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Well, for another client and another purpose, I was goaded into screening my Sweet Tools listing of semantic Web and -related tools and to assemble stuff from every other nook and cranny I could find. The net result is this enclosed listing of some 140 or so tools — most open source — related to semantic Web ontology building in one way or another.
Ever since I wrote my Intrepid Guide to Ontologies nearly three years ago (and one of the more popular articles of this site, though it is now perhaps a bit long in the tooth), I have been intrigued with how these semantic structures are built and maintained. That interest, in no small measure, is why I continue to maintain the Sweet Tools listing.
As far as I know, the following is the largest and most comprehensive listing of ontology building tools available. I broadly interpret the classification of ‘ontology building’; I include, for example, vocabulary extraction and prompting tools, as well as ontology visualization and mapping.
There are some 140 tools, perhaps 90 or so are still in active use. (Given the scope, not every tool could be inspected in detail. Some listed as being perhaps inactive may not be so, and others not in that category perhaps should be.) Of the entire roster of tools, somewhere on the order of 12 to 20 are quite impressive and deserving of local installation, test runs, and close inspection.
There are relatively few tools useful to non-specialists (or useful to engaging knowledgeable publics in the ontology-building exercise). There appear to be key gaps in the entire workflow from domain scoping and initial ontology definition and vocabulary candidates, to longer-term maintenance and revision. For example, spreadsheets would appear to be a possible useful first step in any workflow process (which is why irON is listed), but the spreadsheet tool per se is not listed herein (nor are text editors).
I surely have missed some tools and likely improperly assigned others. Please drop me an email or comment on this post with any revisions or suggestions.
In my own view, there are some tools that definitely deserve a closer look. My favorite candidates — for very different reasons and for very different places in the workflow — are (in no particular order): Apelon DTS, irON, FlexViz, Knoodl, Protégé, diagramic.com, BooWa, COE, ontopia, Anzo, PoolParty, Vine (and voc2rdf), Erca, Graphl, and GrOWL. Each one of these links is more fully described below. Also, all tools in the Vocabulary Prompting Tools category (which also includes extraction) are worth reviewing since all or nearly all have online demos.
Other tools may also be deserving, depending on use case. Some of the more specific analysis and conversion tools, for example, are in the Miscellaneous category.
Also, some purists may quibble with why some tools are listed here (such as inclusion of some stuff related to Topic Maps). Well, my answer to that is there are no real complete solutions, and whatever we can pragmatically do today requires glueing together many disparate parts.
Though all are not relevant, see my post from a couple of years back on large-scale RDF graph software.
If you are like me, you like to clear the decks before the start of major new projects. In Structured Dynamics‘ case, we actually have multiple new initiatives getting underway, so the deck clearing has been especially focused this time.
As a result, we have updated Sweet Tools, AI3’s listing of semantic Web and -related tools, with the addition of some 30 new tools, updates to others, and deletions of five expired entries. The dataset now lists 835 tools. And, as before, there is also now a new structured data view via conStruct (pick the Sweet Tools dataset).
We have also updated SWEETpedia, a listing of 246 research articles that use Wikipedia in one way or another to do semantic-Web related research. Some 20 new papers were added to this update.
Please use the comments section on this post to suggest new tools or new research articles for inclusion in future updates.
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.
In speaking of the semantic Web, it is not infrequent that the open world assumption (OWA) gets mentioned. What this post argues is that this somewhat obscure concept may hold 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.
This is a fairly bold assertion. In order to support it, we first need to look to the logic and mindset assumptions associated with traditional relational data management and the semantic Web. We then need to look to the nature of knowledge itself and its relation to data federation. It is in this intersection that the key of decades of faulty premises may reside.
The main argument is that the closed world assumption (CWA) and its prevalent mindset in traditional database systems have hindered the ability of enterprises and the vendors that support them to adopt incremental, low-risk means to knowledge systems and management. CWA, in turn, has led to over-engineered schema, too-complicated architectures and massive specification efforts that have led to high deployment costs, blown schedules and brittleness.
The good news is that abandoning these failed practices and embracing the open world approach can be done immediately based on existing assets. Simply shifting from the closed world to open world premise can, I argue, improve the odds for enterprise IT success in these areas.
It is time to meet the elephant in the room.
It is, of course, a bit of editorial hyperbole to label most enterprise initiatives in business intelligence and knowledge management as being failures over the past few decades. And, insofar as failures have occurred, I also do not believe they are the result of vendor greed or cynicism, or IT management mistakes or incompetence. Rather, I believe the fault resides in the attempt to pound a square peg (relational model) into a round hole (knowledge representation).
The scope of these failures is not known. We have seen anecdotal claims of trillions of dollars in annual loses due to IT project failures worldwide; failure rates for major IT projects in the 65% to 80% ranges; and analysis of waste and failures in individual firms that are fairly eye-popping [1]. The real point of this post is not to try to quantify these problems. However, in my many years within IT it has been a common perception and concern that many — if not most — large-scale information technology deployments have disappointed in one way or another.
These disappointments range from cost overruns, to late delivery, to unmet objectives, or to low user acceptance. Many initiatives are simply cancelled before any such metrics can be documented. Whatever the absolute quantification, I think most experienced IT managers and executives would agree that these failures and disappointments have been all too commonplace.
Why might this be?
I truly believe the reasons for these disappointments do not reside in bad faith or incompetence. The potential importance of IT knowledge projects to improve competitive position, lower costs, or aid innovation for new markets is understood by all. Dilbert aside, I find it simply incomprehensible that disappointments or failures are rooted in these causes.
Rather, I suspect the root cause resides in the success of the relational model in the enterprise.
As transaction systems and for modeling narrowly bound and structured domains (such as products, inventory or customer lists), the relational model and its proven and optimized RDBMs and SQL query language have been resounding successes. It is natural to take a successful approach and try to extend it to other areas.
However, beginning with data warehouses in the 1980s, business intelligence (BI) systems in the 1990s, and the general issue of most enterprise information being bound up in documents for decades, the application of the relational model to these areas has been disappointing.
The reasons for this do not reside in areas such as storage or hardware; these areas have seen remarkable improvements over the decades. Rather, the problem resides in the nature of the relational model itself, and its lack of suitability to knowledge-based problems.
I have noted the importance of the open world assumption to the semantic enterprise in many of my more recent posts [3,4]. But I, like many others, often refer to the open world assumption with facile summaries such as it means that a lack of information does not imply the missing information to be false. Yet to fully understand the implications of OWA and many of its associated assumptions, it is necessary to delve deeper.
I am using here a shorthand that poses the closed world assumption (CWA) vs. the open world assumption (OWA). Actually, the data models behind these approaches (Datalog or non-monotonic logic in the case of CWA; monotonic in the case of OWA [5]; OWA is also firmly grounded in description logics [4]) tend be coupled with a few other assumptions. I use the shorthand of relational approach vs. (open) semantic Web approach to contrast these two models.
There are instances where the relational model can embrace the open world assumption (for example, the null in SQL) and there are instances where semantic Web approaches can be closed world (as with frame logic or Prolog or other special considerations; see conclusion). But, as generally applied and as generally understood, this contrast between typical relational practice and the semantic Web (based on RDF and OWL) tends to hold.
From a theoretical standpoint, I have found the treatment of Patel-Schneider and Horrocks [6] to be most useful in comparing these approaches. However, the Description Logics Handbook and some other varied sources are also helpful [7,5]. Much of the technical aspects summarized in the table below are from these sources; I refer you to these sources for more informed technical discussions:
| Relational Approach | (Open) Semantic Web Approach |
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Closed World Assumption (CWA) That which is not known to be true is presumed to be false; it needs to be explicitly stated as true. Negation as failure (NAF) is a related assumption, since it assumes as false every predicate that cannot be proven to be true. Under CWA, any statement not known to be true is false. Everything is prohibited until it is permitted. |
Open World Assumption (OWA) The lack of a given assertion or fact being available does not imply whether that possible assertion is true or false: it simply is not known. In other words, lack of knowledge does not imply falsity. Everything is permitted until it is prohibited. |
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Unique Name Assumption (UNA) The unique name assumption (UNA) is premised that different names always refer to different entities in the world. |
Duplicate Labels Allowed OWL allows different synonym labels to be used for the same object; same names may refer to different objects. Identity assertions must be explicitly stated. |
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Complete Information The data system at hand is assumed to be complete. (Missing information is often handled via the null statement in SQL, but that has been controversial and contentious in its own right.) This is also known as the domain-closure assumption. |
Incomplete Information A central tenet of OWA is that information is incomplete. A corollary is that the attributes of specific objects or instances may also be incomplete or partially known. |
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Single Schema (one world) A single schema is necessary to define the scope and interpretation of the world (domain at hand). |
Many World Interpretations Schema and data instance assertions are kept separate. Multiple interpretations (worlds) for the same data are possible. |
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Integrity Constraints Integrity constraints prevent “incorrect” values from being asserted in the relational model. It is useful for validation/parsing/data input and is related to the single model that contains only the facts asserted. Strict cardinality is used for checking validation. |
Logical Axioms (restrictions) Logical axioms provide restrictions through property domains and ranges. Everything can be true unless proven otherwise, and multiple possible models can satisfy the axioms. This provides more powerful inferencing, though can also be unintuitive at times. Cardinality and range restrictions exhibit different behavior for objects (inferred) or datatypes. |
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Non-monotonic Logic The set of conclusions warranted on the basis of a given knowledge base does not increase (in fact, it likely shrinks) with the size of the knowledge base [5]. |
Monotonic Logic The hypotheses of any derived fact may be freely extended with additional assumptions. Additional assertions tend to reduce the inferences or entailments that can be applied. A new piece of knowledge cannot reduce what is known [5]. New knowledge can arise through inference. |
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Fixed and Brittle Changing the schema requires re-architecting the database; not inherently extensible. |
Reusable and Extensible Designed from the ground up to reuse existing ontologies (axioms) and to be extensible. Database design and management can be more agile, with schema evolving incrementally. |
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Flat Structure; Strong Typing Information organized into flat tables; linkages and connections between tables based on foreign keys or joins. Strong data typing orientation. |
Graph Structure; Open Typing Inherent graph structure, supporting of linkage and connectivity analysis. Datatypes are inherently loose, though axioms can add strong types. Datatypes treated in the same way as classes, and datatype values are treated in the same way as individual identiers (i.e., a data value is treated as referring to an object). |
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Querying and Tooling SQL and query optimizers well developed. Tooling well developed. Disjunction not supported; negation must be accommodated through approaches such as NAF. Sums and counts are easier due to unique name premise. Answer closure (one answer passable to a next calculation) is easier than OWA. Most tools are not suitable for any arbitrary schema. |
Querying and Tooling SPARQL and emerging rule languages used for querying; performance at scale and with broad distribution a concern. Queries require contextual information for proper set selection. Negation and disjunction are allowed and are powerful constructs. Tools generally less developed. Exciting opportunities for ontology-driven applications working against a small set of generic tools. |
In well-characterized or self-contained domains (seats on a plane, books in a library, customers of a company, products sold via distribution channels), the traditional relational model works well. A closed-world assumption is performant for transaction operations with easier data validation. The number of negative facts about a given domain is typically much greater than the number of the positive ones. So, in many bounded applications, the number of negative facts is so large that their explicit representation can become practically impossible [7]. In such cases, it is simpler and shorter to state known “true” statements than to enumerate all “false” conditions.
However, the relational model is a paradigm where the information must be complete and it must be described by a single schema. Traditional databases require an agreement on a schema, which must be made before data can be stored and queried. The relational model assumes that the only objects and relationships that exist in the domain are those that are explicitly represented in the database, and that names uniquely identify objects in this domain. The result of these assumptions is that there is a single (canonical) model for relational systems where objects and relationships are in a one-to-one correspondence with the data in the database [6].
This makes CWA and its related assumptions a very poor choice when attempting to combine information from multiple sources, to deal with uncertainty or incompleteness in the world, or to try to integrate internal, proprietary information with external data.
The process of describing an open, semantic Web “world” can proceed incrementally, sequentially asserting new statements or conditions. The schema in the open semantic Web — the ontology — consists of sets of statements (called axioms) that describe characteristics that must be satisfied by the ontology designer’s idea of “reasonable” states of the world. Formally, such statements correspond to logical sentences, and an ontology corresponds to a logical theory [6].
Irregularity and incompleteness are toxic to relational model design. In the open semantic Web, data that is structured differently can still be stored together via RDF triple statements (subject – predicate – object). For example, OWA allows suppliers without cities and names to be stored along alongside suppliers with that information. Information can be combined about similar objects or individuals even though they have different or non-overlapping attributes. Duplicate checking now occurs based on the logic of the system and not unique name evaluations. Data validation in OWA systems can both become more complicated (via testing against restriction statements) or partially easier (via inference).
It is interesting to note that the theoretical underpinnings of CWA by Reiter [8] began to be understood about the same time (1978) that data federation and knowledge representation (KR) activities also began to come to the fore. CWA and later work on (for example) default reasoning [5] appeared to have informed early work in description logics and its alternative OWA approach. This heavily influenced the development of the semantic Web languages RDF and OWL. However, the early path toward KM work based on the relational model also appears to have been set in this timeframe.
We are still reaping the whirlwind from this unfortunate early choice of the relational model for KR, KM and BI purposes. Moreover, though there is quite a bit of theoretical and logical discussion of the alternative OWA and CWA data models, there are surprisingly few discussions of what the implications of these models are to the enterprise. (That is, the elephant in the room.) The next two sections tackle this gap.
The above should make clear that the relational model and CWA are appropriate for defined and bounded systems. However, many of the new knowledge economy challenges are anything but defined and bounded. These applications all reside in the broad category of knowledge management (KM), and include such applications as data federation, data warehousing, enterprise information integration, business intelligence, competitive intelligence, knowledge representation, and so forth.
Let’s looks at the characteristics of such knowledge systems and why they are more appropriately modeled through the open world assumption (OWA) rather than the relational model and CWA:
To be sure, there are many circumstances where large stores of instance data and their analysis are necessary for knowledge purposes. In these cases, hybrid CWA-OWA systems (see conclusion) may make sense.
But, as these points emphasize, the general assembly and organization of knowledge is open world in nature. Trying to fit KM and related applications into the straightjacket of the relational model is folly. The relational model and CWA for KM is the elephant in the room. Three decades of failures and disappointments affirm this fact.
Besides the native match of knowledge systems with OWA, there are sound business arguments for embracing the (open) semantic enterprise as well. These arguments can be summarized as lower risk, lower cost, faster deployment, and more agile responsiveness. What is there not to love?
It should now be clear that 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.
Open world does not necessarily mean open data and it does not mean open source. Open world is simply a way to think about the information we have and how we act on it. OWA technologies are neutral to the question of open or public sources. The techniques can equivalently be applied to internal, closed, proprietary data and structures. Moreover, the technologies can themselves be used as a basis for bringing external information into the enterprise. An open world assumption merely asserts that we never have all necessary information and lacking that information does not itself lead to any conclusions.
Further, we need not 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. However, in leveraging those assets, it is important that the enterprise begin to embrace and understand the open world assumption.
We also see that RDF and OWL, while important behind the scenes as a canonical data model and languages for organizing this information, need not be exposed as such to most users. Most instance data can be expressed as is with the data languages of choice such as XML, JSON or whatever. We are merely using the techniques of the (open) semantic Web as the data model to organize our information assets at hand. These assets need not themselves be represented in the native RDF or OWL languages.
Thus, open world frameworks provide some incredibly important benefits for knowledge management applications in the enterprise:
One might argue, as we believe, that the biggest impediment to the semantic enterprise is the mind shift necessary to start thinking about and accepting the open world premise. Again, this perspective is not applicable to all problems and domains. But, where it is, much can be left in place and leveraged with semantic technologies, so long as the enterprise begins to look at these existing assets through a different open-world lens.
In most real world circumstances, there is much we don’t know and we interact in complex and external environments. Knowledge management inherently occupies this space. Ultimately, data interoperability implies a global context. Open world is the proper logic premise for these circumstances. Via the OWA framework, we can readily change and grow our conceptual understanding and coverage of the world, including incorporation of external ontologies and data. Since this can easily co-exist with underlying closed-world data, the semantic enterprise can readily bridge both worlds.
So, we can now define the open semantic enterprise as one that embraces OWA for its knowledge management applications and engages in rapid and low-risk testing of incremental learning. The open world assumption is the proper framework to reverse decades of failure and disappointment for knowledge projects in the enterprise.
In our own discussions about ABox – TBox splits [10], we have, in essence, supported a hybrid OWA-CWA argument for the enterprise. It is beyond the scope of this current piece to describe these approaches in detail, but some of the options include local CWA, the addition of rule languages and constraints to basic OWA, use of the new OWL 2, TopQuadrant’s SPIN notation, and others [11]. I will address some of these in a later post.
There are also questions about performance and scalability with open semantic technologies. Here, too, progress is rapid, with billion triple thresholds rapidly falling with daily reports of better performance [12]. Fortunately, the incremental approach that we advocate herein dovetails well with these rapid developments. There should be no arguing the benefits of a successful incremental project in a smaller domain, perhaps repeated across multiple domains, in comparison to large, costly initiatives that never produce (even though their underlying technologies are performant).
There are also architecture issues inherent in these OWA designs. In one of our next posts, we return to the topic of Web-oriented architecture and its role in support of these OWA knowledge management initiatives.
In the end, there is no substitute for doing and learning. KM based on OWA for the open semantic enterprise can be started today, in a focused manner with tangible benefits and outcomes, at low cost and risk. Let’s push the elephant out of the room and let the learning and doing begin.
In a former life, I had the nickname of ‘Spreadsheet King’ (perhaps among others that I did not care to hear). I had gotten the nick because of my aggressive use of spreadsheets for financial models, competitors tracking, time series analyses, and the like. However, in all honesty, I have encountered many others in my career much more knowledgeable and capable with spreadsheets than I’ll ever be. So, maybe I was really more like a minor duke or a court jester than true nobility.
Yet, pro or amateur, there are perhaps 1 billion spreadsheet users worldwide [1], making spreadsheets undoubtedly the most prevalent data authoring environment in existence. And, despite moans and wails about how spreadsheets can lead to chaos, spaghetti code, or violations of internal standards, they are here to stay.
Spreadsheets often begin as simple notetaking environments. With the addition of new findings and more analysis, some of these worksheets may evolve to become full-blown datasets. Alternatively, some spreadsheets start from Day One as intended datasets or modeling environments. Whatever the case, clearly there is much accumulated information and data value “locked up” in existing spreadsheets.
How to “unlock” this value for sharing and collaboration was a major stimulus to development of the commON serialization of irON (instance record and Object Notation) [2]. I recently published a case study [3] that describes the reasons and benefits of dataset authoring in a spreadsheet, and provides working examples and code based on Sweet Tools [4] to aid users in understanding and using the commON notation. I summarize portions of that study herein.
The dataset that is the focus of this use case, Sweet Tools, began as an informal tracking spreadsheet about four years ago. I began it as a way to learn about available tools in the semantic Web and -related spaces. I began publishing it and others found it of value so I continued to develop it.
As it grew over time, however, it gained in structure and size. Eventually, it became a reference dataset, with which many other people desired to use and interact. The current version has well over 800 tools listed, characterized by many structured data attributes such as type, programming language, description and so forth. As it has grown, a formal controlled vocabulary has also evolved to bring consistency to the characterization of many of these attributes.
It was natural for me to maintain this listing as a spreadsheet, which was also reinforced when I was one of the first to adopt an Exhibit presentation of the data based on a Google spreadsheet about three years back. Here is a partial view of this spreadsheet as I maintain it locally:
When we began to develop irON in earnest as a simple (”naïve”) dataset authoring framework, it was clear that a comma-separated value, or CSV [5], option should join the other two serializations under consideration, XML and JSON. CSV, though less expressive and capable as a data format than the other serializations, still has an attribute-value pair (also known as key-value pairs and many other variants [6]) orientation. And, via spreadsheets, datasets can be easily authored and inspected, while also providing a rich functional environment including sorting, formatting, data validation, calculations, macros, etc.
As a dataset very familiar to us as irON’s editors, and directly relevant to the semantic Web, Sweet Tools provided a perfect prototype case study for helping to guide the development of irON, and specifically what came to be known as the commON serialization for irON. The Sweet Tools dataset is relatively large for a speciality source, has many different types and attributes, and is characterized by text, images, URLs and similar.
The premise was that if Sweet Tools could be specified and represented in commON sufficiently to be parsed and converted to interoperable RDF, then many similar instance-oriented datasets could likely be so as well. Thus, as we tried and refined notation and vocabulary, we tested applicability against the CSV representation of Sweet Tools in addition to other CSV, JSON and XML datasets.
A large portion of the case study describes the many advantages of authoring small datasets within spreadsheets. The useful thing about the CSV format is that these full functional capabilities of the spreadsheet are available during authoring or later updates and modifications, but, when exported, the CSV provides a relatively clean format for processing and parsing.
So, some of the reasons for small dataset authoring in a spreadsheet include:
The next major section of the case study deals with the minor conventions that must be followed in order to stage spreadsheets for commON. Not much of the specific commON vocabulary or notation is discussed below; for details, see [7].
Because you can create multiple worksheets within a spreadsheet, it is not necessary to modifiy existing worksheets or tabs. Rather, if you are reluctant or can not change existing information, merely create parallel duplicate sheets of the source information. These duplicate sheets have as their sole purpose export to commON CSV. You can maintain your spreadsheet as is while staging for commON.
To do so, use the simple = formula to create cross-references between the existing source spreadsheet tab and the target commON CSV export tab. (You can also do this for complete, highlighted blocks from source to target sheet.) Then, by adding the few minor conventions of commON, you have now created a staged export tab without modifying your source information in the slightest.
In standard form and for Excel and Open Office, single quotes, double quotes and commas when entered into a spreadsheet cell are automatically ‘escaped‘ when issued as CSV. commON allows you to specify your own delimiter for lists (the standard is the pipe ‘|’ character) and what the parser recognizes as the ‘escape’ character (’\’ is the standard). However, you probably should not change for most conditions.
The standard commON parsers and converters are UTF-8 compatible. If your source content has unusual encodings, try to target UTF-8 as your canonical spreadsheet output.
In the irON specification there are a small number of defined modules or processing sections. In commON, these modules are denoted by the double-ampersand character sequence (’&&‘), and apply to lists of instance records (&&recordList), dataset specifications and associated metadata describing the dataset (&&dataset), and mappings of attributes and types to existing schema (&&linkage). Similarly, attributes and types are denoted by a single ampersand prefix (&attributeName).
In commON, any or all of the modules can occur within a single CSV file or in multiple files. In any case, the start of one of these processing modules is signaled by the module keyword and &&keyword convention.
The first spreadsheet figure above shows a Sweet Tools example for the &&recordList module. The module begins with that keyword, indicating one of more instance records will follow. Note that the first line after the &&recordList keyword is devoted to the listing of attributes and types for the instance records (designated by the &attributeName convention in the columns for the first row after the &&recordList keyword is encountered).
The &&recordList format can also include the stacked style (see similar Dataset example below) in addition to the single row style shown above.
At any rate, once a worksheet is ready with its instance records following the straightforward irON and commON conventions, it can then be saved as a CSV file and appropriately named. Here is an example of what this “vanilla” CSV file now looks like when shown again in a spreadsheet:
Alternatively, you could open this same file in a text editor. Here is how this exact same instance record view looks in an editor:
Note that the CSV format separates each column by the comma separator, with escapes shown for the &description attribute when it includes a comma-separated clause. Without word wrap, each record in this format occupies a single row (though, again, for the stacked style, multiple entries are allowed on individual rows so long as a new instance record &id is not encountered in the first column).
The &&dataset module defines the dataset parameters and provides very flexible metadata attributes to describe the dataset [8]. Note the dataset specification is exactly equivalent in form to the instance record (&&recordList) format, and also allows the single row or stacked styles (see these instance record examples), with this one being the stacked style:
The &&linkage module is used to map the structure of the instance records to some structural schema, which can also include external ontologies. The module has a simple, but specific structure.
Either attributes (presented as the &attributeList) or types (presented as the &typeList) are listed sequentially by row until the listing is exhausted [8]. By convention, the second column in the listing is the targeted &mapTo value. Absent a prior &prefixList value, the &mapTo value needs to be a full URL to the corresponding attribute or type in some external schema:
Notice in the case of Sweet Tools that most values are from the actual COSMO mini-ontology underlying the listing. These need to be listed as well, since absent the specifications in commON the system has NO knowledge of linkages and mappings.
In its current state of development, commON does not support a spreadsheet-based means for specifying the schema structure (lightweight ontology) governing the datasets [2]. Another irON serialization, irJSON, does. Either via this irJSON specification or via an offline ontology, a link reference is presently used by commON (and, therefore, Sweet Tools for this case study) to establish the governing structure of the input instance record datasets.
A spreadsheet-based schema structure for commON has been designed and tested in prototype form. commON should be enhanced with this capability in the near future [8].
If the modules are spread across more than one worksheet, then each worksheet must be saved as its own CSV file. In the case of Sweet Tools, as exhibited by its reference current spreadsheet, sweet_tools_20091110.xls, three individual CSV files get saved. These files can be named whatever you would like. However, it is essential that the names be remembered for later referencing.
My own naming convention is to use a format of appname_date_modulename.csv because it sorts well in a file manager accommodating multiple versions (dates) and keeps related files clustered. The appname in the case of Sweet Tools is generally swt. The modulename is generally the dataset, records, or linkage convention. I tend to use the date specification in the YYYYMMDD format. Thus, in the case of the records listings for Sweet Tools, its filename could be something like: swt_20091110_records.csv.
Once saved, these files are now ready to be imported into a structWSF [9] instance, which is where the CSV parsing and conversion to interoperable RDF occurs [8]. In this case study, we used the Drupal-based conStruct SCS system [10]. conStruct exposes the structWSF Web services via a user interface and a user permission and access system. The actual case study write-up offers more details about the import process.
We are now ready to interact with the Sweet Tools structured dataset using conStruct (assuming you have a Drupal installation with the conStruct modules) [10].
The screen capture below shows a couple of aspects of the system:
(click to expand)One of the absolutely cool things about this framework is that all tools, inferencing, user interfaces and data structure are a direct result of the ontology(ies) underlying the system (plus the irON instance ontology, as well). This means that switching datasets or adding datasets causes the entire system structure to now reflect those changes — without lifting a finger!!
Here are a few sample things you can do with these generic tools driven by the Sweet Tools dataset:
Note, if you access this conStruct instance you will do so as a demo user. Unfortunately, as such, you may not be able to see all of the write and update tools, which in this case are reserved for curators or admins. Recall that structWSF has a comprehensive user access and permissions layer.
Of course, one of the real advantages of the irON and structWSF designs is to enable different formats to be interchanged and to interoperate. Upon submission, the commON format and its datasets can then be exported in these alternate formats and serializations [8]:
As should be obvious, one of the real benefits of the irON notation — in addition to easy dataset authoring — is the ability to more-or-less treat RDF, CSV, XML and JSON as interoperable data formats.
The formal Sweet Tools case study based on commON, with sample download files and PDF, is available from Annex: A commON Case Study using Sweet Tools, Supplementary Documentation [3].
.Attribute-values can also be presented as pairs in a form of an associative array, where the first item listed is the attribute, often followed by a separator such as the colon, and then the value. JSON and many simple data struct notations follow this format. This format may also be called attribute-value pairs, key-value pairs, name-value pairs, alists or others. In these cases the “object” is implied, or is introduced as the name of the array..
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!
I have been meaning to write on the semantic enterprise for some time. I have been collecting notes on this topic since the publication by PricewaterhouseCoopers (PWC) of an insightful 58-pp report earlier this year [1]. The PWC folks put their finger squarely on the importance of ontologies and the delivery of semantic information via linked data in that publication.
The recent publication of a special issue of the Cutter IT Journal devoted to the semantic enterprise [2] has prompted me to finally put my notes in order. This Cutter volume has a couple of good articles including its editorial intro [3], but is overall spotty in quality and surprisingly unexciting. I think it gets some topics like the importance of semantics to data integration and business intelligence right, but in other areas is either flat wrong or misses the boat.
The biggest mistake are statements such as “. . . a revolutionary mindset will be needed in the way we’ve traditionally approached enterprise architecture” or that the “. . . semantic enterprise means rethinking everything.”
This is just plain hooey. From the outset, let’s make one thing clear: No one needs to replace anything in their existing architecture to begin with semantic technologies. Such overheated rhetoric is typical consultant hype and fundamentally mischaracterizes the role and use of semantics in the enterprise. (It also tends to scare CIOs and to close wallets.)
As an advocate for semantics in the enterprise, I can appreciate the attraction of framing the issue as one of revolution, paradigm shifts, and The Next Big Thing. Yes, there are manifest benefits and advantages for the semantic enterprise. And, sure, there will be changes and differences. But these changes can occur incrementally and at low risk while experience is gained.
The real key to the semantic enterprise is to build upon and leverage the assets that already exist. Semantic technologies enable us to do just that.
Think about semantic technologies as a new, adaptive layer in an emerging interoperable stack, and not as a wholesale replacement or substitution for all of the good stuff that has come before. Semantics are helping us to bridge and talk across multiple existing systems and schema. They are asking us to become multi-lingual while still allowing us to retain our native tongues. And, hey! we need not be instantly fluent in these new semantic languages in order to begin to gain immediate benefits.
As I noted in my popular article on the Advantages and Myths of RDF from earlier this year:
That is still a key takeaway message from this piece. But, let’s look and list with a fresh perspective the advantages of moving toward the semantic enterprise [4].
For the interconnected reasons noted below, RDF and semantic technologies are inherently incremental, additive and adaptive. The RDF data model and the vocabularies built upon it allow us to progress in the sophistication of our expressions from pidgin English (simple Dick sees Jane triples or assertions) to elegant and expressive King’s English. Premised on the open world assumption (see below), we also have the freedom to only describe partial domains or problem areas.
From a risk standpoint, this is extremely important. To get started with semantic technologies we neither need to: 1) comprehensively describe or tackle the entire enterprise information space; nor 2) do so initially with precision and full expressiveness. We can be partial and somewhat crude or simplistic in our beginning efforts.
Also extremely important is that we can add expressivity and scope as we go. There is no penalty for starting small or simple and then growing in scope or sophistication. Just like progressing from a kindergarten reader to reading Tolstoy or Dickens, we can write and read schema of whatever complexity our current knowledge and understanding allow.
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. Writing and publishing information, sometimes as documents and sometimes as spreadsheets or Web pages, is (and will remain) the major vehicle for communicating within the enterprise and to external constituents.
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. Moreover, as we also know, these activities are undertaken for many different purposes and within many different contexts. The inherent meaning of these activities is also therefore contextual and varied.
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.
These observations — in combination with semantic technologies — can thus lead to a conceptual architecture for the enterprise that recognizes there are “silo” activities that can still be bridged with the semantic layer:
Under this conceptual architecture, “RDFizers” (similar to the ETL function) or information extractors working upon unstructured or semi-structured documents expose their underlying information assets in RDF-ready form. This RDF is characterized by one or more ontologies (multiples are actually natural and preferred [5]), which then can be queried using the semantic querying language, SPARQL.
We have written at length about proper separation of instance records and data and schema, what is called the ABox and TBox, respectively, in description logics [6], a key logic premise to the semantic Web. Thus, through appropriate architecting of existing information assets, it is possible to leave those systems in place while still gaining the interoperability advantages of the semantic enterprise.
Another aspect of this information re-use is also a commitment to leverage existing schema structures, be they industry standards, XML, MDM, relational schema or corporate taxonomies. The mappings of these structures in the resulting ontologies thus become the means to codify the enterprise’s circumstances into an actionable set of relationships bridging across multiple, existing information assets.
Clearly, then, the first obvious benefit to the semantic enterprise is to federate across existing data silos, as featured prominently in the figure above. Data federation has been the Holy Grail of IT systems and enterprises for more than three decades. Expensive and involved efforts from ETL and MDM and then to enterprise information integration (EII), enterprise application integration (EAI) and business intelligence (BI) have been a major focus.
Frankly, it is surprising that no known vendors in these spaces (aside from our own Structured Dynamics, hehe) premise their offerings on RDF and semantic technologies. (Though some claim so.) This is a major opportunity area. (And we don’t mind giving our competitors useful tips.)
Instance-level records and the ABox work well with relational databases. Their schema are simple and relatively fixed. This is fortunate, because such instance records are the basis of transactional systems where performance and throughput are necessary and valued.
But at the level of the enterprise itself — what its business is, its business environment, what is constantly changing around it — trying to model its world with relational schema has proven frustrating, brittle and inflexible. Though relational and RDF schema share much logically, the physical basis of the relational schema does not lend itself to changes and it lacks the flexibility and malleability of the graph-based RDF conceptual structure.
Knowledge management and business intelligence are by no means new concepts for the enterprise. What is new and exciting, however, is how the emergence of RDF and the semantic enterprise will open new doors and perspectives. Once freed of schema constraints, we should see the emergence of “agile KM” similar to the benefits of agile software development.
Because semantic technologies can operate in a layer apart from the standard data basis for the enterprise, there is also a smaller footprint and risk to experimenting at the KM or conceptual level. More options and more testing and much lower costs and risks will surely translate to more innovation.
Just as semantic technologies are poorly suited for transactional or throughput purposes, we should see the complementary and natural migration of KM to the semantic side of the shop. There are no impediments for this migration to begin today. In the process, as yet unforeseen and manifest benefits in agility, experimentation, inferencing and reasoning, and therefore new insights, will emerge.
The same ontologies that guide the data federation and interoperability layer can also do double-duty as the specifications for data-driven applications. The premise is really quite simple: Once it is realized that the inherent information structure contained within ontologies can guide hierarchies, facets, structured retrievals and inferencing, the logical software design is then to “drive” the application solely based on that structure. And, once that insight is realized, then it becomes important, as a best practice, to add further specifications in order to also carry along the information useful for “driving” user interfaces [7].
Thus, while ontologies are often thought solely to be for the purpose of machine interpretation and communication, this double-duty purpose now tells us that useful labels and such for human use and consumption is also an important goal.
When these best practices of structure and useful human labels are made real, it then becomes possible to develop generic software applications, the operations of which vary solely by the nature of the structure and ontologies fed to them. In other words, ontologies now become the application, not custom-written software.
Of course, this does not remove the requirement to develop and write software. But the nature and focus of that development shifts dramatically.
From the outset, data-driven software applications are designed to be responsive to the structure fed them. Granted, specific applications in such areas as search, report writing, analysis, data visualization, import and export, format conversions, and the like, still must be written. But, when done, they require little or no further modification to respond to whatever compliant ontologies are fed to them — irrespective of domain or scope.
It thus becomes possible to see a relatively small number of these generic apps that can respond to any compliant structure.
The shift this represents can be illustrated by two areas that have been traditional choke points for IT within the enterprise: queries to local data stores (in order to get needed information for analysis and decisions) and report writers (necessary to communicate with management and constituents).
It is not unusual to hear of weeks or months delays in IT groups responding to such requests. It is not that the IT departments are lazy or unresponsive, but that the schema and tools used to fulfill their user demands are not flexible.
It is hard to know just how large the huge upside is for data-driven apps and generic tools. But, this may prove to be of even greater import than overcoming the data federation challenge.
In any event, while potentially disruptive, this prospect of data-driven applications can start small and exist in parallel with all existing ways of doing business. Yes, the upside is huge, but it need not be gained by abandoning what already works.
So, assume, then, a knowledge management (KM) environment supported by these data-driven apps. What perspective arises from this prospect?
One obvious perspective is where the KM effort shifts to become 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 foundation of KM activities.
An earlier perspective emphasized how most any existing structure can become a starting basis for 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 worth keeping on the payroll has, by definition, the necessary skills to contribute to useful ontology development and refinement.
With adaptive ontologies powering data-driven apps we thus see a shift in roles and responsibilities away from IT to knowledge workers themselves. This shift acts to democratize the knowledge management function and flatten the organization.
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 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.
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 at the ontology level. 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 and easily accommodated.
As noted, one advantage of open-world reasoning at the ontological level is the ability to readily change and grow the conceptual understanding and coverage of the world, including incorporation of external ontologies and data. Since this can easily co-exist with underlying closed-world data, the semantic enterprise can readily bridge both worlds.
Unfortunately, as a relatively new area there are advantages for some pundits or consultants to present the semantic Web as more complicated and commitment-laden than it need be. Either the proponents of that viewpoint don’t know what they are saying, or are being cynical to the market. The major point underlying the fresh perspectives herein is to iterate that it is quite possible to start small, and do so with low cost and risk.
While it is true that semantic technologies within the enterprise promise some startling upside potentials and disruptions to the old ways of doing business, the total beauty of RDF and its capabilities and this layered model is that those promises can be realized incrementally and without hard choices. No, it is not for free: a commitment to begin the process and to learn is necessary. But, yes, it can be done so with exciting enterprise-wide benefits at a pace and risk level that is comfortable.
The good news about the dedicated issue of the Cutter IT Journal and the earlier PWC publication is that the importance of semantic technologies to the enterprise is now beginning to receive its just due. But as we ramp up this visibility, let’s be sure that we frame these costs and benefits with the right perspectives.
The semantic enterprise offers some important new benefits not obtainable from prior approaches and technologies. And, the best news is that these advantages can be obtained incrementally and at low risk and cost while leveraging prior investments and information assets.
The Message Understanding Conferences (MUC) were initiated in 1987 and financed by DARPA to encourage the development of new and better methods of information extraction (IE). It was a seminal series that resulted in basic measures of retrieval and semantic efficacy, recall (R) and precision (P) and the combined F-measure, and other core terminology and constructs used by IE today.
By the sixth version in the series (MUC-6), in 1995, the task of recognition of named entities and coreference was added. That initial slate of named entities included the basic building blocks of person (PER), location (LOC), and organization (ORG); to these were added the numeric building blocks of time, percentage or quantity. The very terminology of named entity was coined for this seminal meeting, as was the idea of inline markup [1].
The intuition surrounding “named entity” and nameable “things” was that they were discrete and disjoint. A rock is not a person and is not a chemical or an event. As initially used, all “named entities” were distinct individuals. But, there also emerged the understanding that some classes of things could also be treated as more-or-less distinct nameable “things”: beetles are not the same as frogs and are not the same as rocks. While some of these “things” might be a true individual with a discrete name, such as Kermit the Frog, or The Rock at Northwestern University, most instances of such things are unnamed.
The “nameability” (or logical categorization) of things is perhaps best kept separate from other epistemological issues of distinguishing sets, collections, or classes from individuals, members or instances.
In a closed-world system it is easier to enforce clean distinctions. The Cyc knowledge base, for example, the basis for UMBEL (Upper Mapping and Binding Exchange Layer), makes clear the distinction between individuals and collections. In the semantic Web and RDF, this can become smeared a bit with the favored terminology shifting to instances and classes, and in pragmatic, real-world terms we (as humans) readily distinguish John Smith as distinct from Jane Doe but don’t generally (unless we’re entomologists!) make such distinctions for individual beetles, let alone entire genera or species of beetles.
Under precise conditions, these distinctions are important. The fact that Cyc, for example, is assiduous in its application of these distinctions is a major reason for the overall coherence of its knowledge base. But, for most circumstances, we think it is OK to accept a distinction between “nameable” things such as frogs and beetles, but also to accept that there may be nameable individuals at times in those groupings such as Kermit that are truly an individual in that more refined sense.
This digression sets the background for a natural progression from that first MUC-6 conference. If we could cluster persons or organizations, why not other categories of distinct and disjoint things such as frogs or beetles or rocks?
From the first six entity categories of MUC-6 we begin to see an expansion to broader coverage. Readers of this blog will recall that I have been a fan for quite some time of the expanded coverage of 64 classes of entities proposed by BBN or the 200 proposed by Sekine [2] (as discussed, for example in the April 2008 Subject Concepts and Named Entities article). Again, the intuition was that real things in the real world could be logically categorized into discrete and disjoint categories.
Thus, “named entities” inexorably moved to become a categorization system, where the degree of familiarity and distinction dictated whether it was the individual (with a unique name, such as Abraham Lincoln or Mt. Rushmore) or groupings such as animal or plant species and their common names (such as beetle or oak) that was the standard “handle” for assigning a name to the “nameable thing”.
While many can argue these individual <–> grouping distinctions and whether we are talking about true, unique, named individuals or names of convenience, I think that (at least for this blog post and discussion), that misses the real, fundamental point.
The real, fundamental point is that some “things” (whether individuals, instances or classes) are distinct from other “things”. Such disjoint distinctions are a powerful concept that should not be lost sight of by “angels dancing on the head of a pin” epistemological arguments. A frog is not a rock, despite neither are “individuals”, and how can we take advantage of that realilty?
Nearly from the outset of our work with UMBEL as a ‘TBox’ [3] — that is, as a set of 20,000 or so common “subject concepts” — the natural question was what the relation or correspondence was of these concepts to the underlying “things” (entities) that they organized. As we probed the disjoint categories within the Sekine 200 entity types, for example, we began to see significant parallels and overlap. Also gnawing at our sense of order was the rather artificial and arbitrary class of concepts in UMBEL that we termed “Abstract Concepts”.
We introduced Abstract Concepts in the first release of UMBEL. When introduced, we defined “Abstract concepts [as] representing abstract or ephemeral notions such as truth, beauty, evil or justice, or [as] thought constructs useful to organizing or categorizing things but are not readily seen in the experiential world.” In pragmatic terms, Abstract Concepts in UMBEL were often pivotal nodes in the UMBEL subject graph necessary to maintain a high degree of concept interconnectivity.
In any world view that attempts to be more-or-less comprehensive, there is a gradation of concepts from the concrete and observable to the abstract and ephemeral. The recognition that some of these concepts may be more abstract, then, was not the issue. The issue was that there was no definable basis for segregating a concrete Subject Concept from the more Abstract Concept. Where was the bright line? What was the actionable distinction?
Off and on we have probed this question for more than a year, and have looked at what might constitute a more natural and logical ordering and segmentation within UMBEL. After many tests and detailed analysis, we are now releasing the first results of our investigations.
For, like nameable entities or things, we can see a logical segmentation of (mostly) disjoint concepts within the UMBEL TBox. Here are the summary percentages of these high-level splits:
| Disjoint Concepts | 90% |
| Attributes | 1% |
| Classifications | 9% |
| TOTAL | 100% |
(Because the analysis is still being refined, exact counts and percentages for the 20,000 concepts in UMBEL are not provided.)
As we dove deeper into these ideas, not only could we see the basis for a logical segmentation within UMBEL’s concepts, but manifest benefits from doing so as well. Remember that UMBEL’s concept structure performs two main roles. It: 1) provides a coherent framework for relating and “mapping” other external ontologies; and 2) provides conceptual binding points for organizing entities and instances [4]. Via logical segmentation, we get benefits for both roles.
Here are some of the broad areas of benefit from a logical UMBEL segmentation that we have identified:
With these benefits in mind, we have undertaken concerted analysis of UMBEL to discern what this “logical segmentation” might be. This investigation has occurred over three concentrated periods over the past year. (Intervening priorities or other work prevented concentrating solely on this task.)
We are now complete with our first full iteraton of investigation. In this post, and then the subsequent release of UMBEL version 0.80 in the coming weeks, the fruits of this effort should be evident. However, it should also be noted that we are still learning much from this new mindset and approach. UMBEL structure refinement may be likely for some time to come.
Most things and concepts about them are based on real, observable, physical things in the real world. Because most of these things can not occupy both the same moment in time and the same location in physical space, a useful criterion for looking at these things and concepts is disjointedness.
In a broad sense, then, we can split our concepts of the world between those ideas that are disjoint because they pertain to separable objects or ideas and those that are cross-cutting or organizational or classificatory. Attributes, such as color (pink, for example), are often cross-cutting in that they can be used to describe quite disparate things. Inherent classification schemes such as academic fields of study or library catalog systems — while useful ways to organize the world — are not themselves in-and-of the world or discrete from other ideas. Thus, classificatory or organizational concepts are inherently not disjoint.
With the criterion of disjointedness in hand, then, we began an evaluation process of the UMBEL subject concepts. We looked to organizational schema such as the entity types of Sekine or BBN for some starting guidance. We also kept in mind that we also wanted our categories to inform logical clusterings of possible data presentation, such as media types or locations or time.
For terminology, we adopted the term superType to denote the largest cluster designation upon which this disjointedness may occur. As a way to test the basic coherence of these superTypes, we also collected them into larger groups which we termed dimensions.
Our analysis process began with branch-by-branch testing of the UMBEL concept graph using automated scripts, attempting to find pivotal nodes where child instance members were disjoint from other superTypes. This we term the “top-down” method.
This automated analysis was then supplemented with a complete manual inspection of all unassigned and assigned concepts, with a “bottom up” assignment of concepts or corrections to the automated approach. This inspection then led to new insights and identification of missing concepts that needed to be added into UMBEL.
We are still converging between these two methods. Optimally, we should be able to tease out all UMBEL superTypes with a relatively few number of union, intersection, or complement set operations. In its current form, we are close, but there are still some rough spots.
Nonetheless, this analysis method has led us to identify some 33 superTypes [5], clustered into 9 dimensions. Of these, 29 superTypes and 8 dimensions are mostly disjoint. The one dimension of Classificatory includes the four cross-cutting superTypes of attributes and organizational schema that can apply to any of the 29 disjoint superTypes.
Here is the schema, with the descriptions of each:
| Dimension | superType | Description/Sub-types |
| Natural World | Natural Phenomena | This superType includes natural phenomena and natural processes such as weather, weathering, erosion, fires, lightning, earthquakes, tectonics, etc. Clouds and weather processes are specifically included. Also includes climate cycles, general natural events (such as hurricanes) that are not specifically named, and biochemical processes and pathways. |
| Natural Substances | Notable inclusions are minerals, compounds, chemicals, or physical objects that are not the outcome of purposeful human effort, but are found naturally occurring. Other natural objects (such as rock, fossil, etc.) are also found under this superType. | |
| Earthscape | The Earthscape superType consists mostly of the collection of cartographic features that occur on the surface of the Earth. Positive examples include Mountain, Ocean, and Mesa. Artificial features such as canals are excluded. Most instances of these features have a fixed location in space.
Underground and underwater are also explicitly contained. This superType is explicitly disjoint with Extraterrestrial (see below). |
|
| Extraterrestrial | This superType includes all natural things not specifically terrestrial, including celestial bodies (planets, asteroids, stars, galaxies, etc., that can be located within a sky map) | |
| Living Things | Prokaryotes | The Prokaryotes include all prokaryotic organisms, including the Monera, Archaebacteria, Bacteria, and Blue-green algas. Also included in this superType are viruses and prions. |
| Protists or Fungus | This is the remaining cluster of eukaryotic organisms, specifically including the fungus and the protista (protozoans and slime molds). | |
| Plants | This superType includes all plant types and flora, including flowering plants, algae, non-flowering plants, gymnosperms, cycads, and plant parts and body types. Note that all Plant Parts are also included. | |
| Animals | This large superType includes all animal types, including specific animal types and vertebrates, invertebrates, insects, crustaceans, fish, reptiles, amphibia, birds, mammals, and animal body parts. Animal parts are specifically included. Also, groupings of such animals are included. Humans, as an animal, are included (versus as an individual Person). Diseases are specifically excluded. | |
| Diseases | Diseases are atypical or unusual or unhealthy conditions for (mostly human) living things, generally known as conditions, disorders, infections, diseases or syndromes. Diseases only affect living things and sometimes are caused by living things. This superType also includes impairments, disease vectors, wounds and injuries, and poisoning | |
| Person Types | The appropriate superType for all named, individual human beings. This superType also includes the assignment of formal, honorific or cultural titles given to specific human individuals. It further includes names given to humans who conduct specific jobs or activities (the latter case is known as an avocation). Examples include steelworker, waitress, lawyer, plumber, artisan. Ethnic groups are specifically included. | |
| Human Activities | Organizations | Organization is a broad superType and includes formal collections of humans, sometimes by legal means, charter, agreement or some mode of formal understanding. Examples include geopolitical entities such as nations, municipalities or countries; or companies, institutes, governments, universities, militaries, political parties, game groups, international organizations, trade associations, etc. All institutions, for example, are organizations.
Also included are informal collections of humans. Informal or less defined groupings of humans may result from ethnicity or tribes or nationality or from shared interests (such as social networks or mailing lists) or expertise (”communities of practice”). This dimension also includes the notion of identifiable human groups with set members at any given point in time. Examples include music groups, cast members of a play, directors on a corporate Board, TV show members, gangs, mobs, juries, generations, minorities, etc. Finally, Organizations contain the concepts of Industries and Programs and Communities. |
| Finance & Economy | This superType pertains to all things financial and with respect to the economy, including chartable company performance, stock index entities, money, local currencies, taxes, incomes, accounts and accounting, mortgages and property. | |
| Culture, Issues, Beliefs | This category includes concepts related to political systems, laws, rules or cultural mores governing societal or community behavior, or doctrinal, faith or religious bases or entities (such as gods, angels, totems) governing spiritual human matters. Culture, Issues, beliefs and various activisms (most -isms) are included | |
| Activities | These are ongoing activities that result (mostly) from human effort, often conducted by organizations to assist other organizations or individuals (in which case they are known as services, such as medicine, law, printing, consulting or teaching) or individual or group efforts for leisure, fun, sports, games or personal interests (activities) | |
| Human Works | Products | This is the largest superType and includes any instance offered for sale or performed as a commercial service. Often physical object made by humans that is not a conceptual work or a facility, such as vehicles, cars, trains, aircraft, spaceships, ships, foods, beverages, clothes, drugs, weapons. Products also include the concept of ’state’ (e/g/., on/off) |
| Food or Drink | This superType is any edible substance grown, made or harvested by humans. The category also specifically includes the concept of cuisines | |
| Drugs | This superType is an drug, medication or addictive substance | |
| Facilities | Facilities are physical places or buildings constructed by humans, such as schools, public institutions, markets, museums, amusement parks, worship places, stations, airports, ports, carstops, lines, railroads, roads, waterways, tunnels, bridges, parks, sport facilities, monuments. All can be geospatially located.
Facilities also include animal pens and enclosures and general human “activity” areas (golf course, archeology sites, etc.). Importantly, Facilities include infrastructure systems such as roadways and physical networks. Facilities also include the component parts that go into making them (such as foundations, doors, windows, roofs, etc.) |
|
| Information | Chemistry (n.o.c) | This superType is a residual category (n.o.c., not otherwise categorized) for chemical bonds, chemical composition groupings, and the like. It is formed by what is not a natural substance or living thing (organic) substance. |
| Audio Info | This superType is for any audio-only human work. Examples include live music performances, record albums, or radio shows or individual radio broadcasts | |
| Visual Info | This superType includes any still image or picture or streaming video human work, with or without audio. Examples include graphics, pictures, movies, TV shows, individual shows from a TV show, etc. | |
| Written Info | This superType includes any general material written by humans including books, blogs, articles, manuscripts, but any written information conveyed via text. | |
| Structured Info | This information superType is for all kinds of structured information and datasets, including computer programs, databases, files, Web pages and structured data that can be presented in tabular form | |
| Notations & References | Akin to conceptual works, these are codified means of human expression. Examples range from human languages themselves, to more domain-specific cases such as chemical symbols, genetic code (A-G-C-T), protocols, and computer languages, mathematical and set notations, etc.
Identifiers (numeric or alphanumeric identifiers for objects, often in a highly patterned way, such as phone numbers, URLs, zip and postal codes, SKUs, product codes, etc.), Units (any of the various ways in which measurement, space, volume, weight, speed, intensity, temperature, calories, siesmic intensity or other quantitative descriptions of phenomena can be made) and key reference types are also included in this superType |
|
| Numbers | This unique superType is for any abstract representation of numbers and numerics | |
| Human Places | Geopolitical | Named places that have some informal or formal political (authorized) component. Important subcollections include Country, IndependentCountry, State_Geopolitical, City, and Province. |
| Workplaces, etc. | These are various workplaces and areas of human activities, ranging from single person workstations to large aggregations of people (but which are not formal political entities) | |
| Time-related | Events | These are nameable occasions, games, sports events, conferences, natural phenomena, natural disasters, wars, incidents, anniversaries, holidays, or notable moments or periods in time |
| Time | This superType is for specific time or date or period (such as eras, or days, weeks, months type intervals) references in various formats | |
| Descriptive | Attributes | This general superType category is for descriptive attributes of all kinds. Think of the specific attributes in Wikipedia “infoboxes” to understand the purpose and coverage of this superType. It includes colors, shapes, sizes, or other descriptive characteristics about an object |
| Classificatory | Abstract-level | This general superType category is largely composed of former AbstractConcepts, and represent some of the more abstract upper-level nodes for connecting the UMBEL structure together. This superType also includes theories or processes or methods for humans to do stuff or any human technology |
| Topics/Categories | This largely subject-oriented superType is a means for using controlled vocabularies and classification schemes for characterizing what content “is about”. The key constituents of this category are Types, Classifications, Concepts, Topics, and controlled vocabularies | |
| Markets & Industries | This superType is a specialized classificatory system for markets and industries. It could be combined with the superType above, but is kept separate in order to provide a separate, economy-oriented system. |
These may undergo some further refinement prior to release of UMBEL v 0.80, and some of the definitions will be tightened up.
(Note: It should also be mentioned that some of these superTypes further lend themselves to further splits and analysis. The Product superType, for example, is ripe for such treatment.)
The following diagram shows the distribution of these 20,000 UMBEL concepts across major area. By far the largest superType is Products, even with further splits into Food and Drinks and Pharmaceuticals. The next largest categories are Person and Places and Events superTypes, with Organizations and Animals not far behind:
Even in its generic state, UMBEL provides a very rich vocabulary for describing things or for tying in more detailed external ontologies. There are nearly 5,000 concepts across products of all types, for example.
You may recall that our analysis showed 29 of the superTypes to be “mostly disjoint.” This is because there are some concepts — say, MusicPerformingAgent — that can apply to either a person or a group (band or orchestra, for example). Thus, for this concept alone, we have a bit of overlap between the normally disjoint Person and Organization superTypes.
The following shows the resulting interaction matrix where there may be some overlap between superTypes:
This kind of interaction diagram is also useful for further analyzing the concept graph structure, as well.
Of the 29 “mostly” disjoint superTypes, only a relatively few show potential interactions, and then only in minor ways. We can illustrate this (drawn to scale) for the interaction between the Product, Food & Drink and Drug (Pharmaceuticals) superTypes, with the fully disjoint Organization superType thrown in for comparison:
Across all 20,000 concepts, then, fully 85% are disjoint from one another (5% is lost due to overlaps between “mostly” disjoint superTypes). This is a surprising high percentage, with even better likelihood to deliver the benefits previously noted.
These are exciting findings that bode well for UMBEL’s ongoing role and usefulness. Also, the very detailed analysis that has led to these interim findings very much reaffirms the wisdom of basing UMBEL on Cyc. Cyc showed itself to be admirably coherent and remarkably complete. (It also appears that the first versions of UMBEL were also extracted well in terms of good coverage.)
This approach now gives us an understandable and defensible basis for logical segementation of UMBEL. It also provides a much-desired alternative to the earlier Abstract Concepts, which will now be dropped entirely as a schema concept.
One area deserving further attention is in the Attribute superType. We are in the process, for example, of analyzing attributes across Wikipedia and need to look through a slightly different lens at this superType [6]. This area is further important in its strong interaction with the Instance Record Vocabulary that is accompanying this effort on the entity side.
Another lesson for us has been to back away from the terminology of named entity, introduced at MUC-6. The expansions of that idea into other “nameable” things has caused us to embrace the “instance” nomenclature, as evidenced by our emerging IRV.
It is rewarding to prepare this next iteration release of UMBEL with its new mindset of logical segmentation and disjointedness. But — what is also clear — there are many treasures left to mine still hidden in the inherent structure of UMBEL and its Cyc parent.
Sekine’s extended hierarchy proposed in 2002 is made up of 200 subtypes, with 32 larger clusters within that. Here is the top level of the Sekine type system:
| Name-Other | Title | Timex | Frequency |
| Person | Unit | Periodx | Rank |
| Organization | Vocation | Numex-Other | Age |
| Location | Disease | Money | School Age |
| Facility | God | Stock Index | Latitude Longitude |
| Product | ID Number | Point | Measurement |
| Event | Color | Percent | Countx |
| Natural Object | Time-Other | Multiplication | Ordinal Number |
Though developed separately and for different purposes, BBN categories also proposed in 2002 consists of 29 types and 64 subtypes. Here are the BBN types (Note: BBN claims 29 types because there are double entries or considerations for the first five entries):
| Person | Time | Animal |
| NORP (adjectival GPEs) | Percent | Substance |
| Facility | Money | Disease |
| Organization | Quantity | Work of Art |
| GPE (geopolitical places) | Ordinal | Law |
| Location | Cardinal | Language |
| Product | Events | Contact Info |
| Date | Plant | Game |
Of course, other entity extraction systems have similar clusterings and approaches. Though less formal in the sense of a hierarchy or purported complete entity coverage, here for example is the listing of entity types within Calais:
| Anniversary | FaxNumber | NaturalFeature | RadioProgram |
| City | Holiday | OperatingSystem | RadioStation |
| Company | IndustryTerm | Organization | Region |
| Continent | MarketIndex | Person | SportsEvent |
| Country | MedicalCondition | PhoneNumber | SportsGame |
| Currency | Movie | Position | SportsLeague |
| EmailAddress | MusicAlbum | Product | Technology |
| EntertainmentAwardEvent | MusicGroup | ProgrammingLanguage | TVShow |
| Facility | NaturalDisaster | ProvinceOrState | TVStation |
| PublishedMedium | URL |
See further the Wikipedia entry on named entity recognition.
