Title
Common Representational Model And Ontologies For Effective Law Enforcement Solutions
Abstract
Ontologies have developed into a prevailing technique for establishing semantic interoperability among heterogeneous systems transacting information. An ontology is an unambiguous blueprint of a concept. For Artificial Intelligence, only the defined notions can be considered existent. Thus, in relation to AI, an ontology can be understood as part of a program which delineates a collection of descriptions. An ontology, therefore, correlates the labels of the entities in the universe of discourse with wording that holds meaning for humans, explaining what those labels signify, along with the precise principles that force the interpretation and semantic utilization of these labels. An ontology constitutes a proper statement of a logical theory. It is a crucial component of a system with the capability to process, manage, analyze, correlate and reason from the large datasets characterized by heterogeneity. This paper depicts the process of development of a Common Representational Model (CRM) on top of several ontologies, taxonomies and classifications to facilitate computational and data mining functionalities. The building blocks of said CRM are delineated in detail, as well as its application in a specific use case.
Year
DOI
Venue
2020
10.1142/S2196888820020017
VIETNAM JOURNAL OF COMPUTER SCIENCE
Keywords
Field
DocType
Ontology, artificial intelligence, common representational model, semantic interoperability, correlation
Ontology (information science),Data science,Computer science,Artificial intelligence,Law enforcement,Machine learning
Journal
Volume
Issue
ISSN
7
1
2196-8888
Citations 
PageRank 
References 
0
0.34
0
Authors
12