Ontology-Based Text Classification for Filtering Cholangiocarcinoma Documents from PubMed

Sibunruang, Chumsak; Polpinij, Jantima

doi:10.1007/978-3-319-09891-3_25

Cited by 4 publications

(3 citation statements)

References 10 publications

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“…PubMed is by far the biggest biomedical literature repository which contains 18 million plus articles and growing [14]. It is difficult to search the entire corpus to extract relevant knowledge.…”

Section: Literature Reviewmentioning

confidence: 99%

A Comprehensive Framework for Ontology Based Classifier using Unstructured Data

Thangaraj*,

Sivakami

2019

IJEAT

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The knowledge contained within the natural language data can be used to build expert systems. Classifying unstructured data using ontology and text classification algorithms to extract information is one way of approaching the problem of building intelligent systems. One major problem with text processing is most data generated is unstructured and ambiguous, as, data with a structure helps to identify meaningful patterns and eventually exhibit the latent knowledge. Ambiguity in natural language affects accuracy of categorization. Also, Natural Language Processing techniques when combined with semantic data modeling through ontological knowledge will also solve the problem of domain knowledge representation thereby enabling improved data classification facilities, particularly in large datasets where number of features scale to unmanageable proportions. In this paper, the domain knowledge is presented as a knowledge graph, derived from the semantic data modeling. Further, to achieve better Multi Class classification, Multinomial Naive Bayes algorithm is applied to categorize items in their respective classes. For the experiments, Data about various news groups were used for testing the accuracy of the model. Experimental results have proved that the proposed classifier performs better compared to existing systems.

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Section: Literature Reviewmentioning

confidence: 99%

A Comprehensive Framework for Ontology Based Classifier using Unstructured Data

Thangaraj*,

Sivakami

2019

IJEAT

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“…The studies classifying biomedical literatures according to disease categories, such as arrhythmias, coronary heart, and cholangiocarcinoma diseases, are as follows [10]- [11]. Dollah and Aono [10] classified a document by performing ontology alignment between a hierarchy of extracted MeSH keywords and the OHSUMED disease hierarchy.…”

Section: Related Workmentioning

confidence: 99%

“…Dollah and Aono [10] classified a document by performing ontology alignment between a hierarchy of extracted MeSH keywords and the OHSUMED disease hierarchy. Sibunruang and Polpinij [11] proposed a cholangiocarcinoma document classification method using the Cancer Technical Term Net Ontology.…”

Section: Related Workmentioning

confidence: 99%

Classifying Biomedical Literature Providing Protein Function Evidence

Lim¹,

Lee²

2015

ETRI J

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Because protein is a primary element responsible for biological or biochemical roles in living bodies, protein function is the core and basis information for biomedical studies. However, recent advances in bio technologies have created an explosive increase in the amount of published literature; therefore, biomedical researchers have a hard time finding needed protein function information. In this paper, a classification system for biomedical literature providing protein function evidence is proposed. Note that, despite our best efforts, we have been unable to find previous studies on the proposed issue. To classify papers based on protein function evidence, we should consider whether the main claim of a paper is to assert a protein function. We, therefore, propose two novel features — protein and assertion. Our experimental results show a classification performance with 71.89% precision, 90.0% recall, and a 79.94% F‐measure. In addition, to verify the usefulness of the proposed classification system, two case study applications are investigated — information retrieval for protein function and automatic summarization for protein function text. It is shown that the proposed classification system can be successfully applied to these applications.

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Deep Learning Based Topic Identification and Categorization: Mining Diabetes-Related Topics on Chinese Health Websites

Chen

Zhang

et al. 2016

Database Systems for Advanced Applications

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Ontology-Based Text Classification for Filtering Cholangiocarcinoma Documents from PubMed

Cited by 4 publications

References 10 publications

A Comprehensive Framework for Ontology Based Classifier using Unstructured Data

A Comprehensive Framework for Ontology Based Classifier using Unstructured Data

Classifying Biomedical Literature Providing Protein Function Evidence

Deep Learning Based Topic Identification and Categorization: Mining Diabetes-Related Topics on Chinese Health Websites

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