Information-Flow-Based Ontology Mapping
On the Move to Meaningful Internet Systems, 2002 - DOA/CoopIS/ODBASE 2002 Confederated International Conferences DOA, CoopIS and ODBASE 2002
PROMPT: Algorithm and Tool for Automated Ontology Merging and Alignment
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Hierarchy as a new data type for qualitative variables
Expert Systems with Applications: An International Journal
Measuring the understanding between two agents through concept similarity
Expert Systems with Applications: An International Journal
Towards automatic merging of domain ontologies: The HCONE-merge approach
Web Semantics: Science, Services and Agents on the World Wide Web
A multi-strategy knowledge interoperability framework for heterogeneous learning objects
Expert Systems with Applications: An International Journal
Techniques for merging views of software processes
Graph transformations and model-driven engineering
Multi-agent ontology-based Web 2.0 platform for medical rehabilitation
Expert Systems with Applications: An International Journal
An approach for selecting seed URLs of focused crawler based on user-interest ontology
Applied Soft Computing
Hi-index | 12.05 |
In order to compute intelligent answers to complex questions, using the vast amounts of information existing in the Web, computers have (1) to translate such knowledge, typically from text documents, into a data structure suitable for automatic exploitation; (2) to accumulate enough knowledge about a certain topic or area by integrating or fusing these data structures, taking into account new information, additional details, better precision, synonyms, homonyms, redundancies, apparent contradictions and inconsistencies found in the incoming data structures to be added; and (3) to perform deductions from that amassed body of knowledge, most likely through a general query processor. This article seeks to solve point (2) by using a method (OM, Ontology Merging), with its algorithm and implementation, to fuse two ontologies (coming from Web documents) without human intervention, producing a third ontology, taking into account the inconsistencies, contradictions and redundancies between them, thus delivering an answer close to reality. Results of OM working on ontologies extracted from Web documents are shown.