We asked Les to comment on his statement from last year and he confirmed that SKOS continues to be central to the ESA vocabulary business model and that it has also been important for ESA that PoolParty has been flexible enough to support continued publication of non-RDF formats, especially IMS VDEX.

In the course of this project it became more and more obvious that SKOS cannot only be used as yet another format for publishing thesauri but rather as a unified model to build thesauri in general. This approach made possible several improvements to the vocabulary development model and the maintenance process of ESA. Since all data is stored as RDF in a triple store, and SKOS and RDF are flexible formats supporting interoperability and interchangeability of data, many manual transformations that had to be done before are not needed anymore and all other systems using the vocabularies are dynamically fed by PoolParty offering the data in its needed formats (see image below).

Les states that while some manual processes still exist to support legacy systems, PoolParty ensures the integrity and richness of ESA data. Support and customizations for legacy systems can be achieved in the confidence that the linked-data capabilities are centrally managed and stored in the PoolParty triple store.

From the publishing perspective, the previous vocabulary publishing site has been replaced by the PoolParty Linked Data Frontend (LD-Frontend) that has been customized especially for this project to offer more flexibility in the display and the layout of the data. Similar to the frontend for the Austrian Geological Survey mentioned in a previous blog post , the LD-Frontend has been adapted to the ESA styleguide and the display of the data in the HTML view of the frontend has been adapted to be more user-friendly (see screenshot below).

From ESA’s perspective Les commented here that for the vocabulary manager, edits to the frontend styles and templates are intuitive and can be tested in staging environments. But he also stated that for publishing support is important, and that SWC was very responsive.

Example ESA linked data frontend

Of course we asked Les to give a preview of the next steps for ESA. He stated that they include language translation projects so that its vocabularies, especially Schools Online Thesaurus (ScOT), can be accessed by wider markets and by students of other languages. He also stated that PoolParty handles multi-lingual thesauri very well.

We here at SWC are glad to see PoolParty used in more and more applications and usage scenarios. We are looking forward to the next steps that will be done in this project and also to see how the data offered by the ESA vocabulary site is used in other applications.

Thanks to Les Kneebone from ESA for his contribution to his blog post.

Let me introduce you to SKOSsy. I will explain what this web service can do for you:

SKOSsy generates SKOS based thesauri in German or in English for a domain you are interested in. Not any domain but nearly any: SKOSsy extracts data from DBpedia, so it can cover anything which is in DBpedia. Thus, SKOSsy works well whenever a first seed thesaurus should be generated for a certain organisation or project. If you load the automatically generated thesaurus into an editor like PoolParty Thesaurus Manager (PPT) you can start to enrich the knowledge model by additional concepts, relations and links to other LOD sources. But you don´t have to start in the open countryside with your thesaurus project.

Let me give you an example: Imagine you are working for a company which is an international plant builder and you would like to index several thousands of documents the “semantic way”. You have to walk through the following steps:

1. Identify proper categories in Wikipedia/DBpedia which describe best what your business or your domain is all about. Those categories should contain pages / resources which are related to the documents you would like to index. For example: http://dbpedia.org/resource/Category:Metalworking or http://dbpedia.org/resource/Category:Industrial_automation
2. After you have selected proper categories SKOSsy will traverse DBpedia for you and collect all resources, their hierarchical and non-hierarchical relations, alternative labels, definitions and other properties and put them together as a valid SKOS thesaurus; this step will last a couple of minutes. (Find the resulting vocabulary here)
3. Load the resulting thesaurus into PPT, explore it, improve it and enrich it with additional facts.
4. After you´re done you can generate a tailor-made text extractor by using PoolParty Extractor (PPX) which is the second component of PoolParty product family
5. With PPX and its extraction model especially curated for your special use case you can extract named entities from your documents automatically and index your documents in a meaningful way.
6. After a few seconds your semantic search engine is ready to be used. PoolParty Semantic Search (PPS) which is the third PoolParty component will offer some nice facilities like categorized auto-complete, faceted search, content recommendation (similarity search) and smart search suggestions to ease your life as a knowledge worker.

We have constantly discussed the application of thesauri and other knowledge models to improve search over the last years. Many people understood straight away why thesaurus based search is most often much better than search algorithms purely based on statistics. Of course the big contra always was, “the costs are too high to establish a “good-enough” thesaurus or even a “high-quality” one”.

With SKOSsy in place those kinds of arguments become weaker and weaker. To sum up,

• SKOSsy makes heavy use of Linked Data sources, especially DBpedia
• SKOSsy can generate SKOS thesauri for virtually any domain within a few minutes
• Such thesauri can be improved, curated and extended to one´s individual needs but they serve usually as “good-enough” knowledge models for any semantic search application you like
• SKOSsy based semantic search usually outperform search algorithms based on statistics since they contain high-quality information about relations, labels and disambiguation
• SKOSsy works perfectly together with PoolParty product family

If you are interested in the results produced by SKOSsy, just send us a short note about your domain or your project and we will send you an invitation as beta-tester or prepare a demo for you.

Related articles

• Geological Survey Austria launches thesaurus project (semantic-web.at)
• PoolParty 3.0 and its all new Linked Data framework (semantic-web.at)
• PoolParty DemoZone Content Extractor Semantic Search Thesaurus Manager (poolparty.punkt.at)
• Query DBpedia for multiple keywords (stackoverflow.com)

This brainstorming phase is still going on for one more week, so you can chip in with ideas about
– new applications you would like to see
– new ontology terms or integration with other ontologies
– features / bugfixes are you looking for in existing applications
– better explanations of SIOC terms or answers to puzzling questions needed

View the wishes on this wiki page and add your own.

Sören Auer and his team from AKSW at Uni Leipzig are the coordinators of this EU funded project which supports the development of innovative software products. All industry partners are SMEs which offer services for different fields like E-learning, E-tourism or Business Intelligence. Leipzig and OpenLink Software will work on an integration of OntoWiki & Virtuoso.

The first day of the meeting was, of course, dedicated to socialize and get to know each other. The mixture of the project team turned out to be well chosen – and in the evening we flew at higher game: We had a nice overview over Leipzig standing on the highest building of the town.

On the second day of the meeting Orri Erling, Program Manager at OpenLink Software, came up with an idea which is pretty forward: Why shouldn´t we provide OntoWiki as a Linked Data Browser, e.g. on top of DBpedia etc.? One possible outcome of this project.

Some other use cases which make already use of the existing OntoWiki system were demonstrated: Take a look at Vakantieland (…and start to plan your holidays in the Netherlands) and also at LinkedGeoData where a nice user interface can be tried out.

The Kick-Off Meeting will proceed with two workshops dedicated to semantic technologies and to Application Development with the OntoWiki Framework. Thanks to Sören and his team for the excellent hosting of this event!

Last week I had an an encounter with a social scientist (within an academic setting) who argued that discussing the Semantic web would not make sense for him (as a social scientist), because of the present lack of social practices in that field… (*jaw-dropping*) I could not persuade him with the argument that the Linked data cloud itself was the result of a social practice – the view he had of the semantic web (which I assume was not an uneducated one) even led him to denounce that developments like Dbpedia, Twine, Revyu, or the use of metadata in general had anything to do with the Semantic Web.

And this is a big challenge.

On the one hand, it is a good thing that there are social scientists who at least have a certain notion of the Semantic Web – on the other, it seems as if all the exciting ideas and developments that have taken place in the last few years have failed to reach those who have been sensitized for the SemWeb project when the idea was first conceived. I am not meaning to make a statement about social scientists here, but rather about the need to communicate what has further happened to the original idea outside also outside of one’s own community.

Btw: In its current issue, quarterly (German-language) magazine t3n is featuring a Web 3.0 and Applied Semantic Web topic as its opener. And that is a good sign, too!

DBpedia has become the largest RDF repository for encyclopaedic knowledge, extracting structured information from Wikipedia and making it available on the Web of Data. UMBEL, on the other hand, provides an OpenCYC-based, light-weight ontology structure for relating Web content and data to a standard set of subject concepts, with a number of 20,000 concepts currently reached. In the Linked Data Cloud, DBpedia and UMBEL map and cross-reference each other.

In practice this means that UMBEL provides classes to describe the concepts to which “things” are members. For instance, named entities from Wikipedia such as “John F. Kennedy” are mapped with subject concepts such as Leader, Person, Administrator and Graduate, with broader and equivalent classes in CYC and FOAF and broader subject concepts within UMBEL. A link is set to Wikipedia, as well as a ‘same as’ reference to DBpedia. A class structure enables faceted browsing and extraction, inferencing, and navigation and discovery for all datasets linked to that structure.

DBpedia, in turn, returns properties of ‘John J. Kennedy’ (e.g. abstracts in available Wikipedia languages, demographic information such as birth date and place, alma mater, predecessors and successors), and ‘same as’ references, e.g., to the JFK entry in Freebase (who recently released their RDF service) and the aforementioned page in UMBEL. Furthermore, DBpedia maps the URI with available RDF types, for instance foaf:person or yago:AssassinatedAmericanPoliticians and, once again, with UMBEL’s subject concepts Person, Administrator, Graduate and Leader.

Due to its reliance on Wikipedia, DBpedia does a great job at covering a bandwidth of knowledge as broad as the spectrum of the interest of people participating in Wikipedia; it’s within the area of named entities, i.e. entities such as persons, organizations, locations, which have a proper name, but are not necessarily and specifically part of a particular, acknowledged domain or discipline. UMBEL, on the other hand, has as its most apparent advantage its reliance on OpenCyc and with that the strong inferencing and logic capabilities of the CYC knowledge-base which are thus also brought to the Web of Data. DBpedia is a community project started by the University of Leipzig, Free University Berlin and OpenLink Software, while the open and free UMBEL is developed and hosted by Zitgist with support from, again, OpenLink Software.

Now, and in particular with the recent release of Zitgist’s web service endpoints and with the incorporation of UMBEL classes in DBpedia, questions arises as to the relationship of the two projects, and regarding the role of OpenLink Software in the further process. To draw a distinction:

One could say that DBpedia’s goal is to lower the barrier for web developers and end-users in the actual use of the semantic web, while UMBEL aims at bringing “order to the chaos” that is inherent to user-generated, collective knowledge.

Would you agree with this description – and is it a contradiction at all or the kind of dynamic the Semantic Web community has been waiting for?

Mike Bergman: Yes, I would agree with this description, though we have tried many others. For example, in various writings in the past, we have described UMBEL as a roadmap, or middleware, or a backbone, or a concept ontology, or an ‘infocline’, or a meta layer for metadata, and others. Today, what I tend to use, particularly in reference to DBpedia, is the TBox-ABox distinction in computer science and description logics. UMBEL is more of a class or structural and concept relationships schema — a TBox — while DBpedia is more of an an instance and entity layer with attributes — an ABox. I think they are pretty complementary…

Sören Auer: I very much agree with Mike, but would like to add that Wikipedia authors do not have in mind to create a coherent and consistent knowledge base when working on Wikipedia. I think the more we demonstrate the benefits of the semantic representations in DBpedia to the Wikipedia community, these people will start to organize and rearrange content to enable the use of Wikipedia as a knowledge base. Right now, Wikipedia authors just have not yet been confronted with the problem of synonymous infobox properties or the uncleanliness of the category system, for example. I think with a few small and non-invasive changes to Wikipedia, much of the current chaos can be already resolved.

Mike Bergman: I agree, too, with Sören’s adder: I think it is difficult for Wikipedia authors to be consistent or coherent across the entire Wikipedia knowledge base. I think, then, the real question is where does that coherence or structural consistency come from? I think the nature of that task is quite different than creating or editing instance articles.

As for the dynamics and drivers of the community, the role of DBpedia for practical, linked data can not be overstressed. It was the first, remains the biggest, and has brought much visibility and awareness to linked data. I think I was one of the first to give DBpedia some press shortly after its release nearly two years ago, which is still one of my most popular blog posts. I’d like to think that UMBEL is now timely and well-positioned to help provide a complementary resource of concept classes, but that is not proven. DBpedia is.

DBpedia relies on user-generated content, UMBEL, with CYC, is expert-driven. How will a system that combines these divergent approaches continue to grow?

Mike Bergman: DBpedia is in the enviable position of being able to leverage two things: 1) the phenomenal success and growth of its source content, Wikipedia; and 2) the growing sophistication of information and structure extraction techniques built around that phenomenon. In fact, my most recent SWEETpedia listing of research projects based on Wikipedia now exceeds 170 or something projects and we discover and learn about more daily. We now see major research efforts in Germany, Austria, Japan, New Zealand, the US and England (among those I know) aggressively mining and learning from Wikipedia. I’m sure DBpedia will learn more, but others will increasingly contribute as well. This is unprecedented and very exciting. Wikipedia may have as strong a heritage in contributing to research and language and structure understanding as it does as a reference encyclopedia.

But, that is largely instance and attribute data, the ABox to use my earlier terminology. For structural and conceptual relationships — the coherent way to organize and relate things in the world; that is, the TBox — I think there is much subtlety and thinking required. I’ve used the phrase before that creating structural schema is “not like flinging hash”. Efforts such as Cyc, with nearly 1,000 person years of consistent testing and effort behind it, or perhaps others such as SUMO or what is coming out of the biology community with OBO (Open Biomedical Ontologies), offer better coherency and the ability to interoperate across diverse datasets and domains. Perhaps Wikipedia and its data extraction offshoots may someday get to this point — and I truly hope so — but are not anywhere near that today in my opinion.

Sören Auer: I’m actually not so concerned about the lack of structure and coherency as Mike. If we look at the current mostly textual Web and the search engines making it accessible to us humans, there is almost no data, few structure and even less coherency, but search engines still manage to provide an enormous added-value. If we add more data to the Webm much more sophisticated browsing, searching and data integration interfaces can be built. Structure and coherency will then emerge automatically, once people see how their content is indexed and can be easily found (or not).

The same happened by the way with the traditional Web 1.0 – in the beginning nobody used HTML’s meta-data tags. Once search engines started to interpret those for ranking results, meta-tags shifted to the center of attention of every Web content manager. Applied to the Semantic Data Web: once search engines understand foaf:person, everybody will use this concept for describing people.

A little experiment – Web 3.0, Semantic Web, Web of Data, Linked Data: Can you think of an ontology that is able to connect these terms and reveal the concepts behind them?

Mike Bergman: Well, I’d like to think UMBEL is that ontology (smile). That is certainly our intent, though truthfully we are still working out structural details and have not added all of this nice SemWeb terminology. But it is coming shortly (smile).

Sören Auer: A precise definition of these terms in the mathematical sense does not (and probably will never) exist, so articles such as those in Wikipedia (or many other publications) about the terms are from my point of view completely sufficient to reveal the concepts behind them to us. Of course it’s nice to have pretty and world-wide unique identifiers (such as provided by UMBEL) to annotate articles about these terms.

Mike Bergman: Well, UMBEL is about linking to concepts, though we welcome anyone thinking our identifiers are pretty (smile). One key aspect we will see moving forward is how we can translate those concepts into one of the 250 languages now used by Wikipedia while retaining existing structure. That is a real exciting prospect.

What are the concepts you would personally want to employ to explain the over-arching idea of these terms to a newbie?

Mike Bergman: Structured data on the Web is becoming like newly visible stars as nighttime darkens in the desert. Structured data are points of light in a global information space. We need fixed reference points in that sky to find specific stars. We need linkages to extract meanings and constellations from them. So, we both need to expose those stars — as linked data — and to provide fixed references to find them again and connections to draw meaning from them. Objects, references and connections all work in concert to expose the wonder.

Sören Auer: Its difficult for me to add something after Mike’s truly poetic description of pretty technical terms (smile). I see Linked Data or the Web of Data as the next milestone on the road of realizing the vision of the Semantic Web. In this regard, I’m Marxist (smile) and think Marx’s Law of transformation of Quantity into Quality applies: once we have a sufficient quantity of data out there on the Web, a new quality will emerge. Unfortunately, we are still far away from reaching a critical mass, since the Semantic or Data Web as we recently found out (cf.: Triplify – Lightweight Linked Data Publication from Relational Databases, PDF, 332 KB) is effectively shrinking if compared with the growth of the traditional Web.

Kingsley Idehen from OpenLink Software was the first to announce on the W3C mailing list that DBpedia & UMBEL are now “fully connected.” Is Kingsley the bridge-builder between the two projects?

Mike Bergman: Without question. Kingsley has backed both efforts in a big way with people and resources. His company actually did the first RDF linkage between the two projects. What is more remarkable, however, is that DBpedia and UMBEL are but a small slice of the things Kingsley has been backing. He has been a leader in middleware, scalable clusters and cloud computing, RDFizing all data forms, converting relational legacy data to linked RDF data, and providing demos and teaching to newbies on mailing lists. I’m glad you asked this question because Kingsley is a real catalyst and visionary.

Sören Auer: I agree, Kingsley is a mover and shaker in many areas of technology and innovation and in particular the Data Web. However, we should not forget his marvelous team at OpenLink with the database mastermind Orri Erling, Hugh Williams, Ivan Mikhailov, Yrjäna Rankka and all the others.

In the same vein: What are roles that are vital for the LOD-engineering process? Are there also “gardeners” or “bureaucrats“, as Wikipedians would put it?

Mike Bergman: I think the LOD (linking open data) and DBpedia mailing lists have been very effective, and there continues to be good community and organization around those efforts. We know that Wikipedia is not a free for all, with a kind of self-policing plus type of governance. I think that works well at the instance level.

However, structure decisions at the level of conceptual schema such as UMBEL or OpenCyc or even the mapping of classes between ontologies or datasets requires more skill and care. Others may not agree, but I think the schema aspects essential to UMBEL’s purpose — while definitely needing to be open and participatory at the suggestion or input level — possibly require roles more like “priest” or “professional” or “authority” at the actual roll-out level. Without quality, structure is nothing, and all of this is just an elaborate toy.

Sören Auer: Again my philosophy here differs a little from Mike’s: I’m pretty skeptic there will be one ontology or organization scheme for the Semantic Web. Rather, I think structure and homogeneity will be achieved on a peer-to-peer basis first and a community consensus will emerge later.

Mike Bergman: Yeah, this is an excellent point and I’m glad Sören raised it. While it is true we have put much effort into creating a lightweight structure of concept classes for linking disparate datasets, we too do not see “one ontology to rule them all.” I suspect there will often be none, and then many times other frameworks chosen. It really depends on the use case and purpose. UMBEL’s specific purpose is to provide a coherent framework for serious knowledge engineers looking to federate data. After that, other frameworks with a different purposes may then need to do the heavy lifting of actual data interoperability.

With its recent RDF service release, Freebase has risen to the level of a major SemWeb knowledge base, too. Where do you see its role in the future SemWeb ecology, also in relation to your own projects?

Sören Auer: To be honest, I was not very convinced of Freebase from the very beginning although their technology in particular the user interfaces are impressive. From my point of view the Freebase approach was too centralized and proprietary. A better strategy would have been to develop an open (source) technology, which people can deploy on their own Websites combined with server side crawling and search facilities. The first part of this equation is by the way exactly what we are aiming for with our OntoWiki and Triplify projects. However, if Freebase now moves towards more openness and interoperability, this can be only applauded.

Mike Bergman: I think early on that Freebase was needing to get its own feet set and did not do much with regard to open standards or external interoperability. These are good signs we are now seeing. However, I think the revenue model around user-supplied data remains highly suspect. Heck, Wikipedia with all of its tremendous success is daily soliciting contributions from users. It’s hard to get traction without being open and free, and its hard to make money when you are open and free even with traction. But these new announcements now make it much easier for us to use Freebase should our customers request it.

About DBpedia:

DBpedia is a community effort to extract structured information from Wikipedia and to make this information available on the Web. Wikipedia is the by far largest publicly available encyclopedia on the Web. Wikipedia editions are available in over 250 languages with the English one accounting for more than 2.49 million articles. Unfortunately, Wikipedia’s search capabilities are limited to full-text search, which allows very limited access to this valuable knowledge-base. Semantic Web technologies enable expressive queries against structured and interlinked information on the Web. DBpedia allows you to make sophisticated queries against Wikipedia, and to link other data sets on the Web to Wikipedia data. The DBpedia data set currently provides information about more than 2.49 million “things”, including at least 108,000 persons, 392,000 places, 57,000 music albums, and 36,000 films. Altogether, the DBpedia data set consists of 218 million pieces of information (RDF triples).

dbpedia.org (general website)

wiki.dbpedia.org/OnlineAccess (DBpedia Wiki – Online Access)

About UMBEL:

UMBEL (Upper Mapping and Binding Exchange Layer) is a lightweight ontology structure for relating Web content and data to a standard set of 20,000 subject concepts. Its purpose is to provide a fixed set of reference points in a global knowledge space. These subject concepts have defined relationships between them, and can act as binding or attachment points for any Web content or data. UMBEL is like a map of an interstate highway system, a set of roadsigns to help find related content and a way of getting from one big place to another. Once in the right vicinity, other maps (or ontologies) — more akin to detailed street maps — are then necessary to get to specific locations or street addresses. By definition, these more fine-grained maps are beyond UMBEL’s scope. But UMBEL can help provide the context for placing such detailed maps in relation to one another and in relation to the Big Picture of what related content is about.

umbel.org (project website)

umbel.zitgist.com (UMBEL webservice – sandbox)

The interview was led by Andreas Blumauer, SWC

So, instead, let me give you some web search problems. They all came up in my real life, so they are not artificially created. It seems to me that the Semantic Web should make answering them easier, but with the existing web resources, they are really difficult.

• Find all papers having received best paper awards at ISWC conferences. I did that today, and it took me more than 30 minutes. And I’m not sure if I got all of them – indeed I would have missed one of them if I hadn’t known beforehand about that specific paper having received the award. Isn’t this a typical Semantic Web problem? (The results of my search are further below.)
• There’s an owl-like bird in southern German woods, and in colloquial german it’s called Käuzchen. Try to find out the english name for this bird. I actually failed, though I think I got close to the answer when I merged web search with an external knowledge base (in form of a biologist I happen to know). And actually, simply going to Wikipedia and clicking on the English link is not enough, since I’m not looking for the Strix genus of owls, but rather for a particular bird …
• Who is this researcher with the russian looking name who worked on resolution-based methods for the description logic EL? This also looks like a typical Semantic Search problem, which shouldn’t be too difficult if you have the corresponding knowledge (and background knowledge) available. I admit I failed on this one using traditional methods (unless you consider it a traditional method to ask Franz Baader by email about it.)
• Are lobsters spiders? I.e. are lobsters classified as spiders by biologists? This one is actually tougher than you would think using traditional methods. Should be easy using Semantic Web knowledge bases and some simple reasoning, shouldn’t it?

For all these tasks (and many others), it seems to be apparent that Semantic Web Reasoning – and the availability of corresponding knowledge bases – would make the finding of answers much easier. The current reality of the Semantic Web is still quite a bit away from this. But we’re working on it.

Finally, as promised, the results of my inquiry about the ISWC best paper awards:

• 2004: Y. Guo, Z. Pan, and J. Heflin, An Evaluation of Knowledge Base Systems for Large OWL Datasets.  This one compares different semantic web reasoners.
• 2005: The award was split that year. One half was awarded to P. Mika, Ontologies Are Us: A Unified Model of Social Networks and Semantics. The other half was awarded to B. Motik, On the Properties of Metamodeling in OWL. Edit: Apologies to Peter Mika. I accidently forgot his paper and mentioned instead a paper which won the best paper award at the International Symposium on Wearable Computers 2005, which is also abbreviated ISWC. It’s now been corrected. We definitely need Semantic Search!
• 2006: H. Chen, Y. Wang, H. Wang, Y. Mao, J. Tang, C. Zhou, A. Yin, Z. Wu, Towards a Semantic Web of Relational Databases: a Practical Semantic Toolkit and an In-Use Case from Traditional Chinese Medicine.
• 2007: D. Zeginis, Y. Tzitzikas and V. Christophides, On the Foundations of Computing Deltas Between RDF Models. This one is about a diff for RDF, and it involves RDF inferencing.
• 2008: M. Horridge, B. Parsia and U. Sattler, Laconic and Precise Justifications in OWL.

So why did I dig these awards out? Because I noticed that among these 6 papers there are 3 which are explicitly concerned with OWL. And the 2007 paper involves RDF inferencing. Talk about the importance of reasoning for the Semantic Web …

Author: Pascal Hitzler, AIFB, University of Karlsruhe (TH), Germany

Knowledge management typically aims at knowledge that people do not know yet, e.g. (tacit) knowledge that people have acquired in a project and that is suppose to be made explicit and accessible to other people who don’t yet have this knowledge.

But what about the knowledge that everybody knows without them knowing they need to know it? Such as that an apple is a type of fruit, and is green and is red? Common sense knowledge?

I boldly asked Henry Lieberman for a 12 seconds definition of Common Sense Knowledge, a challenge he accomplished with perfect precision:

Henry Lieberman defines Common Sense Knowledge on 12seconds.tv

An intriguing MIT project I hadn’t yet heard about which Henry Lieberman introduced is the Common sense knowledge base Open Mind Common Sense – anyone can sign up to it and contribute. A total of 203 knowledge facts have, for instance, been accumulated about the concept “apple”, including facts such as these:

→ An apple is red
→ An apple is green
→ Apples grow in trees
→ an apple are food.
→ An apple has a core
→ An apple can fall from a tree
→ An apple is a type of fruit

Offered similar concepts are “egg, potato, steak, bread, spinach, frozen food, butter, appl [sic], leftover, grape”. The process of adding knowledge is guided by a list of questions that allow to conceptualize and structure the knowledge, e.g.

MadeOf
What is it made of?
IsA
What kind of thing is it?
UsedFor
What do you use it for?
CapableOf
What can it do?
PartOf
What is it part of?
DefinedAs
How do you define it?

But what are the roles that common sense knowledge can play in interactive applications? Henry Lieberman suggested using common sense knowledge, a system can e.g. anticipate what a user is most likely to do, or it can at least make most likely things easiest to do, e.g. by providing a map from goals to concrete actions in the interface, or by integrating appropriate applications.

Lieberman furthermore introduced a couple of tools which illustrated these benefits, e.g. the prototype for an Event Minder for improved scheduling driven by common sense knowledge. Entering a statement such as “Lunch with Charlie at Miracle next Friday” would for instance calculate the date of ‘next Friday’, call up a calendar application and also a web service to get directions for getting to Miracle.

Regarding the difference between CYC (the common sense knowledge ontology) and the MIT’s common knowledge base Open Mind Common Sense: CYC is an ontology organized by experts with a broader and deeper knowledge – the common knowledge base grants access to anyone and has, for instance, also information about kitten that might not be that relevant to experts. At this stage, there is no mapping to CYC.

Henry Lieberman’s keynote tied in nicely with a presentation by Andrew S. Gordon about “Envisioning with Weblogs”. According to Andrew Gordon, there have been three waves in the 50 year history of common sense knowledge in artifical intelligence:

First wave: Logical formalizations of commons sense knowledge (e.g. CYC)
Second wave: volunteer contributions from web communities (e.g. Open Mind Common Sense)
3rd wave: Knowledge acquisition from the social web (e.g. Envisioning with Weblogs)

First off, what is envisioning? Andrew Gordon described it as a form of reasoning about states and events in time and space, generating answers to questions such as “What’s happening in the world right now?”, or “What is going on in the audience’s mind right now?”, or “How did this person get into the room?”, or “What am I going to have for dinner tonight?”

At the Institute for Creative Technologies (University of Southern California), Andrew is involved in a project called Story Representation and Management, which among other things, is doing research on story interpretation, i.e. “techniques for integrating automated commonsense inference into the processing of narrative text documents, and methodologies for creating very large scale commonsense knowledge bases.”

One of the paths towards the creation of this knowledge base is gathering up stories on weblogs. But can we really gather up all stories ever written in a weblog? In the research conducted and cited by Andrew (Gordon 2007), 4,5 million stories, made up of 66,6 million sentences and 1,06 billion words were extracted from weblogs.

In Gordon’s recipe for envisioning with weblogs, the retrieval of the closest situation provides the best results. Take for instance the quest of formalizing this particular problem in common sense physical reasoning: cracking an egg into a bowl (as described by Morgenstern 1998, Lifschitz 1998, Shanahan 1998).

There are so many things to be considered: Is the bowl big enough? What if the bowl is made of cardboard? What of the egg is hardboiled? Common sense knowledge in stories on weblogs does offer many answers, for instance this story from Amit Asaravala – which also generates further knowledge as to what would happen to a person who does this:

Seeing the little weirdo reminded me of one Saturday morning, a year or so ago, when I cracked an egg into a bowl and found three yolks inside. After tossing the triplets, I cracked another egg from the batch and found yet another three yolks jiggling up at me. Another egg, another trio of blondes.

This continued through all twelve eggs — I kid you not.

Though the episode had me thoroughly creeped out, I must say that I am somewhat intrigued by the thought that, on some farm somewhere, there is a crotchety old hen that consistently lays triple-yolkers.

In the following discussion, some people wondered if weblogs aren’t an unreliable source for a common sense knowledge database. Andrew however doubted that the difference true/false or the difference true/fictitious did really matter. Instead he suggested that in 99% of the cases the same physical reasoning applies in, say, the Star Wars Universe as does apply in the real world.

Common sense knowledge is not about the velocity of spacecrafts crossing the milky way, it’s about what happens if Leia punches Han.

Which is yet another point sustaining that common sense knowledge is so obvious that most of the time we don’t even know we know it. And that’s a challenge to knowledge management.

Oh, and something very nice happened to me today: While I sat in our booth preparing this blog post, someone approached me very politely saying that he had read my name somewhere before, on some blog. Turns out this person – Stefano Bertolo, Project Officer at the Information Society Directorate of the European Commission – has in the past also left a comment on the Flickr page of our “Escape from the Data Silo” logo (which can be used freely by anyone on a CC license). It’s a small world, thanks to Social Media:-) We had a nice conversation at our booth, during which he also recommended the NeON project: Lifecyle Support for Networked Ontologies: a recommendation which I herewith pass on to you, reader of this blog:-)

P.S. There were many more interesting talks and sessions, but the scope of this blogpost is, sadly, limited by the rules of physics: I could only attend one talk at a time.

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• Lecture series on Knowledge Representation

Here is the release announcement Mike communicated via email yesterday:

UMBEL (Upper Mapping and Binding Exchange Layer) [1] is a lightweight ontology for relating Web content and data to a standard set of 20,000 subject concepts. Based on OpenCyc [2], these subject concepts have defined relationships between them, and can act as semantic binding nodes for any data or Web content. A further 1.5 million named entities have been extracted from Wikipedia and mapped to the UMBEL reference structure with cross-links to YAGO [3] and DBpedia [4]. The system can easily be extended with additional dictionaries of named entities, including ones specific to enterprises or domains.

UMBEL is provided as open source under the Creative Commons 3.0 Attribution-Share Alike license. The complete ontology with all subject concepts, definitions, terms and relationships can be freely downloaded [see 5]. All subject concepts and named entities are available as Linked Data [see 5]. Five volumes of documentation [5] are also available.

The release is accompanied by about a dozen Web services [6] for using or manipulating UMBEL, along with a new introductory slide show [7]. Additional release information may be found on Fred’s [8] or my [9] separate blog postings. We welcome those with interest or suggestions for improvements to do so through the UMBEL discussion forum [10]. We will shortly be putting easier services online for such input.

So, enjoy! We look forward to your commentary, suggestions and putting UMBEL under production-grade stress. We know will be doing the same!

Regards, Mike

Great release! They have also given us access to a media-oriented article which you can read on our portal.

A Prototype Knowledge Base for the Life Sciences
W3C Interest Group Note 4 June 2008
Editors: M. Scott Marshall, Eric Prud’hommeaux
Contributors: Alan Ruttenberg, Jonathan Rees, Susie Stephens, Matthias Samwald, Kei-Hoi Cheung
Abstract: The prototype we describe is a biomedical knowledge base, constructed for a demonstration at Banff WWW2007 , that integrates 15 distinct data sources using currently available Semantic Web technologies such as the W3C standard Web Ontology Language [RDF]. This report outlines which resources were integrated, how the knowledge base was constructed using free and open source triple store technology, how it can be queried using the W3C Recommended RDF query language SPARQL [SPARQL], and what resources and inferences are involved in answering complex queries. While the utility of the knowledge base is illustrated by identifying a set of genes involved in Alzheimer’s Disease, the approach described here can be applied to any use case that integrates data from multiple domains.

Experiences with the conversion of SenseLab databases to RDF/OWL
W3C Interest Group Note 4 June 2008
Editors: Matthias Samwald, Kei-Hoi Cheung
Contributors: Alan Ruttenberg, Huajun Chen
Abstract: One of the challenges facing Semantic Web for Health Care and Life Sciences is that of converting relational databases into Semantic Web format. The issues and the steps involved in such a conversion have not been well documented. To this end, we have created this document to describe the process of converting SenseLab databases into OWL. SenseLab is a collection of relational (Oracle) databases for neuroscientific research. The conversion of these databases into RDF/OWL format is an important step towards realizing the benefits of Semantic Web in integrative neuroscience research. This document describes how we represented some of the SenseLab databases in Resource Description Framework (RDF) and Web Ontology Language (OWL), and discusses the advantages and disadvantages of these representations. Our OWL representation is based on the reuse and extension of existing standard OWL ontologies developed in the biomedical ontology communities. The purpose of this document is to share our implementation experience with the community.