Troels Andreasen

On Measuring Similarity for Conceptual Querying

The focus of this paper is approaches to measuring similarity for application in connection with query evaluation. Rather than only comparing at the level of words the issue here is to compare concepts that appear as compound expressions derived from list of words through brief natural language analysis. Concepts refers to and are compared with respect to an ontology describing the domain of the database. We discuss three different principles for measuring similarity between concepts. One in the form of subsumption expansion of concepts and two as different measures of distance in a graphical representation of an ontology.

Perspectives on ontology‐based querying

In this article, we introduce principles for ontology‐based querying of information bases. We consider a framework in which a basis ontology over atomic concepts in combination with a concept language defines a generative ontology. Concepts are assumed to be the basis for an index of the information base, in the sense that these concepts are attached to objects in the information base. Concepts are thus applied to obtain a means for descriptions that generalize classical word‐based information base indexing. We discuss how the ontology influences the matching of values, especially how the different relations of the ontology may contribute to overall similarity between concepts. Further, we discuss a set of major properties to improve a given similarity measures accordance with the semantics of the ontology, and use these properties to guide the choice of function. Finally we implement a prototype search system to evaluate the chosen approach.

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.

On the Weakening of Fuzzy Relational Queries

In this paper, we present a cooperative approach intended to avoid empty answers to conjunctive fuzzy relational queries. We propose a weakening mechanism generating more tolerant queries. This consists in relaxing one or several fuzzy predicates with the help of a fuzzy linguistic modifier which is used to build a lattice of weakened queries expressing a semantic distance between these queries. Selectivity is then used as a heuristic to guide through the lattice, in order to find the query with a non-empty answer which is as close as possible to the initial one.

Grammatical specification of domain ontologies

This paper presents a formalism supporting the analysis and specification of domain ontologies. The method is founded theoretically on conventional context-free grammars. The use of production rules admits recursive formation of compound categories from given categories subjected to combinability constraints.The proposed domain specification methodology is applied to ontology-guided content-based information retrieval in text databases. It is advanced also as a general purpose methodology for ontological engineering.

On Querying Ontologies and Databases

This paper concerns the motivation for and subsequently the analysis of proposed additions, in the form of new operators, to a concept language Ontolog for use in querying a content-based text retrieval system. The expressiveness of the proposed query language introduces the possibility for querying both objects in the base but also mechanisms for direct querying of the ontology, and it furthermore enables the end-user to tailor the evaluation principle of the system by influencing query expansion in the ontology.

Similarity from conceptual relations

The main focus of this paper is how to measure similarity in a content-based information retrieval environment. In the first part we define the information base, which is a generative framework where an ontology in combination with a concept language defines a set of well-formed concepts. Well-formed concepts is assumed to be the basis for an indexing of the information base in the sense that these concepts appear in descriptions attached to objects in the base. Subsequent and last we introduce an approach for measuring similarity in this framework. The measuring problem is divided into to continuous parts where we first narrow what concepts have in common, and secondly use this fragment, a similarity graph, for calculating the similarity between concepts. The purpose of narrowing or restricting what concepts have in common is to manage the generative aspect of the ontology, and to retain the greatest possible number of shared attributes and characteristics of the concepts being compared. Taking the similarity graphs as input we discuss what properties a similarity function need to satisfy to measure the degree of similarity proportional to how close the concepts are or how much they share.

An approach to knowledge-based query evaluation

Abstract We describe here a query evaluation principle based on hierarchical aggregation. Queries are assumed to be free form expressions of lists of words unconnected or forming fragments of natural language (NL). A query is transformed into a nested structure of subsets of attributes. During query evaluation, each subset is treated for individual aggregation and aggregates are thus in turn aggregated, leading to an aggregate for measuring the full query against an object from the database. The key idea with the described principle is to obtain a knowledge-based query evaluation. This is obtained by initiating the query evaluation process through manipulation based on knowledge in a domain-specific knowledge base. An experimental prototype system implementing this line of query evaluation is described.