A crowdsourcing approach to building a legal ontology from text

A crowdsourcing approach to building a legal ontology from text by Anatoly P. Getman and Volodymyr V. Karasiuk.


This article focuses on the problems of application of artificial intelligence to represent legal knowledge. The volume of legal knowledge used in practice is unusually large, and therefore the ontological knowledge representation is proposed to be used for semantic analysis, presentation and use of common vocabulary, and knowledge integration of problem domain. At the same time some features of legal knowledge representation in Ukraine have been taken into account. The software package has been developed to work with the ontology. The main features of the program complex, which has a Web-based interface and supports multi-user filling of the knowledge base, have been described. The crowdsourcing method is due to be used for filling the knowledge base of legal information. The success of this method is explained by the self-organization principle of information. However, as a result of such collective work a number of errors are identified, which are distributed throughout the structure of the ontology. The results of application of this program complex are discussed in the end of the article and the ways of improvement of the considered technique are planned.

Curious how you would compare this attempt to extract an ontology from legal texts to the efforts in the 1960’s and 1970’s to extract logic from the United States Internal Revenue Code? Apologies but my undergraduate notes aren’t accessible so I can’t give you article titles and citations.

If you do dig out some of that literature, pointers would be appreciated. As I recall, capturing the “logic” of those passages was fraught with difficulty.

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