Abstract
Knowledge bases are essential in develo** expert intelligent systems to solve domain specific problems. The low availability of detailed knowledge bases is a byproduct of the fact that addition and reasoning of information entities is largely a manual process. This paper proposes an architecture for automatic generation of knowledge bases from various heterogenous sources. Criminology has been chosen as a domain of choice because extensive knowledge in this area can help in curbing crimes in our society by creating preventive and predictive analysis systems. The proposed architecture utilizes seed criminology ontology as a base and adds information via multiple inputs i.e., Google’s news API, criminology journals, e-books, web portals and available datasets. The approach incorporates classification using LSTM and alignment of the instances using transformers. Furthermore, reasoning has been done using description logics and a large extensive knowledge base has been obtained. The model yields an average accuracy of 95.35% which surpasses the previous approaches.
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Chhatwal, G.S., Deepak, G. (2022). Integrative KnowGen: Integrative Knowledge Base Generation for Criminology as a Domain of Choice. In: Motahhir, S., Bossoufi, B. (eds) Digital Technologies and Applications. ICDTA 2022. Lecture Notes in Networks and Systems, vol 455. Springer, Cham. https://doi.org/10.1007/978-3-031-02447-4_49
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