7,156 research outputs found
Graph-based discovery of ontology change patterns
Ontologies can support a variety of purposes, ranging from capturing conceptual knowledge to the organisation of digital content and information. However, information systems are always subject to change and ontology change management can pose challenges. We investigate ontology change representation and discovery of change patterns.
Ontology changes are formalised as graph-based change logs. We use attributed graphs, which are typed over a generic graph with node and edge attribution.We analyse ontology change logs, represented as graphs, and identify frequent change sequences. Such sequences are applied as a reference in order to discover reusable, often domain-specific and usagedriven change patterns. We describe the pattern discovery algorithms and measure their performance using experimental result
A layered framework for pattern-based ontology evolution
The challenge of ontology-driven modelling of information
components is well known in both academia and industry. In this paper, we present a novel approach to deal with customisation and abstraction of ontology-based model evolution. As a result of an empirical study, we identify a layered change operator framework based on the granularity,
domain-speciïŹcity and abstraction of changes. The implementation of the operator framework is supported through layered change logs. Layered change logs capture the objective of ontology changes at a higher level of granularity and support a comprehensive understanding of ontology evolution. The layered change logs are formalised using a graph-based approach. We identify the recurrent ontology change patterns from an ontology change log for their reuse. The identiïŹed patterns facilitate optimizing and improving the deïŹnition of domain-speciïŹc change patterns
RDF Knowledge Graph Visualization From a Knowledge Extraction System
In this paper, we present a system to visualize RDF knowledge graphs. These
graphs are obtained from a knowledge extraction system designed by
GEOLSemantics. This extraction is performed using natural language processing
and trigger detection. The user can visualize subgraphs by selecting some
ontology features like concepts or individuals. The system is also
multilingual, with the use of the annotated ontology in English, French, Arabic
and Chinese
Dealing with uncertain entities in ontology alignment using rough sets
This is the author's accepted manuscript. The final published article is available from the link below. Copyright @ 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.Ontology alignment facilitates exchange of knowledge among heterogeneous data sources. Many approaches to ontology alignment use multiple similarity measures to map entities between ontologies. However, it remains a key challenge in dealing with uncertain entities for which the employed ontology alignment measures produce conflicting results on similarity of the mapped entities. This paper presents OARS, a rough-set based approach to ontology alignment which achieves a high degree of accuracy in situations where uncertainty arises because of the conflicting results generated by different similarity measures. OARS employs a combinational approach and considers both lexical and structural similarity measures. OARS is extensively evaluated with the benchmark ontologies of the ontology alignment evaluation initiative (OAEI) 2010, and performs best in the aspect of recall in comparison with a number of alignment systems while generating a comparable performance in precision
Expert System for Crop Disease based on Graph Pattern Matching: A proposal
Para la agroindustria, las enfermedades en cultivos constituyen uno de los problemas mĂĄs frecuentes que generan grandes pĂ©rdidas econĂłmicas y baja calidad en la producciĂłn. Por otro lado, desde las ciencias de la computaciĂłn, han surgido diferentes herramientas cuya finalidad es mejorar la prevenciĂłn y el tratamiento de estas enfermedades. En este sentido, investigaciones recientes proponen el desarrollo de sistemas expertos para resolver este problema haciendo uso de tĂ©cnicas de minerĂa de datos e inteligencia artificial, como inferencia basada en reglas, ĂĄrboles de decisiĂłn, redes bayesianas, entre otras. AdemĂĄs, los grafos pueden ser usados para el almacenamiento de los diferentes tipos de variables que se encuentran presentes en un ambiente de cultivos, permitiendo la aplicaciĂłn de tĂ©cnicas de minerĂa de datos en grafos, como el emparejamiento de patrones en los mismos. En este artĂculo presentamos una visiĂłn general de las temĂĄticas mencionadas y una propuesta de un sistema experto para enfermedades en cultivos, basado en emparejamiento de patrones en grafos.For agroindustry, crop diseases constitute one of the most common problems that generate large economic losses and low production quality. On the other hand, from computer science, several tools have emerged in order to improve the prevention and treatment of these diseases. In this sense, recent research proposes the development of expert systems to solve this problem, making use of data mining and artificial intelligence techniques like rule-based inference, decision trees, Bayesian network, among others. Furthermore, graphs can be used for storage of different types of variables that are present in an environment of crops, allowing the application of graph data mining techniques like graph pattern matching. Therefore, in this paper we present an overview of the above issues and a proposal of an expert system for crop disease based on graph pattern matching
QUAL : A Provenance-Aware Quality Model
The research described here is supported by the award made by the RCUK Digital Economy program to the dot.rural Digital Economy Hub; award reference: EP/G066051/1.Peer reviewedPostprin
- âŠ