided by open source. But open source is not a sine qua non: commercial products that embrace these approaches can also be compatible components across the stack.
But — even with these components — a full cure still lacks a couple of crucial factors.
These remaining gaps are emphasized in Andy Mulholland’s recent blog post [3]. His post was occasioned by the press announcement that Structured Dynamics (my firm) had donated its Semantic Enterprise Adoption and Solutions, or SEAS, methodology to MIKE2.0 [5]. Mulholland was suggesting his audience needed to know about this Method for an Integrated Knowledge Environment because some of the major audit partnerships have decided to get behind MIKE2.0 with its explicit and open source purpose of managing knowledge environments and their data and provenance.
As Mulholland notes, “. . . it’s not just more data, it’s the forms of data, and what the data is used for, all of which add to the complications. . . . Sadly the proliferation of data has mostly been in unstructured data in formats suitable for direct human use.”
So, one remaining factor is thus how to extract meaning from unstructured (text) content. It is here that semantics and various natural language processing (NLP) components come in. Implied in the incorporation of data extracted from unstructured sources is a data model expressly designed for such integration.
Yet, without a fulcrum, the semantic lever can still not move the world. Mulholland insightfully nails this fundamental missing piece — the “most crucial issue” — as the use of the open world assumption.
From an enterprise perspective and in relation to the points of this article, an open world assumption is not merely a different way to look at the world. More fundamentally, it is a different way to do business and a very different way to do IT.
I have summarized these points before, but they deserve reiteration. Open world frameworks provide some incredibly important benefits for knowledge management applications in the enterprise:
Archimedes is attributed to the apocryphal quote, “Give me a lever long enough and a fulcrum on which to place it, and I shall move the world.” [6] I have also had lawyer friends tell me that the essence of many court cases is found in a single pivotal assertion or statement in the arguments. I think it fair to say that the open world approach plays such a central role in unlocking the adaptive way for IT to move forward.
As Mulholland notes, we have donated our Open SEAS methodology [7] to MIKE2.0 in the hopes of seeing greater adoption and collaboration. This is useful, and all are welcome to review, comment and contribute to the methodology, indeed as is the case for all aspects of MIKE2.0.
But the essential point of this article is that Open SEAS also embraces most — if not all — of the factors necessary to address the New Normal IT function.
Open SEAS is explicitly designed to facilitate becoming an open semantic enterprise. Namely, this means an organization that uses the languages and standards of the semantic Web, including RDF, RDFS, OWL, SPARQL and others to integrate existing information assets, using the best practices of linked data and the open world assumption, and targeting knowledge management applications. It does so based on Web-oriented architectures and approaches and uses ontologies as an “integration layer” across existing assets.
The foundational approaches to the open semantic enterprise do not necessarily mean open data nor open source (though they are suitable for these purposes with many open source tools available). The techniques can equivalently be applied to internal, closed, proprietary data and structures. The techniques can themselves be used as a basis for bringing external information into the enterprise. ‘Open’ is in reference to the critical use of the open world assumption.
These practices do not require replacing current systems and assets; they can be applied equally to public or proprietary information; and they can be tested and deployed incrementally at low risk and cost. The very foundations of the practice encourage a learn-as-you-go approach and active and agile adaptation. While embracing the open semantic enterprise can lead to quite disruptive benefits and changes, it can be accomplished as such with minimal disruption in itself. This is its most compelling aspect.
We believe this offers IT an exciting, incremental and low-risk path for moving forward. All existing assets can be left in place and — in essence — modernized in place. No massive shifts and no massive commitments are required. As benefits and budgets allow, the extent of the semantic interoperability layer may be extended as needed and as affordable.
The open semantic enterprise is not magic nor some panacea. Simply consider it as bringing rationality to what has become a broken IT system. Embracing the open semantic enterprise can help the New Normal be a good and more adaptive normal.
Today, Structured Dynamics is pleased to release Open SEAS, its methodology for Semantic Enterprise Adoption and Solutions. At the same time, we are donating the framework to the open source MIKE2.0 Method for an Integrated Knowledge Environment project.
Open SEAS provides a framework for the enterprise to establish a coherent, consistent and interoperable layer across its information assets. It is compliant with the MIKE2.0 Semantic Enterprise Solution Offering.
Open SEAS has been developed for enterprises desiring to initiate or extend their involvement with semantic technologies. It is inherently incremental, low-cost and low-risk.
Concurrent with this release, Structured Dynamics is also donating the methodology and all of its related intellectual assets to the MIKE2.0 project. Under Creative Commons license and MIKE2.0’s content governance policies, the community’s current 2000+ members are now free to expand and use the Open SEAS methodology in any manner they see fit.
Last week, I began to introduce MIKE2.0 and its methodology to the readers of this blog. MIKE2.0 provides a complete delivery environment and methodology for information management projects in the enterprise. Solutions — from the specific to the composite — are described and packaged with respect to plans, management communications, products (open source and proprietary), activities, benchmarks, and deliverables. Delivery is accomplished over multiple increments, split into five phases from definition and planning to deployment. The assets associated with this framework first are based on templates and guidelines that can be applied to any information management area. The framework allows for multiple projects to be combined and inter-related, all under a common methodology. More information and a good entry point is provided on the What is MIKE2.0? page on the project’s main Web site.
MIKE2.0 presently has some 800 resources across about 40 solution areas. With Structured Dynamics’ donation, there are now about 40 resources related to the semantic enterprise, many of them major, accompanied by many images and figures. This contribution makes the Semantic Enterprise Solution Offering instantly one of the more complete within MIKE2.0. As noted below, this contribution is also just a beginning of our commitment.
The Open SEAS framework is Structured Dynamics’ specific implementation framework for MIKE2.0’s Semantic Enterprise Solution Offering. This section overviews some of Open SEAS‘ key facets.
Many enterprise information systems, particularly relational ones, embody a closed world assumption that holds that any statement that is not known to be true is false. This premise works well where there is complete coverage of specific items, such as the enumeration of all customers or all products.
Yet, in most areas of the real (”open”) world there is no guarantee or likelihood of complete coverage. Under an open world assumption the lack of a given assertion or fact does not imply whether that possible assertion is true or false: it simply is not known. An open world assumption is one of the key factors that defines the open Semantic Enterprise Offering and enables it to be deployed incrementally. It is also the basis for enabling linkage to external (often incomplete) datasets.
Fortunately, there is no requirement for enterprises to make some philosophical commitment to either closed- or open-world systems or reasoning. It is perfectly acceptable to combine traditional closed-world relational systems with open-world reasoning. It is also not necessary to make any choices or trade-offs about using public v. private data or combinations thereof. All combinations are acceptable when the basis for integration is an open-world one.
Open SEAS is grounded in this “open” style. It can be employed in virtually any enterprise circumstance and at any scope, and expanded in a similar way as budget and needs allow.
Open SEAS is based on seven pillars, which themselves inform the basis for the MIKE2.0 Guiding Principles for the Open Semantic Enterprise. These principles cover data model, architecture, deployment practices and approach for how an enterprise can begin and then extend its use of semantics for information interoperability.
Important aspects are linked data or Web-oriented architecture, but it is really the unique combination of open-world approach and the RDF data model and its semantic power that provide the distinctive differences for Open SEAS. An exciting prospect — but still in its early stages of discovery and implementation — is the role of adaptive ontologies to power ontology-driven applications. These prospects, if fully realized, could totally remake how knowledge workers interact and specify the applications that manage their information environment.
Open SEAS also fully embraces the Layered Semantic Enterprise Architecture of MIKE2.0’s Semantic Enterprise Offering. This architecture acts as a subsequent set of functions or middleware with respect to the MIKE2.0’s standard SAFE Architecture. Most of the existing SAFE architecture resides in the Existing Assets layer. The specific aspects of Open SEAS resides in the layers above, namely Access/Conversion, Ontologies and the Applications Layers.
Stitching together this interoperability layer above existing information and infrastructure assets requires many diverse tools and products, and there still are gaps. The layer figure below shows the semantic enterprise architecture overlaid with some representative open source projects and tools that plug some of those gaps.
Open SEAS also maintains a comprehensive roster of open source and proprietary tools in all aspects of semantic technology, ranging from data storage and converters, to Web services and middleware, and then to ultimate user applications. A database of nearly 1,000 tools in all areas is maintained for potential applicability to the methodology.
The inherently incremental nature of the Open SEAS framework encourages experimentation, affordable deployments, and experience gathering. Because the systems and deployments put into place with this framework are based on the open world approach and use the extensible RDF data model, expansions in scope, sophistication or domain can be incorporated at any time without adverse effects on existing assets or systems or prior Open SEAS deployments.
Quick and (virtually) risk-free increments means that adopting semantic approaches in the enterprise can be accelerated (or not) based on empirical benefits and available budgets.
The Open SEAS framework is built on a solid foundation, but it also one that is incomplete. Deployments of semantic technologies and approaches are still quite early in the enterprise, whether measured in numbers, scope or depth. In order for the framework — and the practice of semantic adoption in general — to continue to expand and be relevant in the enterprise, active learning and documentation is essential. One of the reasons for the affiliation of Open SEAS with MIKE2.0 is to leverage these strong roots in methodological learning.
The nature of Open SEAS and its parent Semantic Enterprise Solution Offering touches most offerings within the MIKE2.0 framework. There is much to be done to integrate the semantic enterprise perspective into these other possibilities, plus much that needs to be learned and documented for the offering itself. The concept of the semantic enterprise, after all, is relatively new with few prominent case studies.
As the offering points out, there are some dozens of addition necessary resources that are available and ready to be packaged and moved into the MIKE2.0 framework. These efforts are a priority, and will continue over the coming weeks.
But, more importantly, beyond that, the experience and practitioner base needs to grow. Much is unknown regarding key aspects of the offering:
Despite these questions, emergence is the way complex systems arise out of a multiple of relatively simple interactions, exhibiting new and unforeseen properties in the process. RDF is an emergent model. It begins as simple “fact” statements of triples, that may then be combined and expanded into ever-more complex structures and stories. As an internal, canonical data model, RDF has advantages for information federation and development over any other approach. It can represent, describe, combine, extend and adapt data and their organizational schema flexibly and at will. Applications built upon RDF can explore and analyze in ways not easily available with other models.
Combined with an open-world approach, new information can be brought in and incorporated to the framework step-by-step. Perhaps the greatest promise in an ongoing transition to become a semantic enterprise is how an inherently incremental and building-block approach might alter prior practices and risks across the entire information management spectrum.
We invite you to join us and to contribute to this effort. I encourage you to join MIKE2.0 if you have not already done so, and check out announcements on this blog for ongoing developments.
The beginning of a new year and a new decade is a perfect opportunity to take stock of how the world is changing and how we can change with it. Over the past year I have been writing on many foundational topics relevant to the use of semantic technologies in enterprises.
In this post I bring those threads together to present a unified view of these foundations — some seven pillars — to the open semantic enterprise.
By open semantic enterprise we mean an organization that uses the languages and standards of the semantic Web, including RDF, RDFS, OWL, SPARQL and others to integrate existing information assets, using the best practices of linked data and the open world assumption, and targeting knowledge management applications. It does so using some or all of the seven foundational pieces (”pillars”) noted herein.
The foundational approaches to the open semantic enterprise do not necessarily mean open data nor open source (though they are suitable for these purposes with many open source tools available [3]). The techniques can equivalently be applied to internal, closed, proprietary data and structures. The techniques can themselves be used as a basis for bringing external information into the enterprise. ‘Open’ is in reference to the critical use of the open world assumption.
These practices do not require replacing current systems and assets; they can be applied equally to public or proprietary information; and they can be tested and deployed incrementally at low risk and cost. The very foundations of the practice encourage a learn-as-you-go approach and active and agile adaptation. While embracing the open semantic enterprise can lead to quite disruptive benefits and changes, it can be accomplished as such with minimal disruption in itself. This is its most compelling aspect.
Like any change in practice or learning, embracing the open semantic enterprise is fundamentally a people process. This is the pivotal piece to the puzzle, but also the one that does not lend itself to ready formula about pillars or best practices. Leadership and vision is necessary to begin the process. People are the fuel for impelling it. So, we’ll take this fuel as a given below, and concentrate instead on the mechanics and techniques by which this vision can be achieved. In this sense, then, there are really eight pillars to the open semantic enterprise, with people residing at the apex.
This article is synthetic, with links to (largely) my preparatory blog postings and topics that preceded it. Assuming you are interested in becoming one of those leaders who wants to bring the benefits of an open semantic enterprise to your organization, I encourage you to follow the reference links for more background and detail.
A Review of the BenefitsOK, so what’s the big deal about an open semantic enterprise and why should my organization care?
We should first be clear that the natural scope of the open semantic enterprise is in knowledge management and representation [1]. Suitable applications include data federation, data warehousing, search, enterprise information integration, business intelligence, competitive intelligence, knowledge representation, and so forth [2]. In the knowledge domain, the benefits for embracing the open semantic enterprise can be summarized as greater insight with lower risk, lower cost, faster deployment, and more agile responsiveness.
The intersection of knowledge domain, semantic technologies and the approaches herein means it is possible to start small in testing the transition to a semantic enterprise. These efforts can be done incrementally and with a focus on early, high-value applications and domains.
There is absolutely no need to abandon past practices. There is much that can be done to leverage existing assets. Indeed, those prior investments are often the requisite starting basis to inform semantic initiatives.
Embracing the pillars of the open semantic enterprise brings these knowledge management benefits:
Moreover, by building on successful Web architectures, we can also put in place loosely coupled, distributed systems that can grow and interoperate in a decentralized manner. These also happen to be perfect architectures for flexible collaboration systems and networks.
These benefits arise both from individual pillars in the open semantic enterprise foundation, as well as in the interactions between them. Let’s now re-introduce these seven pillars.
Pillar #1: The RDF Data ModelAs I stated on the occasion of the 10th birthday of the Resource Description Framework data model, I belief RDF is the single most important foundation to the open semantic enterprise [4]. RDF can be applied equally to all structured, semi-structured and unstructured content. By defining new types and predicates, it is possible to create more expressive vocabularies within RDF. This expressiveness enables RDF to define controlled vocabularies with exact semantics. These features make RDF a powerful data model and language for data federation and interoperability across disparate datasets.
Via various processors or extractors, RDF can capture and convey the metadata or information in unstructured (say, text), semi-structured (say, HTML documents) or structured sources (say, standard databases). This makes RDF almost a “universal solvent” for representing data structure.
Because of this universality, there are now more than 150 off-the-shelf ‘RDFizers’ for converting various non-RDF notations (data formats and serializations) to RDF [5]. Because of its diversity of serializations and simple data model, it is also easy to create new converters. Once in a common RDF representation, it is easy to incorporate new datasets or new attributes. It is also easy to aggregate disparate data sources as if they came from a single source. This enables meaningful compositions of data from different applications regardless of format or serialization.
What this practically means is that the integration layer can be based on RDF, but that all source data and schema can still reside in their native forms [6]. If it is easier or more convenient to author, transfer or represent data in non-RDF forms, great [7]. RDF is only necessary at the point of federation, and not all knowledge workers need be versed in the framework.
Pillar #2: Linked Data TechniquesLinked data is a set of best practices for publishing and deploying instance and class data using the RDF data model. Two of the best practices are to name the data objects using uniform resource identifiers (URIs), and to expose the data for access via the HTTP protocol. Both of these practices enable the Web to become a distributed database, which also means that Web architectures can also be readily employed (see Pillar #5 below).
Linked data is applicable to public or enterprise data, open or proprietary. It is really straightforward to employ. Structured Dynamics has published a useful FAQ on linked data.
Additional linked data best practices relate to how to characterize and classify data, especially in the use of predicates with the proper semantics for establishing the degree of relatedness for linked data items from disparate sources.
Linked data has been a frequent topic of this blog, including how adding linkages creates value for existing data, with a four-part series about a year ago on linked data best practices [8]. As advocated by Structured Dynamics, our linked data best practices are geared to data interconnections, interrelationships and context that is equally useful to both humans and machine agents.
Pillar #3: Adaptive OntologiesOntologies are the guiding structures for how information is interrelated and made coherent using RDF and its related schema and ontology vocabularies, RDFS and OWL [10]. Thousands of off-the-shelf ontologies exist — a minority of which are suitable for re-use — and new ones appropriate to any domain or scope at hand can be readily constructed.
In standard form, semantic Web ontologies may range from the small and simple to the large and complex, and may perform the roles of defining relationships among concepts, integrating instance data, orienting to other knowledge and domains, or mapping to other schema [11]. These are explicit uses in the way that we construct ontologies; we also believe it is important to keep concept definitions and relationships expressed separately from instance data and their attributes [9].
But, in addition to these standard roles, we also look to ontologies to stand on their own as guiding structures for ontology-driven applications (see next pillar). With a relatively few minor and new best practices, ontologies can take on the double role of informing user interfaces in addition to standard information integration.
In this vein we term our structures adaptive ontologies [11,12,13]. Some of the user interface considerations that can be driven by adaptive ontologies include: attribute labels and tooltips; navigation and browsing structures and trees; menu structures; auto-completion of entered data; contextual dropdown list choices; spell checkers; online help systems; etc. Put another way, what makes an ontology adaptive is to supplement the standard machine-readable purpose of ontologies to add human-readable labels, synonyms, definitions and the like.
A neat trick occurs with this slight expansion of roles. The knowledge management effort can now shift to the actual description, nature and relationships of the information environment. In other words, ontologies themselves become the focus of effort and development. The KM problem no longer needs to be abstracted to the IT department or third-party software. The actual concepts, terminology and relations that comprise coherent ontologies now become the explicit focus of KM activities.
Any existing structure (or multiples thereof) can become a starting basis for these ontologies and their vocabularies, from spreadsheets to naïve data structures and lists and taxonomies. So, while producing an operating ontology that meets the best practice thresholds noted herein has certain requirements, kicking off or contributing to this process poses few technical or technology demands.
The skills needed to create these adaptive ontologies are logic, coherent thinking and domain knowledge. That is, any subject matter expert or knowledge worker likely has the necessary skills to contribute to useful ontology development and refinement. With adaptive ontologies powering ontology-driven apps (see next), we thus see a shift in roles and responsibilities away from IT to the knowledge workers themselves. This shift acts to democratize the knowledge management function and flatten the organization.
Pillar #4: Ontology-driven ApplicationsThe complement to adaptive ontologies are ontology-driven applications. By definition, ontology-driven apps are modular, generic software applications designed to operate in accordance with the specifications contained in an adaptive ontology. The relationships and structure of the information driving these applications are based on the standard functions and roles of ontologies, as supplemented by the human and user interface roles noted above [11,12,13].
Ontology-driven apps fulfill specific generic tasks. Examples of current ontology-driven apps include imports and exports in various formats, dataset creation and management, data record creation and management, reporting, browsing, searching, data visualization, user access rights and permissions, and similar. These applications provide their specific functionality in response to the specifications in the ontologies fed to them.
The applications are designed more similarly to widgets or API-based frameworks than to the dedicated software of the past, though the dedicated functionality (e.g., graphing, reporting, etc.) is obviously quite similar. The major change in these ontology-driven apps is to accommodate a relatively common abstraction layer that responds to the structure and conventions of the guiding ontologies. The major advantage is that single generic applications can supply shared functionality based on any properly constructed adaptive ontology.
This design thus limits software brittleness and maximizes software re-use. Moreover, as noted above, it shifts the locus of effort from software development and maintenance to the creation and modification of knowledge structures. The KM emphasis can shift from programming and software to logic and terminology [12].
Pillar #5: A Web-oriented ArchitectureA Web-oriented architecture (WOA) is a subset of the service-oriented architectural (SOA) style, wherein discrete functions are packaged into modular and shareable elements (”services”) that are made available in a distributed and loosely coupled manner. WOA uses the representational state transfer (REST) style. REST provides principles for how resources are defined and used and addressed with simple interfaces without additional messaging layers such as SOAP or RPC. The principles are couched within the framework of a generalized architectural style and are not limited to the Web, though they are a foundation to it [14].
REST and WOA stand in contrast to earlier Web service styles that are often known by the WS-* acronym (such as WSDL, etc.). WOA has proven itself to be highly scalable and robust for decentralized users since all messages and interactions are self-contained.
Enterprises have much to learn from the Web’s success. WOA has a simple design with REST and idempotent operations, simple messaging, distributed and modular services, and simple interfaces. It has a natural synergy with linked data via the use of URI identifiers and the HTTP transport protocol. As we see with the explosion of searchable dynamic databases exposed via the Web, so too can we envision the same architecture and design providing a distributed framework for data federation. Our daily experience with browser access of the Web shows how incredibly diverse and distributed systems can meaningfully interoperate [15].
This same architecture has worked beautifully in linking documents; it is now pointing the way to linking data; and we are seeing but the first phases of linking people and groups together via meaningful collaboration. While generally based on only the most rudimentary basis of connections, today’s social networking platforms are changing the nature of contacts and interaction.
The foundations herein provide a basis for marrying data and documents in a design geared from the ground up for collaboration. These capabilities are proven and deployable today. The only unclear aspects will be the scale and nature of the benefits [16].
Pillar #6: An Incremental, Layered ApproachTo this point, you’ll note that we have been speaking in what are essentially “layers”. We began with existing assets, both internal and external, in many diverse formats. These are then converted or transformed into RDF-capable forms. These various sources are then exposed via a WOA Web services layer for distributed and loosely-coupled access. Then, we integrate and federate this information via adaptive ontologies, which then can be searched, inspected and managed via ontology-driven apps. We have presented this layered architecture before [13], and have also expressed this design in relation to current Structured Dynamics’ products [17].
A slight update of this layered view is presented below, made even more general for the purposes of this foundational discussion:
Semantic technology does not change or alter the fact that most activities of the enterprise are transactional, communicative or documentary in nature. Structured, relational data systems for transactions or records are proven, performant and understood. On its very face, it should be clear that the meaning of these activities — their semantics, if you will — is by nature an augmentation or added layer to how to conduct the activities themselves.
This simple truth affirms that semantic technologies are not a starting basis, then, for these activities, but a way of expressing and interoperating their outcomes. Sure, some semantic understanding and common vocabularies at the front end can help bring consistency and a common language to an enterprise’s activities. This is good practice, and the more that can be done within reason while not stifling innovation, all the better. But we all know that the budget department and function has its own way of doing things separate from sales or R&D. And that is perfectly OK and natural.
Clearly, then, an obvious benefit to the semantic enterprise is to federate across existing data silos. This should be an objective of the first semantic “layer”, and to do so in a way that leverages existing information already in hand. This approach is inherently incremental; if done right, it is also low cost and low risk.
Pillar #7: The Open World MindsetAs these pillars took shape in our thinking and arguments over the past year, an illusive piece seemed always to be missing. It was like having one of those meaningful dreams, and then waking up in the morning wracking your memory trying to recall that essential, missing insight.
As I most recently wrote [1], that missing piece for this story is the open world assumption (OWA). I argue that this somewhat obscure concept holds within it the key as to why there have been decades of too-frequent failures in the enterprise in business intelligence, data warehousing, data integration and federation, and knowledge management.
Enterprises have been captive to the mindset of traditional relational data management and its (most often unstated) closed world assumption (CWA). Given the success of relational systems for transaction and operational systems — applications for which they are still clearly superior — it is understandable and not surprising that this same mindset has seemed logical for knowledge management problems as well. But knowledge and KM are by their nature incomplete, changing and uncertain. A closed-world mindset carries with it certainty and logic implications not supportable by real circumstances.
This is not an esoteric point, but a fundamental one. How one thinks about the world and evaluates it is pivotal to what can be learned and how and with what information. Transactions require completeness and performance; insight requires drawing connections in the face of incompleteness or unknowns.
The absolute applicability of the semantic Web stack to an open-world circumstance is the elephant in the room [1]. By itself, the open world mindset provides no assurance of gaining insight or wisdom. But, absent it, we place thresholds on information and understanding that may neither be affordable nor achievable with traditional, closed-world approaches.
And, by either serendipity or some cosmic beauty, the open world mindset also enables incremental development, testing and refinement. Even if my basic argument of the open world advantage for knowledge management purposes is wrong, we can test that premise at low cost and risk. So, within available budget, pick a doable proof-of-concept, and decide for yourself.
The Foundations for the Open Semantic EnterpriseThe seven pillars above are not magic bullets and each is likely not absolutely essential. But, based on today’s understandings and with still-emerging use cases being developed, we can see our open semantic enterprise as resulting from the interplay of these seven factors:
Thirty years of disappointing knowledge management projects and much wasted money and effort compel that better ways must be found. On the other hand, until recently, too much of the semantic Web discussion has been either revolutionary (“change everything!!”) or argued from pie-in-the-sky bases. Something needs to give.
Our work over the past few years — but especially as focused in the last 12 months — tells us that meaningful semantic Web initiatives can be mounted in the enterprise with potentially huge benefits, all at manageable risks and costs. These seven pillars point to way to how this might happen. What is now required is that eighth pillar — you.
Much has been happening on the Structured Dynamics front of late. Besides welcoming Steve Ardire as a senior advisor to the company, we also have been issuing a steady stream of new products from our semantic Web pipeline.
This new slide show attempts to capture these products and relate them to the various layers in Structured Dynamics’ enterprise product stack:
The show indicates the role of scones, irON, structWSF, UMBEL, conStruct and others and how they leverage existing information assets to enable the semantic enterprise. And, oh, by the way, all of this is done via Web-accessible linked data and our practical technologies.
Enjoy!
The earlier portions of this occasional series have set the groundwork for the role of ontologies in data-driven applications. In this part, I address many of the current misconceptions of what ontologies do or do not do. For, as practiced by Structured Dynamics, our adaptive TBox-level ontologies [1] are definitely not your grandfather’s Oldsmobile.
To share the punch line early, these modern ontologies are fast to develop, easy to change, adaptive to new knowledge and perceptions, robust and flexible. Indeed, it is the structure and nature of these adaptive ontologies that is the heart and secret of data-driven applications.
Any knowledge worker can understand and refine the organization and relationship of information via these structures. And, most importantly, the resulting ontologies are sufficient to drive the generic applications that are based on them. Focusing on data and structure now becomes the emphasis. We can now remove prior bottlenecks arising from the need to customize applications, configure report writers, or wait for IT to generate SQL queries.
But, not all ontologies are created equally and not all practitioners explain or see them in the same way. The purpose of this Part 4 in our series is to present many of the misconceptions, offering a score of takeaway messages for how properly considered and constructed ontologies can achieve these benefits.
To be sure, there are many very large and comprehensive ontologies. Some are focused on specific applications or domains; some are general; and some are the result of large and well-funded projects [2]. I am not arguing that such efforts do not have their role and place. But when viewed as exemplars or notable cases, these complex and comprehensive ontologies can create a misconception that such a scope is an imperative of proper ontology design.
I believe quite the opposite to be true.
An incredible strength of RDF and OWL ontologies is that they can be built incrementally. So long as additions are coherent with some degree of self-consistency in terms of the world view in which they are represented, any of an ontology’s constituent concepts, predicates or entities and datasets can be added and enhanced as needed. This makes ontologies a very different cat from relational schema, which are notoriously brittle with expensive re-architecting required anytime that scope or schema change.
Enterprise consultants that advocate “big” upfront ontology development efforts are doing their clients a massive disservice. They are also cynically playing on the experience with relational schema. As soon as the marketplace begins to realize that ontologies are incredibly plastic and malleable, this huge advantage of ontologies over the relational model for data federation will ring clear.
Takeaway Message #1: Ontologies can (and should!) start small.
Takeaway Message #2: Ontologies can (and should!) grow incrementally.
As a practitioner, two of the most boring arguments I hear are: Ontology X is better than other ontologies and here is why; and, Use of some reference or upper ontology reduces choice and freedom. Both arguments are somewhat grounded in the ‘one ring to rule them all’ mindset — though coming from opposing perspectives — that I think fundamentally misreads the role and purpose of ontologies.
Ontologies provide an organizing context for relating disparate information together and for making meaningful inferences. Without such a framework these purposes can not be achieved. But the framework itself is a function of the world view, context and domain scope at hand. As a result, there is only context, and not some single, universal “truth.” As they say, it all depends.
The trick, then, to properly designed ontologies is to maintain internal coherence and self-consistency [3]. When done, it is then possible to relate disparate information and data to other data and to make intelligent business inferences.
So, the use of an ontology does not limit freedom. It sets the context for making connections and setting relations. And, as long as it is coherent, the “correct” ontology is the one that best captures the scope and domain at hand. Arguing for one ontology v another is wasted energy. Just get on with it.
Takeaway Message #3: There is no single “truth”, only coherence and relevant context.
One of the more pernicious ideas promoted by some practitioners or advocates is the idea of ‘ontological commitment.’ Though some definitions are relatively benign, such as the one offered by the Stanford Knowledge Systems Laboratory (KSL) [4], the unfortunate use of the term “commitment” implies permanence and immutability. (In fact, most definitions of this phrase affirm this interpretation.)
This is really unfortunate, as it again tends to reinforce the inaccurate analogies with brittle and inflexible relational schema.
A much better way to view ontologies is not as a “commitment,” but as a vehicle for developing a common world view within the enterprise. Under this viewpoint, ontology development is somewhat analogous to master data management (MDM) or corporate taxonomies [5]. In this broader sense, then, ontology development can become a means for developing and refining a common language within the enterprise through consensual or community processes.
For the reasons as noted above, as language or conceptual relationships or understandings change, so can the vocabulary or structural character of the ontology change. There is no “lock in”; there is no “commitment”. As long as it is coherent, the ontology can morph to reflect the scope and understandings of the current snapshot in time.
This flexibility results from the fact that the ontologies, properly constructed, can drive a generic set of tools and applications that express themselves based on the underlying structure and vocabulary within those ontologies. The ontologies can thus change at will without any adverse effects whatsoever on the applications based on them.
This data-driven aspect, as noted throughout this series, is quite different from any prior paradigm. So, under this view ontologies have considerably more focus and importance than even some of the strongest ontology advocates claim, yet paradoxically without the theoretical bloat or heaviness many purport. Like human languages, our language and concepts within ontologies change as our world and perceptions change.
Takeaway Message #4: There is no “lock-in” with ontologies; they may be modified and changed at will.
Takeaway Message #5: Like corporate taxonomies or MDM, ontologies provide a framework for enterprises to develop internally consistent common languages or vocabularies.
Takeaway Message #6: Unlike corporate taxonomies or MDM, ontologies can drive directly generic tools and applications.
Ontology development is not some imperative for conceptual “truth”; rather, it is a very adaptable means for stating, testing and refining stuff. Like agile development for software, this refining approach can and should proceed incrementally. Too often ontology efforts get caught like deer in the headlights awaiting some “completeness” threshold before release.
One means to promote this approach is to tackle single datasets or data stores individually before moving on. Having a sense of the eventual scope is useful, of course. But it is also quite acceptable to only fill out those portions of the structure with data available at hand.
These observations reflect a prejudice to action and release, rather than theory. If mistakes are made, fine: simply correct them.
Takeaway Message #7: Understand the full scope, but only build out for the data in hand.
It is advisable to keep relationships (predicates) simple at first. Because, again, like human languages, keeping the verbs simple until fluency is gained is another best practice.
While all of us can see nuances and subtleties heading into a project, trying to accommodate those predicates (relationships) at the outset can introduce unnecessary complexity. This is not an advocacy in any way for inaccurate predicates, but perhaps to err on the side of the general and broader at first.
For organizations familiar with taxonomies, the SKOS vocabulary is a good focus, and there are some other standard starting ontologies that provide a good starting base of predicates [6]. Then, as you work with your data and its requirements, you can later expand to more sophisticated relationships.
In taking this approach you will still see immediate benefits due to the value of connected data through the Linked Data Law [7]. But, at the same time, you will be embracing a simpler language to start and then gain fluency.
Takeaway Message #8: Use simple, well-defined and documented predicates (properties or attributes).
Takeaway Message #9: You are building a common language for the enterprise; do so purposefully.
All of these observations lead to the conclusion that upfront ontology development need not be expensive. Any consultant selling six-figure ontology development to businesses ought to be seriously challenged. Start small and focused. Frankly, a simple spreadsheet taxonomy or quick conversion of existing XML or metadata or vocabulary standards is A-OK to get started.
Takeaway Message #10: Start small with stakeholders to build acceptance and best practices.
Takeaway Message #11: Start immediately to organize and federate existing information.
While it is true that the usefulness of ontologies as advocated by Structured Dynamics is greater than other constructs, these ontologies still just represent a more capable representation of knowledge structures that have been around in various other forms for years. For decades enterprises have created schema, taxonomies, controlled vocabularies, standards, and other knowledge structures that represent untold time, dollars and effort. It would be a waste to not fully leverage these sunk investments.
Further, many ontologies and interoperable structures also exist external to the enterprise, many open source and freely available. And, even if not all are already in proper ontological form, like internal structures these other constructs can be relatively easily leveraged and turned into ontology-ready form.
So, what we are doing with adaptive ontologies is not creating new structures or new representatiions from scratch, but leveraging the expressions of our current world views. These have been hard-earned, codified over years of effort, and are legacy expressions of the enterprise’s knowledge base.
In this vein, then, there is already much richness available to any organization upon which to embark on their ontology efforts. Use them, and gain great leverage.
Takeaway Message #12: Aggressively mine and re-use existing knowledge and structure.
Takeaway Message #13: Leverage and re-use appropriate portions of the “best” existing, external ontologies.
Continuing in this same spirit, it is a mistake to see adaptive ontologies and the associated systems advocated by Structured Dynamics as a replacement for existing data assets. Rather, the idea and advantage is to keep data records in situ as much as possible. These are already performing investments that can be left largely as is. The role of the adaptive ontologies is to act as a federation layer that bridges across these existing assets.
This leverage of existing data assets can occur via the architecture of the system (generally Web-oriented architecture [8]) and a design of the data system and structures providing proper allocation between the ABox and TBox [1].
All of this maintaining of existing assets is aided by the ability to convert in-place data to ontology-ready RDF form. This is a separate topic in its own right and one I discuss elsewhere [9]. There is also a need to make sure that the attributes of the underlying instance records (generally, the columns within a relational table) are also properly modeled within the adaptive ontology. This is part of the best practices guidelines.
Of course, how much of the existing assets can be leveraged “as is” and what degree of modification or conversion might be necessary needs to be evaluated on a case-by-case basis. Generally, however, these mappings can be pretty straightforward and leave in place all existing hardware, software and administration procedures.
Takeaway Message #14: Leverage your existing databases as rich sources of instance records (”ABox”).
Takeaway Message #15: Explicitly design your TBox ontologies to be an interoperability layer over these existing record stores.
Takeaway Message #16: Reconcile the semantics across the enterprise’s data stores at this interoperable TBox layer.
A closed world assumption holds that any statement that is not known to be true is false. Most enterprise database and transaction systems are based on this premise. It works well where there is complete coverage of the entities within a knowledge base, such as the enumeration of all customers or all products of an enterprise.
Yet, in the real (”open”) world there is no guarantee or likelihood of complete coverage. Thus, under an open world assumption the lack of a given assertion or fact being available neither implies whether that possible assertion is true or false: it simply is not known.
An open world assumption is one of the key factors for enabing adaptive ontologies to grow incrementally. It is also the basis for enabling linkage to external (and surely incomplete) datasets.
In fact, systems designed around the open world assumption can still achieve closed world reasoning where the circumstances and completeness of the knowledge base permit. But, rather than being a logical outcome of the framework, such completeness axioms need to be explicitly stated. Thus, open world systems can achieve the same ends as closed ones where applicable, but with greater flexibility and extensibility.
Takeaway Message #17: No enterprise is an island; design according to the open world assumption.
Consultants make their money and academics their reputation by often making things more obscure and jargon-laden than they need be. Ontologies — heck, even the name itself — is no exception.
But what we have laid out as general guidelines herein and their reduction to practice does not require a priesthood. Sure, there are some things to learn and some practices to follow, but these are certainly easier to understand and master than, say, a programming or scripting language. Adaptive ontologies done right can be a participatory activity within most any organization.
Some guidance and mentoring would certainly be helpful. Make sure to pick the right individuals that truly embrace these perspectives.
Also helpful would the assistance of groups skilled in team building and group participation [10].
Takeaway Message #18: Engage all knowledge stakeholders in ontology creation, review and refinement.
Takeaway Message #19: Use selected ontology engineers to help ensure consistency, but not necessarily structure.
The above addresses misconceptions related to how the market perceives current ontologies or how some advocates push the concept. But there are some unique perspectives that Structured Dynamics brings to ontology development specific to the purpose of data-driven applications. From a best practices standpoint, these considerations should also be included.
In order to properly “drive” applications and user interfaces and reports, specific design attention needs to be give to:
Of course, there are other considerations that come to bear. But these lend themselves to some rather simple checklist guidelines during ontology development and maintenance.
Takeaway Message #20: Follow some relatively straightforward best practices to gain all of the advantanges of adaptive ontologies.
Linked data and subject and domain ontologies provide the organizing framework. Techniques for converting, tagging and authoring structure provide the content. In combination, we now have in hand the necessary pieces to enable all of us to “structure the World.”
In this vision, the nature of the links or connections between data need not be complicated to gain tremendous benefit. Similar to Metcalfe’s Law for the increasing value of networks as more nodes (users) get added, adding connections to existing data is a powerful force multiplier.
We can call this the Linked Data Law: the value of a linked data network is proportional to the square of the number of links between data objects [1]. Further, if we are purposeful to include connective links where appropriate as we add more data (that is, nodes), this multiplier effect becomes even stronger.
Structured Dynamics is dedicated to help make this prospect real. Meaningful progress in doing so requires only a relatively few moving parts or techniques. Yet, because we sometimes bounce from talking or focusing on one part versus the others, we can lose context or sight of the overarching vision. The purpose of this article is to re-set and calibrate that overall vision.
The vision is to get all data and information to interoperate, regardless of legacy or form. Much of this data is already structured, either from databases or simpler forms of data structs. Some of this information is unstructured or semi-structured, requiring extraction and tagging techniques. And new information is being constantly generated, which warrants better means to author and stage for interchange and interoperability.
No matter the provenance, all information has context and scope. As a chunk from here, and a piece from there, gets added to our linked data mix, having means to characterize what that data is about and how it can be meaningfully inter-related becomes crucial. Sometimes these contexts are informed by existing schema; sometimes they are not. But, in any case, it is the role of ontologies to both position these datasets into an “aboutness” framework and to help guide how the data can be described and related to other data. This part of the vision invokes semantics and coherent structures (schema or ontologies) for positioning and mapping datasets to one another.
As both the means for representing any extant data format and as the means for describing these conceptual relationships or schema, RDF provides the canonical data model. A single target representation and common data model also means we can develop and design a smaller universe of tools to operate and provide functionality over all of this data. Indeed, because our RDF data model and its ontologies are so richly structured, we can design our tools with generic functionality, the specific operation and expression of which is based on the inherent structure within the data and its relationships. This vision of data-driven apps leads to extreme leverage, incredible flexibility, and inherent “meshup” capabilities for tools.
Further, because we use Web identifiers (URIs) for our data and concepts and because we expose and access this linked data via the Web, we use the proven and scalable architectures of the Web itself for how we design our systems. This Web-oriented architecture (WOA) provides a completely decentralized and loosely coupled deployment model that can work ranging from public and open to private and proprietary, applicable to data and participants alike.
From the outset, it is essential to recognize that thousands of contributors are enabling this vision. So, while Structured Dynamics naturally uses its own tools and techniques to flesh out the various parts of this vision below, realize there are many players and many tools from which to choose [2]. For that is another aspect of this vision that is quite powerful: providing choice and avoiding lock-in.
The core construct — or fulcrum, if you will — of the vision is the RDF (Resource Description Framework) data model [3]. I have written elsewhere on the Advantages and Myths of RDF, which explains more precisely the advantages of that model. RDF provides a common data model to which any external format or schema can be converted and represented. It also provides a logic model and basis for building vocabularies that can inform and drive generic tools.
In the context of data interoperability, a critical premise is that a single, canonical data model is highly desirable. Why?
Simply because of 2N v N2. That is, a single reference (”canon”) structure means that fewer tool variants and converters need be developed to talk to the myriad of data formats in the wild. With a canonical data model, talking to external sources and formats (N) only requires converters to and from the canonical form (2N). Without a canonical model, the combinatorial explosion of required format converters becomes N2 [4].
Note, in general, such a canonical data model merely represents the agreed-upon internal representation. It need not affect data transfer formats. Indeed, in many cases, data systems employ quite different internal data models from what is used for data exchange. Many, in fact, have two or three favored flavors of data exchange such as XML, JSON or the like. More on this is discussed in a section below.
As this diagram shows, then, we have a single internal representation that is the target for all data and format converters and upon which all tools operate. These tools are themselves expressed as Web services so that they may be distributed and conform to general WOA guidelines. In addition, there may be multiple external “hubs” that represent alternative data models or formats or schema conversions (say, for relational databases). So long as we have converters between these alternate “hubs” and our canonical RDF form we can allow a thousand flowers to bloom:
Other canonical forms could be advocated. Yet RDF has the logical basis to represent any data form and any schema or conceptual structure. It is based on a robust set of open standards and languages and tools. It may be serialized in many formats. It can be grounded in description logics and, in appropriate forms, reasoned over and expressed in vocabularies and schema suitable for the most complex of conceptual structures and semantics. RDF is the data model explicitly designed for the Web, the clear global information basis for the foreseeable future.
For more than 30 years — since the widespread adoption of electronic information systems by enterprises — the Holy Grail has been complete, integrated access to all data. With the canonical RDF data model, that promise is now at hand.
Diversity is a truism of human communications as captured by the biblical Tower of Babel and the many thousands of current human languages. Diversity in data formats, serializations, notations and languages is a similar truism. We term the expression of each of these varied forms of data a struct.
While an internal canonical representation of data makes sense for the reasons noted above, pragmatic information systems must recognize the inherent diversity and chaos of data in the real world. The history of trying to find single representations or to impose standards via fiat have singularly failed. That will continue to be so due in part to inertia and legacy, sunk investments, existing infrastructure, and the purposes for the data.
In pursuing a vision of data interoperability, then, conversion is an essential glue for cementing understanding with what exists and will exist.
Arguably the largest source of structured data are enterprise and government information systems, with the predominant data representation being the relational data model managed by relational schema. Much of this data is also cleaner and mission critical compared to other sources in the wild. Fortunately, there are many logical and conceptual affinities between the relational model and the one for RDF [5].
Just as there are many RDFizers for simpler forms of data structs (see next), there are also nice ways to convert relational schema to RDF automatically. Given these overall conceptual and logical affinities the W3C is also in the process of graduating an incubator group to an official work group, RDB2RDF, focused on methods and specifications for mapping relational schema to RDF.
Amongst all techniques covered in this paper, Structured Dynamics views the layering of RDF ontologies over existing relational data stores as one of the most promising and important. Given the advantages of RDF for interoperability, this area should be a major emphasis of current and new vendors and service providers.
Much data, however, resides in much smaller datasets and often for less formal purposes than what is found in enterprise databases. Some of this data is geared for exchange or standardization; much is emerging from Web and Internet applications and uses; and much might be local or personal in nature, such as simple lists or spreadsheets.
RDF is well suited to convert (”RDFize”) these simpler and more naïve data formats. In my original census about 18 months ago, as reported in ‘Structs’: Naïve Data Formats and the ABox, I listed about 90 converters. My most recent update now lists nearly double that number, with about 150 converters [6]:
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URN handlers (in addition to IRI and URI):
RDF
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Many of the sources above come from new and emerging Web-based APIs, which are also huge sources of content growth. Also note that alternative formats to RDF (e.g., microformats) or leading serializations and encodings (e.g, XML, JSON) also have many converter options.
For many typical naïve data structs, the data is represented as attribute-value pairs, which easily lend themselves to conversion to RDF as instance records [8]. See further the Authoring section below.
An apocryphal statistic is that 80% to 85% of all information resides in unstructured text [9]. Besides lacking recent validation, this claim from a decade ago often attributed to Merrill Lynch also precedes much of the Internet and the emergence of metadata and tagging. Nevertheless, what is true is that written text content is ubiquitous and the majority of it remains untagged or uncharacterized by any form of metadata.
While such information can be searched, it only matches when exact terms match. This means that related information, particularly in the form of conceptual relationships and inferencing, can not be applied to untagged text content.
While information extraction — the basis by which tags for entities and concepts can be obtained — has been an active topic of research for two decades, it is only recently that we have begun to see Web-scale extractors appear. Examples include Yahoo’s term extractor, Thomson Reuter’s Calais, or Google’s Squared, to name but a few.
In Structured Dynamics’ case we have been working on the scones (Subject Concepts Or Named EntitieS) extractor for quite a while. scones uses rather simple natural language processing (NLP) methods as informed by concept ontologies and named entity (instance record) dictionaries to help guide the extraction process. The co-occurrence of matches between concepts and entities also aids the disambiguation task (though additional modules may be invoked with alternative disambiguation methods). In prototype forms, the resulting tags can be managed separately or fed to user interfaces or re-injected back into the original content as RDFa.
There are literally dozens of such extractors and services presently available on the Web and many that are available as open source or commercial products. Some are mostly algorithm based using machine-learning techiques or statistics, while others are gazeteer- or dictionary-driven.
These systems will lead to rapid tagging of existing content and the removal of some of the early “chicken-and-egg” challenges associated with the semantic Web. These systems will also be combined with the many existing bookmarking and tagging services.
So, just as we will see federation and interoperability of conventional data, we will also see linkages to relevant and supporting text content accompanying it. This combination, in turn, will also lead to richer browsing and discovery experiences.
In addition to conversion and tagging, authoring is the third leg of the stool to expose structured data. It is a neglected leg to the structured content stool, and one important to make it easier for datasets to be easily exposed as RDF linked data.
One of the reasons for the proliferation of data structs has been the interest in finding notations and conventions for easier reading and authoring of small datasets. There have literally been hundreds of various formats proposed over decades for conveying lightweight data structures. Most have been proprietary or limited to specific domains or users. Some, such as fielded text, structured text, simple declarative language (SDL), or more recently YAML or its simpler cousin JSON, have become more widely adopted and supported by formal specifications, tools or APIs. JSON, especially, is a preferred form for Web 2.0 applications.
What has been less clear or intuitive in these forms, again mostly based on an attribute-value pair orientation, is how to adequately relate them to a more capable data model, such as RDF. In JSON or YAML, for example, the notations include the concepts of objects, arrays and datatypes (among other conventions). Other structures lack even these constructs.
To take the case of JSON as might be related to RDF, there are a couple of efforts to define representation conventions from Talis and GBV for serializing RDF. There was a floated idea for an RDF version of JSON called RDFON that has now evolved into the TURF approach. JDIL (JSON data integration layer) instructs how to add namespaces to JSON to enable encoding RDF. Jim Ley, Kanzaki Masahide and Dave Beckett (likely among others) have written simple and straightforward RDF and Turtle parsers and converters for JSON. And, still further examples are Beckett’s Triplr and Sören Auer’s ASKW Triplify lightweight conversion services involving many different formats.
Because JSON is easily readable, can drive many Web 2.0 applications and widgets, and lends itself to fast conversions and tools in various scripting languages, Structured Dynamics was commissioned by the Bibliographic Knowledge Network (BKN) to formalize a BibJSON specification suitable for BibTeX-like data records and citations with an extensible schema to be converted to RDF.
The emerging result of that BibJSON effort will be published shortly. The specification includes conventions and vocabularies for creating bibliographic and citation instance records, for specifying structural schema, and for creating linkage files between the attributes in the record files with existing and new schema. BibJSON is itself grounded in IRON, which is an instance record and object notation developed by Structured Dyamics that can be serialized as JSON (called irJSON), XML (called irXML) or comma-separated values (or CSV comma-delimited files, called commON).
The purpose of these notations and serializations is to provide easier authoring environments and scripting support to RDF-ready datasets. This approach has the advantage of shielding most users from the nuances or lengthiness of RDF (though the N3 serialization also works well).
The design and development of commON was especially geared to using spreadsheets as authoring environments that would enable easy creation of instance record tables or simple hierarchical or outline structures. For example, here is a sample portion of Sweet Tools specified in a spreadsheet using the commON notation:
Once the philosophy and role of naïve data structs is embraced — with an appreciation of the many converters now available or easily written for translating to RDF — it becomes easier to determine data forms appropriate to the tools and natural work flow of the users and tasks at hand. Under this mindset, the role of RDF is to be the eventual conversion target, but not necessarily what is used for intermediate work tasks, and in particular not for authoring.
OK, so now all of this stuff is converted, tagged or authored. How does it relate? What is the relation of one dataset to another dataset? Is there a context or framework for laying out these conceptual roadmaps?
Two years ago as we looked at the state of RDF and the incipient semantic Web as promised via linked data, we saw that such a specific framework was lacking. (Though there were existing higher-level ontologies, either their complexity or design were not well-suited to these purposes.) It was at that time that Frédérick Giasson and I began to formulate the UMBEL (Upper Mapping and Binding Exchange Layer) ontology, which eventually led to our more formal business partnership and Structured Dynamics.
What we sought to achieve with UMBEL was a coherent reference framework of about 20,000 subject concepts, connected and acting like constellations in the information sky for orienting content and new datasets. At the same time, we wanted to create a general vocabulary and approach that would lend themselves to creation of domain-specific ontologies, which would also naturally tie in and inter-relate to the more general UMBEL structure.
This objective was achieved, though UMBEL deserves an upgrade to OWL 2 and some other pending improvements. A number of domain ontologies have been created and now relate to UMBEL. So, rather than being an end to itself, UMBEL was one of the necessary infrastructure pieces to help make the vision herein a reality.
Similar approaches may be taken by others with new domain ontologies based on the UMBEL vocabulary with tie-in as appropriate to existing subject concepts, or by mapping to the existing UMBEL structure.
Of course, UMBEL is not an absolute condition to the vision herein. However, insofar as users desire to see multiple datasets inter-related, including the use of existing public Web data, something akin to UMBEL and related domain ontologies will be necessary to provide a similar roadmap.
The parts and techniques discussed so far pertain almost exclusively to data and content. But, these structures so created now can inform data-driven applications which also now must be deployed. To do so, Structured Dynamics is committed to what is known as a Web-oriented architecture (WOA):
WOA is a subset of the service-oriented architectural style, wherein discrete functions are packaged into modular and shareable elements (”services”) that are made available in a distributed and loosely coupled manner. WOA generally uses the representational state transfer (REST) architectural style defined by Roy Fielding in his 2000 doctoral thesis; Fielding is also one of the principal authors of the Hypertext Transfer Protocol (HTTP) specification.
REST provides principles for how resources are defined and used and addressed with simple interfaces without additional messaging layers such as SOAP or RPC. The principles are couched within the framework of a generalized architectural style and are not limited to the Web, though they are a foundation to it.
Within this design we need a suite of generic functions and tools that are driven by the structure of the available datasets. The deployment vehicle and design we have implemented to provide this WOA design is structWSF [10].
structWSF is a platform-independent Web services framework for accessing and exposing structured RDF data. Its central organizing perspective is that of the dataset. These datasets contain instance records, with the structural relationships amongst the data and their attributes and concepts defined via ontologies (schema with accompanying vocabularies). The master or controlling Web service in the framework is the module for granting access and use rights to datasets based on permissions.
The structWSF middleware framework is generally RESTful in design and is based on HTTP and Web protocols and open standards. The initial structWSF framework comes packaged with a baseline set of about a dozen Web services in CRUD, browse, search and export and import. More services can readily be added to the system.
All Web services are exposed via APIs and SPARQL endpoints. Each request to an individual Web service returns an HTTP status and a document of resultsets (if the query result is not null). Each results document can be serialized in many ways, and may be expressed as either RDF or pure XML.
In initial release, structWSF has direct interfaces to the Virtuoso RDF triple store (via ODBC, and later HTTP) and the Solr faceted, full-text search engine (via HTTP). However, structWSF has been designed to be fully platform-independent. The framework is open source (Apache 2 license) and designed for extensibility.
Like all visions, there are many aspects and many improvements possible. This vision is definitely a work-in-progress with no end in sight.
But, meaningful movement embracing the full scope of this vision is doable today. Structured Dynamics welcomes inquiries regarding any of these aspects, improvements to them, or application to your specific needs and problems.
We also welcome you to come back and visit our blogs (Fred’s is found here). We try to speak on various aspects of this vision in all of our posts and are pleased to share our experience and insights as gained.
