Computational ontologies that provide an explicit specification of a conceptualization are an important area of computer science and artificial intelligence. Their role in AI is to provide people, or more typically artificial agents, with structured and navigable knowledge about entities and their inter-relations. Ontologies can help people share and reference knowledge about concepts in general or specialist areas, in one or more languages. Ontologies can also be used for information technology tasks such as semantic searches, interoperability between systems, or to facilitate reasoning and problem solving.

The legal domain features some peculiarities that render necessary the design of ad-hoc computational ontologies, called legal ontologies. Laws are written in “legalese” – a domain-specific sublanguage that inherits all the expressivity and ambiguity of natural language with additional terms of its own that are often obscure, subject to changes over time, contextually defined, ill-defined (i.e., some ‘obvious’ elements are not made explicit), subject to interpretation to deal with their vagueness, defined in incompatible ways in different legal sources, or difficult to translate into different languages.

Most legal ontologies are axiomatic ontologies usually written in the Web Ontology Language (OWL), standard; some exceptions have been proposed in the literature, i.e., some non-axiomatic (lightweight) ontologies, notably the European Legal Taxonomy Syllabus.

University of Luxembourg, together with the University of Bologna, is currently developing a legal ontology for the Data Protection domain in the context of the FNR/CORE project DAPRECO.