What’s this

It’s a prototype of a social network service where people are connected automatically via shared topics of interests extracted from their texts. You can find more on the PCN theory in this post.

If you’re interested to play with it, we are glad to invite you to join the testing.

How to join

1. Request a personal invitation code by filling this form. The code will be sent to the provided email address.

2. Register on PCN with your invitation code. Your username must be the same you used in the request above.

3. Download, install and configure the PCN client (Conex) as proposed by the system.

To run the client, an installed Java Runtime Environment is required. You also may need the Java plugin installed into your browser for visualisation of the network in your server profile.

The PCN Client

The PCN client software (Conex, from “CONcept EXtraction”) is a desktop content aggregator collecting pieces of content from different locations (local folders, webs, email, RSS feeds, Del.icio.us, SharePoint servers …). The content is analyzed to extract valuable terms from the texts and assign them as the document tags (manual tagging is possible also). A user can browse her content collections with the tag cloud or  metadata facets, edit document metadata, annotate documents, search in the content collections and do a lot of other things. Those who are interested into details of client functionality and in a underlying technology, can check this site.

To configure PCN connection, select  “PCN Client → Configure PCN Client” menu. At the first time, it will ask your username/password on the server and offer to create one or more contexts for your data. The contexts serves as navigation facets to browse the content collection and are useful to organize the content resources depending on your activities. After the contexts are defined, you can assign existing Conex content locations to them.

Autotagging is applied to individual documents by selecting them and choosing “Autotagging…” in the context menu. This operation can also be automated for all documents in a specific location, if “Apply autotagging for new documents” option is set in the location properties (“Settings” tab of the location dialog box). You can edit the results of autotagging or add new tags for selected documents using “Edit tags” option of the context menu.

As the content is tagged, Conex sends the tagging data and resource descriptions to the server, where a profile of user’s interests is created. All updates in the monitored locations are also sent to the server (in the specified time interval) to keep the user profile up-to-date.

By default, Conex works with the files in your local folders. To enable other types of content locations, you need to install the plugins. A plugin is installed with a single click in the Plugin Management console (“Tools → Manage plugins…” menu) and will be activated after restart.

PDF, MS Office, OpenOffice, HTML, XML and plain text documents are supported out of the box.

For SCAN users

To clarify the things, Conex is neither a proprietary fork, nor a new version of SCAN. It’s basically a rebranded distribution bundled with the common document plugins and integrated into the PCN solution. There is a plugin providing connectivity with the PCN Server and it’s possible to install this plugin into “native” SCAN to use it as a full-featured PCN client. This likely won’t work with the released version, but should be ok with a SVN snapshot. Anyway, new SCAN version enabling the PCN plugin will be released soon, so stay tuned.

The PCN Server

The server receives data from the clients and builds the socio-semantic network of users connected via the tags they have in common. After you’ve submitted some data from Conex, you can check your profile on the server to see your tags and  people appeared in your personal network:

The people listed in your network are the users who have the similar interests. On their profile pages, you can see a detailed information on compatibility between you and a profile owner:

The Network view displays a visualization of your complete network as a map of the overlapping clusters of tags you share with the members of your network:

The map is interactive — double-clicking an element will navigate to a user profile or to a tag page.

The people search is also implemented. You can find users by their names, other profile data or by the tags:

And finally, you can explore the content collections of the network members using faceted navigation with tags and contexts (supplemental metadata facets, such as document authors and  creation dates are also available).

Your privacy

⚠ The full text of the documents is not submitted to the server, however, Conex can use excerpts from the documents to fill the title and description metadata (if it is not defined explicitly). So, if your documents contain some sensitive information, parts of it may appear on the public. Please check and make sure that there are no private documents in the Conex locations you are about to assign to the PCN contexts. Well, you’ve been warned.

Report issues

It’s a beta, so  the bugs are likely to be there. Feel free to report the bugs, either in the Server or the Client,  to the bug tracker (registration is needed). Any other feedback, of course, is also more than welcome.

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.

Back story

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.

PCN basics

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.

The Platform

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

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 Ontology

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:

PCN quadripartite 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.

Extended SCOT Tagging as a skeleton of the PCN model

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.

PCN Ontology

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 API

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“
• Hak-Lae Kim et al, “Review and Alignment of Tag Ontologies for Semantically-Linked Data in Collaborative Tagging Spaces“ , “The State of the Art in Tag Ontologies: A Semantic Model for Tagging and Folksonomies“
• SCOT
• SIOC