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Building a business domain meta-ontology for information pre-processing

Yevgen Biletskiy and J Anthony Brown and Girish R Ranganathan and Ebrahim Bagheri and Ismail Akbari
Reference:
Yevgen Biletskiy; J Anthony Brown; Girish R Ranganathan; Ebrahim Bagheri and Ismail Akbari Building a business domain meta-ontology for information pre-processing. In Information Processing Letters, 2018.
Links to Publication: [www]
Abstract:
Business analysts, along with other business domain software application users, have created a vast amount of business documents, which often do not have any business domain ontologies in the background. This situation leads to misinterpretation of such documents, when being processed by machines, that results in inhibiting the productiveness of computer-assisted analytical work and effectiveness of business solutions due to lack of effective semantics; therefore, business analysts (especially, if rotating) can use well-designed business domain ontologies as a backbone for their official applications. The process of extracting and capturing domain ontologies from these voluminous documents requires extensive involvement of domain experts and application of methods of ontology learning that is substantially labor intensive; therefore, some intermediate solutions which would assist in capturing business domain ontologies must be developed. The present paper proposes a solution in this direction which involves building a meta-ontology as a rapid approach in conceptualizing a business domain from huge amounts of source documents. This meta-ontology can be populated by ontological concepts, attributes and relations from business documents, and then refined in order to form better business domain ontology either through automatic ontology learning methods or some other traditional ontology building approaches.
Bibtex Entry:
@article{ipl2018, author = {Yevgen Biletskiy and J Anthony Brown and Girish R Ranganathan and Ebrahim Bagheri and Ismail Akbari}, title = {Building a business domain meta-ontology for information pre-processing}, journal = {Information Processing Letters}, url = {https://www.journals.elsevier.com/information-processing-letters}, year = {2018}, abstract = {Business analysts, along with other business domain software application users, have created a vast amount of business documents, which often do not have any business domain ontologies in the background. This situation leads to misinterpretation of such documents, when being processed by machines, that results in inhibiting the productiveness of computer-assisted analytical work and effectiveness of business solutions due to lack of effective semantics; therefore, business analysts (especially, if rotating) can use well-designed business domain ontologies as a backbone for their official applications. The process of extracting and capturing domain ontologies from these voluminous documents requires extensive involvement of domain experts and application of methods of ontology learning that is substantially labor intensive; therefore, some intermediate solutions which would assist in capturing business domain ontologies must be developed. The present paper proposes a solution in this direction which involves building a meta-ontology as a rapid approach in conceptualizing a business domain from huge amounts of source documents. This meta-ontology can be populated by ontological concepts, attributes and relations from business documents, and then refined in order to form better business domain ontology either through automatic ontology learning methods or some other traditional ontology building approaches.} }




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