Search results for "Ontology-based data integration"
showing 10 items of 30 documents
Data Mining of Specific-Domain Ontology Components
2008
This paper describes an approach for eliciting ontology components by using knowledge maps. The knowledge contained in a particular domain, any kind of text digital archive, is portrayed by assembling and displaying its ontology components.
Semi-automatic Derivation of Specific-Domain Ontologies for the Semantic Web
2006
This paper describes an approach for helping in the semi-automatic construction of specific-domain ontology components contained in a digital archive. This proposal for extracting knowledge from digital sources allows users to have a view of this knowledge and visualize specific-domain ontology components that with further processing can be shared with software agents by embedding it into digital archives themselves in the context of the Semantic Web. In particular, we deal with the issue of not constructing the ontology from scratch, our approach helps us to speed up the ontology creation process.
An ontology-based retrieval system for mammographic reports
2015
In healthcare domain it can be useful to compare unstructured free-text clinical reports in order to enable the search for similar and/or relevant clinical cases. In data mining and text analysis tasks, the cosine similarity is usually used for texts comparison purposes. It is usually performed by computing the standard document vector cosine similarity between the two vectors representing the report pair under analysis. In this paper a novel system based on text pre-processing techniques and a modelled medical knowledge, using an improved radiological ontology, is proposed. Medical terms organized in a hierarchical tree can assess semantic similarity relationships between unstructured repo…
Learning Path Generation by Domain Ontology Transformation
2005
An approach to automated learning path generation inside a domain ontology supporting a web tutoring system is presented. Even if a terminological ontology definition is needed in real systems to enable reasoning and/or planning techniques, and to take into account the modern learning theories, the task to apply a planner to such an ontology is very hard because the definition of actions along with their preconditions and effects has to take into account the semantics of the relations among concepts, and it results in building an ontology of learning. The proposed methodology is inspired to the Knowledge Space Theory, and proposes some heuristics to transform the original ontology in a weig…
Ontology languages for the semantic web: A never completely updated review
2006
This paper gives a never completely account of approaches that have been used for the research community for representing knowledge. After underlining the importance of a layered approach and the use of standards, it starts with early efforts used for artificial intelligence researchers. Then recent approaches, aimed mainly at the semantic web, are described. Coding examples from the literature are presented in both sections. Finally, the semantic web ontology creation process, as we envision it, is introduced.
Ontology-Based Information System
2014
We describe a novel way for creating information systems based on ontologies. The described solution is aimed at domain experts who would benefit from being able to quickly prototype fully-functional, web-based information system for data input, editing and analysis. The systems backbone is SPARQL 1.1 endpoint that enables organization users to view and edit the data, while outside users can get read-only access to the endpoint. The system prototype is implemented and successfully tested with Latvian medical data ontology with 60 classes and imported 5 000 000 data-level triples.
Adaptive Learning Process for the Evolution of Ontology-Described Classification Model in Big Data Context
2016
International audience; One of the biggest challenges in Big Data is to exploit value from large volumes of variable and changing data. For this, one must focus on analyzing the data in these Big Data sources and classify the data items according to a domain model (e.g. an ontology). To automatically classify unstructured text documents according to an ontology, a hierarchical multi-label classification process called Semantic HMC was proposed. This process uses ontologies to describe the classification model. To prevent cold start and user overload, the classification process automatically learns the ontology-described classification model from a very large set of unstructured text documen…
Semantic oriented data structuration for MABS Application to BIM
2013
International audience; This paper presents a multiagent-based simulation approach to qualify the usage of buildings from the design phase. Our approach combines ontology and evolution process based on machine learning algorithms. The ontology relies on semantic data structures for the representation of environment components, agent knowledge and all data generated during the simulation.
Representing and Reasoning for Spatiotemporal Ontology Integration
2004
International audience; The World-Wide Web hosts many autonomous and heterogeneous information sources. In the near future each source may be described by its own ontology. The distributed nature of ontology development will lead to a large number of local ontologies covering overlapping domains. Ontology integration will then become an essential capability for effective interoperability and information sharing. Integration is known to be a hard problem, whose complexity increases particularly in the presence of spatiotemporal information. Space and time entail additional problems such as the heterogeneity of granularity used in representing spatial and temporal features. Spatio-temporal ob…
HOWERD: A Hidden Markov Model for Automatic OWL-ERD Alignment
2016
The HOWERD model for estimating the most likely alignment between an OWL ontology and an Entity Relation Diagram (ERD) is presented. Automatic alignment between relational schema and ontology represents a big challenge in Semantic Web research due to the different expressiveness of these representations. A relational schema is less expressive than the ontology; this is a non trivial problem when accessing data via an ontology and for ontology storing by means of a relational schema. Existent alignment methodologies fail in loosing some contents of the involved representations because the ontology captures more semantic information, and several elements are left unaligned. HOWERD relies on a…