1,721,042 research outputs found
Ontology Learning from Text: An Overview
Full text linkBuitelaar P, Cimiano P, Magnini B. Ontology Learning from Text: An Overview. In: Buitelaar P, Cimiano P, Magnini B, eds. Ontology Learning from Text: Methods, Evaluation and Applications. Frontiers in Artificial Intelligence and Applications. Vol 123. Amsterdam: IOS Press; 2005: 3-12
Ontology Lexicalization: The lemon perspective
Full text linkBuitelaar P, Cimiano P, McCrae J, Montiel-Ponsoda E, Declerck T. Ontology Lexicalization: The lemon perspective. In: Proceedings of the Workshops - 9th International Conference on Terminology and Artificial Intelligence (TIA 2011). 2011: 33-36
LexOnto: A Model for Ontology Lexicons for Ontology-based NLP
Full text linkCimiano P, Haase P, Herold M, Mantel M, Buitelaar P. LexOnto: A Model for Ontology Lexicons for Ontology-based NLP. In: Proceedings of the OntoLex07 Workshop held in conjunction with ISWC’07. 2007
Enhancing Statistical Machine Translation with Bilingual Terminology in a CAT Environment
No full textLearning Taxonomic Relations from Heterogeneous Sources of Evidence
Full text linkCimiano P, Pivk A, Schmidt-Thieme L, Staab S. Learning Taxonomic Relations from Heterogeneous Sources of Evidence. In: Buitelaar P, Cimiano P, Magnini B, eds. Ontology Learning from Text: Methods, Evaluation and Applications. Frontiers in Artificial Intelligence; 123. Amsterdam: IOS Press; 2005: 59-73
Towards a Language Infrastructure for the Semantic Web
No full textIn recent years, the Internet evolved from a global medium for information exchange (directed mainly towards human users) into a "global, virtual work environment" (for both human users and machines). Building on the world-wide-web, developments such as grid technology, web services and the semantic web contributed to this transformation, the implications of which are now slowly but clearly being integrated into all areas of the new digital society (e-business, e-government, e-science, etc.) In this conctext the semantic web allows for increasingly intelligent and therefore autonomous processing. This development brings new challenges for Human Language Technology (HLT), which require not only some adaptation of processes within the state of the art processing chain of HLT, but also changes at the infrastructure level of HLT resources
LingInfo: Design and Applications of a Model for the Integration of Linguistic Information in Ontologies
Full text linkBuitelaar P, Declerck T, Frank A, et al. LingInfo: Design and Applications of a Model for the Integration of Linguistic Information in Ontologies. In: Proceedings of the OntoLex Workshop at LREC. ELRA; 2006: 28-32
Evaluation dataset and methodology for extracting application-specific taxonomies from the wikipedia knowledge graph
No full textIn this work, we address the task of extracting application-specific taxonomies from the category hierarchy of Wikipedia. Previous work on pruning the Wikipedia knowledge graph relied on silver standard taxonomies which can only be automatically extracted for a small subset of domains rooted in relatively focused nodes, placed at an intermediate level in the knowledge graphs. In this work, we propose an iterative methodology to extract an application-specific gold standard dataset from a knowledge graph and an evaluation framework to comparatively assess the quality of noisy automatically extracted taxonomies. We employ an existing state-of-the-art algorithm in an iterative manner and we propose several sampling strategies to reduce the amount of manual work needed for evaluation. A first gold standard dataset is released to the research community for this task along with a companion evaluation framework. This dataset addresses a real-world application from the medical domain, namely the extraction of food-drug and herb-drug interactions
SemEval-2015 Task 17: Taxonomy Extraction Evaluation (TExEval)
Full text linkThis paper describes the first shared task on Taxonomy Extraction Evaluation organised as part of SemEval-2015. Participants were asked to find hypernym-hyponym relations between given terms. For each of the four selected target domains the participants were provided with two lists of domain-specific terms: a WordNet collection of terms and a well-known terminology extracted from an online publicly available taxonomy. A total of 45 taxonomies submitted by 6 participating teams were evaluated using standard structural measures, the structural similarity with a gold standard taxonomy, and through manual quality assessment of sampled novel relations
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