BioText Search Engine: beyond abstract search

Hearst, Marti A.; Divoli, Anna; Guturu, Harendra; Ksikes, Alex; Nakov, Preslav; Wooldridge, Michael A.; Ye, Jerry

doi:10.1093/bioinformatics/btm301

Cited by 130 publications

(72 citation statements)

References 10 publications

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“…For example the BioText System [19] represents beside extracted figures from relevant articles, query terms highlighted in the title and boldfaced in the text excerpts for communicating reasons the particular results were retrieved. Even though term highlighting can be useful for improving search result list presentations, it does not reveal the semantic interpretation of search results and prevent users from scanning the whole result list for getting an overview.…”

mentioning

confidence: 99%

Semantics Visualization for Fostering Search Result Comprehension

Stab

Nazemi

Breyer

et al. 2012

Lecture Notes in Computer Science

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Abstract. Current search engines present search results in an ordered list even if semantic technologies are used for analyzing user queries and the document contents. The semantic information that is used during the search result generation mostly remains hidden from the user although it significantly supports users in understanding why search results are considered as relevant for their individual query. The approach presented in this paper utilizes visualization techniques for offering visual feedback about the reasons the results were retrieved. It represents the semantic neighborhood of search results, the relations between results and query terms as well as the relevance of search results and the semantic interpretation of query terms for fostering search result comprehension. It also provides visual feedback for query enhancement. Therefore, not only the search results are visualized but also further information that occurs during the search processing is used to improve the visual presentation and to offer more transparency in search result generation. The results of an evaluation in a real application scenario show that the presented approach considerably supports users in assessment and decision-making tasks and alleviates information seeking in digital semantic knowledge bases.

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mentioning

confidence: 99%

Semantics Visualization for Fostering Search Result Comprehension

Stab

Nazemi

Breyer

et al. 2012

Lecture Notes in Computer Science

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“…There are several applications that use tables. For example, the BioText Search engine [8,6] performs information retrieval from text, abstracts, figures and tables in biomedical documents. Wei et al [23] created a question-answering system that looked for answers in tables, using CRF and information retrieval techniques.…”

Section: Introductionmentioning

confidence: 99%

Disentangling the Structure of Tables in Scientific Literature

Milošević

Gregson

Hernandez

et al. 2016

Lecture Notes in Computer Science

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Abstract. Within the scientific literature, tables are commonly used to present factual and statistical information in a compact way, which is easy to digest by readers. The ability to "understand" the structure of tables is key for information extraction in many domains. However, the complexity and variety of presentation layouts and value formats makes it difficult to automatically extract roles and relationships of table cells. In this paper, we present a model that structures tables in a machine readable way and a methodology to automatically disentangle and transform tables into the modelled data structure. The method was tested in the domain of clinical trials: it achieved an F-score of 94.26% for cell function identification and 94.84% for identification of inter-cell relationships.

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“…), as they struggle to manage, access, and share the extremely large scale of data in their daily affairs. In order to facilitate the above process, medical image retrieval techniques have been proposed and adopted in various applications for the past 20 years [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

“…It is generally acknowledged that the retrieval performance of TBMIR systems highly depends on these textual information, in which high preciseness and accuracy is necessary. Although TBMIR is popular in several practical utilizations (e.g., Goldminer from ARRS [2], BioText from Berkeley [3], iMedline from NIH [4], etc. ), it has its own drawbacks.…”

Section: Introductionmentioning

confidence: 99%

A Novel Similarity Learning Method via Relative Comparison for Content-Based Medical Image Retrieval

2013

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Nowadays, the huge volume of medical images represents an enormous challenge towards health-care organizations, as it is often hard for clinicians and researchers to manage, access, and share the image database easily. Content-based medical image retrieval (CBMIR) techniques are employed to facilitate the above process. It is known that a few concrete factors, including visual attributes extracted from images, measures encoding the similarity between images, user interaction, etc. play important roles in determining the retrieval performance. This paper concentrates on the similarity learning problem of CBMIR. A novel similarity learning paradigm is proposed via relative comparison, and a large database composed of 5,000 images is utilized to evaluate the retrieval performance. Extensive experimental results and comprehensive statistical analysis demonstrate the superiority of adopting the newly introduced learning paradigm, compared with several conventional supervised and semi-supervised similarity learning methods, in the presented CBMIR application.

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BioText Search Engine: beyond abstract search

Abstract: http://biosearch.berkeley.edu.

Cited by 130 publications

References 10 publications

Semantics Visualization for Fostering Search Result Comprehension

Semantics Visualization for Fostering Search Result Comprehension

Disentangling the Structure of Tables in Scientific Literature

A Novel Similarity Learning Method via Relative Comparison for Content-Based Medical Image Retrieval

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