Abstract: In this paper, we show how to represent to our formal reasoning and to model social context as knowledge using network models to aggregate heterogeneous information. We show how social context can be efficiently used for well understood tasks in natural language processing (such as context-dependent automated, large scale semantic annotation, term disambiguation, search of similar documents), as well as for novel applications such as social recommender systems which aim to alleviate information overload for social media users by presenting the most attractive and relevant content. We present the algorithms and the architecture of a hybrid recommender system in the activity centric environment Nepomuk-Simple? (EU 6th Framework Project NEPOMUK): recommendations are computed on the fly by network flow methods performing in the unified multidimensional network of concepts from the personal information management ontology augmented with concepts extracted from the documents pertaining to the activity in question.

Keywords: multidimensional networks, graph-based methods, network flow methods, data mining, natural language processing, recommender systems.

ACM Classification Keywords: H.3.4 Information Storage and Retrieval: Systems and Software – information networks; H.3.5 Information Storage and Retrieval: Online Information Services – data sharing.

Link:

SOCIAL CONTEXT AS MACHINE-PROCESSABLE KNOWLEDGE

Alexander Troussov, John Judge, Mikhail Alexandrov, Eugene Levner

http://foibg.com/ibs_isc/ibs-23/ibs-23-p10.pdf