An information retrieval approach to ontology mapping

In this paper, we present a heuristic mapping method and a prototype mapping system that support the process of semi-automatic ontology mapping for the purpose of improving semantic interoperability in heterogeneous systems. The approach is based on the idea of semantic enrichment, i.e., using instance information of the ontology to enrich the original ontology and calculate similarities between concepts in two ontologies. The functional settings for the mapping system are discussed and the evaluation of the prototype implementation of the approach is reported. \ud \u

COMPLIANCE TO QUALITY CRITERIA OF EXISTING REQUIREMENTS ELICITATION METHODS

In this article we define a requirements elicitation method based on natural language modelling. We argue that our method complies with synthesized quality criteria for RE methods, and compare this with the compliance of traditional RE methods (EER, ORM, UML). We show limited empirical evidence to support our theoretical argument.computer science applications;

Artificial Intuition for Automated Decision-Making

Automated decision-making techniques play a crucial role in data science, AI, and general machine learning. However, such techniques need to balance accuracy with computational complexity, as their solution requirements are likely to need exhaustive analysis of the potentially numerous events combinations, which constitute the corresponding scenarios. Intuition is an essential tool in the identification of solutions to problems. More specifically, it can be used to identify, combine and discover knowledge in a “parallel” manner, and therefore more efficiently. As a consequence, the embedding of artificial intuition within data science is likely to provide novel ways to identify and process information. There is extensive research on this topic mainly based on qualitative approaches. However, due to the complexity of this field, limited quantitative models and implementations are available. In this article, the authors have extended the evaluation to include a real-world, multi-disciplinary area in order to provide a more comprehensive assessment. The results demonstrate the value of artificial intuition, when embedded in decision-making and information extraction models and frameworks. In fact, the output produced by the approach discussed in their article was compared with a similar task carried out by a group of experts in the field. This demonstrates comparable results further showing the potential of this framework, as well as artificial intuition as a tool for decision-making and information extraction

Natural Language Analysis for Semantic Document Modeling

Abstract. To ease the retrieval of documents published on the Web, the documents should be classified in a way that users find helpful and meaningful. This paper presents an approach to semantic document classification and retrieval based on Natural Language Analysis and Conceptual Modeling. A conceptual domain model is used in combination with linguistic tools to define a controlled vocabulary for a document collection. Users may browse this domain model and interactively classify documents by selecting model fragments that describe the contents of the documents. Natural language tools are used to analyze the text of the documents and propose relevant model fragments in terms of selected domain model concepts and named relations. The fragments proposed are refined by the users and stored as document descriptions in RDF-XML format. For document retrieval, lexical analysis is used to pre-process search expressions and map these to the domain model for manual query-refinement. A prototype of the system is described, and the approach is illustrated with examples from a document collection published by the Norwegian Center for Medical Informatics (KITH). 1