Linking and Validating Nordic and Baltic Wordnets - A Multilingual Action in META-NORD

This project report describes a multilingual wordnet initiative embarked in the META-NORD project and concerned with the validation and pilot linking between Nordic and Baltic wordnets. The builders of these wordnets have applied very different compilation strategies: The Danish, Icelandic and Swedish wordnets are being developed via monolingual dictionaries and corpora and subsequently linked to Princeton WordNet. In contrast, the Finnish and Norwegian wordnets are applying the expand method by translating from Princeton WordNet and the Danish wordnet, DanNet, respectively. The Estonian wordnet was built as part of the EuroWordNet project and by translating the base concepts from English as a first basis for monolingual extension. The aim of the multilingual action is to test the perspective of a multilingual linking of the Nordic and Baltic wordnets and via this (pilot) linking to perform a tentative comparison and validation of the wordnets along the measures of taxonomical structure, coverage, granularity and completeness.Peer reviewe

Onto.PT: Automatic Construction of a Lexical Ontology for Portuguese

This ongoing research presents an alternative to the man- ual creation of lexical resources and proposes an approach towards the automatic construction of a lexical ontology for Portuguese. Tex- tual sources are exploited in order to obtain a lexical network based on terms and, after clustering and mapping, a wordnet-like lexical on- tology is created. At the end of the paper, current results are shown

Decorrelation and shallow semantic patterns for distributional clustering of nouns and verbs

Distributional approximations to lexical semantics are very useful not only in helping the creation of lexical semantic resources (Kilgariff et al., 2004; Snow et al., 2006), but also when directly applied in tasks that can benefit from large-coverage semantic knowledge such as coreference resolution (Poesio et al., 1998; Gasperin and Vieira, 2004; Versley, 2007), word sense disambiguation (Mc- Carthy et al., 2004) or semantical role labeling (Gordon and Swanson, 2007). We present a model that is built from Webbased corpora using both shallow patterns for grammatical and semantic relations and a window-based approach, using singular value decomposition to decorrelate the feature space which is otherwise too heavily influenced by the skewed topic distribution of Web corpora

OntoAna: Domain Ontology for Human Anatomy

Today, we can find many search engines which provide us with information which is more operational in nature. None of the search engines provide domain specific information. This becomes very troublesome to a novice user who wishes to have information in a particular domain. In this paper, we have developed an ontology which can be used by a domain specific search engine. We have developed an ontology on human anatomy, which captures information regarding cardiovascular system, digestive system, skeleton and nervous system. This information can be used by people working in medical and health care domain.Comment: Proceedings of 5th CSI National Conference on Education and Research. Organized by Lingayay University, Faridabad. Sponsored by Computer Society of India and IEEE Delhi Chapter. Proceedings published by Lingayay University Pres

Automatic Extraction of Semantic Relations for Less­Resourced Languages

Proceedings of the NODALIDA 2009 workshop WordNets and other Lexical Semantic Resources — between Lexical Semantics, Lexicography, Terminology and Formal Ontologies. Editors: Bolette Sandford Pedersen, Anna Braasch, Sanni Nimb and Ruth Vatvedt Fjeld. NEALT Proceedings Series, Vol. 7 (2009), 1-6. © 2009 The editors and contributors. Published by Northern European Association for Language Technology (NEALT) http://omilia.uio.no/nealt . Electronically published at Tartu University Library (Estonia) http://hdl.handle.net/10062/9209

Ontology Enrichment from Free-text Clinical Documents: A Comparison of Alternative Approaches

While the biomedical informatics community widely acknowledges the utility of domain ontologies, there remain many barriers to their effective use. One important requirement of domain ontologies is that they achieve a high degree of coverage of the domain concepts and concept relationships. However, the development of these ontologies is typically a manual, time-consuming, and often error-prone process. Limited resources result in missing concepts and relationships, as well as difficulty in updating the ontology as domain knowledge changes. Methodologies developed in the fields of Natural Language Processing (NLP), Information Extraction (IE), Information Retrieval (IR), and Machine Learning (ML) provide techniques for automating the enrichment of ontology from free-text documents. In this dissertation, I extended these methodologies into biomedical ontology development. First, I reviewed existing methodologies and systems developed in the fields of NLP, IR, and IE, and discussed how existing methods can benefit the development of biomedical ontologies. This previously unconducted review was published in the Journal of Biomedical Informatics. Second, I compared the effectiveness of three methods from two different approaches, the symbolic (the Hearst method) and the statistical (the Church and Lin methods), using clinical free-text documents. Third, I developed a methodological framework for Ontology Learning (OL) evaluation and comparison. This framework permits evaluation of the two types of OL approaches that include three OL methods. The significance of this work is as follows: 1) The results from the comparative study showed the potential of these methods for biomedical ontology enrichment. For the two targeted domains (NCIT and RadLex), the Hearst method revealed an average of 21% and 11% new concept acceptance rates, respectively. The Lin method produced a 74% acceptance rate for NCIT; the Church method, 53%. As a result of this study (published in the Journal of Methods of Information in Medicine), many suggested candidates have been incorporated into the NCIT; 2) The evaluation framework is flexible and general enough that it can analyze the performance of ontology enrichment methods for many domains, thus expediting the process of automation and minimizing the likelihood that key concepts and relationships would be missed as domain knowledge evolves