Context-informed Knowledge Extraction from Document Collections to Support User Navigation
Mario Cataldi, Claudio Schifanella, K. Selçuk Candan, Maria Luisa Sapino, Luigi Di · 2010
Most of the existing document and web search engines rely on keyword-based queries. To find matches, these queries are processed using retrieval algorithms that rely on word frequencies, topic recentness, document authority, and (in some cases) avail-able ontologies. In this paper, we propose an innovative approach to exploring text collections using a novel keywords-by-concepts (KbC) graph, which supports nav-igation using domain-specific concepts as well as keywords that are characterizing the text corpus. The KbC graph is a weighted graph, created by tightly integrat-ing keywords extracted from documents and concepts obtained from domain tax-onomies. Documents in the corpus are associated to the nodes of the graph based on evidence supporting contextual relevance; thus, the KbC graph supports con-textually informed access to these documents. The construction of the KbC graph relies on a spreading-activation like technique which mimics the way the brain links and constructs knowledge. In this paper, we also present CoSeNa (Context-based Search and Navigation) system that leverages the KbC model as the basis for doc-ument exploration as well as contextually-informed media integration.