Thinking about interlinking data contexts

Time to remind the project statement. To build a semantic profiling framework to support the connection of researchers. The main use case and application that the framework has to support is illustrated by what could be called: “the conference case”. Scientists and researchers are interested in very specific topics, this is best verified by the conferences they are attending. Another trend is that they all blog and tweet about these events. This creates huge opportunities for profiling. The attendees tweet about what they notice, what they remark as interesting for their own projects. What if we could connect these users using this information? We could call an application that does just that “Scientific Profiling“. This approach comes from the concept that the data produced in social networks can have true value if properly annotated and interlinked. A second requirement is to create a suitable context in which this information can get meaning. This is very important to identify which ontologies should be used.

Turning the concept around can lead to another use case, let’s name it: “the looking-for-interesting-people case”. Suppose a scientist wants to find either:

  • interesting event (which many people in his subject are going to)
  • people (based on matching interests or events)
  • new challenges (companies, organization, topics which are related to events and topics this scientist is interested in)

We can also call this application “Scientific Profiling”. Now we approached the same application but not from a data perspective, but from the user perspective.

Let’s push a little further and return to the scientific profiling framework that has to support this Scientific Profiling application. We have a framework architecture consisting of three layers:

  1. Extraction layer: Extracts data from various resources and annotates it using relevant ontologies for that specific data context.
  2. Interlinking layer: Is feeded with annotated data (triples) and creates a SPARQL endpoint for it. It is responsible for requesting more data if needed for a certain information query. As mentioned before Semantic Web Pipes can be of great support in this layer to create an information source.
  3. Analysis layer: Here a user information needs are translated into queries that the interlinking layer understands, the interlinking layer will check if data updates are necessary, performs querying.

It is impossible to create a generic framework that supports all data contexts, but we can create a system that supports a broad range of data contexts. For now we are focussing on three data contexts:

  1. User: Social Microblogs, annotated data from twitter users (SIOC, FOAF, DC, GeoNames);
    Purpose: since we are doing profiling, data from the user is an absolute must
  2. Domain: Scientific Conferences annotated data of scientific conferences
    Purpose: to enable the framework to recognize and link to conferences
  3. General: OpenCalais Linked Data, Tag/MOAT Ontology (with links to DBPedia)
    Purpose: to give a meaning to topics and tags from a user.

Extending the framework with more domain knowledge, could quickly increase the number of applications the framework supports. In its current form the “semantic profiling framework” will be able to support most “Research 2.0” use cases. They are very similar to the two use cases presented in this blog. It is about discovering new resources (papers, articles, information) created by researchers and of course the researchers themselves.

Who thinks research and science and people in one sentences, thinks of course “medical”. This is a very important domain within research and it requires very specific needs. A researcher often looks for an exact match for a certain medical case. The user profile (medical record) plays of course a major role. This is described in the following paper: Ontology driven semantic profiling and retrieval in medical information systems. It describes a system to do semantic profiling in medical systems. It also uses an annotated user profile as a starting point to link it with more entities.

It could be interested to keep this case in mind, so we can keep the framework as general as possible. In this way if more domain knowledge is feeded to the interlinking layer, there is no need to completely rewrite the layer. Some additional modules to handle some domain specific queries that come from the analyzing layer should suffice.
At the same time the analysis layer should have ‘no knowledge’ of the data context behind it. It should merely focussing on profiling concepts that a researcher is interested in: “persons”, “organizations/companies/institutions”, “tags/real world entities”, “topics”, “events”,”locations” and maybe others. The interlinking layer will translate queries concerning these concepts to the various contexts it ‘knows’.

The framework could be such modular and extendible with more domain knowledge. It can then support more than just scientific profiling applications, but also for example these medical profiling applications. Both the scientific and the medical fit actually “Research 2.0”. To make the name “semantic profiling framework” last, the framework should allow this extended domain knowledge support in some way.

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

4 Responses to Thinking about interlinking data contexts

  1. Pingback: SPARQL Endpoint set-up and load any twitter profile into the RDF Store « Laurens goes semantic…

  2. Pingback: SPARQL Endpoint set-up and load any twitter profile into the RDF Store | Laurens goes semantic

  3. Pingback: Research notes – Oh! Propagator, really? [Distributed Defeasible Contextual Reasoning in Ambient Computing]

  4. Pingback: User Analysis possibilities: first simple Demo! « Laurens goes semantic…

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