Hanne Erdman Thomsen

Content-based text querying with ontological descriptors

This paper describes a method and a system for content-based querying of texts based on the availability of an ontology for the concepts in the text domain. A key principle in the system is the extraction of conceptual content of noun phrases into descriptors forming an integral part of the ontology.The retrieval of text passages rests on matching descriptors from the text against descriptors from the noun phrases in the query. The match does not need to be exact but is mediated by the ontology invoking in particular taxonomic reasoning with sub- and super-concepts. The paper also reports on a prototype implementation of the system.

Ontology-Based Querying

In this paper we introduce an approach to exploit knowledge represented in an ontology in answers to queries to an information base. We assume that the ontology is embedded in a knowledge base covering the domain of the information base. The ontology is first of all to influence ranking of objects in answers to queries as measured by similarity to the query. We consider a generative framework where an ontology in combination with a concept language defines a set of well-formed concepts. Wellformed concepts is assumed to be the basis for an indexing of the information base in the sense that these concepts appear as descriptors attached to objects in the base. Concepts are thus applied to obtain a means for descriptions that generalizes simple word-based information base indexing. In effect query evaluation is generalized to be a matter of comparison at the level of concepts rather than words.

Ontological Extraction of Content for Text Querying

This paper describes a method and a system ONTOQUERY for content-based querying of texts based on the availability of an ontology for the concepts in the text domain. A key principle in the system is the extraction of conceptual content of noun phrases into descriptors forming an integral part of the ontology.The retrieval of text passages rests on matching descriptors from the text against descriptors from the noun phrases in the query. The match need not be exact but is mediated by the ontology, invoking in particular taxonomic reasoning with sub- and super concepts. The paper also reports on a prototype implementation of the system.

Terminological concept modelling and conceptual data modelling

Ontologies are useful for many purposes. The use of an ontology is, for example, crucial for writing consistent definitions of concepts within a specific domain. In this paper, we will argue that the principles of rigorous terminology work are useful for building consistent ontologies. In many cases, developers of IT systems encounter severe problems, because they neglect the necessity of developing a proper ontology (concept model) before they develop a conceptual data model as a basis for an IT system. In this paper, we will argue that the development of an ontology is crucial for setting up a conceptual data model, and therefore it should always be added as an initial stage to data modelling. Also we will give some examples of the mapping between ontologies and conceptual data models. Future research will reveal to what extent it will be possible to set up rules for automatic mapping of concepts of an ontology into classes and attributes of a conceptual data model.