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 .
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  (as discussed, for example in the April 2008 Subject Concepts and Named Entities article). Again, the intuition was that real things in the real world could be logically categorized into discrete and disjoint categories.
Thus, “named entities” inexorably moved to become a categorization system, where the degree of familiarity and distinction dictated whether it was the individual (with a unique name, such as Abraham Lincoln or Mt. Rushmore) or groupings such as animal or plant species and their common names (such as beetle or oak) that was the standard “handle” for assigning a name to the “nameable thing”.
While many can argue these individual <–> grouping distinctions and whether we are talking about true, unique, named individuals or names of convenience, I think that (at least for this blog post and discussion), that misses the real, fundamental point.
The real, fundamental point is that some “things” (whether individuals, instances or classes) are distinct from other “things”. Such disjoint distinctions are a powerful concept that should not be lost sight of by “angels dancing on the head of a pin” epistemological arguments. A frog is not a rock, despite neither are “individuals”, and how can we take advantage of that realilty?
Nearly from the outset of our work with UMBEL as a ‘TBox’  — 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.)
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 . Via logical segmentation, we get benefits for both roles.
Here are some of the broad areas of benefit from a logical UMBEL segmentation that we have identified:
With these benefits in mind, we have undertaken concerted analysis of UMBEL to discern what this “logical segmentation” might be. This investigation has occurred over three concentrated periods over the past year. (Intervening priorities or other work prevented concentrating solely on this task.)
We are now complete with our first full iteraton of investigation. In this post, and then the subsequent release of UMBEL version 0.80 in the coming weeks, the fruits of this effort should be evident. However, it should also be noted that we are still learning much from this new mindset and approach. UMBEL structure refinement may be likely for some time to come.
Most things and concepts about them are based on real, observable, physical things in the real world. Because most of these things can not occupy both the same moment in time and the same location in physical space, a useful criterion for looking at these things and concepts is disjointedness.
In a broad sense, then, we can split our concepts of the world between those ideas that are disjoint because they pertain to separable objects or ideas and those that are cross-cutting or organizational or classificatory. Attributes, such as color (pink, for example), are often cross-cutting in that they can be used to describe quite disparate things. Inherent classification schemes such as academic fields of study or library catalog systems — while useful ways to organize the world — are not themselves in-and-of the world or discrete from other ideas. Thus, classificatory or organizational concepts are inherently not disjoint.
With the criterion of disjointedness in hand, then, we began an evaluation process of the UMBEL subject concepts. We looked to organizational schema such as the entity types of Sekine or BBN for some starting guidance. We also kept in mind that we also wanted our categories to inform logical clusterings of possible data presentation, such as media types or locations or time.
For terminology, we adopted the term superType to denote the largest cluster designation upon which this disjointedness may occur. As a way to test the basic coherence of these superTypes, we also collected them into larger groups which we termed dimensions.
Our analysis process began with branch-by-branch testing of the UMBEL concept graph using automated scripts, attempting to find pivotal nodes where child instance members were disjoint from other superTypes. This we term the “top-down” method.
This automated analysis was then supplemented with a complete manual inspection of all unassigned and assigned concepts, with a “bottom up” assignment of concepts or corrections to the automated approach. This inspection then led to new insights and identification of missing concepts that needed to be added into UMBEL.
We are still converging between these two methods. Optimally, we should be able to tease out all UMBEL superTypes with a relatively few number of union, intersection, or complement set operations. In its current form, we are close, but there are still some rough spots.
Nonetheless, this analysis method has led us to identify some 33 superTypes , clustered into 9 dimensions. Of these, 29 superTypes and 8 dimensions are mostly disjoint. The one dimension of Classificatory includes the four cross-cutting superTypes of attributes and organizational schema that can apply to any of the 29 disjoint superTypes.
Here is the schema, with the descriptions of each:
|Natural World||Natural Phenomena||This superType includes natural phenomena and natural processes such as weather, weathering, erosion, fires, lightning, earthquakes, tectonics, etc. Clouds and weather processes are specifically included. Also includes climate cycles, general natural events (such as hurricanes) that are not specifically named, and biochemical processes and pathways.|
|Natural Substances||Notable inclusions are minerals, compounds, chemicals, or physical objects that are not the outcome of purposeful human effort, but are found naturally occurring. Other natural objects (such as rock, fossil, etc.) are also found under this superType.|
|Earthscape||The Earthscape superType consists mostly of the collection of cartographic features that occur on the surface of the Earth. Positive examples include Mountain, Ocean, and Mesa. Artificial features such as canals are excluded. Most instances of these features have a fixed location in space.
Underground and underwater are also explicitly contained.
This superType is explicitly disjoint with Extraterrestrial (see below).
|Extraterrestrial||This superType includes all natural things not specifically terrestrial, including celestial bodies (planets, asteroids, stars, galaxies, etc., that can be located within a sky map)|
|Living Things||Prokaryotes||The Prokaryotes include all prokaryotic organisms, including the Monera, Archaebacteria, Bacteria, and Blue-green algas. Also included in this superType are viruses and prions.|
|Protists or Fungus||This is the remaining cluster of eukaryotic organisms, specifically including the fungus and the protista (protozoans and slime molds).|
|Plants||This superType includes all plant types and flora, including flowering plants, algae, non-flowering plants, gymnosperms, cycads, and plant parts and body types. Note that all Plant Parts are also included.|
|Animals||This large superType includes all animal types, including specific animal types and vertebrates, invertebrates, insects, crustaceans, fish, reptiles, amphibia, birds, mammals, and animal body parts. Animal parts are specifically included. Also, groupings of such animals are included. Humans, as an animal, are included (versus as an individual Person). Diseases are specifically excluded.|
|Diseases||Diseases are atypical or unusual or unhealthy conditions for (mostly human) living things, generally known as conditions, disorders, infections, diseases or syndromes. Diseases only affect living things and sometimes are caused by living things. This superType also includes impairments, disease vectors, wounds and injuries, and poisoning|
|Person Types||The appropriate superType for all named, individual human beings. This superType also includes the assignment of formal, honorific or cultural titles given to specific human individuals. It further includes names given to humans who conduct specific jobs or activities (the latter case is known as an avocation). Examples include steelworker, waitress, lawyer, plumber, artisan. Ethnic groups are specifically included.|
|Human Activities||Organizations||Organization is a broad superType and includes formal collections of humans, sometimes by legal means, charter, agreement or some mode of formal understanding. Examples include geopolitical entities such as nations, municipalities or countries; or companies, institutes, governments, universities, militaries, political parties, game groups, international organizations, trade associations, etc. All institutions, for example, are organizations.
Also included are informal collections of humans. Informal or less defined groupings of humans may result from ethnicity or tribes or nationality or from shared interests (such as social networks or mailing lists) or expertise (“communities of practice”). This dimension also includes the notion of identifiable human groups with set members at any given point in time. Examples include music groups, cast members of a play, directors on a corporate Board, TV show members, gangs, mobs, juries, generations, minorities, etc.
Finally, Organizations contain the concepts of Industries and Programs and Communities.
|Finance & Economy||This superType pertains to all things financial and with respect to the economy, including chartable company performance, stock index entities, money, local currencies, taxes, incomes, accounts and accounting, mortgages and property.|
|Culture, Issues, Beliefs||This category includes concepts related to political systems, laws, rules or cultural mores governing societal or community behavior, or doctrinal, faith or religious bases or entities (such as gods, angels, totems) governing spiritual human matters. Culture, Issues, beliefs and various activisms (most -isms) are included|
|Activities||These are ongoing activities that result (mostly) from human effort, often conducted by organizations to assist other organizations or individuals (in which case they are known as services, such as medicine, law, printing, consulting or teaching) or individual or group efforts for leisure, fun, sports, games or personal interests (activities)|
|Human Works||Products||This is the largest superType and includes any instance offered for sale or performed as a commercial service. Often physical object made by humans that is not a conceptual work or a facility, such as vehicles, cars, trains, aircraft, spaceships, ships, foods, beverages, clothes, drugs, weapons. Products also include the concept of ‘state’ (e/g/., on/off)|
|Food or Drink||This superType is any edible substance grown, made or harvested by humans. The category also specifically includes the concept of cuisines|
|Drugs||This superType is an drug, medication or addictive substance|
|Facilities||Facilities are physical places or buildings constructed by humans, such as schools, public institutions, markets, museums, amusement parks, worship places, stations, airports, ports, carstops, lines, railroads, roads, waterways, tunnels, bridges, parks, sport facilities, monuments. All can be geospatially located.
Facilities also include animal pens and enclosures and general human “activity” areas (golf course, archeology sites, etc.). Importantly, Facilities include infrastructure systems such as roadways and physical networks.
Facilities also include the component parts that go into making them (such as foundations, doors, windows, roofs, etc.)
|Information||Chemistry (n.o.c)||This superType is a residual category (n.o.c., not otherwise categorized) for chemical bonds, chemical composition groupings, and the like. It is formed by what is not a natural substance or living thing (organic) substance.|
|Audio Info||This superType is for any audio-only human work. Examples include live music performances, record albums, or radio shows or individual radio broadcasts|
|Visual Info||This superType includes any still image or picture or streaming video human work, with or without audio. Examples include graphics, pictures, movies, TV shows, individual shows from a TV show, etc.|
|Written Info||This superType includes any general material written by humans including books, blogs, articles, manuscripts, but any written information conveyed via text.|
|Structured Info||This information superType is for all kinds of structured information and datasets, including computer programs, databases, files, Web pages and structured data that can be presented in tabular form|
|Notations & References||Akin to conceptual works, these are codified means of human expression. Examples range from human languages themselves, to more domain-specific cases such as chemical symbols, genetic code (A-G-C-T), protocols, and computer languages, mathematical and set notations, etc.
Identifiers (numeric or alphanumeric identifiers for objects, often in a highly patterned way, such as phone numbers, URLs, zip and postal codes, SKUs, product codes, etc.), Units (any of the various ways in which measurement, space, volume, weight, speed, intensity, temperature, calories, siesmic intensity or other quantitative descriptions of phenomena can be made) and key reference types are also included in this superType
|Numbers||This unique superType is for any abstract representation of numbers and numerics|
|Human Places||Geopolitical||Named places that have some informal or formal political (authorized) component. Important subcollections include Country, IndependentCountry, State_Geopolitical, City, and Province.|
|Workplaces, etc.||These are various workplaces and areas of human activities, ranging from single person workstations to large aggregations of people (but which are not formal political entities)|
|Time-related||Events||These are nameable occasions, games, sports events, conferences, natural phenomena, natural disasters, wars, incidents, anniversaries, holidays, or notable moments or periods in time|
|Time||This superType is for specific time or date or period (such as eras, or days, weeks, months type intervals) references in various formats|
|Descriptive||Attributes||This general superType category is for descriptive attributes of all kinds. Think of the specific attributes in Wikipedia “infoboxes” to understand the purpose and coverage of this superType. It includes colors, shapes, sizes, or other descriptive characteristics about an object|
|Classificatory||Abstract-level||This general superType category is largely composed of former AbstractConcepts, and represent some of the more abstract upper-level nodes for connecting the UMBEL structure together. This superType also includes theories or processes or methods for humans to do stuff or any human technology|
|Topics/Categories||This largely subject-oriented superType is a means for using controlled vocabularies and classification schemes for characterizing what content “is about”. The key constituents of this category are Types, Classifications, Concepts, Topics, and controlled vocabularies|
|Markets & Industries||This superType is a specialized classificatory system for markets and industries. It could be combined with the superType above, but is kept separate in order to provide a separate, economy-oriented system.|
These may undergo some further refinement prior to release of UMBEL v 0.80, and some of the definitions will be tightened up.
(Note: It should also be mentioned that some of these superTypes further lend themselves to further splits and analysis. The Product superType, for example, is ripe for such treatment.)
The following diagram shows the distribution of these 20,000 UMBEL concepts across major area. By far the largest superType is Products, even with further splits into Food and Drinks and Pharmaceuticals. The next largest categories are Person and Places and Events superTypes, with Organizations and Animals not far behind:
Even in its generic state, UMBEL provides a very rich vocabulary for describing things or for tying in more detailed external ontologies. There are nearly 5,000 concepts across products of all types, for example.
You may recall that our analysis showed 29 of the superTypes to be “mostly disjoint.” This is because there are some concepts — say, MusicPerformingAgent — that can apply to either a person or a group (band or orchestra, for example). Thus, for this concept alone, we have a bit of overlap between the normally disjoint Person and Organization superTypes.
The following shows the resulting interaction matrix where there may be some overlap between superTypes:
This kind of interaction diagram is also useful for further analyzing the concept graph structure, as well.
Of the 29 “mostly” disjoint superTypes, only a relatively few show potential interactions, and then only in minor ways. We can illustrate this (drawn to scale) for the interaction between the Product, Food & Drink and Drug (Pharmaceuticals) superTypes, with the fully disjoint Organization superType thrown in for comparison:
Across all 20,000 concepts, then, fully 85% are disjoint from one another (5% is lost due to overlaps between “mostly” disjoint superTypes). This is a surprising high percentage, with even better likelihood to deliver the benefits previously noted.
These are exciting findings that bode well for UMBEL’s ongoing role and usefulness. Also, the very detailed analysis that has led to these interim findings very much reaffirms the wisdom of basing UMBEL on Cyc. Cyc showed itself to be admirably coherent and remarkably complete. (It also appears that the first versions of UMBEL were also extracted well in terms of good coverage.)
This approach now gives us an understandable and defensible basis for logical segementation of UMBEL. It also provides a much-desired alternative to the earlier Abstract Concepts, which will now be dropped entirely as a schema concept.
One area deserving further attention is in the Attribute superType. We are in the process, for example, of analyzing attributes across Wikipedia and need to look through a slightly different lens at this superType . This area is further important in its strong interaction with the Instance Record Vocabulary that is accompanying this effort on the entity side.
Another lesson for us has been to back away from the terminology of named entity, introduced at MUC-6. The expansions of that idea into other “nameable” things has caused us to embrace the “instance” nomenclature, as evidenced by our emerging IRV.
It is rewarding to prepare this next iteration release of UMBEL with its new mindset of logical segmentation and disjointedness. But — what is also clear — there are many treasures left to mine still hidden in the inherent structure of UMBEL and its Cyc parent.
Sekine’s extended hierarchy proposed in 2002 is made up of 200 subtypes, with 32 larger clusters within that. Here is the top level of the Sekine type system:
|Facility||God||Stock Index||Latitude Longitude|
|Natural Object||Time-Other||Multiplication||Ordinal Number|
Though developed separately and for different purposes, BBN categories also proposed in 2002 consists of 29 types and 64 subtypes. Here are the BBN types (Note: BBN claims 29 types because there are double entries or considerations for the first five entries):
|NORP (adjectival GPEs)||Percent||Substance|
|Organization||Quantity||Work of Art|
|GPE (geopolitical places)||Ordinal||Law|
Of course, other entity extraction systems have similar clusterings and approaches. Though less formal in the sense of a hierarchy or purported complete entity coverage, here for example is the listing of entity types within Calais:
See further the Wikipedia entry on named entity recognition.
The need to undertake some recent research has provided the occasion for me to update the AI3‘s listing — called SWEETpedia — of (largely) academic papers pertaining to the use of Wikipedia for semantic Web-related topics. These papers cover such things as information extraction, named entity recognition, word sense disambiguation, concept hierarchies, ontology and question/answer support, and so forth.
Please go here to see this alphabetized and updated SWEETpedia listing of 207 papers. It is really quite impressive, and represents 44 new papers since my last (and second) update nearly a year ago. While the pace of academic attention seems to be tailling off a bit — now that the usefulness of Wikipedia to these topics has become clear — the quality remains high.
As with past listings, I encourage any researchers that have had their paper inadvertently missed to comment on this blog post and I will make sure the oversight is corrected in the next listing.
Oh, by the way, there are many strategies I employ to find these papers, but here are a few tips you can apply on your own depending on your specific interests:
Again, please provide any missing papers by commenting below. And, of course, enjoy the updated SWEETpedia listing!
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 . Further, if we are purposeful to include connective links where appropriate as we add more data (that is, nodes), this multiplier effect becomes even stronger.
Structured Dynamics is dedicated to help make this prospect real. Meaningful progress in doing so requires only a relatively few moving parts or techniques. Yet, because we sometimes bounce from talking or focusing on one part versus the others, we can lose context or sight of the overarching vision. The purpose of this article is to re-set and calibrate that overall vision.
The vision is to get all data and information to interoperate, regardless of legacy or form. Much of this data is already structured, either from databases or simpler forms of data structs. Some of this information is unstructured or semi-structured, requiring extraction and tagging techniques. And new information is being constantly generated, which warrants better means to author and stage for interchange and interoperability.
No matter the provenance, all information has context and scope. As a chunk from here, and a piece from there, gets added to our linked data mix, having means to characterize what that data is about and how it can be meaningfully inter-related becomes crucial. Sometimes these contexts are informed by existing schema; sometimes they are not. But, in any case, it is the role of ontologies to both position these datasets into an “aboutness” framework and to help guide how the data can be described and related to other data. This part of the vision invokes semantics and coherent structures (schema or ontologies) for positioning and mapping datasets to one another.
As both the means for representing any extant data format and as the means for describing these conceptual relationships or schema, RDF provides the canonical data model. A single target representation and common data model also means we can develop and design a smaller universe of tools to operate and provide functionality over all of this data. Indeed, because our RDF data model and its ontologies are so richly structured, we can design our tools with generic functionality, the specific operation and expression of which is based on the inherent structure within the data and its relationships. This vision of data-driven apps leads to extreme leverage, incredible flexibility, and inherent “meshup” capabilities for tools.
Further, because we use Web identifiers (URIs) for our data and concepts and because we expose and access this linked data via the Web, we use the proven and scalable architectures of the Web itself for how we design our systems. This Web-oriented architecture (WOA) provides a completely decentralized and loosely coupled deployment model that can work ranging from public and open to private and proprietary, applicable to data and participants alike.
From the outset, it is essential to recognize that thousands of contributors are enabling this vision. So, while Structured Dynamics naturally uses its own tools and techniques to flesh out the various parts of this vision below, realize there are many players and many tools from which to choose . For that is another aspect of this vision that is quite powerful: providing choice and avoiding lock-in.
The core construct — or fulcrum, if you will — of the vision is the RDF (Resource Description Framework) data model . 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 .
Note, in general, such a canonical data model merely represents the agreed-upon internal representation. It need not affect data transfer formats. Indeed, in many cases, data systems employ quite different internal data models from what is used for data exchange. Many, in fact, have two or three favored flavors of data exchange such as XML, JSON or the like. More on this is discussed in a section below.
As this diagram shows, then, we have a single internal representation that is the target for all data and format converters and upon which all tools operate. These tools are themselves expressed as Web services so that they may be distributed and conform to general WOA guidelines. In addition, there may be multiple external “hubs” that represent alternative data models or formats or schema conversions (say, for relational databases). So long as we have converters between these alternate “hubs” and our canonical RDF form we can allow a thousand flowers to bloom:
Other canonical forms could be advocated. Yet RDF has the logical basis to represent any data form and any schema or conceptual structure. It is based on a robust set of open standards and languages and tools. It may be serialized in many formats. It can be grounded in description logics and, in appropriate forms, reasoned over and expressed in vocabularies and schema suitable for the most complex of conceptual structures and semantics. RDF is the data model explicitly designed for the Web, the clear global information basis for the foreseeable future.
For more than 30 years — since the widespread adoption of electronic information systems by enterprises — the Holy Grail has been complete, integrated access to all data. With the canonical RDF data model, that promise is now at hand.
Diversity is a truism of human communications as captured by the biblical Tower of Babel and the many thousands of current human languages. Diversity in data formats, serializations, notations and languages is a similar truism. We term the expression of each of these varied forms of data a struct.
While an internal canonical representation of data makes sense for the reasons noted above, pragmatic information systems must recognize the inherent diversity and chaos of data in the real world. The history of trying to find single representations or to impose standards via fiat have singularly failed. That will continue to be so due in part to inertia and legacy, sunk investments, existing infrastructure, and the purposes for the data.
In pursuing a vision of data interoperability, then, conversion is an essential glue for cementing understanding with what exists and will exist.
Arguably the largest source of structured data are enterprise and government information systems, with the predominant data representation being the relational data model managed by relational schema. Much of this data is also cleaner and mission critical compared to other sources in the wild. Fortunately, there are many logical and conceptual affinities between the relational model and the one for RDF .
Just as there are many RDFizers for simpler forms of data structs (see next), there are also nice ways to convert relational schema to RDF automatically. Given these overall conceptual and logical affinities the W3C is also in the process of graduating an incubator group to an official work group, RDB2RDF, focused on methods and specifications for mapping relational schema to RDF.
Amongst all techniques covered in this paper, Structured Dynamics views the layering of RDF ontologies over existing relational data stores as one of the most promising and important. Given the advantages of RDF for interoperability, this area should be a major emphasis of current and new vendors and service providers.
Much data, however, resides in much smaller datasets and often for less formal purposes than what is found in enterprise databases. Some of this data is geared for exchange or standardization; much is emerging from Web and Internet applications and uses; and much might be local or personal in nature, such as simple lists or spreadsheets.
RDF is well suited to convert (“RDFize”) these simpler and more naïve data formats. In my original census about 18 months ago, as reported in ‘Structs’: Naïve Data Formats and the ABox, I listed about 90 converters. My most recent update now lists nearly double that number, with about 150 converters :
URN handlers (in addition to IRI and URI):
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 . See further the Authoring section below.
An apocryphal statistic is that 80% to 85% of all information resides in unstructured text . Besides lacking recent validation, this claim from a decade ago often attributed to Merrill Lynch also precedes much of the Internet and the emergence of metadata and tagging. Nevertheless, what is true is that written text content is ubiquitous and the majority of it remains untagged or uncharacterized by any form of metadata.
While such information can be searched, it only matches when exact terms match. This means that related information, particularly in the form of conceptual relationships and inferencing, can not be applied to untagged text content.
While information extraction — the basis by which tags for entities and concepts can be obtained — has been an active topic of research for two decades, it is only recently that we have begun to see Web-scale extractors appear. Examples include Yahoo’s term extractor, Thomson Reuter’s Calais, or Google’s Squared, to name but a few.
In Structured Dynamics’ case we have been working on the scones (Subject Concepts Or Named EntitieS) extractor for quite a while. scones uses rather simple natural language processing (NLP) methods as informed by concept ontologies and named entity (instance record) dictionaries to help guide the extraction process. The co-occurrence of matches between concepts and entities also aids the disambiguation task (though additional modules may be invoked with alternative disambiguation methods). In prototype forms, the resulting tags can be managed separately or fed to user interfaces or re-injected back into the original content as RDFa.
There are literally dozens of such extractors and services presently available on the Web and many that are available as open source or commercial products. Some are mostly algorithm based using machine-learning techiques or statistics, while others are gazeteer- or dictionary-driven.
These systems will lead to rapid tagging of existing content and the removal of some of the early “chicken-and-egg” challenges associated with the semantic Web. These systems will also be combined with the many existing bookmarking and tagging services.
So, just as we will see federation and interoperability of conventional data, we will also see linkages to relevant and supporting text content accompanying it. This combination, in turn, will also lead to richer browsing and discovery experiences.
In addition to conversion and tagging, authoring is the third leg of the stool to expose structured data. It is a neglected leg to the structured content stool, and one important to make it easier for datasets to be easily exposed as RDF linked data.
One of the reasons for the proliferation of data structs has been the interest in finding notations and conventions for easier reading and authoring of small datasets. There have literally been hundreds of various formats proposed over decades for conveying lightweight data structures. Most have been proprietary or limited to specific domains or users. Some, such as fielded text, structured text, simple declarative language (SDL), or more recently YAML or its simpler cousin JSON, have become more widely adopted and supported by formal specifications, tools or APIs. JSON, especially, is a preferred form for Web 2.0 applications.
What has been less clear or intuitive in these forms, again mostly based on an attribute-value pair orientation, is how to adequately relate them to a more capable data model, such as RDF. In JSON or YAML, for example, the notations include the concepts of objects, arrays and datatypes (among other conventions). Other structures lack even these constructs.
To take the case of JSON as might be related to RDF, there are a couple of efforts to define representation conventions from Talis and GBV for serializing RDF. There was a floated idea for an RDF version of JSON called RDFON that has now evolved into the TURF approach. JDIL (JSON data integration layer) instructs how to add namespaces to JSON to enable encoding RDF. Jim Ley, Kanzaki Masahide and Dave Beckett (likely among others) have written simple and straightforward RDF and Turtle parsers and converters for JSON. And, still further examples are Beckett’s Triplr and Sören Auer‘s ASKW Triplify lightweight conversion services involving many different formats.
Because JSON is easily readable, can drive many Web 2.0 applications and widgets, and lends itself to fast conversions and tools in various scripting languages, Structured Dynamics was commissioned by the Bibliographic Knowledge Network (BKN) to formalize a BibJSON specification suitable for BibTeX-like data records and citations with an extensible schema to be converted to RDF.
The emerging result of that BibJSON effort will be published shortly. The specification includes conventions and vocabularies for creating bibliographic and citation instance records, for specifying structural schema, and for creating linkage files between the attributes in the record files with existing and new schema. BibJSON is itself grounded in IRON, which is an instance record and object notation developed by Structured Dyamics that can be serialized as JSON (called irJSON), XML (called irXML) or comma-separated values (or CSV comma-delimited files, called commON).
The purpose of these notations and serializations is to provide easier authoring environments and scripting support to RDF-ready datasets. This approach has the advantage of shielding most users from the nuances or lengthiness of RDF (though the N3 serialization also works well).
The design and development of commON was especially geared to using spreadsheets as authoring environments that would enable easy creation of instance record tables or simple hierarchical or outline structures. For example, here is a sample portion of Sweet Tools specified in a spreadsheet using the commON notation:
Once the philosophy and role of naïve data structs is embraced — with an appreciation of the many converters now available or easily written for translating to RDF — it becomes easier to determine data forms appropriate to the tools and natural work flow of the users and tasks at hand. Under this mindset, the role of RDF is to be the eventual conversion target, but not necessarily what is used for intermediate work tasks, and in particular not for authoring.
OK, so now all of this stuff is converted, tagged or authored. How does it relate? What is the relation of one dataset to another dataset? Is there a context or framework for laying out these conceptual roadmaps?
Two years ago as we looked at the state of RDF and the incipient semantic Web as promised via linked data, we saw that such a specific framework was lacking. (Though there were existing higher-level ontologies, either their complexity or design were not well-suited to these purposes.) It was at that time that Frédérick Giasson and I began to formulate the UMBEL (Upper Mapping and Binding Exchange Layer) ontology, which eventually led to our more formal business partnership and Structured Dynamics.
What we sought to achieve with UMBEL was a coherent reference framework of about 20,000 subject concepts, connected and acting like constellations in the information sky for orienting content and new datasets. At the same time, we wanted to create a general vocabulary and approach that would lend themselves to creation of domain-specific ontologies, which would also naturally tie in and inter-relate to the more general UMBEL structure.
This objective was achieved, though UMBEL deserves an upgrade to OWL 2 and some other pending improvements. A number of domain ontologies have been created and now relate to UMBEL. So, rather than being an end to itself, UMBEL was one of the necessary infrastructure pieces to help make the vision herein a reality.
Similar approaches may be taken by others with new domain ontologies based on the UMBEL vocabulary with tie-in as appropriate to existing subject concepts, or by mapping to the existing UMBEL structure.
Of course, UMBEL is not an absolute condition to the vision herein. However, insofar as users desire to see multiple datasets inter-related, including the use of existing public Web data, something akin to UMBEL and related domain ontologies will be necessary to provide a similar roadmap.
The parts and techniques discussed so far pertain almost exclusively to data and content. But, these structures so created now can inform data-driven applications which also now must be deployed. To do so, Structured Dynamics is committed to what is known as a Web-oriented architecture (WOA):
WOA is a subset of the service-oriented architectural style, wherein discrete functions are packaged into modular and shareable elements (“services”) that are made available in a distributed and loosely coupled manner. WOA generally uses the representational state transfer (REST) architectural style defined by Roy Fielding in his 2000 doctoral thesis; Fielding is also one of the principal authors of the Hypertext Transfer Protocol (HTTP) specification.
REST provides principles for how resources are defined and used and addressed with simple interfaces without additional messaging layers such as SOAP or RPC. The principles are couched within the framework of a generalized architectural style and are not limited to the Web, though they are a foundation to it.
Within this design we need a suite of generic functions and tools that are driven by the structure of the available datasets. The deployment vehicle and design we have implemented to provide this WOA design is structWSF .
structWSF is a platform-independent Web services framework for accessing and exposing structured RDF data. Its central organizing perspective is that of the dataset. These datasets contain instance records, with the structural relationships amongst the data and their attributes and concepts defined via ontologies (schema with accompanying vocabularies). The master or controlling Web service in the framework is the module for granting access and use rights to datasets based on permissions.
The structWSF middleware framework is generally RESTful in design and is based on HTTP and Web protocols and open standards. The initial structWSF framework comes packaged with a baseline set of about a dozen Web services in CRUD, browse, search and export and import. More services can readily be added to the system.
All Web services are exposed via APIs and SPARQL endpoints. Each request to an individual Web service returns an HTTP status and a document of resultsets (if the query result is not null). Each results document can be serialized in many ways, and may be expressed as either RDF or pure XML.
In initial release, structWSF has direct interfaces to the Virtuoso RDF triple store (via ODBC, and later HTTP) and the Solr faceted, full-text search engine (via HTTP). However, structWSF has been designed to be fully platform-independent. The framework is open source (Apache 2 license) and designed for extensibility.
Like all visions, there are many aspects and many improvements possible. This vision is definitely a work-in-progress with no end in sight.
But, meaningful movement embracing the full scope of this vision is doable today. Structured Dynamics welcomes inquiries regarding any of these aspects, improvements to them, or application to your specific needs and problems.
We also welcome you to come back and visit our blogs (Fred’s is found here). We try to speak on various aspects of this vision in all of our posts and are pleased to share our experience and insights as gained.
OK, I admit it. I’m a dweeb and a suck-up. I have just returned from the Semantic Technology Conference in San Jose (CA) and I could not be more impressed. For real semantic Web action, this was the place to be. And, I’m sure, it will continue to be so if its leadership stays intact for some time to come.
I know, it is really not my style to applaud others when I could be patting myself on the back. But, hey, this was such a remarkable meeting in so many ways that I feel I have to break from precedent.
In terms of dislcosure let me be clear: I hope I get invited back to speak again next year (only this time in a bigger forum. Hint. Hint.) But, even if not invited to speak, me and my company will be there with bells on for this simple reason: this is the semantic Web confab that matters.
Unlike others that have noted specific talks, etc., I will not do so. Not because there were not talks that warrant such visibility; indeed, there were a tremendous number. My biggest regret of the meeting is that I could only taste a portion of all of the talks because so much was going on. But, rather than singling out any specific talks, I’d like to comment more thematically.
The conference organizers reported that more than 500 paper suggestions were put forward for what ended up being about 150 speaking slots. This was in addition to many tutorials, keynotes and many rump meetups. My best guess is that there were on the order of 1200 in total attendance. It was a packed five days or so. San Jose and the facilities were excellent.
When tech shows reportedly are down on average 40% or so from prior years, it is pretty remarkable to have one exceed its prior attendance, which SemTech 2009 apparently did. There were real customers, real use cases and real interest at every turn and in every conversation. Many hard problems were brought forward; some without acceptable solutions yet.
The real theme I kept hearing was: data federation, data federation, data federation. The potential for semantic Web technologies via the RDF data model and OWL and ontologies for finally breaking down the barriers between data silos was hammered and probed. The timing, I think, could not have been better than to have received shortly before the conference the timely PricewaterhouseCoopers technology quarterly report on linked data in the enterprise that I reviewed a couple of weeks back.
I think we can safely say that the advances from linked data in the past couple of years have been huge enablers and eye-openers to these prospects. But I also had the sense that the discourse is now moving beyond linked data as practiced so far. Web identifiers and Web access, I think, have won the argument. It is now time to move on to real data, interoperability and efficient tools and build-out.
To be sure there were discussions of more consumer-oriented apps and search. But the major energy and action seemed to center on the enterprise.
The idea is how can RDF bring us leverage, not replace what already exists. After 30 years of frustration, how can we finally solve the data federation problem? How can we remove the historical brittleness of applications and report writers? How can we actually begin to extract business intelligence from the massive data assets we already have at hand?
Asking enterprises to junk what they have for promises and prayers will not cut it. The winning strategy, and the challenge I kept hearing was: How can we layer on semantic technologies and RDF to bridge our existing data stores? How can we leave our RDBMs in place while gaining the goodness of ontologies and semantics?
We clearly see all around us the power of open source and the withering of proprietary apps and approaches. But, much data and information will remain private and needs to have access and rights restrictions. What answers does semantic technologies offer in these areas?
Then, as suppliers in this brave new world of open source and low software rents, what is the winning business model? Tom Tague helped articulate the importance of revenue models and options in his keynote; it resonated with already ongoing discussions in the hallways.
I’ve been in this space for more years than I care to admit. My observation from prior years is that some new “big thing” is identified, given blessing and push by the industry analysts (always with a new acronym), and then hyped like hell. Maybe it is the current challenged economic climate, but it feels like those days are over. For good.
Hype will not open wallets anymore. Case studies and real warts will help bring confidence that something truly different is at hand with semantic technologies. Our central challenge as suppliers to this market is to respond to today’s pragmatic imperatives. We must demonstrate more with less and faster. We must emphasize leverage and re-use. We must respect the trillions in already sunk IT assets.
I think this matters much for three different communities.
For enterprises, I think it means that it is time for pilots and engagements. Both the market and the suppliers can not move this space forward rapidly without meaningful engagement. We’re ready, and it seems like many of you are as well. Push it with your bosses; we’ll deliver.
For the linked data community, where do you go next? I, too, heard some of the criticisms about too much “ontology.” But such discussion risks wasting the gains already achieved. If we do not listen and respond to the market’s imperatives and voice, we will become irrelevant. Let’s accept linked data as a tremendously helpful step in an ongoing progression, but continue to mature.
For some of the more established semantic technology providers, we have to make it simpler and faster. Expensive ontology development, too, will become irrelevant if we are indeed going to replace conventional software development with data-driven apps. Fortunately, I saw much, much exciting in this space and really had my eyes opened to tremendous innovation.
Outside of the venue, I heard from some of my prior Silicon Valley colleagues that this was the most constrained VC situation they have seen in decades. Funds may exist, but capital calls are not being made. What little powder there is, is being kept dry to triage existing investments. It is a good thing capital requirements for new start-ups have declined so much in recent years, because VCs are unlikely to fund the gap. And, aside from some big, prominent initiatives like data.gov or health care digitization, most savvy observers would bet that US and EU funding will also begin drying up in the coming years.
All of this can sound like bad news, but I think it is an opportunity: As technologists and suppliers, we must be relentlessly revenue focused and deliver what the market is demanding: more with less faster while preserving existing investments.
The organizers from Wilshire Conferences and their entire staff did an absolutely tremendous job. Tony Shaw, Eric Franzon, Steve Bastasini and Eric Hoffer (I know Eric, you were only pinch hitting), plus the many on-site staff, were uniformly professional and unobtrusive. Sally Khudairi on PR and the A/V and registration crew were also excellent.
I once had responsibility for an annual technical meeting that averaged more than 2000 attendees and 150 exhibitors and I appreciate how many moving parts there are behind the scenes. Things work when nothing gets noticed. My guess is few noticed any issues or problems at this conference.
The stated aim on the intro slide to each session was to educate, and the agenda certainly achieved that. A/V was professional; time was kept; coffee did not run out; wi-fi glitches were quickly solved.
Sure, like any business, there is some pay-to-play in such conferences. Big sponsors get more slots and visibility. This reality, however, was also well balanced with new voices and innovative presenters. My “to do” notes and contacts resulting from the conference will take quite some time to work through.
One of the things I really appreciated was how the time slots and composition of talks and activities were varied each day. I have not attended a meeting before that did such a good job of mixing the schedule up to keep things feeling fresh over so many intense days.
Much, thanks, folks, for a conference exceedingly well done.
If I had to note a quibble I guess it would be to start the conference with more challenges and innovations. While the tutorials are very helpful, the first opening talks, I hope, could not be quite so introductory in nature. I think things are maturing fast. But, I could be wrong. First-time attendees from the marketplace should probably guide how such events start ramping up the engine.
So, start saving for your travel budget now. This is “must see” semantic Web. And I look forward to seeing you there in a year!
After six months of dedicated effort, we are pleased to announce two new products: conStruct, which is a set of modules for bringing structured (RDF) content capabilities to Drupal and structWSF, the platform-independent Web services framework that underlies it.
There has been some promising effort to expose RDF data from Drupal for some time, and expressing internal data within Drupal as RDFa is being implemented by others as part of the upcoming version 7 of Drupal. These are exciting prospects that we wholeheartedly applaud. In fact, they will also be great sources to our products noted below.
However, our innovation looks through the other end of the telescope: Our new conStruct structured content system (SCS) enables external structured data to actually ‘drive the application‘. We think Drupal is the perfect host to demonstrate this new paradigm of ‘data-driven apps’.
The conStruct Drupal module makes the connections between existing Drupal capabilities and the structWSF Web services framework. structWSF provides a standard suite of Web services, an innovative means to access and manage datasets, and the hooks to underlying structured data stores and full-text search engines.
Combined with the existing efforts to expose RDF from Drupal, we think these two new products now promise a two-way highway for structured data thorugh Drupal.
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 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.
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.
conStruct SCS is a structured content system that extends the basic Drupal content management framework. conStruct enables structured data and its controlling vocabularies (ontologies) to drive applications and user interfaces.
Users and groups can flexibly access and manage any or all datasets exposed by the system depending on roles and permissions. Report and presentation templates are easily defined, styled or modified based on the underlying datasets and structure. Collaboration networks can readily be established across multiple installations and non-Drupal endpoints. Powerful linked data integration can be included to embrace data anywhere on the Web.
conStruct provides Drupal-level CRUD (create – read – update – delete), data display templating, faceted browsing, full-text search, and import and export over structured data stores based on RDF. Depending on roles and permissions, a given user may or may not see specific datasets or tools within the Drupal interface. Search and browse results are similarly sequestered depending on access rights. There is a core conStruct project on Drupal, with the additional optional modules of structDisplay and structOntology coming soon thereafter.
In addition to the products themselves, two different Web sites accompany our announcement, both based on Drupal.
OpenStructs.org is dedicated to the platform-independent offerings. All OpenStructs tools are premised on the canonical RDF (Resource Description Framework) data model. OpenStructs tools either convert existing data structures to RDF, extract structure from content as RDF, or manage and manipulate RDF. All OpenStructs tools and approaches are compliant with existing open standards from the W3C. The intent is to achieve maximum data and software interoperabililty.
The main software distribution from OpenStructs is structWSF. Over time, OpenStructs is also meant to be the distribution point for user-generated “structs” in data display templating, and data extractors and converters, in addition to additional Web services compliant with the structWSF framework.
conStruct SCS is a knowledge site dedicated to conStruct and provides demos and sandboxes for the system. It accompanies the actual project sites on Drupal itself.
We unveiled and demoed the two products yesterday at the 2009 Semantic Technology Conference in San Jose, California. I did so during my talk on, "BKN: Building Communities through Knowledge, and Knowledge Through Communities." SemTech 2009 is a premier semantic Web event, which has been steadily growing and now exceeds 1000 attendees.
structWSF has been under development by Structured Dynamics for some time. Its linkage and incorporation within the Drupal system has more recently been supported by the Bibliographic Knowledge Network.
BKN is a major, two-year, NSF-funded project jointly sponsored by the University of California, Berkeley, Harvard University, Stanford University, and the American Institute of Mathematics, with broad private sector and community support. BKN is developing a suite of tools and infrastructure for citations and bibliographies within the mathematics and statistics domain based on semantic technologies for professionals, students or researchers to form new communities.
An alpha version of structWSF will released for download from the OpenStructs (http://openstructs.org) Web site on June 30. The conStruct system will be released at the same time under GPL license. See its home site at http://constructscs.com or within the Drupal module system (http://drupal.org/project/construct).
Structured Dynamics has as its mission to assist enterprises and non-profit organizations and projects to adopt Web-accessible and interoperable data. These are our first product offerings geared to address this mission.
The basic premise is that the data itself becomes the application. Via structured, linked data and a combination of products and Web services, information in any form and from any source can now be integrated and made interoperable. Linked data is based on open standards to interconnect any form of relevant information on the Web — on demand and in context.
One of the most exciting aspects of the overall architecture behind these two products is their suitability to support distributed collaboration, across diverse and definable datasets, all supported by sensitivity to role-based data and tools (Web services) access.
We’ll be speaking much more on these topics now that we have this foundation in place. We also, of course, have much to learn about the deployment and use case potentials of these frameworks.
These two products signal our (SD’s) commitment to open source. We hope some of you also see the promise in these frameworks to provide an adaptive infrastructure to linked and structured data. We welcome your participation!