Semi-automatic ontology construction for improving comprehension of legal documents

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Abstract

In this paper we present a new method based on semi-automatic ontology construction that can be used to improve the understandability of legal documents. Legal documents typically extensively define requirements and procedures in a specific legislative area; usually, they are hard to comprehend for citizens without a proper legal knowledge. However, a vast majority of today's e-Government activities for citizens (G2C) are governed by legal documents. Therefore, by improving the citizen's comprehension many intricacies that occasionally occur during G2C activities can be avoided. Our method first divides a legal document into several paragraphs. From the paragraphs it semi-automatically constructs ontology of the field by using a tool OntoGen. Ontology concepts are then used to classify each paragraph and the resulting classification is visualized in a simple matrix, where rows represent paragraphs and columns represent top-level ontology concepts. Based on the visualization, paragraphs that need revising are identified; they can be relocated to more suitable context within the document or rewritten using more appropriate wording. We demonstrate the presented method on the document defining the tender for selling flats at favorable prices at the Housing Fund of the Republic of Slovenia, a public fund. We argue that by using the new method we were able to substantially improve the comprehension of the document. In addition, the constructed ontology helped the Fund's officers improve the structure of their knowledge about the underlying business process. © Springer-Verlag Berlin Heidelberg 2008.

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APA

Cestnik, B., Kern, A., & Modrijan, H. (2008). Semi-automatic ontology construction for improving comprehension of legal documents. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5184 LNCS, pp. 328–339). https://doi.org/10.1007/978-3-540-85204-9_28

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