Abstract
Motivated from related entity finding problem, in this paper, we introduce a novel approach to query answering called “NMiner.” NMiner takes advantage of heuristics to find answers to complex semantic queries. It uses a combination of natural language processing techniques to parse sentences and extract entities, hypertext structure of the documents to derive relational information, and semantic web data to extract relevant entities as search result candidates. Further, a bimodal network of sentences and entities is created from the search result candidates. Content Centric Ranking (CCR) and Cumulative Structural Similarity (CSS), are proposed to rank the candidate entities. Our empirical study on the ClueWeb09 corpus (with approximately 25 terabytes of web documents) shows that both CSS and CCR outperform PageRank and HITS. Moreover, NMiner proved to be significant in solving the problem of answering complex queries performed against a largely unstructured corpus of text documents.
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Martha, V., Wallace, S., Bisgin, H., Xu, X., Agarwal, N., Joshi, H. (2012). NMiner: A System for Finding Related Entities by Mining a Bimodal Network. In: Hou, Y., Nie, JY., Sun, L., Wang, B., Zhang, P. (eds) Information Retrieval Technology. AIRS 2012. Lecture Notes in Computer Science, vol 7675. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35341-3_30
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