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Title A Context-Driven Subgraph Model for Literature-Based Discovery
Publication Date
Degree PhD
Discipline/Department Computer Science and Engineering PhD
Degree Level doctoral
University/Publisher Wright State University
Abstract Literature-Based Discovery (LBD) refers to the process of uncovering hidden connections that are implicit in scientific literature. Numerous hypotheses have been generated from scientific literature using the LBD paradigm, which influenced innovations in diagnosis, treatment, preventions and overall public health. However, much of the existing research on discovering hidden connections among concepts have used distributional statistics and graph-theoretic measures to capture implicit associations. Such metrics do not explicitly capture the semantics of hidden connections. Rather, they only allude to the existence of meaningful underlying associations. To gain in-depth insights into the meaning of hidden (and other) connections, complementary methods have often been employed. Some of these methods include: 1) the use of domain expertise for concept filtering and knowledge exploration, 2) leveraging structured background knowledge for context and to supplement concept filtering, and 3) developing heuristics a priori to help eliminate spurious connections.While effective in some situations, the practice of relying on domain expertise, structured background knowledge, and heuristics to complement distributional and graph-theoretic approaches, has serious limitations. The main issue is that the intricate context of complex associations is not always known a priori and cannot easily be computed without understanding the underlying semantics of the associations. Complex associations should not be overlooked, since they are often needed to elucidate the mechanisms of interaction and causality relationships among concepts. Moreover, they can capture the broader aspects of a biomedical sub-domain by segregating associations along different thematic dimensions, such as Metabolic Function, Pharmaceutical Treatment, and Neurological Activity. This dissertation proposes an innovative context-driven, automatic subgraph creation method for finding hidden and complex associations among concepts, along multiple thematic dimensions. It outlines definitions for context and shared context, based on implicit and explicit (or formal) semantics, which compensate for deficiencies in statistical and graph-based metrics. It also eliminates the need for heuristics a priori. An evidence-based evaluation of the proposed framework showed that 8 out of 9 existing scientific discoveries could be recovered using this approach. Additionally, insights into the meaning of associations could be obtained using provenance provided by the system. In a statistical evaluation to determine the interestingness of the generated subgraphs, it was observed that an arbitrary association is mentioned in only approximately 4 articles in MEDLINE, on average. These results suggest that leveraging implicit and explicit context, as defined in this dissertation, is a significant advancement of the state-of-the-art in LBD research.
Subjects/Keywords Computer Science; Biomedical Research; Information Systems; Semantic Predications; Graph mining; Path clustering; Semantic relatedness; Literature-based discovery
Contributors Sheth , Amit (Advisor)
Language en
Rights unrestricted ; This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws.
Country of Publication us
Format application/pdf
Record ID oai:etd.ohiolink.edu:wright1417034001
Repository ohiolink
Date Indexed 2016-12-22
Grantor Wright State University

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…Rarity and Interestingness of the subgraphs in the rediscoveries . . . . . . 53 55 57 64 . 72 . 75 . 77 . 83 . 99 . 105 . 108 . 112 . 115 . 117 . 119 . 123 B.1 Literature-Based Discovery Systems and Tools . . . . . . . . . . . . . . . 157 ix…

…this dissertation. Special thanks to Dr. Thomas C. Rindflesch, whose role as a leading researcher in the field of Literature-Based Discovery (LBD) continues to be recognized in the broader research community. Interactions with him during both…

…developed in this research. 8 Overview Literature-Based Discovery (LBD) is characterized by uncovering hidden but novel information, implicit in non-interacting literatures. The notion of LBD was first proposed by Don R. Swanson (1924–2012…

…QA), 3) Document Summarization, and 4) Literature-Based Discovery (LBD). 2.1.1 Information Retrieval Information Retrieval (IR) is a process of finding information, within large collections, to satisfy an…

…focused on finding such implicit connections, which may lead to the discovery of new knowledge. 2.1.4 Literature-Based Discovery Literature-Based Discovery (LBD) is a challenging aspect of biomedical text mining. It involves uncovering hidden…

…capturing context. In the next Section paradigms, modes, and methodologies for LBD that leverage assertional and definitional knowledge are discussed. 2.3 Literature-Based Discovery Research The field of Literature-Based Discovery was pioneered by American…

…processing system that leverages rich representations of textual content, based on implicit and explicit semantics, can provide an effective means for making discoveries from scientific literature. This has been convincingly demonstrated in this research…

…connections that are implicit in scientific literature. In reference to the challenging nature of knowledge discovery, in general, Professor James Caruthers, in a Purdue University News Service Report (October 19, 2004)1 noted that “knowledge…