A Knowledge Discovery from Full-Text Document Collections Using Clustering and Interpretable Genetic-Fuzzy Systems

2018 ◽  
pp. 434-443
Author(s):  
Filip Rudziński
Keyword(s):  
Knowledge Discovery ◽  
Full Text ◽  
Fuzzy Systems ◽  
Document Collections ◽  
Genetic Fuzzy Systems ◽  
Text Document

scholarly journals PubTator central: automated concept annotation for biomedical full text articles

2019 ◽  
Vol 47 (W1) ◽  
pp. W587-W593 ◽  
Author(s):  
Chih-Hsuan Wei ◽  
Alexis Allot ◽  
Robert Leaman ◽  
Zhiyong Lu
Keyword(s):  
Text Mining ◽  
Web Service ◽  
Full Text ◽  
Use Cases ◽  
Third Party ◽  
Document Collections ◽  
Server Side ◽  
Text Document ◽  
Restful Web Service ◽  
Disease Analysis

AbstractPubTator Central (https://www.ncbi.nlm.nih.gov/research/pubtator/) is a web service for viewing and retrieving bioconcept annotations in full text biomedical articles. PubTator Central (PTC) provides automated annotations from state-of-the-art text mining systems for genes/proteins, genetic variants, diseases, chemicals, species and cell lines, all available for immediate download. PTC annotates PubMed (29 million abstracts) and the PMC Text Mining subset (3 million full text articles). The new PTC web interface allows users to build full text document collections and visualize concept annotations in each document. Annotations are downloadable in multiple formats (XML, JSON and tab delimited) via the online interface, a RESTful web service and bulk FTP. Improved concept identification systems and a new disambiguation module based on deep learning increase annotation accuracy, and the new server-side architecture is significantly faster. PTC is synchronized with PubMed and PubMed Central, with new articles added daily. The original PubTator service has served annotated abstracts for ∼300 million requests, enabling third-party research in use cases such as biocuration support, gene prioritization, genetic disease analysis, and literature-based knowledge discovery. We demonstrate the full text results in PTC significantly increase biomedical concept coverage and anticipate this expansion will both enhance existing downstream applications and enable new use cases.

Conceptual Clustering of Textual Documents and Some Insights for Knowledge Discovery

2008 ◽  
pp. 223-243 ◽  
Author(s):  
Leandro Krug Wives ◽  
José Palazzo Moreira de Oliveira ◽  
Stanley Loh
Keyword(s):  
Knowledge Discovery ◽  
Real World ◽  
Document Clustering ◽  
Relevant Information ◽  
Conceptual Clustering ◽  
Document Collections ◽  
Clustering Techniques ◽  
Related Information

This chapter introduces a technique to cluster textual documents using concepts. Document clustering is a technique capable of organizing large amounts of documents in clusters of related information, which helps the localization of relevant information. Traditional document clustering techniques use words to represent the contents of the documents and the use of words may cause semantic mistakes. Concepts, instead, represent real world events and objects, and people employ them to express ideas, thoughts, opinions and intentions. Thus, concepts are more appropriate to represent the contents of a document and its use helps the comprehension of large document collections, since it is possible to summarize each cluster and rapidly identify its contents (i.e. concepts). To perform this task, the chapter presents a methodology to cluster documents using concepts and presents some practical experiments in a case study to demonstrate that the proposed approach achieves better results than the use of words.

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