Posted:February 24, 2014

Smell the MoneyTo Combat a Decline in Mindshare, Follow What is Pragmatic

A secret of the semantic Web community is that energy, innovation and participation have slipped over, say, the past three or four years. This has been obvious for some time. I began collecting statistics on such things as prevalence in Google searches, attendance at SemTech or xSWC meetings, postings to user groups, blog postings, heck, even stupid and lengthy controversies on the mailing lists, or the sale and then sale and then sale of SemTech itself.

Fortunately, I realized that my observation of a decline did not depend on having documentary backup: the trend was obvious. So, I could stop collecting time-sucking statistics. I’m sure many of the participants in the formation of the semWeb know exactly of this decline in energy and focus of which I speak.

Other endeavors have kept me from worrying too much about such matters, but recent griping in public forums about the state of the semantic Web got me again thinking about premises and the state of semantic technologies. Such re-thinks are useful because they help put current circumstances into context, and because they help guide how to spot emerging opportunities.

While I am not feeling overwhelmingly passionate about such matters, there does appear to be a villain in this story, what I might term the FYN crowd [1]. But, like all good villains and stories, villainy is mostly a matter of context, with the winners being the ones writing the history. So, accept my thoughts as arising as much from my own worldview as from anything else . . . .

Galileo’s BallsGalileo's Balls

Once one embraces an intellectual domain with the premise of semantics, then meaning and context a priori become first citizens. Depending on viewpoint, what the semantic Web means to one individual can differ substantially from another individual. Moreover, the space becomes a sort of cipher for expressing any worldview, legitimately. For example, one tension at the heart of the semantic Web enterprise has been bottom up v top down; another has been anything goes v more structure and formalism. Hot buttons arise when worldviews differ, as they always surely do. The semantic Web is no exception.

Yet the stated bases for these semantic Web hot buttons, I would claim, are simplistic. What really occurs in the semantic technology space is something more akin to the Galileo thermometer, multiple viewpoints finding multiple resting points. Only in the semantic Web case, the natural resting points don’t just simply occur along a single dimension of, say, formalism, but other viewpoints as well. So, what we end up with is something more akin to a 3D- or multi-dimensional column. There are an infinite number of resting points in reasoned discourse.

Why should this be strange or threatening? Of course, upon inspection, it is not. The understanding that needs to arise is that semantics is truly about differences at all levels of human experience, perceptions and language. A pragmatic semantics must reflect this reality.

I don’t think that these sentiments will ever translate into precision or algorithms. But they can be modeled approximately with algorithms and refined with judgment. Much of their essence can also be captured by ontologies. These are viewpoints that can be captured in silico and used to help humans make better decisions. Semantics are essential to these prospects. At the heart of any pragmatic semantics must be an accommodation of viewpoints and terminology.

The real point in all of this — actually, also the major reason for semantic technologies in the first place — is that for any topic of normal human discourse there is a variety of viewpoints. Only a system expressly designed to respect these differences can be an effective digital means of interoperability.

Tribal Diversities

There are many tribes within the semantic technology space. Academic researchers are the most visible tribe. Because of funding nuances and general interest and tradition (though there are real differences between the US, Queen’s countries, EU or Asia), academics have — and sometimes continue to — set the tone for the semantic Web community. This has been useful to establish a coherent and (generally) logical basis to the underpinnings of the semantic Web. But most in the community would also acknowledge this basis is not sufficient to achieve commercial breakthrough.

In the US, there is a strange mix, with many semantic researchers flying below the radar, because they work for the three-letter intelligence agencies. Also, there is a very strong biomedical community, often funded from the National Library of Medicine. The biomedical community has been an exemplar innovator. Because of this community’s efforts, we now can see how an entire domain — biomedical — can develop and leverage ontologies, establish common vocabularies or standards, or cooperate on tools development. There is no public community more advanced in semantic techology developments than the biomedical one.

Another tribe in this space is the successful hunter, able to use semantic technology capabilities to attract and secure paying customers. Most of the activities of these tribe members is hidden from view, because their paying efforts are by nature infrastructural and concentrated on enterprise and commercial customers. But, also, many individuals within this tribe actively contribute to public efforts and conferences. Many of the more visible semantic technology companies, including my own, occupy this space.

But the most enriched tribe of the semantic Web has been the background semantic orchestrator, generally through infrastructure-based initiatives like broadscale knowledge representation, statistical analysis of massive text corpora, well-considered ontologies, or knowledge structures. The semantic efforts of the search engine vendors, including Bing and Google’s knowledge graph, are members of this tribe, as is Siri, now part of Apple.

These differences in market focus and visibility have tended to play out in expected ways. Academic researchers, Web enthusiasts and those committed to open data have been most vocal about “linked data”. They tend to be the more visible participants in semantic Web mailing lists and forums. Casual followers of the semantic technology space, or those new to it, mostly hear these same voices. By default, the apparent health and status of the semantic Web is more-or-less defined by these voices.

When I said in the intro that the semantic Web has slipped over the past few years, that perception is mostly the result of the lowered volume and fewer messages coming from the vocal tribe. But there are two problems with the accuracy resulting from that. The first, as argued above, is that the vocal and visible linked data advocates are not the only representatives of the community. And, the second, which I’ll get to in a moment, is that the vocal community’s prescriptions for the semantic Web, in my opinion, are no longer the most meaningful ones.

Branding, Terminology and Marketing Messages

Pig SnoutsMany early proponents of the semantic Web, I think it fair to observe, would say that two positioning mistakes (from their perspective) have kept the paradigm from grabbing greater hold. The first reason often cited is the use of XML as the initial syntax of RDF. At first blush, I agree with this observation, given that when I was first entering into the dark chambers of the semantic Web it was at times difficult to separate XML from RDF. Today, though, most semWeb practitioners prefer the use of alternative serializations. I personally don’t think that any difficulties that semantic Web understanding and adoption may pose today are any longer influenced by a decade-old XML confusion. In Web years, these are eons.

The second reason seems to have been the flat-out retreat from “semantic Web” terminology. The conscious decision to switch to the “linked data” branding began in earnest about 2008. I find this shift interesting. I think it relates to looking to the wrong measures of success. What seemed like a clever re-branding at that time has both set the focus in the wrong direction and consequently set the wrong targets for measuring success.

In the areas of standards and movements, moral authority, suasion and prominence often become the bases for who is viewed as “owning” a new concept. There has been much of this posturing around the “semantic Web” and “linked data”, with parry thrusts from “Web 3.0″ and “big data” and “open this or that”. So, I’m not surprised that branding many of the concepts of the semantic Web with a new term — “linked data” — was pushed and took hold. But why original semantic Web advocates adopted this term and its shift in focus from an ecosystem to data representation and exchange does surprise me.

The strange thing, in my opinion, is the monadic emphasis on “linked data” that acts to partially kill the semantic Web minding. Whether by design or fallout, “linked data” inexorably shifts the focus to how data is represented and transmitted. It is a royal pain in the ass for publishers to publish “linked data” and then, when done, there is surprisingly little consumption of it. The MusicBrainz announcement it was dropping RDFa last week is telliing [2]. We are seeing the representation of structured Web data being driven on other bases, as evidenced by the success of JSON, something that linked data enthusiasts have only lately come to embrace, and the schema.org initiative of the major search engines.

Once linked data was raised as the lead banner, other branding messages followed. The first add-on message was “follow-your-nose”. FYN represents clcking from link to link following data references of interest on the Web [1]. In order for that be facilitated, but also as a means to clear up some confusions about linked data, the quality standard of “5-star linked data” was also put forth. To achieve all five stars, linked data should conform to open standards such as RDF and link to other data for context [3].

Today, on virtually all “official” semantic Web forums you will see mention of the brands of linked data, FYN, 5-star linked data, and open data. Publishing of data according to best practices that enables global links from datum to datum across the Giant Global Graph has become the sort of gold standard associated with this new branding.

What is the Measure of Success?

Success is always measured against our premises and values. In the case of the vocal tribe, the premises and values relate to linked and open data. By these measures, the semantic Web is a mixed bag. On the positive front, many laudable sources of quality data — most recently the Getty Museum [4], but also the Library of Congress and arts and humanities publishers across Europe, but also including many science realms beyond biology, and of course hundreds of others made famous by the LOD cloud [5]  — are published as linked data. or in the process being so. Open data sets are coming from government at all levels [6].

On the negative front, the growth of pubished linked data has fallen behind the pace of publishing structured data in general, and notable evidence for where the consumption of linked data has made a difference is pretty hard to find. Linked data advocates only rarely discuss integration with “closed”, proprietary data or enterprise use, integration and realities. Shitty sameAs assertions abound everywhere. Markets find it hard to get excited when the arguments and reference frameworks don’t relate well to their actual problems and pain points. DBpedia can only go so far, and a mountain of links to it without relevance, context or quality is just so much more noise [7].

The point here is not to mount a screed against linked data, but to caution: Be careful how you brand yourself. By the measures of growth and penetration and uptake of linked data, moreover linked open data, the semantic Web space is generally not attracting developer interest, media attention or venture dollars. I hope the release of meaningful linked data continues, but setting that goal as the measure of the semantic Web’s success is selling the wrong product.

Rather than setting a FYN objective as to whether our semantic technology efforts to date have been a success, I suggest we adopt a “follow the money” (FT$) premise. Who is investing or making money off of this stuff, and how and why? Herein lies a different measure of success.Money River

If we look to the approaches taken by those making money in this market, we find that the:

  • Challenges of meaningful connections
  • Interoperability
  • Integration across document and structured data
  • Discovering new patterns and relationships
  • Facilitating semantic understanding across disparate communities and legacy data sources, and
  • Providing quality characteristics for new entities,

are where the bucks are being made. These activities are all at the heart of the knowledge worker’s job responsibilities. Even the earliest advocates of the semantic Web must have had aspirations that the semantic Web had the promise to address these meaningful challenges.

Another secret to systems like Freebase, Google knowledge graph, Bing, Watson, Siri, or similar innovations is their use and reliance on Wikipedia, at least in their formative stages. Though often DBpedia was the structural form of ingest, the core basis of these systems’ capabilities comes from content — Wikipedia — the access to which was only made easier via DBpedia.

The sentiment to follow the money is not a sell out or a political statement. It is a recognition that work worth doing is work others appreciate and are willing to pay for. It is the best signal amidst the noise of what is valuable to work on.

It’s Time for the Side B Hit

I’ve been a fairly active participant in the semantic Web for nearly 10 years. I sometimes have the image of an aspiring music artist from the ’50s or ’60s arguing with the record execs which song should be the favored Side A cut on the 45. The visible voices of the semantic Web want to push FYN and linked data as Side A, but it really isn’t selling, according to the advocates’ own success measures.

The Side B of interoperability, RDF and OWL is not just “filler” to the main promotion, but where I clearly think the hit resides. Some have heard that track, buy it, and are enthused about it. It would be nice if the record execs could see what is right before their face and begin promoting it as well.

FYN and its vocal proponents risk the perception of failure of the semantic Web enterprise from the simple fact of putting linked data front and center. Sure, it is a good approach with potentially rich information so long as you can trust the source both for the content itself and the quality of its RDF expression. No one is arguing with that.

But SGML and ASN.1, one could argue, in similar veins, amongst actually dozens of others, were great and useful notations, yet are now mostly historical footnotes. If a trusted source is going to serve me up 5-star linked data, I will take it. Yet the truth is I would take structured data in any form from a trusted source, but take no linked data from an unknown source or one with poor linkages. We spend much time looking at these issues for our clients, and it is the rare linked data set that becomes part of our solution. Even then, we carefully scrutinize all assumed connections.

The Side B semantic Web of vetted and interlinked, interoperable data organized by competent graphs is the winning side. It is the only location where true economic transactions are taking place around the semantic Web. To understand where the semantic Web makes sense, follow the Side B money to your answers.

The insight gained from a FT$ approach clearly points to the failure of FYN. I say, do linked data if you can, it is the best ingest format around. But don’t get too hung up on that. Spend your time figuring out how to bridge meaningful gaps in semantics or data across any enterprise, global or local. Information is not truffles, and following your nose is not the primary argument for the semantic Web.

[1] FYN. or Follow Your Nose, reflects is the general practice of performing web retrieval on URIs in a knowledge base to obtain more knowledge. Two W3C articles provide additional commentary. In the linked data context, FYN represents clcking from link to link following data references of interest. FYN is a specific pattern of linked data. Ed Summers provided one of the better overviews of the use of FYN in the context of linked data and the Web of Data.
[2] See the MusicBrainz blog from February 18, 2014.
[3] Tim Berners-Lee describes 5-star linked open data in this article.
[4] The Getty Museum recently made a portion of its Arts and Architecture Thesaurus (AAT) open source using linked data; see http://blogs.getty.edu/iris/art-architecture-thesaurus-now-available-as-linked-open-data/.
[5] The linked open data (LOD) cloud diagram and supporting information is maintained at http://lod-cloud.net/.
[6] I have often written on the problems with linked and open data as presently practiced. See Practical P-P-P-Problems with Linked Data (October 4, 2010) and The Nature of Connectedness on the Web (November 22, 2010) as two examples. Specific commentary on open data in government is provided in When Linked Data Rules Fail (November 16, 2009).
[7] For another assessment of the state of the semantic Web, see Brian Sletten’s recent Keep On Keeping On article on semanticweb.com (January 13, 2014).
Posted:May 21, 2013

Neighbourhoods of Winnipeg - NOWFirst and Largest Local Government Site to Exclusively Embrace Semantic Technologies

The City of Winnipeg, the capital and largest city of Manitoba, Canada, just released its “NOW” portal celebrating its diverse and historical 236 neighborhoods. The NOW portal is one of the largest releases of open data by a local government to date, with some 57 varied datasets now available ranging from local neighborhood amenities such as pools and recreation centers, to detailed real estate and economic development information. Nearly one-half million individual Web pages comprise the site, driven exclusively by semantic technologies. Nearly 10 million RDF triples underly the site.

In announcing the site, Winnipeg Mayor Sam Katz said, “We want to attract new investment to the city and, at the same time, ensure that Winnipeg remains healthy and viable for existing businesses to thrive and grow.” He added, “The new web portal, Neighbourhoods of Winnipeg—or NOW—is one way that we are making it easy to do business within the City of Winnipeg.”

NOW provides a single point of access for information such as location of schools and libraries, Census and demographic information, historical data and mapping information. A new Economic Development feature included in the portal was developed in partnership with Economic Development Winnipeg Inc. (EDW) and Winnipeg REALTORS®.

Our company, Structured Dynamics, was the lead contractor for the effort. An intro to the technical details powering the Winnipeg site is provided in the complementary blog post by SD’s chief technologist, Fred Giasson. These intro announcements by SD will be later followed by more detailed discussions on relevant NOW portal topics in the coming weeks.

Background and Formal Release

But the NOW story is really one of municipal innovation and a demonstration of what a staff of city employees can accomplish when given the right tools and frameworks. SD’s real pleasure over the past two years of development and data conversion for this site has been our role as consultants and advisors as the City itself converted the data and worked the tools. The City of Winnipeg NOW (Neighbourhoods of Winnipeg) site is testament to the ability of semantic technologies to be learned and effectively used and deployed by subject matter professionals from any venue.

In announcing the site on May 13, Mayor Sam Katz also released a short four-minute introductory video about the site:

What we find most exciting about this site is how our open source Open Semantic Framework can be adopted to cutting-edge municipal open data and community-oriented portals. Without any semantic technology background at the start of the project, the City has demonstrated its ability to manage, master and then tailor the OSF framework to its specific purposes.

Key Emphases

As its name implies, the entire thrust of the Winnipeg portal is on its varied and historical neighborhoods. The NOW portal itself is divided into seven major site sections with 2,245 static pages and a further 425,000 record-oriented pages. The number of dynamic pages that may be generated from the site given various filtering or slicing-and-dicing choices is essentially infinite.

Neighborhoods

The fulcrum around which all data is organized on the NOW portal are the 236 neighborhoods within the City of Winnipeg, organized into 14 community areas, 15 political wards, and 23 neighborhood clusters. These neighborhood references link to thousands of City of Winnipeg and external sites, as well as have many descriptive pages of their own.

Some 57 different datasets contribute the information to the site, some authored specifically for the NOW portal with others migrated from legacy City databases. Coverage ranges from parks, schools, recreational and sports facilities, and zoning, to libraries, bus routes, police stations, day care facilities, community gardens and more. More than 1,400 attributes characterize this data, all of which may be used for filtering or slicing the data.

Property and Economic Development

A key aspect of the site is its real estate, assessment and zoning information. Every address and parcel in the city — a count nearing 190,000 in the current portal — may be looked up and related to its local and neighborhood amenities. Up to three areas of the City may be mapped and compared to one another, felt to be a useful tool for screening economic development potentials.

Census Data

All of the neighborhood and neighborhood clusters may be investigated and compared for Census data in two time periods (2001 and 2006). Types of Census informaton includes population, education, labor and work, transportation, education, languages, income, minorities and immigration, religion, marital status, and other family and household measures.

Any and all neighborhoods may be compared to one another on any or all of these measures, with results available in chart, table or export form.

Images and History

Images and history pages are provided for each Winnipeg neighborhood.

Mapping

Throughout, there are rich mapping options that can be sliced and displayed on any of these dimensions of locality or type of information or attribute.

More to Come!

The basic dataset authoring framework will enable City staff (and, perhaps, external parties or citizens) to add additional datasets to the portal over time.

Key Functionality and Statistics

The NOW site is rich in functionality and display and visualization options. Some of this functionality includes the:

NOW Ontology Graph

NOW Graph Structure

NOW is entirely an ontology-driven site, with both domain and administrative ontologies guiding all aspects of search, retrieval and organization. There are 12 domain ontologies govering the site, two of which are specific to NOW (the NOW ontology and a Canadian Census ontology). Ten external ontologies (such as FOAF, GeoNames, etc) are also used.

The NOW ontology, shown to the left, has more than 2500 subject concepts within it covering all aspects of municipal governance and specific Winnipeg factors.

Relation Browser

All of the 2500 linked concepts in the NOW ontology graph can be interactively explored and navigated via the relation browser. The central “bubble” also presents related, linked information such as images, Census data, descriptive material and the like. As adjacent “bubbles” are clicked, the user can navigate or “swim through” the NOW graph.

NOW Relation Browser

NOW Web Maps

Web Map

Nearly all of the information on the NOW site — or about 420,000 records — contains geolocational information of one form or another. There are about 200,000 points of interest records, another 200,000 area or polygon records, and about 7,000 paths and routes such as bus routes in the system.

All 190,000 property addresses in Winnipeg may be looked up and mapped.

Virtually all of the 57 datasets in the system may be filtered by category or type or attribute. This information can be filtered or searched using about 1400 different facets, singly or in combination with one another.

Various map perspectives are provided from facilities (schools, parks, etc.) to economic development and history, transportation routes and bus stops, and property, real estate and zoning records.

Templates

Depending on the type of object at hand, one of more than 50 templates may be invoked to govern the display of its record information. These templates are selected contextually from the ontology and present different layouts of map, image, record attribute or other information, all keyed by the governing type.

Each template is thus geared to present relevant information for the type of object at hand, in a layout specific to that object.

Objects lacking their own specific templates default to the display type of their parent or grandparent objects such that no object type lacks a display format.

Multiple templates are displayed on search pages, depending upon the diversity of object types returned by the given search.

Example of a NOW Record Template

Example of a NOW Census Chart

Graph Statistics

The NOW site provides a rich set of Census statistics by neighborhood or community area for comparison purposes. The nearly half million data points may be compared between neighborhoods (make sure and pick more than one) in graph form (shown) or in tabular form (not shown).

Census information spans from demographics and income to health, schooling and other measures of community well-being.

Like all other displays, the selected results can also be exported as open data (see below).

Image Gallery

The NOW portal presently has about 2700 images on site organized by object type, neighborhood, and historical. These images are contextually available in multiple locations throughout the site.

The History topic section also matches these images to historical neighborhood narratives.

Example of a NOW Image Gallery

Example conStruct Tool: structOntology

conStruct Tools

A series of twenty or so back office tools are available to City of Winnipeg staff to grow, manage and otherwise maintain the portal. Some of these tools are exposed in read-only form to the general public (see Geeky Tools next).

The example at left is the structOntology tool for managing the various ontologies on the site.

Geeky Tools

As a means to show what happens behind the scenes, the Geeky Tools section presents a number of the back office tools in read-only form. These are also good ways to see the semantic technologies in action.

The Geeky Tools section provides access to Search, Browse, Ontology, and Export (see next) tools.

NOW's Geeky Tools

The NOW Export Function

Open Data Exports

On virtually any display or after any filter selection, there is an “export” button that allows the active data to be exported in a variety of formats. Under Geeky Tools it is also possible to export whole datasets or slices of them. Some of the key formats include:

Some of these are serializations that are not standard ones for RDF, but follow a notation that retains the unique RDF aspects.

Some Early Lessons

Though the technical aspects of the NOW site have been ready for quite some time, with limited staff and budget it took City staff some time to convert all of its starting datasets and to learn how to develop and manage the site on its own. As a result, some of the design decisions made a couple of years back now appear a bit dated.

For example, the host content management system is Drupal 6, though Drupal 8 is getting close to its own release. Similarly, some of the display widgets are based on Flash, which Adobe announced last year it will continue to maintain, but will no longer develop. In the two years since design decisions were originally made, the importance of mobile apps and smartphones and tablets has also grown tremendously in importance.

These kinds of upgrades are a constant in the technology world, and apply to NOW as well. Fortunately, the underlying basis of the entire portal in its data and stack were architected to enable eventual upgrades.

Another key aspect of the site will be the degree to which external parties contribute additional data. It would be nice, for example, to see the site incorporate events announcements and non-City information on commercial and non-profit services and facilities.

Conclusion

Structured Dynamics is proud about the suitability of our OSF technology stack and is impressed with all the data that is being exposed. Our informal surveys suggest this is the largest open data portal by a major city worldwide to be released to date. It is certainly the first to be powered exclusively by semantic technologies.

Yet, despite those impressive claims, we submit that the real achievement of this project is something different. The fact that this entire portal is fully maintained and operated by the City’s own internal IT staff is a game changer. The IT staff of the City of Winnipeg had no prior internal semantic Web knowledge, nor any knowledge in RDF, OWL or any other underlying technologies used by the portal. What they had is a vision of their project and what they wanted. They placed significant faith and made a commitment to master the OSF technology stack, and the underlying semantic Web concepts and principles to make their vision a reality. Much of SD’s 430+ documents on the OSF TechWiki are a result of this collaborative technology transfer between us and the City.

We are truly grateful that the City of Winnipeg has taken open source and open data quite seriously. In our partnership wth them they have been extremely supportive of what we have done to progress the technology, release it as open source, and then to document our lessons and experiences for other parties to utilize as documented on the TechWiki. The City of Winnipeg truly shows enlightened government at its best. Thank you, especially to our project managers, Kelly Goldstrand and Don Conolly.

Structured Dynamics has long stated its philosophy as, “We are successful when we are no longer needed. We’re extremely pleased and proud that the NOW portal and the City of Winnipeg show this objective is within realistic reach.

Posted:May 15, 2013

So Many QuestionsThinking About the Interstices of the Journey

It actually is a dimmer memory than I would like: the decision to begin a blog eight years ago, nearly to the day ([1]). Since then, for every month and more often many more times per month, I have posted the results of my investigations or ramblings, mostly like clockwork. But, in a creeping realization, I see that I have not posted any new thoughts on this venue for more than two months! Until that hiatus, I had been biologically consistent.

Maybe, as some say, and I don’t disagree, the high-water mark of the blog has passed. Certainly blog activity has dropped dramatically. The emergence of ‘snippet communications’ now appears dominant based on messages and bandwidth. I don’t loathe it, nor fear it, but I find a world dominated by 140 characters and instant babbling mostly a bore.

From a data mining perspective — similar to peristalsis or the wave in a sports stadium — there is worth in the “crowd” coherence/incoherence and spontaneity. We can see the waves, but most are transient. I actually think that broad scrutiny helps promote separating the wheat from chaff. We can expose free radicals to the sanitizing effect of sunlight. Yet these are waves, only very rarely trends, and most generally not truths. That truth stuff is some really slippery stuff.

But, that is really not what is happening for me. (Though I really live to chaw on truth.) Mostly, I just had nothing interesting to say, so there was no reason to blog about it. And, now, as I look at why I broke my disciplined approach to blogging and why it has gone on hiatus, I still am a bit left scratching my head as to why my pontifications stalled.

Two obvious reasons are that our business is doing gangbusters, and it is hard to sneak away from company good-fortune. Another is that my family and children have been joyously demanding.

Yet all of that deflects from the more relevant explanation. The real reason, I think, that I have not been writing more recently actually relates to the circumstance of semantic techologies. Yes, progress is being made, some instances are notable, but the general “semantic web” or “linked data” enterprise is stalled. The narrative for these things — let alone their expression and relevance — needs to change substantially.

I feel we are in the midst of this intense interstice, but the framing perspective for the next discussions have yet to emerge.

The strange thing about that statement is not the basis in semantic technologies, which are now understood and powerful, but the incorporation of these advantages into enterprise practices and environments. In this sense, semantic technologies are now growing up. Their logic and role is clear and explainable, but how they fit into corporate practice with acceptable maintenance costs is still being discovered.

Posted:September 10, 2012

Open WorldThe Foundation of Knowledge Applications Should Reflect Their Nature

Every couple of months I return to the idea of the open world assumption (OWA) [1] and its fundamental importance to knowledge applications. What it is that makes us human — in health and in sickness — is but a further line of evidence for the importance of an open world viewpoint. I’ll use three personal anecdotes to make this case.

Cell Symbionts

Believe it or not, Alfred Wegener‘s theory of continental drift was only becoming accepted by mainstream scientists in my high school years. I experienced déjà vu regarding a science revolution while a botany major at Pomona College in the early 1970s. A young American biologist at that time, Lynn Margulis, was postulating the theory of endosymbiosis; that is, that certain cell organelles originated from initially free-living bacteria.

This idea of longstanding symbionts in the cell — indeed, even forming what was our overall conception of cells and their parts — was truly revolutionary. It was revolutionary because of the implications for the nature and potential degree of symbiosis. And it was revolutionary in adding a different arrow in the quiver of biotic change over time than classical Darwinian evolution.

Today, Margulis’ theory is now widely accepted and is understood to embrace cell organelles from mitochondria to chloroplasts and ribosomes. The seemingly fundamental unit of all organisms — the cell — is itself an amalgam of archaic symbionts and bacteria-like lifeforms. Truly remarkable.

The Vanishing Ulcer

In the early 1990s, my oldest child, Erin, then in elementary school, had been going through a debilitating bout of periodic and severe stomach upsets. I sort of thought this might be inherited, since my paternal grandmother had suffered from ulcers for many decades (as did many at that time).

We were good friends with our pediatrician in our small town and knew him to be a thoughtful and well-informed MD. His counsel was that Erin was likely suffering from an ulcer and we began taking great care about her diet. But Erin’s symptoms did not seem to improve.

My wife, Wendy, is a biomedical researcher and began to investigate this problem on her own. She discovered some early findings implicating a gastrointestinal (gut) bacteria with similar symptoms and brought this research to our doctor’s attention. He, too, was intrigued, and prescribed a rather straightforward antibiotic regimen for Erin. Her symptoms immediately ceased, and she has been clear of further symptoms in the twenty years since.

The nearly universal role of the Helicobacter bacteria in ulcers is now widely understood. The understanding of peptic ulcers that had stood for centuries no longer applies in most cases. Though ulcers may arise from many other conditions, because of these new understandings the prevalence and discussion of ulcers has nearly fallen off the radar screen.

Humans as Walking Ecosystems

A few years back I began to show symptoms of rosacea, a facial skin condition characterized by redness. My local dermatologist recommended a daily dose of antibiotics as the preferred course of action. I was initially reluctant to follow this advice. I knew about the growing problem of bacterial resistance, and did not think that my constant use of tetracycline would help that issue. I also knew some about the controversial use of antibiotics in animal feeds, and had hesitations for that reason as well.

Nonetheless, I took the doctor’s advice. I rarely take any kind of medicine and immediately began to notice GI problems. My digestive regularity was immediately thrown out of kilter with other adverse effects as well. I immediately stopped using the antibiotics, and soon returned to (largely) my pre-regime conditions. (I also switched doctors.)

Over the past five years, due to a revolution in DNA sequencing [2], we are now beginning to understand the why of my observed reactions to antibiotics. Because we can now analyze skin and fecal samples for foreign DNA, we are coming to realize that humans (as is likely true for all higher organisms) are walking, teeming ecosystems of thousands of different species, mostly bacteria [3].

While there are some 23,000 genes in the native humane genome, there are more than 3 million estimated as arising from these fellow travelers. While we are still learning much, and rapidly, we know that our ecosystem of bacteria is involved in nutrition and digestion, contributing perhaps as much as 15% of the energy value we get from food. We also know that imbalances of various sorts in our walking ecosystem can also lead to diseases and other chronic conditions.

Though the degree and nature is still quite uncertain, our “microbiome” of symbiotic bacteria has been implicated in heart disease, Type II diabetes, obesity, malnutrition, multiple sclerosis, other auto-immune diseases, asthma, eczema, liver disease, bowel cancer and autism, among others. The breadth and extent of implications on well-being is staggering, especially since all of these implications have been learned over the past five years.

There are considerable differences between different human populations and cultures, too, in terms of differing compositions of the microbiome. And these effects are not limited to the gut. Skin and orifices to the outside world have their own denizens as well, likely also involved with both health and disease. Humans are not just complicated beasts, but a world of other species unique unto ourselves.

We Are Not Yet an Open Book, But We Are an Open World

Each of these three anecdotes — and there are many others — point to phenomenal changes in our understanding of the human organism. This new knowledge has also arisen over a remarkably short period. Who knows when the pace of these insights might slow, if ever?

These anecdotes are exemplary about the fundamental nature of knowledge: it is constantly expanding with new connections and heretofore unforeseen relationships constantly emerging. These anecdotes also point to the fact that most knowledge problems are systems problems, intimately involved with the connections and inter-relationships among a diversity of players and factors.

It makes sense that how we choose to organize and analyze the information that constitutes our knowledge should have a structure and underlying logic premise consistent with expansion and new relationships. This premise is the central feature of the open world assumption and semantic Web technologies.

Fixed, closed, brittle schema of transaction systems and relational databases are a clear mismatch with knowledge problems and knowledge applications. We need systems where schema and structure can evolve with new information and knowledge. The foundational importance of open world approaches to understanding and modeling knowledge problems continues to be the elephant in the room.

It is perhaps not surprising that one of the fields most aggressive in embracing ontologies and semantic technologies is the life sciences. Practitioners in this field experience daily the explosion in new knowledge and understandings. Knowledge workers in other fields would be well-advised to follow the lead of the life sciences in re-thinking their own foundations for knowledge representation and management. It is good to remember that if your world is not open, then your understanding of it is closed.


[1]  See M. K. Bergman, 2009. The Open World Assumption: Elephant in the Room, December 21, 2009. The open world assumption (OWA) generally asserts that the lack of a given assertion or fact being available does not imply whether that possible assertion is true or false: it simply is not known. In other words, lack of knowledge does not imply falsity. Another way to say it is that everything is permitted until it is prohibited. OWA lends itself to incremental and incomplete approaches to various modeling problems.
OWA is a formal logic assumption that the truth-value of a statement is independent of whether or not it is known by any single observer or agent to be true. OWA is used in knowledge representation to codify the informal notion that in general no single agent or observer has complete knowledge, and therefore cannot make the closed world assumption. The OWA limits the kinds of inference and deductions an agent can make to those that follow from statements that are known to the agent to be true. OWA is useful when we represent knowledge within a system as we discover it, and where we cannot guarantee that we have discovered or will discover complete information. In the OWA, statements about knowledge that are not included in or inferred from the knowledge explicitly recorded in the system may be considered unknown, rather than wrong or false. Semantic Web languages such as OWL make the open world assumption.
Also, you can search on OWA on this blog.
[2] Automatic DNA sequencing machines now allow direct samples to be sequenced without the need to grow up cultures of organisms. This advance has freed up the ability to take direct samples — such as from soil, seawater, skin, feces or secretions — to identify all DNA present. DNA not matching a host organism or which matches patterns for other known organisms then allows the presence of foreign organisms to be identified.
[3] An excellent piece for lay readers providing more background on this topic may be found in “The Human Microbiome: Me, Myself, Us,” in The Economist, August 18, 2012, pp. 69-72.

Posted by AI3's author, Mike Bergman Posted on September 10, 2012 at 10:03 pm in Adaptive Information, Semantic Web | Comments (0)
The URI link reference to this post is: http://www.mkbergman.com/1024/we-are-an-open-world/
The URI to trackback this post is: http://www.mkbergman.com/1024/we-are-an-open-world/trackback/
Posted:July 9, 2012
Abrogans; earliest glossary (from Wikipedia)

There are many semantic technology terms relevant to the context of a semantic technology installation [1]. Some of these are general terms related to language standards, as well as to  ontologies or the dataset concept.

ABox
An ABox (for assertions, the basis for A in ABox) is an “assertion component”; that is, a fact associated with a terminological vocabulary within a knowledge base. ABox are TBox-compliant statements about instances belonging to the concept of an ontology.
Adaptive ontology
An adaptive ontology is a conventional knowledge representational ontology that has added to it a number of specific best practices, including modeling the ABox and TBox constructs separately; information that relates specific types to different and appropriate display templates or visualization components; use of preferred labels for user interfaces, as well as alternative labels and hidden labels; defined concepts; and a design that adheres to the open world assumption.
Administrative ontology
Administrative ontologies govern internal application use and user interface interactions.
Annotation
An annotation, specifically as an annotation property, is a way to provide metadata or to describe vocabularies and properties used within an ontology. Annotations do not participate in reasoning or coherency testing for ontologies.
Atom
The name Atom applies to a pair of related standards. The Atom Syndication Format is an XML language used for web feeds, while the Atom Publishing Protocol (APP for short) is a simple HTTP-based protocol for creating and updating Web resources.
Attributes
These are the aspects, properties, features, characteristics, or parameters that objects (and classes) may have. They are the descriptive characteristics of a thing. Key-value pairs match an attribute with a value; the value may be a reference to another object, an actual value or a descriptive label or string. In an RDF statement, an attribute is expressed as a property (or predicate or relation). In intensional logic, all attributes or characteristics of similarly classifiable items define the membership in that set.
Axiom
An axiom is a premise or starting point of reasoning. In an ontology, each statement (assertion) is an axiom.
Binding
Binding is the creation of a simple reference to something that is larger and more complicated and used frequently. The simple reference can be used instead of having to repeat the larger thing.
Class
A class is a collection of sets or instances (or sometimes other mathematical objects) which can be unambiguously defined by a property that all of its members share. In ontologies, classes may also be known as sets, collections, concepts, types of objects, or kinds of things.
Closed World Assumption
CWA is the presumption that what is not currently known to be true, is false. CWA also has a logical formalization. CWA is the most common logic applied to relational database systems, and is particularly useful for transaction-type systems. In knowledge management, the closed world assumption is used in at least two situations: 1) when the knowledge base is known to be complete (e.g., a corporate database containing records for every employee), and 2) when the knowledge base is known to be incomplete but a “best” definite answer must be derived from incomplete information. See contrast to the open world assumption.
Data Space
A data space may be personal, collective or topical, and is a virtual “container” for related information irrespective of storage location, schema or structure.
Dataset
An aggregation of similar kinds of things or items, mostly comprised of instance records.
DBpedia
A project that extracts structured content from Wikipedia, and then makes that data available as linked data. There are millions of entities characterized by DBpedia in this way. As such, DBpedia is one of the largest — and most central — hubs for linked data on the Web.
DOAP
DOAP (Description Of A Project) is an RDF schema and XML vocabulary to describe open-source projects.
Description logics
Description logics and their semantics traditionally split concepts and their relationships from the different treatment of instances and their attributes and roles, expressed as fact assertions. The concept split is known as the TBox and represents the schema or taxonomy of the domain at hand. The TBox is the structural and intensional component of conceptual relationships. The second split of instances is known as the ABox and describes the attributes of instances (and individuals), the roles between instances, and other assertions about instances regarding their class membership with the TBox concepts.
Domain ontology
Domain (or content) ontologies embody more of the traditional ontology functions such as information interoperability, inferencing, reasoning and conceptual and knowledge capture of the applicable domain.
Entity
An individual object or member of a class; when affixed with a proper name or label is also known as a named entity (thus, named entities are a subset of all entities).
Entity–attribute–value model
EAV is a data model to describe entities where the number of attributes (properties, parameters) that can be used to describe them is potentially vast, but the number that will actually apply to a given entity is relatively modest. In the EAV data model, each attribute-value pair is a fact describing an entity. EAV systems trade off simplicity in the physical and logical structure of the data for complexity in their metadata, which, among other things, plays the role that database constraints and referential integrity do in standard database designs.
Extensional
The extension of a class, concept, idea, or sign consists of the things to which it applies, in contrast with its intension. For example, the extension of the word “dog” is the set of all (past, present and future) dogs in the world. The extension is most akin to the attributes or characteristics of the instances in a set defining its class membership.
FOAF
FOAF (Friend of a Friend) is an RDF schema for machine-readable modeling of homepage-like profiles and social networks.
Folksonomy
A folksonomy is a user-generated set of open-ended labels called tags organized in some manner and used to categorize and retrieve Web content such as Web pages, photographs, and Web links.
GeoNames
GeoNames integrates geographical data such as names of places in various languages, elevation, population and others from various sources.
GRDDL
GRDDL is a markup format for Gleaning Resource Descriptions from Dialects of Languages; that is, for getting RDF data out of XML and XHTML documents using explicitly associated transformation algorithms, typically represented in XSLT.
High-level Subject
A high-level subject is both a subject proxy and category label used in a hierarchical subject classification scheme (taxonomy). Higher-level subjects are classes for more atomic subjects, with the height of the level representing broader or more aggregate classes.
Individual
See Instance.
Inferencing
Inference is the act or process of deriving logical conclusions from premises known or assumed to be true. The logic within and between statements in an ontology is the basis for inferring new conclusions from it, using software applications known as inference engines or reasoners.
Instance
Instances are the basic, “ground level” components of an ontology. An instance is individual member of a class, also used synonomously with entity. The instances in an ontology may include concrete objects such as people, animals, tables, automobiles, molecules, and planets, as well as abstract instances such as numbers and words. An instance is also known as an individual, with member and entity also used somewhat interchangeably.
Instance record
An instance with one or more attributes also provided.
irON
irON (instance record and Object Notation) is a abstract notation and associated vocabulary for specifying RDF (Resource Description Framework) triples and schema in non-RDF forms. Its purpose is to allow users and tools in non-RDF formats to stage interoperable datasets using RDF.
Intensional
The intension of a class is what is intended as a definition of what characteristics its members should have; it is akin to a definition of a concept and what is intended for a class to contain. It is therefore like the schema aspects (or TBox) in an ontology.
Key-value pair
Also known as a name–value pair or attribute–value pair, a key-value pair is a fundamental, open-ended data representation. All or part of the data model may be expressed as a collection of tuples <attribute name, value> where each element is a key-value pair. The key is the defined attribute and the value may be a reference to another object or a literal string or value. In RDF triple terms, the subject is implied in a key-value pair by nature of the instance record at hand.
Kind
Used synonomously herein with class.
Knowledge base
A knowledge base (abbreviated KB or kb) is a special kind of database for knowledge management. A knowledge base provides a means for information to be collected, organized, shared, searched and utilized. Formally, the combination of a TBox and ABox is a knowledge base.
Linkage
A specification that relates an object or attribute name to its full URI (as required in the RDF language).
Linked data
Linked data is a set of best practices for publishing and deploying instance and class data using the RDF data model, and uses uniform resource identifiers (URIs) to name the data objects. The approach exposes the data for access via the HTTP protocol, while emphasizing data interconnections, interrelationships and context useful to both humans and machine agents.
Mapping
A considered correlation of objects in two different sources to one another, with the relation between the objects defined via a specific property. Linkage is a subset of possible mappings.
Member
Used synonomously herein with instance.
Metadata
Metadata (metacontent) is supplementary data that provides information about one or more aspects of the content at hand such as means of creation, purpose, when created or modified, author or provenance, where located, topic or subject matter, standards used, or other annotation characteristics. It is “data about data”, or the means by which data objects or aggregations can be described. Contrasted to an attribute, which is an individual characteristic intrinsic to a data object or instance, metadata is a description about that data, such as how or when created or by whom.
Metamodeling
Metamodeling is the analysis, construction and development of the frames, rules, constraints, models and theories applicable and useful for modeling a predefined class of problems.
Microdata
Microdata is a proposed specification used to nest semantics within existing content on web pages. Microdata is an attempt to provide a simpler way of annotating HTML elements with machine-readable tags than the similar approaches of using RDFa or microformats.
Microformats
A microformat (sometimes abbreviated μF or uF) is a piece of mark up that allows expression of semantics in an HTML (or XHTML) web page. Programs can extract meaning from a web page that is marked up with one or more microformats.
Natural language processing
NLP is the process of a computer extracting meaningful information from natural language input and/or producing natural language output. NLP is one method for assigning structured data characterizations to text content for use in semantic technologies. (Hand assignment is another method.) Some of the specific NLP techniques and applications relevant to semantic technologies include automatic summarization, coreference resolution, machine translation, named entity recognition (NER), question answering, relationship extraction, topic segmentation and recognition, word segmentation, and word sense disambiguation, among others.
OBIE
Information extraction (IE) is the task of automatically extracting structured information from unstructured and/or semi-structured machine-readable documents. Ontology-based information extraction (OBIE) is the use of an ontology to inform a “tagger” or information extraction program when doing natural language processing. Input ontologies thus become the basis for generating metadata tags when tagging text or documents.
Ontology
An ontology is a data model that represents a set of concepts within a domain and the relationships between those concepts. Loosely defined, ontologies on the Web can have a broad range of formalism, or expressiveness or reasoning power.
Ontology-driven application
Ontology-driven applications (or ODapps) are modular, generic software applications designed to operate in accordance with the specifications contained in one or more ontologies. The relationships and structure of the information driving these applications are based on the standard functions and roles of ontologies (namely as domain ontologies), as supplemented by UI and instruction sets and validations and rules.
Open Semantic Framework
The open semantic framework, or OSF, is a combination of a layered architecture and an open-source, modular software stack. The stack combines many leading third-party software packages with open source semantic technology developments from Structured Dynamics.
Open World Assumption
OWA is a formal logic assumption that the truth-value of a statement is independent of whether or not it is known by any single observer or agent to be true. OWA is used in knowledge representation to codify the informal notion that in general no single agent or observer has complete knowledge, and therefore cannot make the closed world assumption. The OWA limits the kinds of inference and deductions an agent can make to those that follow from statements that are known to the agent to be true. OWA is useful when we represent knowledge within a system as we discover it, and where we cannot guarantee that we have discovered or will discover complete information. In the OWA, statements about knowledge that are not included in or inferred from the knowledge explicitly recorded in the system may be considered unknown, rather than wrong or false. Semantic Web languages such as OWL make the open world assumption. See contrast to the closed world assumption.
OPML
OPML (Outline Processor Markup Language) is an XML format for outlines, and is commonly used to exchange lists of web feeds between web feed aggregators.
OWL
The Web Ontology Language (OWL) is designed for defining and instantiating formal Web ontologies. An OWL ontology may include descriptions of classes, along with their related properties and instances. There are also a variety of OWL dialects.
Predicate
See Property.
Property
Properties are the ways in which classes and instances can be related to one another. Properties are thus a relationship, and are also known as predicates. Properties are used to define an attribute relation for an instance.
Punning
In computer science, punning refers to a programming technique that subverts or circumvents the type system of a programming language, by allowing a value of a certain type to be manipulated as a value of a different type. When used for ontologies, it means to treat a thing as both a class and an instance, with the use depending on context.
RDF
Resource Description Framework (RDF) is a family of World Wide Web Consortium (W3C) specifications originally designed as a metadata model but which has come to be used as a general method of modeling information, through a variety of syntax formats. The RDF metadata model is based upon the idea of making statements about resources in the form of subject-predicate-object expressions, called triples in RDF terminology. The subject denotes the resource, and the predicate denotes traits or aspects of the resource and expresses a relationship between the subject and the object.
RDFa
RDFa 1.0 is a set of extensions to XHTML that is a W3C Recommendation. RDFa uses attributes from meta and link elements, and generalizes them so that they are usable on all elements allowing annotation markup with semantics. A W3C Working draft is presently underway that expands RDFa into version 1.1 with HTML5 and SVG support, among other changes.
RDF Schema
RDFS or RDF Schema is an extensible knowledge representation language, providing basic elements for the description of ontologies, otherwise called RDF vocabularies, intended to structure RDF resources.
Reasoner
A semantic reasoner, reasoning engine, rules engine, or simply a reasoner, is a piece of software able to infer logical consequences from a set of asserted facts or axioms. The notion of a semantic reasoner generalizes that of an inference engine, by providing a richer set of mechanisms.
Reasoning
Reasoning is one of many logical tests using inference rules as commonly specified by means of an ontology language, and often a description language. Many reasoners use first-order predicate logic to perform reasoning; inference commonly proceeds by forward chaining or backward chaining.
Record
As used herein, a shorthand reference to an instance record.
Relation
Used synonomously herein with attribute.
RSS
RSS (an acronym for Really Simple Syndication) is a family of web feed formats used to publish frequently updated digital content, such as blogs, news feeds or podcasts.
schema.org
Schema.org is an initiative launched by the major search engines of Bing, Google and Yahoo!, and later jointed by Yandex, in order to create and support a common set of schemas for structured data markup on web pages. schema.org provided a starter set of schema and extension mechanisms for adding to them. schema.org supports markup in microdata, microformat and RDFa formats.
Semantic enterprise
An organization that uses semantic technologies and the languages and standards of the semantic Web, including RDF, RDFS, OWL, SPARQL and others to integrate existing information assets, using the best practices of linked data and the open world assumption, and targeting knowledge management applications.
Semantic technology
Semantic technologies are a combination of software and semantic specifications that encodes meanings separately from data and content files and separately from application code. This approach enables machines as well as people to understand, share and reason with data and specifications separately. With semantic technologies, adding, changing and implementing new relationships or interconnecting programs in a different way can be as simple as changing the external model that these programs share. New data can also be brought into the system and visualized or worked upon based on the existing schema. Semantic technologies provide an abstraction layer above existing IT technologies that enables bridging and interconnection of data, content, and processes.
Semantic Web
The Semantic Web is a collaborative movement led by the World Wide Web Consortium (W3C) that promotes common formats for data on the World Wide Web. By encouraging the inclusion of semantic content in web pages, the Semantic Web aims at converting the current web of unstructured documents into a “web of data”. It builds on the W3C’s Resource Description Framework (RDF).
Semset
A semset is the use of a series of alternate labels and terms to describe a concept or entity. These alternatives include true synonyms, but may also be more expansive and include jargon, slang, acronyms or alternative terms that usage suggests refers to the same concept.
SIOC
Semantically-Interlinked Online Communities Project (SIOC) is based on RDF and is an ontology defined using RDFS for interconnecting discussion methods such as blogs, forums and mailing lists to each other.
SKOS
SKOS or Simple Knowledge Organisation System is a family of formal languages designed for representation of thesauri, classification schemes, taxonomies, subject-heading systems, or any other type of structured controlled vocabulary; it is built upon RDF and RDFS.
SKSI
Semantic Knowledge Source Integration provides a declarative mapping language and API between external sources of structured knowledge and the Cyc knowledge base.
SPARQL
SPARQL (pronounced “sparkle”) is an RDF query language; its name is a recursive acronym that stands for SPARQL Protocol and RDF Query Language.
Statement
A statement is a “triple” in an ontology, which consists of a subject – predicate – object (S-P-O) assertion. By definition, each statement is a “fact” or axiom within an ontology.
Subject
A subject is always a noun or compound noun and is a reference or definition to a particular object, thing or topic, or groups of such items. Subjects are also often referred to as concepts or topics.
Subject extraction
Subject extraction is an automatic process for retrieving and selecting subject names from existing knowledge bases or data sets. Extraction methods involve parsing and tokenization, and then generally the application of one or more information extraction techniques or algorithms.
Subject proxy
A subject proxy as a canonical name or label for a particular object; other terms or controlled vocabularies may be mapped to this label to assist disambiguation. A subject proxy is always representative of its object but is not the object itself.
Tag
A tag is a keyword or term associated with or assigned to a piece of information (e.g., a picture, article, or video clip), thus describing the item and enabling keyword-based classification of information. Tags are usually chosen informally by either the creator or consumer of the item.
TBox
A TBox (for terminological knowledge, the basis for T in TBox) is a “terminological component”; that is, a conceptualization associated with a set of facts. TBox statements describe a conceptualization, a set of concepts and properties for these concepts. The TBox is sufficient to describe an ontology (best practice often suggests keeping a split between instance records — and ABox — and the TBox schema).
Taxonomy
In the context of knowledge systems, taxonomy is the hierarchical classification of entities of interest of an enterprise, organization or administration, used to classify documents, digital assets and other information. Taxonomies can cover virtually any type of physical or conceptual entities (products, processes, knowledge fields, human groups, etc.) at any level of granularity.
Topic
The topic (or theme) is the part of the proposition that is being talked about (predicated). In topic maps, the topic may represent any concept, from people, countries, and organizations to software modules, individual files, and events. Topics and subjects are closely related.
Topic Map
Topic maps are an ISO standard for the representation and interchange of knowledge. A topic map represents information using topics, associations (similar to a predicate relationship), and occurrences (which represent relationships between topics and information resources relevant to them), quite similar in concept to the RDF triple.
Triple
A basic statement in the RDF language, which is comprised of a subjectproperty – object construct, with the subject and property (and object optionally) referenced by URIs.
Type
Used synonomously herein with class.
UMBEL
UMBEL, short for Upper Mapping and Binding Exchange Layer, is an upper ontology of about 28,000 reference concepts, designed to provide common mapping points for relating different ontologies or schema to one another, and a vocabulary for aiding that ontology mapping, including expressions of likelihood relationships distinct from exact identity or equivalence. This vocabulary is also designed for interoperable domain ontologies.
Upper ontology
An upper ontology (also known as a top-level ontology or foundation ontology) is an ontology that describes very general concepts that are the same across all knowledge domains. An important function of an upper ontology is to support very broad semantic interoperability between a large number of ontologies that are accessible ranking “under” this upper ontology.
Vocabulary
A vocabulary in the sense of knowledge systems or ontologies are controlled vocabularies. They provide a way to organize knowledge for subsequent retrieval. They are used in subject indexing schemes, subject headings, thesauri, taxonomies and other form of knowledge organization systems.
WordNet
WordNet is a lexical database for the English language. It groups English words into sets of synonyms called synsets, provides short, general definitions, and records the various semantic relations between these synonym sets. The purpose is twofold: to produce a combination of dictionary and thesaurus that is more intuitively usable, and to support automatic text analysis and artificial intelligence applications. The database and software tools can be downloaded and used freely. Multiple language versions exist, and WordNet is a frequent reference structure for semantic applications.
YAGO
“Yet another great ontology” is a WordNet structure placed on top of Wikipedia.

[1] This glossary is based on the one provided on the OSF TechWiki. For the latest version, please refer to this link.