Abstract:
Search engines or localized software systems developed for information searching, play an important role in knowledge discovery. Proliferation of data in the web and social media has posed significant challenges in finding relevant information efficiently even using those search engines or other software systems. Moreover, those systems or engines tend to collect large number of data, which could be useful for end users in various ways but have overlooked the meaning of the search phrases, hence generate irrelevant search results. A unit level searching i.e. searching information within a website or page is also not effective as they follow exact keyword matching techniques and ignore the semantic level matching of search phrases. In order to address those deficiencies, this research proposes a hybrid approach which use the semantics of data, community preferences as well as collaborative filtering techniques for semantic information retrieval. More specifically, Topic modeling based on Latent Dirichlet Allocation together with topic-driven based community detection methods are applied for identifying personalized search results generation and hence improve the relatedness of the research results. Based on the proposed hybrid approach a framework for semantic search that can easily be integrated to a software application has been implemented. The evaluation results confirm the effectiveness of search results which outperform benchmark approaches that follow traditional keyword search algorithms.
