MINING STRONG RELEVANCE BETWEEN HETEROGENEOUS ENTITIES FROM THEIR CO-OCURRENCES

Given two heterogeneous entities, the prevalence of text data provides rich co-occurrence information for them. However, the co-occurrence only is noisy-not only may the co-occurrence just imply an accidental writing, but also it may just reflect the domain-specific common words. Only those strong r...

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Bibliographic Details
Main Authors JI MING, HE QI, SPANGLER W. SCOTT
Format Patent
LanguageEnglish
Published 19.11.2015
Subjects
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
ELECTRIC DIGITAL DATA PROCESSING
HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATIONTECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING ORPROCESSING OF MEDICAL OR HEALTHCARE DATA
INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTEDFOR SPECIFIC APPLICATION FIELDS
PHYSICS
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Summary:Given two heterogeneous entities, the prevalence of text data provides rich co-occurrence information for them. However, the co-occurrence only is noisy-not only may the co-occurrence just imply an accidental writing, but also it may just reflect the domain-specific common words. Only those strong relevance between entities supported by rich relevance contexts in data can indicate meaningful entity relationships. Strong relevance between heterogeneous entities are mined from their co-occurrences. Drug-disease therapeutic relationships are used as the example to demonstrate an application of this work.
Bibliography:Application Number: US201414279617