Informatica

In the legal domain, ontologies organize legal concepts and their relationships, while knowledge graphs connect these concepts to specific entities in legal documents. This study proposes a solution for integrating ontology and knowledge graph, called Legal-Onto model, to construct a knowledge base of an intelligent retrieval system in the legal domain. The Legal-Onto model combines ontology as the conceptual layer and knowledge graphs as the implementation layer for representing the content of legal documents. This relational model is integrated with a structure of knowledge graph to identify relations between concepts and entities extracted from ontology in the determined domain. Moreover, this research addresses inherent challenges in semantic-based knowledge-driven search. The specific objective is to accurately extract relevant information from legal documents to respond to entered queries. The experimental results show that this method is more effective than state-of-the-art methods in natural language processing and large language models, which are without specific legal domain knowledge.

ProObj is a Prolog based system for knowledge representation which was strongly influenced by object-oriented and frame-based systems. The paper shortly describes ProObj and then presents a classification mechanism which is based on the ideas of classifiers in KL-ONE like systems.

Rule-based systems are usually interpreted as a shallow expert systems realization tool. The paper analyses how the applicability of production rules can be extended using the proposed rule base structuring discipline. Its main constructions are rule grouping according to elementary aspects of investigation, and decomposition of actions. In addition, the rule cycle construction is used for discrete time simulation tasks. The proposed method is illustrated by 2 applications: the expert subsystem for a database, and the simulator of a water heater.