A Linguistic Ontology for the Semantic Web (original) (raw)

Natural Language Processing provides a very significant contribution to various research areas such as the e-health, e-business, education and antiterrorism. However, understanding the meaning, scope and usage of linguistic knowledge is a tedious task for a heterogeneity of potential users. Several approaches have been proposed to represent heterogeneous linguistic knowledge covering specific features of some languages. However, these approaches focused only on the data aspect of linguistic knowledge and neglected the processing one. Moreover, most of them do not support multilingualism and lack of powerful semantic representation and reasoning abilities. In this paper, we propose a multilingual linguistic domain ontology, called LingOnto, that represents and reasons about (1) linguistic data, (2) linguistic processing functionalities and (3) linguistic processing features. Our ontology supports English, French and Arabic languages and can be used by linguistically under-skilled use...

Understanding the differences underlying the scope, usage and content of language data requires the provision of a clarifying terminological basis which is integrated in the metadata describing a particular language resource. While terminological resources such as the SIL Glossary of Linguistic Terms, ISOcat or the GOLD ontology provide a considerable amount of linguistic terms, their practical usage is limited to a look up of a defined term whose relation to other terms is unspecified or insufficient. Therefore, in this paper we propose an ontology for linguistic terminology, called OnLiT. It is a data model which can be used to represent linguistic terms and concepts in a semantically interrelated data structure and, thus, overcomes prevalent isolating definition-based term descriptions. OnLiT is based on the LiDo Glossary of Linguistic Terms and enables the creation of RDF datasets, that represent linguistic terms and their meanings within the whole or a subdomain of linguistics.

Abstract Ontologies provide formal models for representing domain knowledge, which reveal to be useful in several contexts where efficient organization of available data and an shared understanding of its content reveals to be crucial. The Semantic Web offers the most appropriate scenario for exploiting ontologies' potentialities, due to the large amount of information which is to be exposed and accessed.

Abstract We introduce here a framework for adding Linguistic Expressivity to conceptual knowledge, as represented in ontologies. Both the multilingual aspects which characterize the (Semantic) Web and the demand for more easy-to-share forms of knowledge representation, being equally accessible by humans and machines, push in fact the need for a linguistically motivated approach to ontology development.

This paper implements the first process of the task ontology of a dynamic semantic ontological framework (MODS) for the Semantic Web, presented in [1], which is the lexical-morphological analysis. MODS is an interpreter of natural language queries to the semantic web. For its implementation, we design and develop a lexical- morphological analysis, which interacts with the lexicon, as well as the MODS learning component, which updates dynamically the lexicon in the case that a lexical component is not present in it. In this paper is presented the process of lexical-morphological analysis as part of the task ontology of the MODS.

The Web already passed the level of syntax, now the web is Semantics. Semantic gives the web new way of information description and structure, by meaning "reference" and structure "relations" Computers are now able to identify a word, idea, or concept and recognize the relations that connect it with other words, ideas, or concepts.

The aim of natural language ontology is to uncover the ontological categories and structures that are implicit in the use of natural language. This paper aims to clarify what exactly the subject matter of natural language ontology is, what sorts of linguistic data it should take into account, how natural language ontology relates to other sorts of projects in metaphysics, in what ways natural language ontology is important, and in what ways the ontology of natural language may be driven by the use of natural language itself.