Deriving concept hierarchies from text
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Automatic acquisition of hyponyms from large text corpora
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
Automatic Discovery of Part-Whole Relations
Computational Linguistics
Ontology Learning and Population from Text: Algorithms, Evaluation and Applications
Ontology Learning and Population from Text: Algorithms, Evaluation and Applications
Ontology Matching
Word sense disambiguation: A survey
ACM Computing Surveys (CSUR)
SOFIE: a self-organizing framework for information extraction
Proceedings of the 18th international conference on World wide web
Learning Expressive Ontologies
Proceedings of the 2008 conference on Ontology Learning and Population: Bridging the Gap between Text and Knowledge
International Journal of Human-Computer Studies
Deriving a large scale taxonomy from Wikipedia
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
DBpedia - A crystallization point for the Web of Data
Web Semantics: Science, Services and Agents on the World Wide Web
Unsupervised learning of semantic relations between concepts of a molecular biology ontology
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
An efficient and scalable algorithm for segmented alignment of ontologies of arbitrary size
Web Semantics: Science, Services and Agents on the World Wide Web
A method to combine linguistic ontology-mapping techniques
ISWC'05 Proceedings of the 4th international conference on The Semantic Web
Matching unstructured vocabularies using a background ontology
EKAW'06 Proceedings of the 15th international conference on Managing Knowledge in a World of Networks
Hi-index | 0.01 |
When multiple ontologies are used within one application system, aligning the ontologies is a prerequisite for interoperability and unhampered semantic navigation and search. Various methods have been proposed to compute mappings between elements from different ontologies, the majority of which being based on various kinds of similarity measures. As a major shortcoming of these methods it is difficult to decode the semantics of the results achieved. In addition, in many cases they miss important mappings due to poorly developed ontology structures or dissimilar ontology designs. I propose a complementary approach making massive use of relation extraction techniques applied to broad-coverage text corpora. This approach is able to detect different types of semantic relations, dependent on the extraction techniques used. Furthermore, exploiting external background knowledge, it can detect relations even without clear evidence in the input ontologies themselves.