Acquiring Domain-Specific Knowledge for WordNet from a Terminological Database

In this research we explore a terminological database (Termoteca) in order to expand the Portuguese and Galician wordnets (PULO and Galnet) with the addition of new synset variants (word forms for a concept), usage examples for the variants, and synset glosses or definitions. The methodology applied in this experiment is based on the alignment between concepts of WordNet (synsets) and concepts described in Termoteca (terminological records), taking into account the lexical forms in both resources, their morphological category and their knowledge domains, using the information provided by the WordNet Domains Hierarchy and the Termoteca field domains to reduce the incidence of polysemy and homography in the results of the experiment. The results obtained confirm our hypothesis that the combined use of the semantic domain information included in both resources makes it possible to minimise the problem of lexical ambiguity and to obtain a very acceptable index of precision in terminological information extraction tasks, attaining a precision above 89% when there are two or more different languages sharing at least one lexical form between the synset in Galnet and the Termoteca record

The impact of geographic location on the development of a specialty field. A case study on Sloan Digital Sky Survey in Astronomy.

International audienceWe analyze the scientific discourse of researchers in a specialty field in Astronomy by examining the influence that geographic location may have on the development of this field. Using as a case study the Sloan Digital Sky Survey (SDSS) pro- ject, we analyzed texts from bibliographic records along three geographic axes: US-only publications, non-US publications and international collaboration. Each geographic region reflected authors affiliated to research institutions in that region. Interna- tional collaboration refers to papers published by both US-based and non-US based institutions. Through clustering of domain terms used in titles and abstracts fields of the bibliographic records, we were able to automatically identify the topology of to- pics peculiar to each geographic region and identify the research topics common to the three geographic zones. The results showed that US-only and non-US research in SDSS shared more commonalities with international collaboration than with one another, thus indicating that the former two focused on rather distinct topics

Mapping data elements to terminological resources for integrating biomedical data sources

BACKGROUND: Data integration is a crucial task in the biomedical domain and integrating data sources is one approach to integrating data. Data elements (DEs) in particular play an important role in data integration. We combine schema- and instance-based approaches to mapping DEs to terminological resources in order to facilitate data sources integration. METHODS: We extracted DEs from eleven disparate biomedical sources. We compared these DEs to concepts and/or terms in biomedical controlled vocabularies and to reference DEs. We also exploited DE values to disambiguate underspecified DEs and to identify additional mappings. RESULTS: 82.5% of the 474 DEs studied are mapped to entries of a terminological resource and 74.7% of the whole set can be associated with reference DEs. Only 6.6% of the DEs had values that could be semantically typed. CONCLUSION: Our study suggests that the integration of biomedical sources can be achieved automatically with limited precision and largely facilitated by mapping DEs to terminological resources

GRISP: A Massive Multilingual Terminological Database for Scientific and Technical Domains

International audienceThe development of a multilingual terminology is a very long and costly process. We present the creation of a multilingual terminological database called GRISP covering multiple technical and scientific fields from various open resources. A crucial aspect is the merging of the different resources which is based in our proposal on the definition of a sound conceptual model, different domain mapping and the use of structural constraints and machine learning techniques for controlling the fusion process. The result is a massive terminological database of several millions terms, concepts, semantic relations and definitions. This resource has allowed us to improve significantly the mean average precision of an information retrieval system applied to a large collection of multilingual and multidomain patent documents

Challenges for the Multilingual Web of Data

The Web has witnessed an enormous growth in the amount of semantic information published in recent years. This growth has been stimulated to a large extent by the emergence of Linked Data. Although this brings us a big step closer to the vision of a Semantic Web, it also raises new issues such as the need for dealing with information expressed in different natural languages. Indeed, although the Web of Data can contain any kind of information in any language, it still lacks explicit mechanisms to automatically reconcile such information when it is expressed in ifferent languages. This leads to situations in which data expressed in a certain language is not easily accessible to speakers of other languages. The Web of Data shows the potential for being extended to a truly multilingual web as vocabularies and data can be published in a language-independent fashion, while associated language-dependent (linguistic) information supporting the access across languages can be stored separately. In this sense, the multilingual Web of Data can be realized in our view as a layer of services and resources on top of the existing Linked Data infrastructure adding i) linguistic information for data and vocabularies in different languages, ii) mappings between data with labels in different languages, and iii) services to dynamically access and traverse Linked Data across different languages. In this article we present this vision of a multilingual Web of Data. We discuss challenges that need to be addressed to make this vision come true and discuss the role that techniques such as ontology localization, ontology mapping, and cross-lingual ontology-based information access and presentation will play in achieving this. Further, we propose an initial architecture and describe a roadmap that can provide a basis for the implementation of this vision

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

Coping with Alternate Formulations of Questions and Answers

We present in this chapter the QALC system which has participated in the four TREC QA evaluations. We focus here on the problem of linguistic variation in order to be able to relate questions and answers. We present first, variation at the term level which consists in retrieving questions terms in document sentences even if morphologic, syntactic or semantic variations alter them. Our second subject matter concerns variation at the sentence level that we handle as different partial reformulations of questions. Questions are associated with extraction patterns based on the question syntactic type and the object that is under query. We present the whole system thus allowing situating how QALC deals with variation, and different evaluations