Abstract
When developing information-analytical systems (IAS) for various purposes it is often necessary to gather thematic facts which are of interest to experts in the field. The paper presents an approach that allows one to increase the completeness of fact extraction by using basic domain knowledge. The main idea of the approach is deriving new facts on the basis of facts explicitly stated in the text and basic knowledge contained in the corresponding ontologies. An architecture and algorithms of the system are discussed. The approach is illustrated by an example of extracting relevant facts using inference rules.
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References
Feldman, R., Sanger, J.: The Text Mining Textbook: Advanced Approaches in Analyzing Unstructured Data. Cambridge Univ. Press (2007)
Wimalasuriya, D., Dou, D.: Ontology-based information extraction: An introduction and a survey of current approaches. J. of Inf. Science 36(3), 306–323 (2010)
Anantharangachar, R., Ramani, S., Rajagopalan, S.: Ontology Guided Information Extraction from Unstructured Text. Int. J. of Web & Sem. Tech. 4(1), 19–36 (2013)
Buitelaar, P., Cimiano, P., Frank, A., Hartung, M., Racioppa, S.: Ontology-based Information Extraction and Integration from Heterogeneous Data Sources. Int. J. of Human Computer Studies 66, 759–788 (2008)
Petasis, G., Möller, R., Karkaletsis, V.: BOEMIE: Reasoning-based Information Extraction. In: Proceedings of the 1st Workshop on Natural Language Processing and Automated Reasoning, pp. 60–75 (2013)
Suchanek, F.M., Sozio, M., Weikum, G.: SOFIE: A self-organizing framework for information extraction. In: Proceedings of the 18th International Conference on World Wide Web, Madrid, Spain, pp. 631–640 (2009)
Apache Http Client, http://hc.apache.org
Apache Tika, http://tika.apache.org/
GATE: General Architecture for Text Engineering, https://gate.ac.uk/
Apache Open NLP, https://opennlp.apache.org/
Apache Lucene, http://lucene.apache.org
Apache Jena Core, https://jena.apache.org/documentation/rdf/
Apache Jena SDB, http://jena.apache.org/documentation/sdb/
A PROMISING HIGH-SPEED HELICOPTER (PSV) V-37, http://bastion-karpenko.ru/v-37_psv/
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Yelagina, N., Panteleyev, M. (2014). Deriving of Thematic Facts from Unstructured Texts and Background Knowledge. In: Klinov, P., Mouromtsev, D. (eds) Knowledge Engineering and the Semantic Web. KESW 2014. Communications in Computer and Information Science, vol 468. Springer, Cham. https://doi.org/10.1007/978-3-319-11716-4_18
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DOI: https://doi.org/10.1007/978-3-319-11716-4_18
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11715-7
Online ISBN: 978-3-319-11716-4
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