1,721,012 research outputs found
Web Science and the two (hundred) cultures: representation of disciplines publishing in Web Science
No full textWeb Science is an interdisciplinary field. Motivated by the unforeseen scale and impact of the web, it addresses web related research questions in a holistic manner, incorporating epistemologies from a broad set of disciplines. There has been ongoing discussion about which disciplines are more or less present in the community, and about defining Web Science itself: there is, however, a dearth of empirical work in this area.This paper presents an analysis of the presence of different disciplines in Web Science. We applied Natural Language Processing and topic extraction to a corpus of Web Science material, analysing it with graphing and visualisation tools, MatLab and an expert survey. We discovered four communities within Web Science, and trends in the conference series over time (a strong impact from collocation) and format (posters covering a broader range of topics than papers). The expert survey linked highly ranked terms with disciplines, yielding strong links with Communication, Computer Science, Psychology, and Sociology. Controversially, experts described highly ranked topics and suggested disciplines (extracted from WebSci CFPs) as not reflecting the nature of Web Science
A disciplinary analysis of Internet Science
Full text linkInternet Science is an interdisciplinary field. Motivated by the unforeseen scale and impact of the Internet, it addresses Internet-related research questions in a holistic manner, incorporating epistemologies from a broad set of disciplines. Nonetheless, there is little empirical evidence of the levels of disciplinary representation within this field.This paper describes an analysis of the presence of different disciplines in Internet Science based on techniques from Natural Language Processing and network analysis. Key terms from Internet Science are identified, as are nine application contexts. The results are compared with a disciplinary analysis of Web Science, showing a surprisingly low amount of overlap between these two related fields. A practical use of the results within Internet Science is described. Finally, next steps are presented that will consolidate the analysis regarding representation of less technologically-oriented disciplines within Internet Science
Domain adaptive extraction of topical hierarchies for Expertise Mining
No full textIn this age of pervasive internet access we have become accustomed to rely
on web search for our most basic information needs. But complex queries in
knowledge-intensive organisations, as well as in the academic environment, are still best answered by direct interaction with domain experts. Experts
produce large amounts of text in their daily activities that can be analysed to automatically map expertise and provide services that allow users to search for experts instead of documents. Current approaches for expert finding are based on keyphrase search, relying on exact string matches to identify experts. What is needed instead is support for exploratory search and discovery of expertise topics and experts, and in-depth measures of expertise, that can be provided by extracting expertise topics and the relations between them.
This dissertation examines methods for extracting knowledge structures from text and their application to expert search. Towards this goal, we introduce a novel methodology called Expertise Mining, that provides solutions for expertise topic extraction, expert profiling and expert finding through text analysis. In particular, we propose a term extraction approach that considers the level of specificity of a term within a domain, as a solution for expertise topic extraction. We investigate relations between expertise topics, proposing a high-coverage method for topical hierarchy construction based on a global generality measure and a graph-based algorithm. We show that topical hierarchies can be used to improve expert finding, by measuring how well an individual covers the subtopics of a field.
Additionally, automatically extracted expertise topics are used to construct
expert profiles that provide context to the expertise of a person.This work has been part of the Saffron project, at the Digital Enterprise Research Institute (DERI), NUI Galway. The Saffron system currently provides insight into different Computer Science domains and was deployed at several conferences as a tool for finding collaborators
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Public Health and Epidemiology Informatics: Recent Research Trends
No full textInternational audienceOBJECTIVES: To introduce and analyse current trends in Public Health and Epidemiology Informatics. METHODS: PubMed search of 2020 literature on public health and epidemiology informatics was conducted and all retrieved references were reviewed by the two section editors. Then, 15 candidate best papers were selected among the 920 references. These papers were then peer-reviewed by the two section editors, two chief editors, and external reviewers, including at least two senior faculty, to allow the Editorial Committee of the 2021 International Medical Informatics Association (IMIA) Yearbook to make an informed decision regarding the selection of the best papers. RESULTS: Among the 920 references retrieved from PubMed, four were suggested as best papers and the first three were finally selected. The fourth paper was excluded because of reproducibility issues. The first best paper is a very public health focused paper with health informatics and biostatistics methods applied to stratify patients within a cohort in order to identify those at risk of suicide; the second paper describes the use of a randomized design to test the likely impact of fear-based messages, with and without empowering self-management elements, on patient consultations or antibiotic requests for influenza-like illnesses. The third selected paper evaluates the perception among communities of routine use of Whole Genome Sequencing and Big Data technologies to capture more detailed and specific personal information. CONCLUSIONS: The findings from the three studies suggest that using Public Health and Epidemiology Informatics methods could leverage, when combined with Deep Learning, early interventions and appropriate treatments to mitigate suicide risk. Further, they also demonstrate that well informing and empowering patients could help them to be involved more in their care process
Semantic representation and enrichment of information retrieval experimental data
No full textJournal articleExperimental evaluation carried out in international large-scale campaigns is a fundamental pillar of the scientific and technological advancement of information retrieval (IR) systems. Such evaluation activities produce a large quantity of scientific and experimental data, which are the foundation for all the subsequent scientific production and development of new systems. In this work, we discuss how to semantically annotate and interlink this data, with the goal of enhancing their interpretation, sharing, and reuse. We discuss the underlying evaluation workflow and propose a resource description framework model for those workflow parts. We use expertise retrieval as a case study to demonstrate the benefits of our semantic representation approach. We employ this model as a means for exposing experimental data as linked open data (LOD) on the Web and as a basis for enriching and automatically connecting this data with expertise topics and expert profiles. In this context, a topic-centric approach for expert search is proposed, addressing the extraction of expertise topics, their semantic grounding with the LOD cloud, and their connection to IR experimental data. Several methods for expert profiling and expert finding are analysed and evaluated. Our results show that it is possible to construct expert profiles starting from automatically extracted expertise topics and that topic-centric approaches outperform state-of-the-art language modelling approaches for expert finding.Science Foundation Ireland grant # SFI/12/RC/2289 (INSIGHT)Not peer reviewed2017-05-2
Benchmarking Domain-Specific Expert Search Using Workshop Program Committees
No full textConference paperTraditionally, relevance assessments for expert search have been gathered through self-assessment or based on the opinions of co-workers. We introduce three benchmark datasets1 for expert search that use conference workshops for relevance assessment. Our data sets cover entire research domains as opposed to single institutions. In addition, they provide a larger number of topic-person associations and allow a more objective and fine-grained evaluation of expertise than existing data sets do. We present and discuss baseline results for a language modelling and a topic-centric approach to expert search. We find that the topic-centric approach achieves the best results on domain-specific datasets.EU Grant No. 258191 (PROMISE project); Science Foundation Ireland Grant No, SFI/12/RC/228
Benchmarking Domain-Specific Expert Search Using Workshop Program Committees
No full textConference paperTraditionally, relevance assessments for expert search have been gathered through self-assessment or based on the opinions of co-workers. We introduce three benchmark datasets1 for expert search that use conference workshops for relevance assessment. Our data sets cover entire research domains as opposed to single institutions. In addition, they provide a larger number of topic-person associations and allow a more objective and fine-grained evaluation of expertise than existing data sets do. We present and discuss baseline results for a language modelling and a topic-centric approach to expert search. We find that the topic-centric approach achieves the best results on domain-specific datasets.EU Grant No. 258191 (PROMISE project); Science Foundation Ireland Grant No, SFI/12/RC/2289peer-reviewe
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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