Structural and semantic proximity in information networks

This research includes the investigation, design and experimentationof models and measures of semantic and structural proximity for knowledgeextraction and link prediction. The aim is to measure, predict and elicit, inparticular, data from social or collaborative sources of heterogeneous information.The general idea is to use the information about entities (i.e. users) andrelationships in collaborative or social repositories as an information source toinfer the semantic context, and relations among the heterogeneous multimediaobjects of any kind to extract the relevant structural knowledge. Contexts canthen be used to narrow the domains and improve the performances of tasks suchas disambiguation of entities, query expansion, emotion recognition and multimediaretrieval, just to mention a few.There is thus the need for techniques able to produce results, even approximated,with respect to a given query, for ranking a set of promising candidates.Tools to reach the rich information already exist: web search engines, whichresults can be calculated with web-based proximity measures. Semantic proximityis used to compute attributes e.g. textual information. On the other hand,non-textual (i.e. structural, topological) information in collaborative or socialrepositories is used in contexts where the object is located. Both web-based andstructural similarity measures can make profit from suboptimal results ofcomputations. Which measure to use, and how to optimize the extraction and theutility of the extracted information, are the open issues that we address in ourwork.