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Contextual Text Mining Framework for Unstructured Textual Judicial Corpora through Ontologies

Zubair Nabi1, Ramzan Talib1,*, Muhammad Kashif Hanif1, Muhammad Awais2

1 Department of Computer Science, Government College University, Faisalabad, 38000, Pakistan
2 Department of Software Engineering, Government College University, Faisalabad, 38000, Pakistan

* Corresponding Author: Ramzan Talib. Email:

Computer Systems Science and Engineering 2022, 43(3), 1357-1374.


Digitalization has changed the way of information processing, and new techniques of legal data processing are evolving. Text mining helps to analyze and search different court cases available in the form of digital text documents to extract case reasoning and related data. This sort of case processing helps professionals and researchers to refer the previous case with more accuracy in reduced time. The rapid development of judicial ontologies seems to deliver interesting problem solving to legal knowledge formalization. Mining context information through ontologies from corpora is a challenging and interesting field. This research paper presents a three tier contextual text mining framework through ontologies for judicial corpora. This framework comprises on the judicial corpus, text mining processing resources and ontologies for mining contextual text from corpora to make text and data mining more reliable and fast. A top-down ontology construction approach has been adopted in this paper. The judicial corpus has been selected with a sufficient dataset to process and evaluate the results. The experimental results and evaluations show significant improvements in comparison with the available techniques.


Cite This Article

Z. Nabi, R. Talib, M. Kashif Hanif and M. Awais, "Contextual text mining framework for unstructured textual judicial corpora through ontologies," Computer Systems Science and Engineering, vol. 43, no.3, pp. 1357–1374, 2022.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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