Natural Language Processing methods and systems for biomedical ontology learning

Liu, Kaihong; Hogan, William R.; Crowley, Rebecca S.

doi:10.1016/j.jbi.2010.07.006

Cited by 128 publications

(94 citation statements)

References 84 publications

(94 reference statements)

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“…The use of effective linguistic tools such as grammar, syntax, and textual patterns are very effective for learning and assessment of text. [4] …”

Section: Methodsmentioning

confidence: 99%

“…Corpora are very effective, which provides a large number of computational data for spoken and written language. For example, in British English, BNC (the British National Corpus) provides a large data about the vocabulary usage [4,5]. The large collection of information provides sufficient data regarding the usage of words, which assist enhancing the information and academic skills of the students.…”

Section: A Nlp and Educational Settingmentioning

confidence: 99%

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Natural Language Processing and its Use in Education

Alhawiti¹

2014

ijacsa

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Abstract-Natural Language Processing (NLP) is an effective approach for bringing improvement in educational setting. Implementing NLP involves initiating the process of learning through the natural acquisition in the educational systems. It is based on effective approaches for providing a solution for various problems and issues in education. Natural Language Processing provides solution in a variety of different fields associated with the social and cultural context of language learning. It is an effective approach for teachers, students, authors and educators for providing assistance for writing, analysis, and assessment procedures. Natural Language Processing is widely integrated with the large number of educational contexts such as research, science, linguistics, e-learning, evaluations system, and contributes resulting positive outcomes in other educational settings such as schools, higher education system, and universities. The paper aims to address the process of natural language learning and its implication in the educational settings. The study also highlights how NLP can be utilized with scientific computer programs to enhance the process of education. The study follows qualitative approach. Data is collected from the secondary resources in order to identify problems faced by the teachers and students for understanding the context due to obstacles of language. Results provide effectiveness of linguistic tools such as grammar, syntax, and textual patterns that are fairly productive in educational context for learning and assessment.

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“…The use of effective linguistic tools such as grammar, syntax, and textual patterns are very effective for learning and assessment of text. [4] …”

Section: Methodsmentioning

confidence: 99%

Section: A Nlp and Educational Settingmentioning

confidence: 99%

Natural Language Processing and its Use in Education

Alhawiti¹

2014

ijacsa

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“…Alleviating the bottleneck of automated maintenance requires the application of advanced text mining and related techniques. In the past, in the majority of the cases the developed techniques utilized term recognition and pattern-based relationship extraction [9] algorithms. Perhaps closer to our work are the approaches that either apply analysis of Web search results and PubMed articles [5], or machine learning to predict the regions that may be expanded [12].…”

Section: Biomedical Ontology Evolution and Extensionmentioning

confidence: 99%

Temporal Classifiers for Predicting the Expansion of Medical Subject Headings

Tsatsaronis

Varlamis

Kanhabua

et al. 2013

Computational Linguistics and Intelligent Text Processing

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Abstract. Ontologies such as the Medical Subject Headings (MeSH) and theGene Ontology (GO) play a major role in biology and medicine since they facilitate data integration and the consistent exchange of information between different entities. They can also be used to index and annotate data and literature, thus enabling efficient search and analysis. Unfortunately, maintaining the ontologies manually is a complex, error-prone, and time and personnel-consuming effort. One major problem is the continuous growth of the biomedical literature, which expands by almost 1 million new scientific papers per year, indexed by Medline. The enormous annual increase of scientific publications constitutes the task of monitoring and following the changes and trends in the biomedical domain extremely difficult. For this purpose, approaches that try to learn and maintain ontologies automatically from text and data have been developed in the past. The goal of this paper is to develop temporal classifiers in order to create, for the first time to the best of our knowledge, an automated method that may predict which regions of the MeSH ontology will expand in the near future.

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“…OntoUSP (Poon and Domingos, 2010) induced and populated a probabilistic ontology by parsing the syntactic structure of a sentence, which learns the ISA hierarchy by clusters of logical expression. Liu et al (2011) reviewed and discussed the existing methods of biomedical ontology learning from free texts, which concern natural language processing, information extraction, and machine learning.…”

Section: Introductionmentioning

confidence: 99%

Interaction Relation Ontology Learning

Wang

Zhang

et al. 2014

Journal of Computational Biology

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Ontology is widely used in semantic computing and reasoning, and various biomedicine ontologies have become institutionalized to make the heterogeneous knowledge computationally amenable. Relation words, especially verbs, play an important role when describing the interaction between biological entities in molecular function, biological process, and cellular component; however, comprehensive research and analysis are still lacking. In this article, we propose an automatic method to build interaction relation ontology by investigating relation verbs, analyzing the syntactic relation of PubMed abstracts to perform relation vocabulary expansion, and integrating WordNet into our method to construct the hierarchy of relation vocabulary. Five attributes are populated automatically for each word in interaction relation ontology. As a result, the interaction relation ontology is constructed; it contains a total of 963 words and covers the most relation words used in existing methods of proteins interaction relation.

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Natural Language Processing methods and systems for biomedical ontology learning

Cited by 128 publications

References 84 publications

Natural Language Processing and its Use in Education

Natural Language Processing and its Use in Education

Temporal Classifiers for Predicting the Expansion of Medical Subject Headings

Interaction Relation Ontology Learning

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