Today at University of Nottingham, we deployed an alpha of the People-Concept Networking platform prototype. It’s the alpha, so it doesn’t do a lot as yet and is released only for internal overview and testing of the basic infrastructure. It is however, an occasion to disclose what I am working at now, along with an introduction to some PCN theory.
Disclaimer: Like it says on the author page, I work for UoN and Laboranova project, but I cannot speak for it officially in this blog. These are my own views as a project insider and the other’s may differ.
As far back as in early 2008, I’ve been introduced into PCN proposal and invited for collaboration on a R&D for its prototype implementation. It was very interesting for me and we developed a vision and basic design principles of the PCN solution to start the prototype development at the beginning of 2009.
This work is a part of Laboranova — a large EU project aimed at new ways of collaboration between knowledge workers and sharing ideas and competencies. Apart from PCN, there are lots of other interesting thngs the project partners do there.
The idea is a social networking where the members were connected via shared areas of expertise. These areas are identified by the topics of interests, discovered at user’s content.
Like other social network services connect people via shared professional activities, schools, hobbies or music tastes, PCN approach employs the shared semantics of texts the people read or write. There is an optimistic assumption that such semantics, expressed as sets of the weighted keywords (or, tag clouds), may reflect an area of user’s expertise and constitute a profile of his professional interests.
Multiple user profiles are aggregated to create expertise information and form a socio-semantic network of people and concepts. Comparing profiles of different users, we can evaluate a similarity of their expertises and thus, estimate their social distance in the network. We also can take a specific area of expertise to find who are the best experts relevant to it, and explore the related overlapping areas. With this network analysis, it is possible to generate a variety of individual recommendations to help people to discover new collaboration opportunities and areas of knowledge.
There is a client-server architecture including a central PCN server and a number of clients connected to it. The PCN client software is installed locally and analyses content from different locations specified by its owner: file folders, webs, email, RSS, delicious accounts, SharePoint servers and so on.
A user can let the software extract the keywords from the content automatically, edit the results or choose to tag the content manually. Then he chooses what locations should be submitted to the server and assigns them to one or more named contexts which help to organize the concepts within the user profile. Document metadata and tagging information is uploaded to the server where an individual profile of tags is created and published. As a location is submitted to the server, it is monitored for changes to synchronize the profile with the actual state of the content (by sending incremental updates at a specified time interval).
The client part is based on SCAN, so there is a zero barrier to start working with it for SCAN users. Actually, from a SCAN user perspective, there is no difference from usual everyday work — you can enjoy a full set of features one can find in native “offline” SCAN, but also use it for populating your public profile of interests at the PCN server.
Also, variety of location types and document formats are supported by the client thanks to the plugins from the SCAN repository.
The server receives metadata about content resources as SIOC RDF, so in theory, it may work with any SIOC provider, apart from the default PCN client. The server augments resource metadata with relationships to the user profile, concepts and contexts, thus forming the quadripartite PCN ontology model:
To describe the PCN model, we adopted SCOT (Semantic Clouds Of Tags) ontology aimed at conceptualization of the structure and semantics of tagging data with strong focus on social interoperability. It is an extension and further development of the Tag Ontology project that describes the relationship between an agent, an arbitrary resource, and one or more tags.
The SCOT (Tag) ontology is based on a tripartite (User—Tag—Resource) model. These three core concepts are connected together via a central concept of Tagging representing the tagging activity. Every Tagging instance can be considered as a result of a single tagging action defining a user who performed it, the tagged resource and what tags have been used. It also can carry auxiliary information about the action, such as the time of tagging.
For users and resources, SCOT relies upon concepts from SIOC — specifically, sioc:User and sioc:Item classes. For our PCN ontology, we extended the SCOT model with the notion of context by adding another class of entities and a property to relate them with the Tagging instances.
Using SCOT Tagging model, it is possible to avoid excessive verbosity in the PCN ontology, as the relationships between core PCN classes (the edges of the tetrahedron) are inferable from their relationships with the central Tagging class (the skeleton). In the diagram below, the implicit relations are shown as dashed.
An interesting possibilities comes from the fact that the scot:Tag is actually a subclass of the SKOS Concept, so all kinds of SKOS reasonings about the concepts are possible in the future. Moreover, MOAT features incorporated in SCOT open a way to integrate the tag descriptions with the Linked Data web.
The client talks to the server via a simple RESTful API for adding and modifying the users metadata. As said above, the metadata is described with SIOC, so it is theoretically possible to use that API and integrate the PCN server with any system which can provide SIOC metadata about the content.
An endpoint for SPARQL queries is planned also.
For more information:
- An early description of the work package (Laboranova Profile System) on Laboranova web-site.
- Marc Pallot et al, “Future Workplaces, towards the ‘Collaborative’ Web“
- Peter Mika, “Ontologies are us : A unified model of social networks and semantics“
- Review and Alignment of Tag Ontologies for Semantically-Linked Data in Collaborative Tagging SpacesThe State of the Art in Tag Ontologies: A Semantic Model for Tagging and Folksonomies