Rule-based extraction of experimental evidence in the biomedical domain

Regev, Yizhar; Finkelstein-Landau, Michal; Feldman, Ronen; Gorodetsky, Maya; Zheng, Xin; Lévy, Samuel; Charlab, Rosane; Lawrence, Charles; Lippert, Ross A.; Zhang, Qing; Shatkay, Hagit

doi:10.1145/772862.772874

Cited by 59 publications

(25 citation statements)

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“…The rst, GenTheme, is based on information retrieval (Shatkay et al, , 2002 while the second, LitMiner, is an information extraction system which was the winning system in the KDD-cup 2002 competition (Regev et al, 2002;Yeh et al, 2002).…”

Section: Literature Mining Systems-examplesmentioning

confidence: 99%

Mining the Biomedical Literature in the Genomic Era: An Overview

Shatkay

Feldman

2003

Journal of Computational Biology

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206

113

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The past decade has seen a tremendous growth in the amount of experimental and computational biomedical data, specifically in the areas of genomics and proteomics. This growth is accompanied by an accelerated increase in the number of biomedical publications discussing the findings. In the last few years, there has been a lot of interest within the scientific community in literature-mining tools to help sort through this abundance of literature and find the nuggets of information most relevant and useful for specific analysis tasks. This paper provides a road map to the various literature-mining methods, both in general and within bioinformatics. It surveys the disciplines involved in unstructured-text analysis, categorizes current work in biomedical literature mining with respect to these disciplines, and provides examples of text analysis methods applied towards meeting some of the current challenges in bioinformatics.

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Section: Literature Mining Systems-examplesmentioning

confidence: 99%

Mining the Biomedical Literature in the Genomic Era: An Overview

Shatkay

Feldman

2003

Journal of Computational Biology

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206

113

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“…The ClearForest and Celera team developed the winning system of the KDD Cup task [18]. Their system was implemented through a rule-based general Information Extraction language.…”

Section: Discussionmentioning

confidence: 99%

Classifying biological articles using web resources

Couto

Martins

Silva

2004

Proceedings of the 2004 ACM Symposium on Applied Computing

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Text classification systems on biomedical literature aim to select relevant articles to a specific issue from large corpora. Most systems with an acceptable accuracy are based on domain knowledge, which is very expensive and does not provide a general solution. This paper presents a novel approach for text classification on biomedical literature, involving the use of information extracted from related web resources. We validated this approach by implementing the proposed method and testing it on the KDD2002 Cup challenge: bio-text task. Results show that our approach can effectively improve efficiency on text classification systems for biomedical literature.

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“…Recently, several rule based information extraction systems were developed. Among them we cite: TextMarker [3], the AVATAR Information Extraction System [4] and, different systems for the medical [5] and biological (gene analysis) domains [6]. Furthermore, in the last decade, a number of ontology development related research directions emerged as: the automation of ontology development (KYOTO Project [7]), ontology and lexicon integration [8,9], or ontology learning and population [1,10].…”

Section: Related Workmentioning

confidence: 99%

An Architecture for Data and Knowledge Acquisition for the Semantic Web: The AGROVOC Use Case

Pazienza

Stellato

Tudorache

et al. 2012

On the Move to Meaningful Internet Systems: OTM 2012 Workshops

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Abstract. We are surrounded by ever growing volumes of unstructured and weakly-structured information, and for a human being, domain expert or not, it is nearly impossible to read, understand and categorize such information in a fair amount of time. Moreover, different user categories have different expectations: final users need easy-to-use tools and services for specific tasks, knowledge engineers require robust tools for knowledge acquisition, knowledge categorization and semantic resources development, while semantic applications developers demand for flexible frameworks for fast and easy, standardized development of complex applications. This work represents an experience report on the use of the CODA framework for rapid prototyping and deployment of knowledge acquisition systems for RDF. The system integrates independent NLP tools and custom libraries complying with UIMA standards. For our experiment a document set has been processed to populate the AGROVOC thesaurus with two new relationships.

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Rule-based extraction of experimental evidence in the biomedical domain

Cited by 59 publications

References 1 publication

Mining the Biomedical Literature in the Genomic Era: An Overview

Mining the Biomedical Literature in the Genomic Era: An Overview

Classifying biological articles using web resources

An Architecture for Data and Knowledge Acquisition for the Semantic Web: The AGROVOC Use Case

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