714 research outputs found

    Contextualization of topics - browsing through terms, authors, journals and cluster allocations

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    This paper builds on an innovative Information Retrieval tool, Ariadne. The tool has been developed as an interactive network visualization and browsing tool for large-scale bibliographic databases. It basically allows to gain insights into a topic by contextualizing a search query (Koopman et al., 2015). In this paper, we apply the Ariadne tool to a far smaller dataset of 111,616 documents in astronomy and astrophysics. Labeled as the Berlin dataset, this data have been used by several research teams to apply and later compare different clustering algorithms. The quest for this team effort is how to delineate topics. This paper contributes to this challenge in two different ways. First, we produce one of the different cluster solution and second, we use Ariadne (the method behind it, and the interface - called LittleAriadne) to display cluster solutions of the different group members. By providing a tool that allows the visual inspection of the similarity of article clusters produced by different algorithms, we present a complementary approach to other possible means of comparison. More particular, we discuss how we can - with LittleAriadne - browse through the network of topical terms, authors, journals and cluster solutions in the Berlin dataset and compare cluster solutions as well as see their context.Comment: proceedings of the ISSI 2015 conference (accepted

    The MeSH-gram Neural Network Model: Extending Word Embedding Vectors with MeSH Concepts for UMLS Semantic Similarity and Relatedness in the Biomedical Domain

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    Eliciting semantic similarity between concepts in the biomedical domain remains a challenging task. Recent approaches founded on embedding vectors have gained in popularity as they risen to efficiently capture semantic relationships The underlying idea is that two words that have close meaning gather similar contexts. In this study, we propose a new neural network model named MeSH-gram which relies on a straighforward approach that extends the skip-gram neural network model by considering MeSH (Medical Subject Headings) descriptors instead words. Trained on publicly available corpus PubMed MEDLINE, MeSH-gram is evaluated on reference standards manually annotated for semantic similarity. MeSH-gram is first compared to skip-gram with vectors of size 300 and at several windows contexts. A deeper comparison is performed with tewenty existing models. All the obtained results of Spearman's rank correlations between human scores and computed similarities show that MeSH-gram outperforms the skip-gram model, and is comparable to the best methods but that need more computation and external resources.Comment: 6 pages, 2 table

    Ariadne's Thread - Interactive Navigation in a World of Networked Information

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    This work-in-progress paper introduces an interface for the interactive visual exploration of the context of queries using the ArticleFirst database, a product of OCLC. We describe a workflow which allows the user to browse live entities associated with 65 million articles. In the on-line interface, each query leads to a specific network representation of the most prevailing entities: topics (words), authors, journals and Dewey decimal classes linked to the set of terms in the query. This network represents the context of a query. Each of the network nodes is clickable: by clicking through, a user traverses a large space of articles along dimensions of authors, journals, Dewey classes and words simultaneously. We present different use cases of such an interface. This paper provides a link between the quest for maps of science and on-going debates in HCI about the use of interactive information visualisation to empower users in their search.Comment: CHI'15 Extended Abstracts, April 18-23, 2015, Seoul, Republic of Korea. ACM 978-1-4503-3146-3/15/0

    Retrieval, crawling and fusion of entity-centric data on the web

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    While the Web of (entity-centric) data has seen tremendous growth over the past years, take-up and re-use is still limited. Data vary heavily with respect to their scale, quality, coverage or dynamics, what poses challenges for tasks such as entity retrieval or search. This chapter provides an overview of approaches to deal with the increasing heterogeneity of Web data. On the one hand, recommendation, linking, profiling and retrieval can provide efficient means to enable discovery and search of entity-centric data, specifically when dealing with traditional knowledge graphs and linked data. On the other hand, embedded markup such as Microdata and RDFa has emerged a novel, Web-scale source of entitycentric knowledge. While markup has seen increasing adoption over the last few years, driven by initiatives such as schema.org, it constitutes an increasingly important source of entity-centric data on the Web, being in the same order of magnitude as the Web itself with regards to dynamics and scale. To this end, markup data lends itself as a data source for aiding tasks such as knowledge base augmentation, where data fusion techniques are required to address the inherent characteristics of markup data, such as its redundancy, heterogeneity and lack of links. Future directions are concerned with the exploitation of the complementary nature of markup data and traditional knowledge graphs. The final publication is available at Springer via http://dx.doi.org/ 10.1007/978-3-319-53640-8_1

    Role for positive schizotypy and hallucination proneness in semantic processing

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    Semantic processing underpins the organization of verbal information for both storage and retrieval. Deficits in semantic processing are associated with both the risk for and symptoms presented in schizophrenia. However, studies are mixed and could reflect the confounding effects of medication and symptom heterogeneity. Therefore, we considered whether two risk phenotypes, positive schizotypy and hallucinatory predisposition, present in the general population were associated with differential responding profiles for a semantic processing task. One hundred and eighty-three participants completed the Schizotypal Personality Questionnaire, Launay-Slade Hallucination Scale, National Adult Reading Test, a handedness measure, and a computerized semantic relatedness judgment task. Pairs of words were related through their dominant or subordinate meanings, or unrelated. Participants were divided into four groups using a mean split on cognitive-perceptual (positive) schizotypy and hallucination proneness. Significant differences between groups were found for reaction time on the semantic relatedness task, with the high cognitive-perceptual schizotypy groups responding significantly slower to all word pairs compared to their low scoring counterparts. There was some evidence that high hallucination proneness was associated with significantly faster reaction times which may reflect disinhibitive processes, however additional support is required. The results suggest that these two components of psychosis risk are associated with different patterns of responding to semantic processing. More diffuse activation of semantic information appeared to be associated with positive schizotypy, while those predisposed to hallucinations appeared to respond quicker. These results have significant implications in the re-conceptualization of hallucination proneness as distinct from positive schizotypy. Additional research is required to investigate the association between psychotic-like experiences separate from personality variables such as positive schizotypy and semantic processing

    FolkRank: A Ranking Algorithm for Folksonomies

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    In social bookmark tools users are setting up lightweight conceptual structures called folksonomies. Currently, the information retrieval support is limited. We present a formal model and a new search algorithm for folksonomies, called FolkRank, that exploits the structure of the folksonomy. The proposed algorithm is also applied to find communities within the folksonomy and is used to structure search results. All findings are demonstrated on a large scale dataset. A long version of this paper has been published at the European Semantic Web Conference 2006

    The Open Research Web: A Preview of the Optimal and the Inevitable

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    The multiple online research impact metrics we are developing will allow the rich new database , the Research Web, to be navigated, analyzed, mined and evaluated in powerful new ways that were not even conceivable in the paper era – nor even in the online era, until the database and the tools became openly accessible for online use by all: by researchers, research institutions, research funders, teachers, students, and even by the general public that funds the research and for whose benefit it is being conducted: Which research is being used most? By whom? Which research is growing most quickly? In what direction? under whose influence? Which research is showing immediate short-term usefulness, which shows delayed, longer term usefulness, and which has sustained long-lasting impact? Which research and researchers are the most authoritative? Whose research is most using this authoritative research, and whose research is the authoritative research using? Which are the best pointers (“hubs”) to the authoritative research? Is there any way to predict what research will have later citation impact (based on its earlier download impact), so junior researchers can be given resources before their work has had a chance to make itself felt through citations? Can research trends and directions be predicted from the online database? Can text content be used to find and compare related research, for influence, overlap, direction? Can a layman, unfamiliar with the specialized content of a field, be guided to the most relevant and important work? These are just a sample of the new online-age questions that the Open Research Web will begin to answer
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