Rasmus Knappe

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.

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.

Similarity for Conceptual Querying

The focus of this paper is approaches to measuring similarity for application in content-based query evaluation. Rather than only comparing at the level of words, the issue here is conceptual resemblance. The basis is a knowledge base defining major concepts of the domain and may include taxonomic and ontological domain knowledge. The challenge for support of queries in this context is an evaluation principle that on the one hand respects the formation rules for concepts in the concept language and on the other is sufficiently efficient to candidate as a realistic principle for query evaluation. We present and discuss principles where efficiency is obtained by reducing the matching problem – which basically is a matter of conceptual reasoning – to numerical similarity computation.

On automatic modeling and use of domain-specific ontologies

In this paper, we firstly introduce an approach to the modeling of a domain-specific ontology for use in connection with a given document collection. Secondly, we present a methodology for deriving conceptual similarity from the domain-specific ontology. Adopted for ontology representation is a specific lattice-based concept algebraic language by which ontologies are inherently generative. The modeling of a domain specific ontology is based on a general ontology built upon common knowledge resources as dictionaries and thesauri. Based on analysis of concept occurrences in the object document collection the general ontology is restricted to a domain specific ontology encompassing concepts instantiated in the collection. The resulting domain specific ontology and similarity can be applied for surveying the collection through key concepts and conceptual relations and provides a means for topic-based navigation. Finally, a measure of concept similarity is derived from the domain specific ontology based on occurrences, commonalities, and distances in the ontology.

Perspectives on ontology-based querying: Research Articles

Over the years database management systems have evolved to include spatially referenced data. Because spatial data are complex and have a number of unique constraints (i.e., spatial components and uncertain properties), spatial database systems can be effective only if the spatial data are properly handled at the physical level. Therefore, it is important to develop an effective spatial and aspatial indexing technique to facilitate flexible spatial and/or aspatial querying for such databases. For this purpose we introduce an indexing approach to use (fuzzy) spatial and (fuzzy) aspatial data. We use a number of spatial index structures, such as Multilevel Grid File (MLGF), G-tree, R-tree, and Ra-tree, for fuzzy spatial databases and compare the performances of these structures for various flexible queries.