Reasoning about Social Semantic Web Applications using String Similarity and Frame Logic

Social semantic Web or Web 3.0 application gained major attention from academia and industry in recent times. Such applications try to take advantage of user supplied meta data, using ideas from the semantic Web initiative, in order to provide better services. An open problem is the formalization of such meta data, due to its complex and often inconsistent nature. A possible solution to inconsistencies are string similarity metrics which are explained and analyzed. A study of performance and applicability in a frame logic environment is conducted on the case of agent reasoning about multiple domains in TaOPis - a social semantic Web application for self-organizing communities. Results show that the NYSIIS metric yields surprisingly good results on Croatian words and phrases

Knowledge Extraction from Open Data Repository

The explosion of affluent social networks, online communities, and jointly generated information resources has accelerated the convergence of technological and social networks producing environments that reveal both the framework of the underlying information arrangements and the collective formation of their members. In studying the consequences of these developments, we face the opportunity to analyze the POD repository at unprecedented scale levels and extract useful information from query log data. This chapter aim is to improve the performance of a POD repository from a different point of view. Firstly, we propose a novel query recommender system to help users shorten their query sessions. The idea is to find shortcuts to speed up the user interaction with the open data repository and decrease the number of queries submitted. The proposed model, based on pseudo-relevance feedback, formalizes exploiting the knowledge mined from query logs to help users rapidly satisfy their information need