DocumentCode
2727290
Title
An ontology-based rule chaining algorithm for legal expert systems
Author
Förhécz, András ; Strausz, György
Author_Institution
Multilogic Kft., Budapest, Hungary
fYear
2011
fDate
21-22 Nov. 2011
Firstpage
443
Lastpage
447
Abstract
In the field of law the most prominent tasks are legal assessment and qualification. These tasks aim at determining whether a legal case is allowed and which legal categories does it fulfil given an appropriate body of legal norms. We have developed a legal modelling framework called Emerald, which builds on Semantic Web standards and is able to solve these legal problems even providing means for information collection. Our proposed formal modelling approach of Emerald uses OWL 2 description logic combined with SWRL rules to answer legal qualification problems. In this paper we present an algorithm to collect input case specification from end users by conducting an interactive dialogue. A backward chaining algorithm generates relevant questions with respect to the qualification problem, taking into consideration any partial case description available and other constraints in description logic.
Keywords
expert systems; formal logic; formal specification; interactive systems; knowledge representation languages; law administration; ontologies (artificial intelligence); qualifications; semantic Web; Emerald; OWL 2 description logic; SWRL rules; formal modelling; input case specification; interactive dialogue; legal assessment; legal case; legal expert systems; legal modelling framework; legal norms; legal qualification; ontology based rule chaining algorithm; semantic Web standards; Inference algorithms; Knowledge based systems; Law; OWL; Ontologies; Semantics; description logic; information collection; rule systems;
fLanguage
English
Publisher
ieee
Conference_Titel
Computational Intelligence and Informatics (CINTI), 2011 IEEE 12th International Symposium on
Conference_Location
Budapest
Print_ISBN
978-1-4577-0044-6
Type
conf
DOI
10.1109/CINTI.2011.6108546
Filename
6108546
Link To Document