Integrating 3 semantic web layers: Communities, ontologies and content

Reading a comment in Trends & Controversies in IEEE Intelligent Systems by Steffen Saab led me to an interesting paper by Peter Mika. It supports my conviction that the integration of social network data from different sources is very important. The information produced in social networks has true value since it contains many knowledge. This knowledge is being communicated there between people that are a members from a specific (research) group or community. There are however some issues to be considered. They are outlined very clearly in this article.

As Saab stated:

Social networks have interesting properties. They influence our lives enormously without us being aware of the implications they raise: How does a kind of fashion become en vogue? How does a virus spread and infect people? How does a research topic become a hot topic? Why are some companies successful and others aren’t? All these questions affect us, and understanding them by building and investigating computational models might give us a powerful tool to improve our health system, increase individual and general wealth, or just increase awareness about how the people around us actually influence our opinions, which we frequently believe that we shape.

Peter Mika considered a particular form of influence: the way that people agree on terminology and this phenomenon’s implications for the way we build ontologies and the Semantic Web. In a nut- shell, he concluded that the Semantic Web will either include social networks’ influence in its architecture or wither away.

According to him, there seem to be some issues who have been passed around like ‘hot potatatoes’ among Semantic Web researchers. Two in particular stick out from the thick proceedings volumes: ontology learning and ontology mapping. Ontology learning or extraction is the attempt to recreate a conceptual model from existing knowledge sources, in particular natural text. Ontology mapping (also known as merging, alignment, and so on) refers to finding and reconciling the relations between two or more conceptual models and creating a single model that captures their intentions and the relationships between them.

He argued that  the underlying reason that the automation of these tasks is difficult because of the lack of our machines’ understanding. This is the problem the semantic web set out to solve. As the well-known slide about what it’s like to be a computer illustrates, although the machine doesn’t understand strings of human symbols, it has no problem identify- ing the parts in angled brackets. Mika stated that what the machine can’t do is access what we think those symbols’ interpretations are, and therein lies the problem. Unfortunately, this is the crux of ontology learning and mapping.

Then the author described how semantics in fact ‘are us’. Creating and reconciling interpretations is a human-complex problem. He illustrated this with an old experemint of linking two familiar concepts to humans by some concept. This concept was illustrated as a blank in the article. The question was: “What concept is hidden under the blank.” Knowledge engineers collected in this way the 1973 Edinburgh Associative Thesaurus by handing a list of words to students and instructing them to write as quickly as possible next to each stimulus word the first word it made them think of.  The EAT is actually an ontology. This ontology-engineering method revealed that once an initial set of words was selected, the only parameter left to the process is the population chosen. The experiment’s subjects collective mindset drove many of the aggregated associations. The well-known dynamics of social networks create this collective mindset: interaction creates similarity and vice versa.

Mika then moved back to the semantic web and communities. He structured in a table how we should bow to the social sciences at this point and consider knowledge’s embeddedness in the social context. Communities aren’t merely sets of users, but an integral and dynamic part of the architecture. Without intending or even realizing, we’ve elevated them to this place by integrating the notions of ontology and semantics into the first Web’s technological framework. Communities will start creating their own ontologies that reflect their identities, lan- guages, and collective intelligence, develop- ing them through interaction surrounding a practice or interest. With these ontologies, the communities will annotate their online or offline content.

Mika continued and wrote that the challenge is not only the differences between ontologies from different communities. The change of conceptualizations as communities evolve poses another challenge. This challenge is of course the “Ontology Mapping” he refered to earlier in his article. The more unstable knowledge is, the more difficulty we can expect in formalizing and sharing it on a large scale. Mika included an illustration that shows how communities, ontologies, and content make up the three layers of the Semantic Web.


Staab, S. Mika, P. Social Networks Applied. IEEE Intelligent Systems, Volume 20, Number 1 (2005) pp. 1-14


About laurensdv
Computer Science Student, interested in creating more innovating user experiences for information access. Fond of travelling around Europe!

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