Article

Text Mining through Entity-Relationship Based Information Extraction

Authors:

Lipika Dey,

Muhammad Abulaish,

Jahiruddin,

Gaurav SharmaAuthors Info & Claims

WI-IATW '07: Proceedings of the 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Workshops

Pages 177 - 180

Published: 02 November 2007 Publication History

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Abstract

In this paper we describe an information extraction and text mining system which identifies key information components from text documents. The information components are centered on domain entities and their relationships. The components mined from a repository are chained using an n-gram-based algorithm. The information chains provide a comprehensive view of the collection and can be also used for inferential reasoning.

References

[1]

Beeferman, D., Lexical discovery with an enriched semantic network, in: Proceedings of the ACL/COLING Workshop on Applications of WordNet in Natural Language Processing Systems, 1998, pp. 358-364.

Google Scholar

[2]

Ciaramita, M., Gangemi, A., Ratsch, E., Saric, J., Rojas, I., Unsupervised Learning of Semantic Relations between Concepts of a Molecular Biology Ontology, in: Proceedings of the 19th International Joint Conference on Artificial Intelligence (IJCAI'05), pp. 659-664.

Digital Library

Google Scholar

[3]

Feldman, R. & Dagan, I.: 1995, Knowledge Discovery in Textual Databases, in: Proceedings of KDD'95, pp. 112-117.

Google Scholar

[4]

Friedman, C., Kra, P., Yu, H., Krauthammer, M., A. Rzhetsky, GENIES: A Natural-Language Processing System for the Extraction of Molecular Pathways from Journal Articles, Bioinformatics, vol. 17, Suppl. 1, 2001, pp. s74-s82.

Crossref

Google Scholar

[5]

Fukuda, K., Tsunoda, T., Tamura, A., Takagi, T., Toward Information Extraction: Identifying Protein Names from Biological papers, in: Proceedings of the Pacific Symposium on Biocomputing, Hawaii, 1998, pp. 707-718.

Google Scholar

[6]

Hearst, M. A., Untangling Text Data Mining, in: Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics, College Park, Maryland, 1999, pp. 3-10.

Digital Library

Google Scholar

[7]

Jenssen, T. -K., Laegreid, A., Komorowski, J., Hovig, E., A Literature Network of Human Genes for High-Throughput Analysis of Gene Expression, Nat. Genet 2001, pp. 28: 21-28.

Google Scholar

[8]

Sekimizu, T., Park, H. S., Tsujii, J., Identifying the Interactions Between Genes and Genes Products based on Frequently Seen Verbs in MEDLINE Abstract, Genome Informatics 9, 1998, pp. 62-71.

Google Scholar

[9]

Swanson, D. R., and Smalheiser, N. R., An interactive system for finding complementary literatures: a stimulus to scientific discovery, Artificial Intelligence 91, 1997, pp. 183-203.

Digital Library

Google Scholar

Cited By

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Wu Y(2018)Enriching a thesaurus as a better question-answering tool and information retrieval aidJournal of Information Science10.1177/016555151770621944:4(512-525)Online publication date: 1-Aug-2018
https://dl.acm.org/doi/10.1177/0165551517706219
Mehmood RMaurer HWeippl EIndrawan-Santiago MSteinbauer MKotsis GKhalil I(2013)Aspects of Information Integration in a WikiProceedings of International Conference on Information Integration and Web-based Applications & Services10.1145/2539150.2539230(575-579)Online publication date: 2-Dec-2013
https://dl.acm.org/doi/10.1145/2539150.2539230

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Text Mining through Entity-Relationship Based Information Extraction

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Published In

WI-IATW '07: Proceedings of the 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Workshops

November 2007

513 pages

ISBN:0769530281

Publisher

IEEE Computer Society

United States

Publication History

Published: 02 November 2007

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Wu Y(2018)Enriching a thesaurus as a better question-answering tool and information retrieval aidJournal of Information Science10.1177/016555151770621944:4(512-525)Online publication date: 1-Aug-2018
https://dl.acm.org/doi/10.1177/0165551517706219
Mehmood RMaurer HWeippl EIndrawan-Santiago MSteinbauer MKotsis GKhalil I(2013)Aspects of Information Integration in a WikiProceedings of International Conference on Information Integration and Web-based Applications & Services10.1145/2539150.2539230(575-579)Online publication date: 2-Dec-2013
https://dl.acm.org/doi/10.1145/2539150.2539230

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Index Terms

Recommendations

Automatic office document classification and information extraction

Logical structure based semantic relationship extraction from semi-structured documents

Text association mining with cross-sentence inference, structure-based document model and multi-relational text mining