Natural language processing

Beginning with the basic issues of NLP, this chapter aims to chart the major research activities in this area since the last ARIST Chapter in 1996 (Haas, 1996), including: (i) natural language text processing systems - text summarization, information extraction, information retrieval, etc., including domain-specific applications; (ii) natural language interfaces; (iii) NLP in the context of www and digital libraries ; and (iv) evaluation of NLP systems

PhenDisco: phenotype discovery system for the database of genotypes and phenotypes.

The database of genotypes and phenotypes (dbGaP) developed by the National Center for Biotechnology Information (NCBI) is a resource that contains information on various genome-wide association studies (GWAS) and is currently available via NCBI's dbGaP Entrez interface. The database is an important resource, providing GWAS data that can be used for new exploratory research or cross-study validation by authorized users. However, finding studies relevant to a particular phenotype of interest is challenging, as phenotype information is presented in a non-standardized way. To address this issue, we developed PhenDisco (phenotype discoverer), a new information retrieval system for dbGaP. PhenDisco consists of two main components: (1) text processing tools that standardize phenotype variables and study metadata, and (2) information retrieval tools that support queries from users and return ranked results. In a preliminary comparison involving 18 search scenarios, PhenDisco showed promising performance for both unranked and ranked search comparisons with dbGaP's search engine Entrez. The system can be accessed at http://pfindr.net

User evaluation of a pilot terminologies server for a distributed multi-scheme environment

The present paper reports on a user-centred evaluation of a pilot terminology service developed as part of the High Level Thesaurus (HILT) project at the Centre for Digital Library Research (CDLR) in the University of Strathclyde in Glasgow. The pilot terminology service was developed as an experimental platform to investigate issues relating to mapping between various subject schemes, namely Dewey Decimal Classification (DDC), Library of Congress Subject Headings (LCSH), the Unesco thesaurus, and the MeSH thesaurus, in order to cater for cross-browsing and cross-searching across distributed digital collections and services. The aim of the evaluation reported here was to investigate users' thought processes, perceptions, and attitudes towards the pilot terminology service and to identify user requirements for developing a full-blown pilot terminology service

Improving Knowledge Retrieval in Digital Libraries Applying Intelligent Techniques

Nowadays an enormous quantity of heterogeneous and distributed information is stored in the digital University. Exploring online collections to find knowledge relevant to a user’s interests is a challenging work. The artificial intelligence and Semantic Web provide a common framework that allows knowledge to be shared and reused in an efficient way. In this work we propose a comprehensive approach for discovering E-learning objects in large digital collections based on analysis of recorded semantic metadata in those objects and the application of expert system technologies. We have used Case Based-Reasoning methodology to develop a prototype for supporting efficient retrieval knowledge from online repositories. We suggest a conceptual architecture for a semantic search engine. OntoUS is a collaborative effort that proposes a new form of interaction between users and digital libraries, where the latter are adapted to users and their surroundings

Improving average ranking precision in user searches for biomedical research datasets

Availability of research datasets is keystone for health and life science study reproducibility and scientific progress. Due to the heterogeneity and complexity of these data, a main challenge to be overcome by research data management systems is to provide users with the best answers for their search queries. In the context of the 2016 bioCADDIE Dataset Retrieval Challenge, we investigate a novel ranking pipeline to improve the search of datasets used in biomedical experiments. Our system comprises a query expansion model based on word embeddings, a similarity measure algorithm that takes into consideration the relevance of the query terms, and a dataset categorisation method that boosts the rank of datasets matching query constraints. The system was evaluated using a corpus with 800k datasets and 21 annotated user queries. Our system provides competitive results when compared to the other challenge participants. In the official run, it achieved the highest infAP among the participants, being +22.3% higher than the median infAP of the participant's best submissions. Overall, it is ranked at top 2 if an aggregated metric using the best official measures per participant is considered. The query expansion method showed positive impact on the system's performance increasing our baseline up to +5.0% and +3.4% for the infAP and infNDCG metrics, respectively. Our similarity measure algorithm seems to be robust, in particular compared to Divergence From Randomness framework, having smaller performance variations under different training conditions. Finally, the result categorization did not have significant impact on the system's performance. We believe that our solution could be used to enhance biomedical dataset management systems. In particular, the use of data driven query expansion methods could be an alternative to the complexity of biomedical terminologies

Thesaurus-assisted search term selection and query expansion: a review of user-centred studies

This paper provides a review of the literature related to the application of domain-specific thesauri in the search and retrieval process. Focusing on studies which adopt a user-centred approach, the review presents a survey of the methodologies and results from empirical studies undertaken on the use of thesauri as sources of term selection for query formulation and expansion during the search process. It summaries the ways in which domain-specific thesauri from different disciplines have been used by various types of users and how these tools aid users in the selection of search terms. The review consists of two main sections covering, firstly studies on thesaurus-aided search term selection and secondly those dealing with query expansion using thesauri. Both sections are illustrated with case studies that have adopted a user-centred approach