Ontology–based Representation of Simulation Models

Ontologies have been used in a variety of domains for multiple purposes such as establishing common terminology, organizing domain knowledge and describing domain in a machine-readable form. Moreover, ontologies are the foundation of the Semantic Web and often semantic integration is achieved using ontology. Even though simulation demonstrates a number of similar characteristics to Semantic Web or semantic integration, including heterogeneity in the simulation domain, representation and semantics, the application of ontology in the simulation domain is still in its infancy. This paper proposes an ontology-based representation of simulation models. The goal of this research is to facilitate comparison among simulation models, querying, making inferences and reuse of existing simulation models. Specifically, such models represented in the domain simulation engine environment serve as an information source for their representation as instances of an ontology. Therefore, the ontology-based representation is created from existing simulation models in their proprietary file formats, consequently eliminating the need to perform the simulation modeling directly in the ontology. The proposed approach is evaluated on a case study involving the I2Sim interdependency simulator

Extending document models to incorporate semantic information for complex standards

This paper presents the concept of hybrid semantic-document models to aid information management when using standards for complex technical domains such as military data communication. These standards are traditionally text based documents for human interpretation, but prose sections can often be ambiguous and can lead to discrepancies and subsequent implementation problems. Many organisations will produce semantic representations of the material to ensure common understanding and to exploit computer aided development. In developing these semantic representations, no relationship is maintained to the original prose. Maintaining relationships between the original prose and the semantic model has key benefits, including assessing conformance at a semantic level rather than prose, and enabling original content authors to explicitly define their intentions, thus reducing ambiguity and facilitating computer aided functionality. A framework of relationships is proposed which can integrate with common document modeling techniques and provide the necessary functionality to allow semantic content to be mapped into document views. These relationships are then generalised for applicability to a wider context

Cheating to achieve Formal Concept Analysis over a large formal context

International audienceResearchers are facing one of the main problems of the Information Era. As more articles are made electronically available, it gets harder to follow trends in the different domains of research. Cheap, coherent and fast to construct knowledge models of research domains will be much required when information becomes unmanageable. While Formal Concept Analysis (FCA) has been widely used on several areas to construct knowledge artifacts for this purpose (Ontology development, Information Retrieval, Software Refactoring, Knowledge Discovery), the large amount of documents and terminology used on research domains makes it not a very good option (because of the high computational cost and humanly-unprocessable output). In this article we propose a novel heuristic to create a taxonomy from a large term-document dataset using Latent Semantic Analysis and Formal Concept Analysis. We provide and discuss its implementation on a real dataset from the Software Architecture community obtained from the ISI Web of Knowledge (4400 documents)

Knowledge-enhanced latent semantic indexing (KELSI): algorithms and applications

Latent Semantic Indexing (LSI) is a popular information retrieval model for concept-based searching. As with many vector space IR models, LSI requires an existing term-document association structure such as a term-by-document ma-trix. The term-by-document matrix, constructed during document parsing, can only capture weighted vocabulary occurrence patterns in the documents. How-ever, for many knowledge domains (e.g., medicine) there are pre-existing semantic structures that could be used to organize and to categorize information. The goals of this study are to demonstrate how such semantic structures can be incorporated into the LSI vector space model and to measure their overall effect on query match-ing performance. The new approach, called Knowledge-Enhanced LSI (KELSI), is applied to documents in the OHSUMED medical abstracts using the semantic structures provided by the UMLS Semantic Network and MeSH. Results based on precision-recall graphs and 11-point average precision values (P) indicate that a MeSH-enhanced search index is capable of delivering noticeable incremental performance gain over the original LSI model - 28% improvement for P=.01 and 100% improvement for P=.30. This performance gain is achieved by replacing the original query with the MeSH heading extracted from the query text via regular expression matchs