A New Slide Show Consolidates, Explains Recent Developments

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!

Fred Giasson has just announced that Structured Dynamics has moved and is now hosting the new UMBEL Web services. Check out his “New Home for UMBEL Web Services” post to learn more.

I should mention that Structured Dynamics has also used this migration to update parts of its Web site, as well.

The Significant Advantages to a Logically Segmented TBox

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].

What is a ‘Nameable Thing’?

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?

What Works for Entities, Works for Concepts

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:

(Because the analysis is still being refined, exact counts and percentages for the 20,000 concepts in UMBEL are not provided.)

Why a Logical Segmentation?

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:

• Template-driven — as we discuss elsewhere, Structured Dynamics also uses its ontologies to “drive applications” and the user interfaces (UI) that support them. By proper segmentation of UMBEL concepts, we are able to determine to what “cluster” of things (which we call either dimensions or superTypes; see below) a given thing belongs. This identification means we can also determine how best to display information about that “thing”. This determination can include either the attributes or the display templates appropriate for that thing. For example, location-based things or time-based things might invoke map or calendar or timeline type displays. Moreover, because of the logical segmentation of concepts, we can also use the power of the concept graph to infer more generic display templates when specific matches are absent
• Computational Efficiency — as the percentages above indicate, once we identify what superType concept to which a given instance belongs, we can eliminate nearly all remaining UMBEL concepts from consideration. This logical winnowing leads to computational efficiencies at all levels in the system. The fastest computational work is not to do it, and when large chunks of data are removed from consideration, many performance advantages accrue
• Disambiguation — via this approach we now can assess concept matches in addition to entity matches. This means we can triangulate between the two assessments to aid disambiguation. Because of these logical segmentations, we also have multiple “clusters” (that is, either the concept, type, superType or dimension) upon which to do our disambiguation evaluations, either between concepts and entities or within the various concept clusters. We can do so via either multiple semantic vectors (for statistical-based methods) or multiple features (for machine learning methods). In other words, because of logical segmentation, we have increased the informational power of our concept graph
• Structure and Integrity Testing — the very mindset of looking for logical segmentation has led to much learning about the UMBEL structure and OpenCyc upon which it is based. In the process, missing nodes (concepts), erroneous assignments, and superfluous nodes are all being discovered. Further, many of these tests can be automated using basic logical and inference approaches. The net result is a constant improvement to the scope and completeness of the structure. Lastly, these same approaches can be applied when mapping external ontologies to UMBEL, providing similar consistency benefits.

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.

UMBEL Analysis

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.

UMBEL superTypes

Here is the schema, with the descriptions of each:

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.)

Distribution of superTypes

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.

Possible Overlaps (non-disjoint) between superTypes

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.

Even Where Overlaps Occur, They are Minor

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.

Interim Conclusions and Observations

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.

Ontology Best Practices for Data-driven Applications: Part 4

The earlier portions of this occasional series have set the groundwork for the role of ontologies in data-driven applications. In this part, I address many of the current misconceptions of what ontologies do or do not do. For, as practiced by Structured Dynamics, our adaptive TBox-level ontologies [1] are definitely not your grandfather’s Oldsmobile.

To share the punch line early, these modern ontologies are fast to develop, easy to change, adaptive to new knowledge and perceptions, robust and flexible. Indeed, it is the structure and nature of these adaptive ontologies that is the heart and secret of data-driven applications.

Any knowledge worker can understand and refine the organization and relationship of information via these structures. And, most importantly, the resulting ontologies are sufficient to drive the generic applications that are based on them. Focusing on data and structure now becomes the emphasis. We can now remove prior bottlenecks arising from the need to customize applications, configure report writers, or wait for IT to generate SQL queries.

But, not all ontologies are created equally and not all practitioners explain or see them in the same way. The purpose of this Part 4 in our series is to present many of the misconceptions, offering a score of takeaway messages for how properly considered and constructed ontologies can achieve these benefits.

Misconception: No ‘Big Bang’ Needed

To be sure, there are many very large and comprehensive ontologies. Some are focused on specific applications or domains; some are general; and some are the result of large and well-funded projects [2]. I am not arguing that such efforts do not have their role and place. But when viewed as exemplars or notable cases, these complex and comprehensive ontologies can create a misconception that such a scope is an imperative of proper ontology design.

I believe quite the opposite to be true.

An incredible strength of RDF and OWL ontologies is that they can be built incrementally. So long as additions are coherent with some degree of self-consistency in terms of the world view in which they are represented, any of an ontology’s constituent concepts, predicates or entities and datasets can be added and enhanced as needed. This makes ontologies a very different cat from relational schema, which are notoriously brittle with expensive re-architecting required anytime that scope or schema change.

Enterprise consultants that advocate “big” upfront ontology development efforts are doing their clients a massive disservice. They are also cynically playing on the experience with relational schema. As soon as the marketplace begins to realize that ontologies are incredibly plastic and malleable, this huge advantage of ontologies over the relational model for data federation will ring clear.

Takeaway Message #1: Ontologies can (and should!) start small.

Takeaway Message #2: Ontologies can (and should!) grow incrementally.

Misconception: No ‘One Ring to Rule Them All’

As a practitioner, two of the most boring arguments I hear are: Ontology X is better than other ontologies and here is why; and, Use of some reference or upper ontology reduces choice and freedom. Both arguments are somewhat grounded in the ‘one ring to rule them all’ mindset — though coming from opposing perspectives — that I think fundamentally misreads the role and purpose of ontologies.

Ontologies provide an organizing context for relating disparate information together and for making meaningful inferences. Without such a framework these purposes can not be achieved. But the framework itself is a function of the world view, context and domain scope at hand. As a result, there is only context, and not some single, universal “truth.” As they say, it all depends.

The trick, then, to properly designed ontologies is to maintain internal coherence and self-consistency [3]. When done, it is then possible to relate disparate information and data to other data and to make intelligent business inferences.

So, the use of an ontology does not limit freedom. It sets the context for making connections and setting relations. And, as long as it is coherent, the “correct” ontology is the one that best captures the scope and domain at hand. Arguing for one ontology v another is wasted energy. Just get on with it.

Takeaway Message #3: There is no single “truth”, only coherence and relevant context.

Misconception: No Such Thing as an ‘Ontological Commitment’

One of the more pernicious ideas promoted by some practitioners or advocates is the idea of ‘ontological commitment.’ Though some definitions are relatively benign, such as the one offered by the Stanford Knowledge Systems Laboratory (KSL) [4], the unfortunate use of the term “commitment” implies permanence and immutability. (In fact, most definitions of this phrase affirm this interpretation.)

This is really unfortunate, as it again tends to reinforce the inaccurate analogies with brittle and inflexible relational schema.

A much better way to view ontologies is not as a “commitment,” but as a vehicle for developing a common world view within the enterprise. Under this viewpoint, ontology development is somewhat analogous to master data management (MDM) or corporate taxonomies [5]. In this broader sense, then, ontology development can become a means for developing and refining a common language within the enterprise through consensual or community processes.

For the reasons as noted above, as language or conceptual relationships or understandings change, so can the vocabulary or structural character of the ontology change. There is no “lock in”; there is no “commitment”. As long as it is coherent, the ontology can morph to reflect the scope and understandings of the current snapshot in time.

This flexibility results from the fact that the ontologies, properly constructed, can drive a generic set of tools and applications that express themselves based on the underlying structure and vocabulary within those ontologies. The ontologies can thus change at will without any adverse effects whatsoever on the applications based on them.

This data-driven aspect, as noted throughout this series, is quite different from any prior paradigm. So, under this view ontologies have considerably more focus and importance than even some of the strongest ontology advocates claim, yet paradoxically without the theoretical bloat or heaviness many purport. Like human languages, our language and concepts within ontologies change as our world and perceptions change.

Takeaway Message #4: There is no “lock-in” with ontologies; they may be modified and changed at will.

Takeaway Message #5: Like corporate taxonomies or MDM, ontologies provide a framework for enterprises to develop internally consistent common languages or vocabularies.

Takeaway Message #6: Unlike corporate taxonomies or MDM, ontologies can drive directly generic tools and applications.

Misconception: No Need for Completeness or Comprehensiveness

Ontology development is not some imperative for conceptual “truth”; rather, it is a very adaptable means for stating, testing and refining stuff. Like agile development for software, this refining approach can and should proceed incrementally. Too often ontology efforts get caught like deer in the headlights awaiting some “completeness” threshold before release.

One means to promote this approach is to tackle single datasets or data stores individually before moving on. Having a sense of the eventual scope is useful, of course. But it is also quite acceptable to only fill out those portions of the structure with data available at hand.

These observations reflect a prejudice to action and release, rather than theory. If mistakes are made, fine: simply correct them.

Takeaway Message #7: Understand the full scope, but only build out for the data in hand.

Misconception: No Need for Predicate Bloat

It is advisable to keep relationships (predicates) simple at first. Because, again, like human languages, keeping the verbs simple until fluency is gained is another best practice.

While all of us can see nuances and subtleties heading into a project, trying to accommodate those predicates (relationships) at the outset can introduce unnecessary complexity. This is not an advocacy in any way for inaccurate predicates, but perhaps to err on the side of the general and broader at first.

For organizations familiar with taxonomies, the SKOS vocabulary is a good focus, and there are some other standard starting ontologies that provide a good starting base of predicates [6]. Then, as you work with your data and its requirements, you can later expand to more sophisticated relationships.

In taking this approach you will still see immediate benefits due to the value of connected data through the Linked Data Law [7]. But, at the same time, you will be embracing a simpler language to start and then gain fluency.

Takeaway Message #8: Use simple, well-defined and documented predicates (properties or attributes).

Takeaway Message #9: You are building a common language for the enterprise; do so purposefully.

Misconception: No Need for Expensive Up-front Engineering

All of these observations lead to the conclusion that upfront ontology development need not be expensive. Any consultant selling six-figure ontology development to businesses ought to be seriously challenged. Start small and focused. Frankly, a simple spreadsheet taxonomy or quick conversion of existing XML or metadata or vocabulary standards is A-OK to get started.

Takeaway Message #10: Start small with stakeholders to build acceptance and best practices.

Takeaway Message #11: Start immediately to organize and federate existing information.

Misconception: No Need to Reinvent the Wheel

While it is true that the usefulness of ontologies as advocated by Structured Dynamics is greater than other constructs, these ontologies still just represent a more capable representation of knowledge structures that have been around in various other forms for years. For decades enterprises have created schema, taxonomies, controlled vocabularies, standards, and other knowledge structures that represent untold time, dollars and effort. It would be a waste to not fully leverage these sunk investments.

Further, many ontologies and interoperable structures also exist external to the enterprise, many open source and freely available. And, even if not all are already in proper ontological form, like internal structures these other constructs can be relatively easily leveraged and turned into ontology-ready form.

So, what we are doing with adaptive ontologies is not creating new structures or new representatiions from scratch, but leveraging the expressions of our current world views. These have been hard-earned, codified over years of effort, and are legacy expressions of the enterprise’s knowledge base.

In this vein, then, there is already much richness available to any organization upon which to embark on their ontology efforts. Use them, and gain great leverage.

Takeaway Message #12: Aggressively mine and re-use existing knowledge and structure.

Takeaway Message #13: Leverage and re-use appropriate portions of the “best” existing, external ontologies.

Misconception: No Requirement to Displace Existing Assets

Continuing in this same spirit, it is a mistake to see adaptive ontologies and the associated systems advocated by Structured Dynamics as a replacement for existing data assets. Rather, the idea and advantage is to keep data records in situ as much as possible. These are already performing investments that can be left largely as is. The role of the adaptive ontologies is to act as a federation layer that bridges across these existing assets.

This leverage of existing data assets can occur via the architecture of the system (generally Web-oriented architecture [8]) and a design of the data system and structures providing proper allocation between the ABox and TBox [1].

All of this maintaining of existing assets is aided by the ability to convert in-place data to ontology-ready RDF form. This is a separate topic in its own right and one I discuss elsewhere [9]. There is also a need to make sure that the attributes of the underlying instance records (generally, the columns within a relational table) are also properly modeled within the adaptive ontology. This is part of the best practices guidelines.

Of course, how much of the existing assets can be leveraged “as is” and what degree of modification or conversion might be necessary needs to be evaluated on a case-by-case basis. Generally, however, these mappings can be pretty straightforward and leave in place all existing hardware, software and administration procedures.

Takeaway Message #14: Leverage your existing databases as rich sources of instance records (”ABox”).

Takeaway Message #15: Explicitly design your TBox ontologies to be an interoperability layer over these existing record stores.

Takeaway Message #16: Reconcile the semantics across the enterprise’s data stores at this interoperable TBox layer.

Misconception: No Closed World Assumptions

A closed world assumption holds that any statement that is not known to be true is false. Most enterprise database and transaction systems are based on this premise. It works well where there is complete coverage of the entities within a knowledge base, such as the enumeration of all customers or all products of an enterprise.

Yet, in the real (”open”) world there is no guarantee or likelihood of complete coverage. Thus, under an open world assumption the lack of a given assertion or fact being available neither implies whether that possible assertion is true or false: it simply is not known.

An open world assumption is one of the key factors for enabing adaptive ontologies to grow incrementally. It is also the basis for enabling linkage to external (and surely incomplete) datasets.

In fact, systems designed around the open world assumption can still achieve closed world reasoning where the circumstances and completeness of the knowledge base permit. But, rather than being a logical outcome of the framework, such completeness axioms need to be explicitly stated. Thus, open world systems can achieve the same ends as closed ones where applicable, but with greater flexibility and extensibility.

Takeaway Message #17: No enterprise is an island; design according to the open world assumption.

Misconception: No Restriction to a Dedicated Priesthood

Consultants make their money and academics their reputation by often making things more obscure and jargon-laden than they need be. Ontologies — heck, even the name itself — is no exception.

But what we have laid out as general guidelines herein and their reduction to practice does not require a priesthood. Sure, there are some things to learn and some practices to follow, but these are certainly easier to understand and master than, say, a programming or scripting language. Adaptive ontologies done right can be a participatory activity within most any organization.

Some guidance and mentoring would certainly be helpful. Make sure to pick the right individuals that truly embrace these perspectives.

Also helpful would the assistance of groups skilled in team building and group participation [10].

Takeaway Message #18: Engage all knowledge stakeholders in ontology creation, review and refinement.

Takeaway Message #19: Use selected ontology engineers to help ensure consistency, but not necessarily structure.

Design for Data-driven Apps

The above addresses misconceptions related to how the market perceives current ontologies or how some advocates push the concept. But there are some unique perspectives that Structured Dynamics brings to ontology development specific to the purpose of data-driven applications. From a best practices standpoint, these considerations should also be included.

In order to properly “drive” applications and user interfaces and reports, specific design attention needs to be give to:

• Linked data, and the use and accessibility of URIs as resource identifiers
• Context- and instance-sensitive data display, including templates, and
• Driving user interfaces via the inclusion of preferred and alternate labels in the ontology.

Of course, there are other considerations that come to bear. But these lend themselves to some rather simple checklist guidelines during ontology development and maintenance.

Takeaway Message #20: Follow some relatively straightforward best practices to gain all of the advantanges of adaptive ontologies.

Multiple Techniques and Data Structs can Make the Vision a Reality

Linked data and subject and domain ontologies provide the organizing framework. Techniques for converting, tagging and authoring structure provide the content. In combination, we now have in hand the necessary pieces to enable all of us to “structure the World.”

In this vision, the nature of the links or connections between data need not be complicated to gain tremendous benefit. Similar to Metcalfe’s Law for the increasing value of networks as more nodes (users) get added, adding connections to existing data is a powerful force multiplier.

We can call this the Linked Data Law: the value of a linked data network is proportional to the square of the number of links between data objects [1]. Further, if we are purposeful to include connective links where appropriate as we add more data (that is, nodes), this multiplier effect becomes even stronger.

Structured Dynamics is dedicated to help make this prospect real. Meaningful progress in doing so requires only a relatively few moving parts or techniques. Yet, because we sometimes bounce from talking or focusing on one part versus the others, we can lose context or sight of the overarching vision. The purpose of this article is to re-set and calibrate that overall vision.

The Vision: Data Federation of Any Desired Content

The vision is to get all data and information to interoperate, regardless of legacy or form. Much of this data is already structured, either from databases or simpler forms of data structs. Some of this information is unstructured or semi-structured, requiring extraction and tagging techniques. And new information is being constantly generated, which warrants better means to author and stage for interchange and interoperability.

No matter the provenance, all information has context and scope. As a chunk from here, and a piece from there, gets added to our linked data mix, having means to characterize what that data is about and how it can be meaningfully inter-related becomes crucial. Sometimes these contexts are informed by existing schema; sometimes they are not. But, in any case, it is the role of ontologies to both position these datasets into an “aboutness” framework and to help guide how the data can be described and related to other data. This part of the vision invokes semantics and coherent structures (schema or ontologies) for positioning and mapping datasets to one another.

As both the means for representing any extant data format and as the means for describing these conceptual relationships or schema, RDF provides the canonical data model. A single target representation and common data model also means we can develop and design a smaller universe of tools to operate and provide functionality over all of this data. Indeed, because our RDF data model and its ontologies are so richly structured, we can design our tools with generic functionality, the specific operation and expression of which is based on the inherent structure within the data and its relationships. This vision of data-driven apps leads to extreme leverage, incredible flexibility, and inherent “meshup” capabilities for tools.

Further, because we use Web identifiers (URIs) for our data and concepts and because we expose and access this linked data via the Web, we use the proven and scalable architectures of the Web itself for how we design our systems. This Web-oriented architecture (WOA) provides a completely decentralized and loosely coupled deployment model that can work ranging from public and open to private and proprietary, applicable to data and participants alike.

From the outset, it is essential to recognize that thousands of contributors are enabling this vision. So, while Structured Dynamics naturally uses its own tools and techniques to flesh out the various parts of this vision below, realize there are many players and many tools from which to choose [2]. For that is another aspect of this vision that is quite powerful: providing choice and avoiding lock-in.

RDF: The Canonical Data Model

The core construct — or fulcrum, if you will — of the vision is the RDF (Resource Description Framework) data model [3]. I have written elsewhere on the Advantages and Myths of RDF, which explains more precisely the advantages of that model. RDF provides a common data model to which any external format or schema can be converted and represented. It also provides a logic model and basis for building vocabularies that can inform and drive generic tools.

In the context of data interoperability, a critical premise is that a single, canonical data model is highly desirable. Why?

Simply because of 2N v N². That is, a single reference (”canon”) structure means that fewer tool variants and converters need be developed to talk to the myriad of data formats in the wild. With a canonical data model, talking to external sources and formats (N) only requires converters to and from the canonical form (2N). Without a canonical model, the combinatorial explosion of required format converters becomes N² [4].

Note, in general, such a canonical data model merely represents the agreed-upon internal representation. It need not affect data transfer formats. Indeed, in many cases, data systems employ quite different internal data models from what is used for data exchange. Many, in fact, have two or three favored flavors of data exchange such as XML, JSON or the like. More on this is discussed in a section below.

As this diagram shows, then, we have a single internal representation that is the target for all data and format converters and upon which all tools operate. These tools are themselves expressed as Web services so that they may be distributed and conform to general WOA guidelines. In addition, there may be multiple external “hubs” that represent alternative data models or formats or schema conversions (say, for relational databases). So long as we have converters between these alternate “hubs” and our canonical RDF form we can allow a thousand flowers to bloom:

Other canonical forms could be advocated. Yet RDF has the logical basis to represent any data form and any schema or conceptual structure. It is based on a robust set of open standards and languages and tools. It may be serialized in many formats. It can be grounded in description logics and, in appropriate forms, reasoned over and expressed in vocabularies and schema suitable for the most complex of conceptual structures and semantics. RDF is the data model explicitly designed for the Web, the clear global information basis for the foreseeable future.

For more than 30 years — since the widespread adoption of electronic information systems by enterprises — the Holy Grail has been complete, integrated access to all data. With the canonical RDF data model, that promise is now at hand.

Conversion: So Many Structs, So Little Time

Diversity is a truism of human communications as captured by the biblical Tower of Babel and the many thousands of current human languages. Diversity in data formats, serializations, notations and languages is a similar truism. We term the expression of each of these varied forms of data a struct.

While an internal canonical representation of data makes sense for the reasons noted above, pragmatic information systems must recognize the inherent diversity and chaos of data in the real world. The history of trying to find single representations or to impose standards via fiat have singularly failed. That will continue to be so due in part to inertia and legacy, sunk investments, existing infrastructure, and the purposes for the data.

In pursuing a vision of data interoperability, then, conversion is an essential glue for cementing understanding with what exists and will exist.

RDB-to-RDF

Arguably the largest source of structured data are enterprise and government information systems, with the predominant data representation being the relational data model managed by relational schema. Much of this data is also cleaner and mission critical compared to other sources in the wild. Fortunately, there are many logical and conceptual affinities between the relational model and the one for RDF [5].

Just as there are many RDFizers for simpler forms of data structs (see next), there are also nice ways to convert relational schema to RDF automatically. Given these overall conceptual and logical affinities the W3C is also in the process of graduating an incubator group to an official work group, RDB2RDF, focused on methods and specifications for mapping relational schema to RDF.

Amongst all techniques covered in this paper, Structured Dynamics views the layering of RDF ontologies over existing relational data stores as one of the most promising and important. Given the advantages of RDF for interoperability, this area should be a major emphasis of current and new vendors and service providers.

RDFizers

Much data, however, resides in much smaller datasets and often for less formal purposes than what is found in enterprise databases. Some of this data is geared for exchange or standardization; much is emerging from Web and Internet applications and uses; and much might be local or personal in nature, such as simple lists or spreadsheets.

RDF is well suited to convert (”RDFize”) these simpler and more naïve data formats. In my original census about 18 months ago, as reported in ‘Structs’: Naïve Data Formats and the ABox, I listed about 90 converters. My most recent update now lists nearly double that number, with about 150 converters [6]:

Many of the sources above come from new and emerging Web-based APIs, which are also huge sources of content growth. Also note that alternative formats to RDF (e.g., microformats) or leading serializations and encodings (e.g, XML, JSON) also have many converter options.

For many typical naïve data structs, the data is represented as attribute-value pairs, which easily lend themselves to conversion to RDF as instance records [8]. See further the Authoring section below.

Tagging: The 80% Solution

An apocryphal statistic is that 80% to 85% of all information resides in unstructured text [9]. Besides lacking recent validation, this claim from a decade ago often attributed to Merrill Lynch also precedes much of the Internet and the emergence of metadata and tagging. Nevertheless, what is true is that written text content is ubiquitous and the majority of it remains untagged or uncharacterized by any form of metadata.

While such information can be searched, it only matches when exact terms match. This means that related information, particularly in the form of conceptual relationships and inferencing, can not be applied to untagged text content.

While information extraction — the basis by which tags for entities and concepts can be obtained — has been an active topic of research for two decades, it is only recently that we have begun to see Web-scale extractors appear. Examples include Yahoo’s term extractor, Thomson Reuter’s Calais, or Google’s Squared, to name but a few.

In Structured Dynamics’ case we have been working on the scones (Subject Concepts Or Named EntitieS) extractor for quite a while. scones uses rather simple natural language processing (NLP) methods as informed by concept ontologies and named entity (instance record) dictionaries to help guide the extraction process. The co-occurrence of matches between concepts and entities also aids the disambiguation task (though additional modules may be invoked with alternative disambiguation methods). In prototype forms, the resulting tags can be managed separately or fed to user interfaces or re-injected back into the original content as RDFa.

There are literally dozens of such extractors and services presently available on the Web and many that are available as open source or commercial products. Some are mostly algorithm based using machine-learning techiques or statistics, while others are gazeteer- or dictionary-driven.

These systems will lead to rapid tagging of existing content and the removal of some of the early “chicken-and-egg” challenges associated with the semantic Web. These systems will also be combined with the many existing bookmarking and tagging services.

So, just as we will see federation and interoperability of conventional data, we will also see linkages to relevant and supporting text content accompanying it. This combination, in turn, will also lead to richer browsing and discovery experiences.

Authoring: The Neglected Third Leg of the Stool

In addition to conversion and tagging, authoring is the third leg of the stool to expose structured data. It is a neglected leg to the structured content stool, and one important to make it easier for datasets to be easily exposed as RDF linked data.

One of the reasons for the proliferation of data structs has been the interest in finding notations and conventions for easier reading and authoring of small datasets. There have literally been hundreds of various formats proposed over decades for conveying lightweight data structures. Most have been proprietary or limited to specific domains or users. Some, such as fielded text, structured text, simple declarative language (SDL), or more recently YAML or its simpler cousin JSON, have become more widely adopted and supported by formal specifications, tools or APIs. JSON, especially, is a preferred form for Web 2.0 applications.

What has been less clear or intuitive in these forms, again mostly based on an attribute-value pair orientation, is how to adequately relate them to a more capable data model, such as RDF. In JSON or YAML, for example, the notations include the concepts of objects, arrays and datatypes (among other conventions). Other structures lack even these constructs.

To take the case of JSON as might be related to RDF, there are a couple of efforts to define representation conventions from Talis and GBV for serializing RDF. There was a floated idea for an RDF version of JSON called RDFON that has now evolved into the TURF approach. JDIL (JSON data integration layer) instructs how to add namespaces to JSON to enable encoding RDF. Jim Ley, Kanzaki Masahide and Dave Beckett (likely among others) have written simple and straightforward RDF and Turtle parsers and converters for JSON. And, still further examples are Beckett’s Triplr and Sören Auer’s ASKW Triplify lightweight conversion services involving many different formats.

Because JSON is easily readable, can drive many Web 2.0 applications and widgets, and lends itself to fast conversions and tools in various scripting languages, Structured Dynamics was commissioned by the Bibliographic Knowledge Network (BKN) to formalize a BibJSON specification suitable for BibTeX-like data records and citations with an extensible schema to be converted to RDF.

The emerging result of that BibJSON effort will be published shortly. The specification includes conventions and vocabularies for creating bibliographic and citation instance records, for specifying structural schema, and for creating linkage files between the attributes in the record files with existing and new schema. BibJSON is itself grounded in IRON, which is an instance record and object notation developed by Structured Dyamics that can be serialized as JSON (called irJSON), XML (called irXML) or comma-separated values (or CSV comma-delimited files, called commON).

The purpose of these notations and serializations is to provide easier authoring environments and scripting support to RDF-ready datasets. This approach has the advantage of shielding most users from the nuances or lengthiness of RDF (though the N3 serialization also works well).

The design and development of commON was especially geared to using spreadsheets as authoring environments that would enable easy creation of instance record tables or simple hierarchical or outline structures. For example, here is a sample portion of Sweet Tools specified in a spreadsheet using the commON notation:

Once the philosophy and role of naïve data structs is embraced — with an appreciation of the many converters now available or easily written for translating to RDF — it becomes easier to determine data forms appropriate to the tools and natural work flow of the users and tasks at hand. Under this mindset, the role of RDF is to be the eventual conversion target, but not necessarily what is used for intermediate work tasks, and in particular not for authoring.

Getting it All Organized

OK, so now all of this stuff is converted, tagged or authored. How does it relate? What is the relation of one dataset to another dataset? Is there a context or framework for laying out these conceptual roadmaps?

Two years ago as we looked at the state of RDF and the incipient semantic Web as promised via linked data, we saw that such a specific framework was lacking. (Though there were existing higher-level ontologies, either their complexity or design were not well-suited to these purposes.) It was at that time that Frédérick Giasson and I began to formulate the UMBEL (Upper Mapping and Binding Exchange Layer) ontology, which eventually led to our more formal business partnership and Structured Dynamics.

What we sought to achieve with UMBEL was a coherent reference framework of about 20,000 subject concepts, connected and acting like constellations in the information sky for orienting content and new datasets. At the same time, we wanted to create a general vocabulary and approach that would lend themselves to creation of domain-specific ontologies, which would also naturally tie in and inter-relate to the more general UMBEL structure.

This objective was achieved, though UMBEL deserves an upgrade to OWL 2 and some other pending improvements. A number of domain ontologies have been created and now relate to UMBEL. So, rather than being an end to itself, UMBEL was one of the necessary infrastructure pieces to help make the vision herein a reality.

Similar approaches may be taken by others with new domain ontologies based on the UMBEL vocabulary with tie-in as appropriate to existing subject concepts, or by mapping to the existing UMBEL structure.

Of course, UMBEL is not an absolute condition to the vision herein. However, insofar as users desire to see multiple datasets inter-related, including the use of existing public Web data, something akin to UMBEL and related domain ontologies will be necessary to provide a similar roadmap.

Making it All Available

The parts and techniques discussed so far pertain almost exclusively to data and content. But, these structures so created now can inform data-driven applications which also now must be deployed. To do so, Structured Dynamics is committed to what is known as a Web-oriented architecture (WOA):

WOA is a subset of the service-oriented architectural style, wherein discrete functions are packaged into modular and shareable elements (”services”) that are made available in a distributed and loosely coupled manner. WOA generally uses the representational state transfer (REST) architectural style defined by Roy Fielding in his 2000 doctoral thesis; Fielding is also one of the principal authors of the Hypertext Transfer Protocol (HTTP) specification.

REST provides principles for how resources are defined and used and addressed with simple interfaces without additional messaging layers such as SOAP or RPC. The principles are couched within the framework of a generalized architectural style and are not limited to the Web, though they are a foundation to it.

Within this design we need a suite of generic functions and tools that are driven by the structure of the available datasets. The deployment vehicle and design we have implemented to provide this WOA design is structWSF [10].

structWSF is a platform-independent Web services framework for accessing and exposing structured RDF data. Its central organizing perspective is that of the dataset. These datasets contain instance records, with the structural relationships amongst the data and their attributes and concepts defined via ontologies (schema with accompanying vocabularies). The master or controlling Web service in the framework is the module for granting access and use rights to datasets based on permissions.

The structWSF middleware framework is generally RESTful in design and is based on HTTP and Web protocols and open standards. The initial structWSF framework comes packaged with a baseline set of about a dozen Web services in CRUD, browse, search and export and import. More services can readily be added to the system.

All Web services are exposed via APIs and SPARQL endpoints. Each request to an individual Web service returns an HTTP status and a document of resultsets (if the query result is not null). Each results document can be serialized in many ways, and may be expressed as either RDF or pure XML.

In initial release, structWSF has direct interfaces to the Virtuoso RDF triple store (via ODBC, and later HTTP) and the Solr faceted, full-text search engine (via HTTP). However, structWSF has been designed to be fully platform-independent. The framework is open source (Apache 2 license) and designed for extensibility.

No End in Sight

Like all visions, there are many aspects and many improvements possible. This vision is definitely a work-in-progress with no end in sight.

But, meaningful movement embracing the full scope of this vision is doable today. Structured Dynamics welcomes inquiries regarding any of these aspects, improvements to them, or application to your specific needs and problems.

We also welcome you to come back and visit our blogs (Fred’s is found here). We try to speak on various aspects of this vision in all of our posts and are pleased to share our experience and insights as gained.

Ontology Best Practices for Data-driven Applications: Part 3

In my Intrepid Guide to Ontologies from a couple of years back, I noted that “Ontology is one of the more daunting terms for those exposed for the first time to the semantic Web.” And, for sure, if one starts to peruse the current discussions ranging from the Ontolog Forum to major academic symposia (not meaning to single anyone out), it is clear that the idea of developing “ontologies” is often freighted with much weight, hot air, and (by implication) cost.

This is both a shame because, firstly, it is unnecessary and not often true. And, secondly, because the whole pragmatic idea of what an ontology is and what it can do has often gotten lost in the shuffle.

To be sure, there have been massive standards efforts and EU-funded mega-projects devoted to ontologies. There are certainly cases where coordination of specific domains such as petroleum or integration with a complicated supplier base such as in airline manufacture warrant these massive, complicated ontology development efforts.

But, from my vantage, these extremes overshadow the vast majority of more prosaic, pragmatic applications of ontologies. Remember, ontologies are merely a means of describing a conceptual view of the world [1]. If one defines that “world” within focused and appropriate scope, it is surprising (we believe) how much mileage can be extracted from these suckers.

As we see a breakthrough of interest in semantic Web and linked data principles applied to the enterprise, as wonderfully described in the recent seminal PricewaterhouseCoopers quarterly Technology Forecast, also notably with a prominent focus on ontologies, I think it is time to direct all guns on prior bad assumptions and bad anecdotes. To wit:

• Ontology development need not be a comprehensive, self-contained definition of a “big picture.” Ontologies can be focused, limited, and grow and change as needed
• Ontology development need not be expensive. Whoever is selling six-figure ontology development to businesses ought to be taken out and shot. Start small and focused; frankly, a simple spreadsheet taxonomy or quick conversion of existing XML or metadata or vocabulary standards is A-OK to get started
• Ontology development is not massive and static: rather, it is small and flexible and incremental as more is brought in and more is learned
• Ontology development is not some imperative for conceptual “truth”; rather, it is a very adaptable means for stating, testing and refining stuff
• Ontology development is certainly no massive relational schema; by its nature it is malleable with nary a whiff of “lock-in”, and
• Most importantly, ontology development is a way of “driving” applications and user interfaces and reports.

In fact, it is the last point that no one is discussing today, but it is the most important of the lot: Ontologies, properly crafted, can be the ‘engines’ for data-driven applications.

It is this latter point that is a true paradigm shift and one of the most exciting prospects of ontologies.

Manifest Uses

Ontologies, for sure, are a formal representation of conceptual relations, a “world view.” But, that world view need not carry with it the freight of trying to describe all human knowledge. It can (and should) be restricted to an understandable scope (domain) and purpose. In that vein, what does such a “world view” need?

Let’s first talk about scope. We don’t need a “global ontology” that is accepted by everybody on Earth. What we need are focused ontology(ies) for describing things within a given problem space (whose data may reside in a single dataset or aggregate of datasets). We need to communicate how this system describes the things within its domain and how it understands the concepts and attributes associated with its problem space and data. This communication is published as the ontology. Rather than a global, comprehensive schema, we simply need these well constructed bricks, one by one.

Then, the ontology itself needs to be understandable and manageable. Ontologies should be readable by machines, but too many see ontologies solely through the lens of machines. I believe that to be a mistake. While importantly needing to be designed for machine ingest, I believe the real purpose of ontologies is for humans. How do we label things? How do we describe and define things? How do we find things? How do we organize things? How can these understandings be brought before us in the software that we use?

These types of questions lead us to the pragmatic and pull us back from the abstract. If we keep foremost the simple idea that ontologies are merely structures for how to organize (schema) and describe (vocabularies) our problem space at hand, then we can actually get on with cutting the bull and getting real stuff done.

Let’s take as an example our structWSF Web services framework that I will be announcing and demoing for the first time at SemTech 2009 next week. We developed a simple and flexible ontology to describe what a “Web services framework” should be. Then, we developed and implemented the software to make it happen. This means that an ontology development task can be seen as a specification task, too.

Pragmatic Applications of Ontologies

So, OK, what do these exhortations mean? Without respect to any particular scope or domain, let me then list below some important functional areas to which ontologies — properly and pragmatically designed — can contribute.

Conceptual Relationships

The traditional lens for viewing ontologies is as a means to express conceptual relationships. We agree.

However, ontologies need not have large and nuanced predicate (relationship) vocabularies in order to be useful. Relatively simple but powerful structures with hierarchical or part-of relationships can be very effectively employed for inferencing or faceted searching. From a pragmatic standpoint, let’s first agree on what “things” (nouns) there are in our domains, then let’s worry about how they relate (verbs).

The idea here has long been known as successive approximation: Let’s first get ourselves into the right country, then right province, then right city, then right neighborhood, then right house, and then right room. Only then should we worry about the condition of the paint or the age of the floors.

Endless harangues about “true” conceptual relations are a hindrance, not a help, to this perspective. It is much better (and faster, cheaper and more pragmatic) to put forward simple but coherent relationships than to worry about what all of that “really” means. From a business perspective, isn’t being able to utilize the assertion that the hip bone is connected to the thigh bone more important than having to await a full explication of all of the muscles and ligaments and tendons that might comprise them?

Once such simple relationship structures are embraced, then amazing inferencing power comes to the fore. If one searches on thigh bone, inferencing can also bring forward the hip because of its relationships to the leg.

Integrating Instance Data

OK, so now at least we have a coherent scaffolding of concepts and their straightforward relationships. That is, is concept A a “bigger” one (class or super set) than concept B? (Other simple relations could be substituted.) If so, we now see a bit of an organizing “world view” begin to emerge.

So, now we begin to bring in external data. But that data and its schema describe themselves differently. In one realm it is “foo”; in the other, “bar”.

While this different terminology for the same “thing” or related things may not be known at the outset, it is discoverable. And, when discovered, it is quite easy to associate the idea or concept of “foo” in one dataset to “bar” in another. In this manner, through learning and accretion, we are able to associate more and more similar things to one another.

We did not need to begin with some global, cosmic view to begin relating this data to one another. We only needed the right framework and structure that allowed this association to evolve as the learning occurred.

And, oh, by the way: this very same process is akin to documenting the organization’s institutional memory.

Orienting to Other Knowledge and Domains

Being able to relate and “classify” or “organize” some things to other things also means that we are now beginning to create a roadmap for how “stuff” in a broad sense relates to other “stuff.” For example, if I develop a detailed understanding of the hip bone, I can now bring that body of knowledge into the context above to relate this new information to the thigh and to the leg.

Frankly, at this juncture, while perhaps ultimately important, it is helpful merely to know that Domain A (hip) is somehow related to Domain B (thigh). Think of the issue more like trying to get into the right map vicinity on the globe, and not whether individual streets intersect.

Again, the mindset here is one of letting ontologies and their concepts get related knowledge bases into the same ballpark. Whether we are trying to match Little League ball players with Major League ballplayers is beside the point: accept that both are playing baseball, then decide the importance and specifics of the relationship in a later step.

Again, “ballpark” is more helpful than no connection whatsoever. Silly statements about “ontological commitments” really mean nothing. Ontologies, like any other tool, can play different roles at different times. When helping to get like-related things into the same ballpark, ontologies are easy and quite effective.

(As an aside, it is useful to note here that our efforts with the UMBEL upper-level subject ontology are solely premised on this “roadmap” purpose. In and of itself, UMBEL is not a very complicated explication of the world. But it does provide a comprehensive set of 20,000 subject concepts for orienting quite disparate datasets and information to one another. This very same approach could be replicated and then applied to the granularity of individual domains, kind of like zooming in on Google Maps, to provide similar benefits at smaller scale for domain-specific roadmaps. In fact, that is a common approach we apply in our own client ontologies, which we then also make sure we tie into UMBEL for global orientation.)

Mapping to Other Schema

OK, so with this foundation now built, we can next raise the bar a bit further. Once one begins to express these “world views” formally as an ontology, even with reduced ambitions as presented above, one still ends up with a formal specification of that conceptualization. And, that means, we now also have a basis with standard languages for mapping two disparate or separately developed ontologies to one another.

This is powerful. Through such mapping, we end up, in the memorable phrase of my colleague, Fred Giasson, “exploding the domain.”

Moreover, we also have found a means for stitching together datasets with disparate schema to one another. Voilà: We now have met the Holy Grail of data interoperability.

In my opinion, this is the money shot from all of this effort. But, again, if we set the deployment threshold to the unrealistic levels that some ontology pundits suggest, this payoff is unlikely to happen. We are not trying to state absolute, universal truth about anything nor to be unrealistically comprehensive. All we are trying to do is make defensible assertions that one portion of a world view is similar or related to a portion of a different world view.

Now, does that sound that scary? No, of course not. It is merely a reasonable and pragmatic means for relating two structures together.

A key aspect to this mapping ability is to enrich the description of our concepts with what we call “semsets.” Semsets are a listing of related terms and phrases that provide synonyms, aliases, jargon and related context for alternative ways to describe or bound the concept at hand. This terminological “grist” is the basis for relatively straightforward natural language processing techniques to suggest matches between concepts in different ontologies (which might also be combined with other ontology components such as preferred labels, descriptions or structural placements in the schema).

Like many of the points above, these semsets can be built incrementally and over time as new jargon and terminology is discovered.

Linked Data, with Federated and Comprehensive Data

These techniques of mapping datasets and their ontology structures can be leveraged still further with the proper application of linked data practices. Via linked data, we place our data into Web-accessible (HTTP) networks and give them Web-scalable identifiers (URIs). This means we can now integrate and interoperate with much external public Web data and break down our own internal data silos.

Our instance records can be fleshed out with supplementary sources to provide more comprehensive attibutes and characterizations. Uniformity of treatment and coverage is promoted. Data interoperability is finally at hand.

A key best practice to this, of course, needs to be the recognition that not all data or information is public and not all users have the same roles or should have the same access to different sets of data. Thus, to embrace global mechanisms for data interoperability, there must also be local methods for enforcing access, privacy and confidentiality.

Properly designed ontologies can fulfill this requirement, as well. By organizing information into datasets and setting profiles for access and CRUD (create – read – update – delete) rights, an effective environment for data sharing and federation is established.

Context- and Instance-sensitive Data Display

To this point, we have taken almost an exclusively data- or schema-centric view of ontologies. But, as structures, pure and simple, their structural nature can be exploited in other ways. It is here, frankly, that less is spoken of the potential for ontologies than in the more “conceptual” areas noted above.

The first of these new areas is in instance-sensitive data display. Each instance record is associated with an instance type in a governing ontology. Detecting this type means that context-sensitive display templates can then be invoked.

Detecting that something refers to a city, for example, can invoke a template providing a map, population figures, area size, city governance method and the like. In contrast, detecting an instance as a camera might invoke an entirely different display template focusing on product features or price or store and purchasing locations. Such instance-type displays are common; they are known as “infoboxes” within Wikipedia articles, as one example.

But this power of data display templates can be generalized further. What if we detect our instance represents a camera but do not have a display template specific to cameras? Well, the ontology and simple inferencing can tell us that cameras are a form of digital or optical products, which more generally are part of a product concept, which more generally is a form of a human-made artifact, or similar.

By tracing this inferencing chain from the specifc to the more general we can “fall back” until a somewhat OK display template is discovered, even in the absence of the better and more specific one. Then, if we find we are trying to display information on cameras frequently, we only need take one of the more general, parent templates and specifically modify it for the desired camera attributes. We also keep presentation separate from data so that the styling and presentation mode of these templates is also freely modifiable.

This parallel set of display structures to the domain ontology provides a highly reusable and leveraged data presentation framework. For 30 years organizations have struggled with report generators and all sorts of complicated systems for responsive reporting and data display. When driven by ontologies, this challenge is greatly simplified.

Driving User Interfaces

The careful reader of the above will note that our ontologies now have a number of interesting characteristics, all of which can be leveraged within the user interface. For example, we have:

• Human-readable labels for our “things”
• Alternative labels in our semsets that can characterize those same “things”
• A readable description of each “thing”
• An organized and logical schema for how each “thing” relates to other things.

This very information, when indexed in a supplementary full-text search engine with faceting capabilities (such as the Solr engine we use), can be leveraged in the user interface for these types of desired UI capabilities:

• 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.

This is absolutely mindblowing power!

We can now design generic tools that do patterned functions. Then, based on the data at hand and the ontologies that describe them, we can now see completely modified and tailored interfaces. And all of this is done without modifying a single line of application code!

Applications in this brave new world now consist of assembling a proper suite of generic tools, and then spending the bulk of our time on describing and characterizing our data via ontologies and refining templates for displaying or reporting the types of specific instances within our current problem space.

Conclusions

All of the points made above are doable and being done today. Properly designed ontologies can readily deliver all of the aspects noted above. Later parts in this ongoing series will address many of those aspects in greater detail.

Ontologies are not magic. Properly done — an important emphasis — ontologies are the pivot point for faster and more adaptable ways of doing business. A simple, pragmatic mindset can help.

Our perspective is that ontologies are really the “flour that gets backed into the cake”. While viewable and definable as their own structures, properly constructed ontologies actually should exist everywhere within applications and contribute everywhere to applications. This is what we mean by “data-driven applications.”

To be sure, we are suggesting a paradigm shift from 30 years of IT frustrations: schema no longer must be fragile; reports no longer must be costly and delayed; and data can finally be made interoperable.

We will continue to give you our best thinking on these topics over the coming weeks and how they might be important to you.

Sound too good to be true? Read the material above again. And, then, we welcome getting your call.

SearchMonkey’s Recommended Vocabularies a Useful Resource

I am pleased to report that UMBEL is now included as one of the recommended vocabularies for the Yahoo! SearchMonkey service. Using SearchMonkey, developers and site owners can use structured data to enhance the value of standard Yahoo! search results and customize their presentation, including through “infobars“. SearchMonkey is integral to a concerted effort by Yahoo! to embrace structured data, RDF and the semantic Web.

SearchMonkey was first announced in February 2008 with a beta release in April and then public release in May with 28 supported vocabularies. Then, last October, an additional set of common, external vocabularies were recommended for the system including DBpedia, Freebase, GoodRelations and SIOC. At the same time, some further internal Yahoo! vocabularies and standard Web languages (e.g., OWL, XHTML) were also added.

This is the first vocabulary update since then. Besides UMBEL, the AB Meta and Semantic Tags vocabularies have also been added to this latest revision. (There have also been a few deprecations over time.)

A recommended vocabulary means that its namespace prefix is recognized by SearchMonkey. The namespaces for the recommended vocabularies are reserved. Though site owners may customize and add new SearchMonkey structure, they must be explicitly defined in specific DataRSS feeds.

Structured data may be included in Yahoo! search results from these sources:

• Yahoo! Index — the core Yahoo! search data with limited structure such as the page’s title, summary, file size, MIME type, etc. This structure is only provided by Yahoo!
• Semantic Web Data — including microformats and RDF data embedded in the host page
• Data Feed — A feed of Yahoo! native DataRSS provided by a third party site
• Custom Data Service — Any data extracted from an (X)HTML page or web service and represented within SearchMonkey as DataRSS.

As a recommended vocabulary, UMBEL namespace references can now be embedded and recognized (and then presented) in Yahoo! search results.

The Current Vocabulary Set

Here are the 34 current vocabularies (plus five deprecated) recognized by the system:

In addition, there are a number of standard datatypes recognized by SearchMonkey, mostly a superset of XSD (XML Schema datatypes).

What is emerging from this Yahoo! initiative is a very useful set of structured data definitions and vocabularies. These same resources can be great starting points for non-SearchMonkey applications as well.

For More Information

There is quite a bit of online material now available for SearchMonkey, with new expansions and revisions also accompanying this most recent release. As some starting points, I recommend:

• SearchMonkey: Getting Started
• The online SearchMonkey Guide for developers, and
• The 243-pp SearchMonkey Guide in PDF.

Concluding with a Simplified Instance Record Vocabulary for Linked Data ABoxes

In Part 1 of this series, I advocated the placement of linked data in an ABox construct from description logics [1] based on a separation of concerns argument. In Part 2, I reinforced that argument from the perspective of the work to be done within a knowledge base. In Part 3 we surveyed some of the key literature, finding justification for the split of the TBox from the ABox and the use of specialty RDFS and OWL dialects for work-oriented reasoning in the context of an integral logics.

We now conclude this series and try to bring these threads full circle to address what might be a vocabulary for an ABox instance record design. We’d very much like to thank Dr. Jim Pitman of the Bibliographic Knowledge Network project for having stimulated much of the thinking about the benefits and design of simple, human-authored and -readable instance records.

A Re-cap

Up until about six to eight months ago Fred Giasson and I were spending much of our thinking and design time on UMBEL, ontologies and what we now more precisely define as the TBox. Our intent all along was to get our process and thinking down pat there, and then turn ourselves to the representation of the actual entity data.

We have wanted to keep data records separate from logic and structure all along. Some clients have their own specific data records but may still want to interact with Web stuff or apply similar logic. Moreover, some client data is proprietary, some public. By organizing the data into “named entity dictionaries” we could modularize the architecture to allow swapping in and out of data appropriate to the customer or circumstance at hand.

Our initial design of this and what we share publicly has UMBEL and various standard public ontologies (FOAF, DC, SIOC, BIBO, etc) for the TBox, with Wikipedia entities and stuff from the BBC at the entity level (the ABox).

However, earlier work with another client showed us that our initial named entity structure was not sufficiently general or robust. That company’s records have complex relationships, such as affiliations for entities embedded in the same data record.

For linked data to become truly successful, we need to find easier ways for data publishers to write, expose and share structured data on the Web.

In order to improve the design, we went back to the drawing board to see if we could find guidance from the literature and other researchers as to how to “best” architect instance data in relation to the logic in the TBox (though we were not yet thinking and framing our questions viz description logics, or DL).

This series of postings itself, and some of its predecessor articles, were motivated by probing the description logics space and the guidance it might provide to help determine performant architectures and designs.

Folks, We’re Making Linked Data Just Too Tough

For linked data to become truly successful, we need to find easier ways for data publishers to write, expose and share structured data on the Web.

As anyone who reads my blog knows, I frequently rail against poor semantics or other aspects of the linked data space that I feel are counterproductive. At the same time, I’d like to think that I am also a vocal advocate and proponent for linked data. I am indeed a fan.

To me, the fundamental precepts of RDF as a data model able to capture virtually any data structure or relationship, and the use of Web URIs as linkable identifiers for a global ‘Web of Data’, are simply foundational and game changing. Stuff like this quickens my pulse.

But look at what it takes someone today to publish linked data:

• He must understand the terminology and standards and best practices — and actually, even amongst current practitioners, few do
• She must assign Web identifiers (URIs) to her data objects, which means finding them and making them (gawd, I hate this word) “dereferencable”
• He must understand the semantics of the relationships and linkages his data asserts (which, unfortunately, many don’t)
• She must present her data in serialized subject-predicate-object “triples”, which are arcane and difficult for most to understand, and
• They both often confuse data and instances with structure and world views.

Now, come on. This is not the recipe to success.

Simple and unbreakable and forgiving is the recipe to success.

As I noted in an earlier posting, there are many different data structures (’structs‘) for describing and conveying (transmitting) data records. Most of these are easy to understand and easy to read. We know that microformats have tried to capture a part of this space, but so has in other ways data serializations such as JSON or others. What can we learn from such formats?

Well, one thing I have learned is that many on the Web positively want to expose their data. Another thing I have learned is that there is much structured data that will not get exposed without hurdle rates that are small.

Revenge of the ABox

The phrase ‘revenge of the ABox’ comes from Heiko Stoermer’s thesis [2]; it conveys well, I think, the fact that everyone wants to capture and structure “world views” via ontologies and the big picture, but many do not want to grub around at the level of individual instances and data records. As he states, “. . . the most valuable knowledge is typically the one about individuals, but research on ontology integration has traditionally concentrated on concepts and relations.”

(The perverse outcome of this is that even though linked data as practiced to date is almost 100% about instance data, the discussion rarely looks at ABox-level work or instance data integrity.)

As this series and its predecessor posts have argued, description logics (DL) is an excellent guiding framework for how to make architectural and design decisions about linked data. DL and the ABox – TBox have meshed beautifully with our earlier intuition to split ontologies and a structural and organizational view of the world (TBox) from the instance records (ABox, or what we had been calling internally our ‘named entity dictionaries’).

As this four-part series and its predecessor pieces indicate, not only can we gain better conceptual understanding and realization of some of this semantic Web stuff by using DL, but also, perhaps, many of today’s silly or inefficient design practices may be remedied by better grounding our architectures in these logics.

One area, for example, that has helped us much is to get away from the confusing terminology of ‘individuals’ v ‘instances’. Once we come to see an instance record as just that (so, that is why collections can play on an equal footing with individual things, for example), we now only need worry about asserting the attributes of the instance. We can defer all of the logic and reasoning about individuals and members and sets and collections and classes, etc., to the TBox and just get on with capturing and conveying our instance record, as an ABox.

For this reason alone (but there are others), Structured Dynamics has now abandoned the terminology of a ‘named entity dictionary’ in favor or ‘instance dictionaries’ or ABox (either term of which is understood to contain one or more instance records).

The ‘Instance Record’

An instance record is simply a means to either represent or convey the information (”attributes”) of a given instance. An instance is the thing at hand, and need not represent an individual; it could, for example, represent the entire holdings or collection of books in a given library.

An instance record may convey information about multiple instances, but each block of information for each instance is about that instance alone. Thus, for example, if the instance is a paper citation, the instance is the paper. If as attributes it asserts multiple authors, each with different institutional affiliations, those affiliations get asserted in a separate instance for each author. They are attributes of the authors, not of the paper.

In this manner it is easy to see attributes as only pertaining to a given instance. If the overall information to be conveyed discusses attributes for multiple instances, than the instance record presents in series each instance that is characterized.

The Simplicity of Key-Value Pairs

The objective is to make it easy for data owners to write, read and publish data. This means the starting format should be a human readable, easily writable means for authoring and conveying these instance records (that is, instances and their attributes and assigned values).

The simplest, naïve format (independent of syntax or serialization) is the key-value (name-value) pair. In the key-value pair, the subject is always implied. So, for me, MikeBergman, as the subject:

first_name:Mike

sex:male

citizenship:USA

town:Iowa City

Because an instance record only describes attributes for a single instance at a time, all assertions can easily be transformed into the subject-predicate-object (s-p-o) “triples” of RDF. So,

<subject:MikeBergman> <hasFirstName> <Mike>

Now, of course, in conventional linked data many of these entries need to be expressed as URIs in order to “define” the item. Our design allows for that, of course, but also allows the user to simply provide literals (that is, not identifiers, but text strings or numeric or actual values) for each item. Thus, the declaration of a “new” attribute only need occur by its expression, with its value also as simply declared.

Separate, specialized services (see below) may be (and often will need to be!) employed to look up and de-reference URIs, do datatype or data instance validation checks, evaluate identity relationships, disambiguate terms and so forth. The data supplier may choose to publish more-or-less complete “records” on their own, or they may not.

Through this design, nothing need change with regard to how linked data is being done today (other than the addition of some simple converters to accommodate the new format; see below). But, by shifting testing and validation work to external services, we can make it much easier for more data to get exposed and published. It is now time for linked data intermediaries and services to evolve in the linked data ecosystem.

In its most naïve form, this key-value pair format allows for fast and easy instance record creation with the ability to create instances and new attributes on the fly. Sure, these assertions need to be checked, but so does most data when it is asked to participate in any meaningful work.

This simple design, then, is very much in keeping with the limited roles and work associated with an ABox. Only attributes and metadata for an instance are being asserted. Conceptual relationships and specialized work that might be applied against the ABox to determine data validity or whatever is shifted to be external to the instance record, where it properly and logically belongs.

Relation to RDF

In Part 3 we discussed how fragments of the RDF and OWL languages can be used for specialized purposes within a knowledge base while keeping the overall logics of the system integral and decidable. Clearly, this instance record approach where the sole purpose is to assert attributes and values for an instance does not require any OWL. In fact, most linked data to date only brings OWL into the picture for the owl:sameAs property, the common errors of which we discussed in Part 2.

The instance record only requires a small subset of the RDF language. But it does require use of RDFS (Schema) because of the appropriate use of datatypes within the instance data record.

At the level of the TBox and the “specialized work” areas, there are other fragments of OWL, now called profiles in the soon to be released OWL 2 [3], that similarly can be applied to areas such as instance checking and validation, identity relation testing, etc., that I mentioned above. In other words, we can logically fragment RDF and OWL to do the individual parts of a complete system in order to simplify things and aid performance and computational efficiency.

The Instance Record Vocabulary

We are implementing this design internally through what we call the Instance Record Vocabulary (QName: irv). It is still quite experimental and we are testing some important aspects, some of which we describe below. As we get these nuances worked out better, we will release this vocabulary publicly for any to use and comment.

As we presently see it, the namespace languages required for the IRV vocabulary are RDF, RDFS, DCterms and XSD. The RDFS (Schema) is required because, at minimum, of the incorporation of XML Schema datatypes (XSD), which we think to be a desirable requirement for what is, after all, an instance data specification and transfer protocol. However, the actual RDF and RDFS vocabulary used would be extremely minimal, with no OWL required.

In pseudo-form, with many serializations and simple syntaxes possible, this Instance Record Vocabulary has the following properties. Note as discussed above that the <s> in s-p-o is implied. Thus, in its naïve or handwritten form, it could be expressed in pretty simple key-value pairs:

<InstanceRecord>
<Instance>
<hasLabel> <[literal]> @en
<hasAltLabel> <[literal]> @en
<hasURI> <[URI]>
<hasDescription> <[literal]> @en
<Attribute>
<hasAttribute1> <[literal with optional XSD (@en) or URI]>
<hasAttribute2> <[literal with optional XSD (@en) or URI]>
<hasAttribute3> <[literal with optional XSD (@en) or URI]>
<hasAttributeX> <[literal with optional XSD (@en) or URI]>
</Attribute>
<assertIdentity> <[literal or URI]>
<assertType> <[literal or URI]>
<hasSource> <[literal or URI]>
<hasVetting> <[literal or URI]>
</Instance>
<Instance>
. . . repeat as needed . . . 
</Instance>
</InstanceRecord>

Note that most values allow either literal or URI specifications. Some of the properties are obviously optional, others, such as hasLabel, will be required. hasURI, for example, is one case of an optional property that then may require a separate lookup service to complete it as a linked data record.

Instance records with literal specifications would need to be validated and checked before actually used for standard linked data or meaningful data purposes. However, this approach is already well-proved through, for example, OpenLink’s Virtuoso Sponger cartridges and design. Sure some work would need to be done at time of ingest, but there are no technical challenges.

The language used to write a literal can be specified for any kind of attribute (metadata or not). The language is specified using the “@lang-tag” at the end of the literal. This method is similar to the N3 serialization of RDF, which is also equivalent to the XML serialization of RDF using the “xml:lang” attribute.

Metadata

Most of the first properties are simply metadata describing the instance. The strings could be qualified by language.

Attributes

The bulk of the instance record is devoted to the attributes and their values. Attributes could be optionally declared with XSD datatypes. URI references could be specified or later substituted by vetting services (see below).

Attributes could also optionally be characterized in a list format, similar to the Lists specification for Notation 3 (N3).

Asserted Relations

Identity and class membership (rdf:type) assertions could be made; these could later be checked for correctness or identity relations with external or specialized services. The assertIdentity property, in particularly, is the replacement with more appropriate ABox semantics for owl:sameAs.

hasSource

A separate Source record is being developed to cover source or dataset characterizations. A single instance extraction from a Web page, for example, would be accompanied by a simple source characterization. Instances of particular types, such as microformats for example, would be so noted (as they might invoke specialized processors or carry certain authority). Instances from large datasets would have a still longer list of possible characterizations.

This property may look closely at what is also being done for the voiD dataset vocabulary.

Certain parameters in a Source record, such as language for example, may also be applied in special ways by the IRV parser at time of ingest with respect to specific literal specifications.

In any event, this is one of the properties still needing much more thought and definition.

hasVetting

This property, too, needs much more thought and definition.

The hasVetting property, for which multiples are allowed, would identify the specific checks and services applied to the instance data. Depending on service, such checks might include URI lookup or de-referencing, identity relations and testing, record completeness and sufficiency checks, data type checking and validation, general instance checking, disambiguation, and so forth (see “Specialized Work” below).

Some services might also re-write the instance record with corrected values or URIs returned in place of literals.

Best practice for external services would suggest identifying them by URI, though literals would also be allowed to identify internal checks or for lookup purposes.

This property is meant to be a key indicator of how third parties may want to rely on the data. Combined with hasSource, these hasVetting entries provide essential authority and provenance information about the data at hand.

Putting it All Together

This diagram attempts to show the relationship of how many of these pieces may interact:

Some of these bubbles deserve some additional commentary.

Hand-crafted Input

An important objective in this design is to allow naïve, simple text specifications to be hand-crafted for instance records. There are many relatively simple formats for specifying key-value pairs with a relatively few conventions, ranging from BibTeX to YAML and JSON and others. There are literally hundreds of such formats available, as my earlier overview of Naïve Representations and Structs discussed.

There may be justification for still another form in relation to this Instance Record Vocabulary or not; this topic is still under active discussion.

External Structs

However, whether there is a separate format or not, that same earlier piece overviewed the many simple data structs presently out there. It also noted the nearly 100 existing converters for these forms to RDF. These same converters, with quite slight modifications, could all output the Instance Record Vocabulary in an appropriate serialization as well.

Hooks to Functional and Scripting Languages

Another option is to combine this design with a functional language front-end to generate these records. (Though they could be produced in other ways, as well.) For example, lambda calculus or even a domain-specific language (DSL) could be used to create this very simple record generator. This simple system, in turn, could have a straightforward API that would allow existing scripting languages (such as Python or others) to be used as well.

Specialized Work

So, in fact, we can also now see the specialized work (see also Part 2) that itself is not part of the ABox but can and often should be applied to the instance data in the ABox:

Though, strictly speaking, such specialty work could be seen to occur at the TBox level, it is actually different and separate logic from “standard” inferencing or reasoning. Specialized work can therefore often occur as separate tests or in batch mode with fragments of OWL or other dedicated indexes and algorithms. Some of this specialized work may take advantage of the conceptual relationships in the TBox, but may not necessarily need to do so. In these manners, the inferencing work of the TBox can be kept clean and efficient.

Beyond Browsing and Unvalidated Queries

Today, linked data has largely been used for browsing and providing unvalidated responses to queries; focus and attention to its ABox roles are important to move beyond this baseline into meaningful work [2]. In those limited instances where this linked data has been looked at and evaluated as a complete knowledge base, such as the SWSE search engine with the SAOR approach as discussed in Part 3, more than 97% of the RDF triples provided in those cases were removed from consideration, often for logical or mis-assertion reasons [4].

The ideas presented here for a simpler linked data specification that can be easily represented in readable text is not new. RDF in JSON has been looked at in this way by Talis and JDIL, YAML has been looked at similarly, and similar and simpler approaches have been looked at closely for topic maps. There are other examples.

A key thrust of these efforts is to make it easier for the data publisher, thereby encouraging the exposure of more structured data.

These emerging ideas do not change in any way the usefulness of current linked data. Our suggested approach interoperates seamlessly with current practices and easily co-resides with them. But, these ideas do:

• Provide a simpler path for writing and publishing human-readable instance data
• Provide an ABox instance record structure that can have much specialized work applied against it in a consistent way, and
• Contributes to an overall logic and architecture that is performant and scalable for doing meaningful work.

Though still needing further thought and refinement, this broad outline of roles and architecture and structure for the ABox completes the last missing piece to Structured Dynamics’ overall approach to linked data and RDF. Much time, thought and research have gone into it. Again, we’d very much like to thank Jim Pitman for his ideas that have helped catalyze this design [5].

We think the combination of a generalized Instance Record Vocabulary that can be reasoned over for ABox-level data checking, and that works with a simple, text-based key-value pair input format, might be a winning combination.