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There is an exponential growth in user-generated contents in the form of customer reviews on the Web. But, most of the contents are stored in either unstructured or semi-structured format due to which distillation of knowledge from this huge repository is a challenging task. In addition, on analysis we found that most of the users use fuzzy terms instead of crisp terms to express opinions on product features. Considering these facts, in this paper, we present an opinion-based query answering framework which mines product features and opinionated words to handle user queries over review documents. The proposed framework uses BK-FIRM (Bandler-Kohout Fuzzy Information Retrieval Model) that facilitates the formulation of imprecise queries using linguistic qualifiers, retrieves relevant opinion documents, and presents them in the order of their degree of relevance. The efficacy of the system is established through experiments over customer reviews on different models of digital camera, and mp3 player. [ABSTRACT FROM AUTHOR]

In this paper, we present an opinion mining system to identify product features and opinions from review documents. The features and opinions are extracted using semantic and linguistic analysis of text documents. The polarity of opinion sentences is established using polarity scores of the opinion words through Senti-WordNet to generate a feature-based summary of review documents. The system is also integrated with a visualization module to present feature-based summary of review documents in a comprehendible way. [ABSTRACT FROM AUTHOR]

In this paper, we present a relation mining and visualization framework to identify important semi-structured information components using semantic and linguistic analysis of text documents. The novelty of the paper lies in identifying key snippet from text to validate the interaction between a pair of entities. The extracted information components are exploited to generate semantic network which provides distinct user perspectives and allows navigation over documents with similar information components. The efficacy of the proposed framework is established through experiments carried out on biomedical text documents extracted through PubMed search engine. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel approach for finding association rules from locally frequent itemsets using rough set and boolean reasoning. The rules mined so are termed as local association rules. The efficacy of the proposed approach is established through experiment over retail dataset that contains retail market basket data from an anonymous Belgian retail store. [ABSTRACT FROM AUTHOR]

In this paper, we present a rough-fuzzy ontology generation framework which supports rough concept descriptions at multiple levels of granularity, using fuzzy property descriptors. The rough approximations for concept descriptions and valid fuzzy property descriptors can be obtained through text-mining. [ABSTRACT FROM AUTHOR]

Semantic frameworks can be used to improve the accuracy and expressiveness of natural language processing for the purpose of extracting meaning from text documents. Such a framework represents knowledge using semantic networks and can be generated using information mined from text documents. The key issue however is to identify relevant concepts and their inter-relationships. In this paper, we have presented a scheme for semantic integration of information extracted from text documents. The extraction principle is based on linguistic and semantic analysis of text. Entities and relations are extracted using Natural Language Processing techniques. A method for collating information extracted from multiple sources to generate the semantic net is also presented. The efficacy of the proposed semantic framework is established through experiments carried out for visualizing information embedded in biomedical texts extracted from PubMed database. [ABSTRACT FROM AUTHOR]

In this paper we investigate the problem of enriching an existing biological concept ontology into a fuzzy relational ontology structure using generic biological relations and their strengths mined from tagged biological text documents. Though biological relations in a text are defined between a pair of entities, the entities are usually tagged by their concept names in a tagged corpus. Since the tags themselves are related taxonomically, as given in the ontology, the mined relations have to be properly characterized before entering them into the ontology. We have proposed a mechanism to generalize each relation to be defined at the most appropriate level of specificity, before it can be added to the ontology. Since the mined relations have varying degrees of associations with various biological concepts, an appropriate fuzzy membership generation mechanism is proposed to fuzzify the strengths of the relations. Extensive experimentation has been conducted over the entire GENIA corpus and the results of enhancing the GENIA ontology are presented in the paper. [ABSTRACT FROM AUTHOR]

Biological information embedded within the large repository of unstructured or semi-structured text documents can be extracted more efficiently through effective semantic analysis of the texts in collaboration with structured domain knowledge. The GENIA corpus houses tagged MEDLINE abstracts, manually annotated according to the GENIA ontology, for this purpose. However, manual tagging of all texts is impossible and special purpose storage and retrieval mechanisms are required to reduce information overload for users. In this paper we have proposed an ontology-based biological Information Extraction and Query Answering (BIEQA) system that has four components: an ontology-based tag analyzer for analyzing tagged texts to extract Biological and lexical patterns, an ontology-based tagger for tagging new texts, a knowledge base enhancer which enhances the ontology, and incorporates new knowledge in the form of biological entities and relationships into the knowledge base, and a query processor for handling user queries. Keywords: Ontology-based text mining, Biological information extraction, Automatic tagging. [ABSTRACT FROM AUTHOR]

In this paper we have proposed a system that performs both ontology-based text information extraction and ontology update using the extracted information. The system employs text-mining techniques to mine information from text documents guided by an underlying ontology. It also enhances the existing ontology with new concepts and their descriptors which may be precise and/ or imprecise, mined from the text. All extracted information related to concepts and concept descriptors are also stored in a structured knowledge base. Keywords: Text mining, Information extraction, Ontology enhancement. [ABSTRACT FROM AUTHOR]