Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
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Domain relevance on term weighting
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While classic information retrieval methods return whole documents as a result of a query, many information demands would be better satisfied by fine-grain access inside the documents. One way to support this goal is to make the semantics of small document regions explicit, e.g. as XML labels, so that query engines can exploit them. To this purpose, the topics of the small document regions must be discovered from the texts; differently from document labelling applications, fine-grain topics cannot be listed in advance for arbitrary collections. Text-understanding approaches can derive the topic of a document region but are less appropriate for the construction of a small set of topics that can be used in queries. To address this challenge we propose the coupling of text mining, prior knowledge explicated in ontologies and human expertise and present the system RELFIN, which is designed to assis the human expert in the discovery of topics appropriate for (i) ontology enhancement with additional concepts or relationships, (ii) semantic characterization and tagging of document regions. RELFIN performs data mining upon linguistically preprocessed corpora to group document regions on topics and constructing the topic labels for them, so that the labels are characteristic of the regions and thus helpful in ontology-based search. We show our first results of applying RELFIN on a case study of text analysis and retrieval.