Data Linkage Discovery Applications

Data Linkage Discovery Applications

Richard S. Segall (Arkansas State University, USA) and Shen Lu (University of South Florida, USA)
Copyright: © 2018 |Pages: 11
DOI: 10.4018/978-1-5225-2255-3.ch155
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Background

This section discusses some of the terminology of text mining that is also used in linkage discovery that is web-based form of knowledge discovery. Latent Semantic Analysis (LSA) can be used to discover knowledge from text with a general mathematical learning method without knowing prior linguistic or perceptual similarity knowledge.

Latent Semantic Analysis (LSA) is a Natural Language Processing (NLP) technique that is based on similarity of words but not grammatical or syntactical structure and extracts knowledge through the similarity of individual words. The motivation of LSA in terms of psychology is that people learn knowledge only from similarity of individual words taken as units, not with knowledge of their syntactical or grammatical function.

Experimental result for linkage discovery for glossaries was shown in Lu et al. (2011) and Lu et al. (2012) and by other investigators that, by combining glossaries with the text, we can extract more meaningful words from the text and then link similar sections together.

Latent Semantic Analysis (LSA) can provide the meanings of the terms based on the context. However, one article cannot include all of the domain knowledge and the definition extracted from the context where the term appears in that article is not accurate. But, in glossaries, all of the terms are defined clearly. In Lu et al. (2011) and Lu et al. (2012), we manually put the definitions of the terms in glossaries to those words in an article and use those definitions to improve the accuracy of the background knowledge we can extract from the context. In this way, we can define meaningful words and use them to decide the theme of the corresponding sections.

Ferret (2002) presented a method, called TOPICOLL, for using collocations for topic segmentation and link detection. Figure 1 below illustrates the automation of the algorithm of Ferret (2002) for detecting topic shifts.

Figure 1.

Automation for topic shift detection

[Ferret (2002)]

TOPICOLL Algorithm [Source: Ferret (2002)]

Parameters:

Key Terms in this Chapter

Semantic Analysis: In linguistics is the process of relating syntactic structures, from the levels of phrases, clauses, sentences and paragraphs to the level of the writing as a whole, to their language-independent meanings (Wikipedia, 2016) AU80: The citation "Wikipedia (2016)" matches multiple references. Please add letters (e.g. "Smith 2000a"), or additional authors to the citation, to uniquely match references and citations. .

Information Retrieval: The activity of obtaining information resources relevant to an information need from a collection of information resources. Searches can be based on metadata or on full-text indexing.

Ontology-Based: Borrowing a word from traditional philosophy

Glossary: An alphabetical list of terms in a particular domain of knowledge with the definitions for those terms (Wikipedia, 2016) AU79: The citation "Wikipedia (2016)" matches multiple references. Please add letters (e.g. "Smith 2000a"), or additional authors to the citation, to uniquely match references and citations. .

Information-Theoretic: Based upon information theory such as subfields of information security and language processing.

Linkage Discovery: Applications include discovery of linkage between different sections in electron publications.

Latent Semantic Analysis: Statistical model of word usage that permits comparisons of semantic similarity between pieces of textual information ( Foltz, 1996) .

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