On the role of ontologies in information extraction

On the role of ontologies in information extraction. Sen, S., Tao, J., & Deokar, A., V. Annals of Information Systems (Special Issue: Reshaping Society through Analytics, Collaboration, and Decision Support: Role of Business Intelligence and Social Media), Springer, 2014.
abstract bibtex

The ubiquity of unstructured/semi-structured data in business decision-making presents a unique challenge as data management methods developed for structured data are not directly applicable. While such non-traditional data have already become part of many organizations’ product/service offerings; most data managers admit that they lack the capability to leverage such data assets to elicit meaningful information. In this context, we discuss the use of Information Extraction (IE) methodologies to aid in the decision making process that utilizes un/semi-structured data. We focus on knowledge-based IE methodologies that are particularly suitable for business domains characterized by few subject matter experts’ tacit and uncodified domain knowledge. Ontologies that encapsulate and represent domain knowledge can play a key role in enabling knowledge-based IE. In this article we conduct a comprehensive review of the extant literature on Ontology-Based Information Extraction (OBIE) and articulate four different roles ontologies play in such knowledge-based IE systems. We discuss these various roles of ontologies in relation to the various IE phases and illustrate them with a case study involving IT service contracts, which is an example of a OBIE system. Finally, we discuss open research issues related to the use of ontologies, evaluation metrics, and applications of IE in decision-making.

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 abstract = {The ubiquity of unstructured/semi-structured data in business decision-making presents a unique challenge as data management methods developed for structured data are not directly applicable. While such non-traditional data have already become part of many organizations’ product/service offerings; most data managers admit that they lack the capability to leverage such data assets to elicit meaningful information. In this context, we discuss the use of Information Extraction (IE) methodologies to aid in the decision making process that utilizes un/semi-structured data. We focus on knowledge-based IE methodologies that are particularly suitable for business domains characterized by few subject matter experts’ tacit and uncodified domain knowledge. Ontologies that encapsulate and represent domain knowledge can play a key role in enabling knowledge-based IE. In this article we conduct a comprehensive review of the extant literature on Ontology-Based Information Extraction (OBIE) and articulate four different roles ontologies play in such knowledge-based IE systems. We discuss these various roles of ontologies in relation to the various IE phases and illustrate them with a case study involving IT service contracts, which is an example of a OBIE system. Finally, we discuss open research issues related to the use of ontologies, evaluation metrics, and applications of IE in decision-making.},
 bibtype = {article},
 author = {Sen, Sagnika and Tao, Jie and Deokar, Amit V.},
 journal = {Annals of Information Systems (Special Issue: Reshaping Society through Analytics, Collaboration, and Decision Support: Role of Business Intelligence and Social Media)}
}

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