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1
Personalized Prediction of Suicide Risk for Web-based Intervention
In: Krishnaprasad Thirunarayan (2019)
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Personalized Prediction of Suicide Risk for Web-based Intervention
In: Amit P. Sheth (2019)
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3
Personalized Prediction of Suicide Risk for Web-based Intervention
In: Kno.e.sis Publications (2018)
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4
Intent Classification of Short-Text on Social Media
In: Valerie Shalin (2017)
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5
Intent Classification of Short-Text on Social Media
In: Amit P. Sheth (2016)
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6
Intent Classification of Short-Text on Social Media
In: Krishnaprasad Thirunarayan (2016)
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7
FACES: Diversity-Aware Entity Summarization using Incremental Hierarchical Conceptual Clustering
In: Kno.e.sis Publications (2015)
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8
Context-Driven Automatic Subgraph Creation for Literature-Based Discovery
In: Kno.e.sis Publications (2015)
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9
Intent Classification of Short-Text on Social Media
In: Kno.e.sis Publications (2015)
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10
iExplore: Interactive Browsing and Exploring Biomedical Knowledge
In: Amit P. Sheth (2014)
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11
FACES: Diversity-Aware Entity Summarization using Incremental Hierarchical Conceptual Clustering
In: Amit P. Sheth (2014)
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12
A Graph-Based Recovery and Decomposition of Swanson’s Hypothesis using Semantic Predications
In: Krishnaprasad Thirunarayan (2014)
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13
Context-Driven Automatic Subgraph Creation for Literature-Based Discovery
In: Amit P. Sheth (2014)
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14
FACES: Diversity-Aware Entity Summarization using Incremental Hierarchical Conceptual Clustering
In: Krishnaprasad Thirunarayan (2014)
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15
Context-Driven Automatic Subgraph Creation for Literature-Based Discovery
In: Krishnaprasad Thirunarayan (2014)
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16
A Graph-Based Recovery and Decomposition of Swanson’s Hypothesis using Semantic Predications
In: Amit P. Sheth (2014)
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17
iExplore: Interactive Browsing and Exploring Biomedical Knowledge
In: Krishnaprasad Thirunarayan (2014)
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18
A Graph-Based Recovery and Decomposition of Swanson’s Hypothesis using Semantic Predications
In: Kno.e.sis Publications (2013)
Abstract: Objectives This paper presents a methodology for recovering and decomposing Swanson’s Raynaud Syndrome–Fish Oil hypothesis semi-automatically. The methodology leverages the semantics of assertions extracted from biomedical literature (called semantic predications) along with structured background knowledge and graph-based algorithms to semi-automatically capture the informative associations originally discovered manually by Swanson. Demonstrating that Swanson’s manually intensive techniques can be undertaken semi-automatically, paves the way for fully automatic semantics-based hypothesis generation from scientific literature. Methods Semantic predications obtained from biomedical literature allow the construction of labeled directed graphs which contain various associations among concepts from the literature. By aggregating such associations into informative subgraphs, some of the relevant details originally articulated by Swanson have been uncovered. However, by leveraging background knowledge to bridge important knowledge gaps in the literature, a methodology for semi-automatically capturing the detailed associations originally explicated in natural language by Swanson, has been developed. Results Our methodology not only recovered the three associations commonly recognized as Swanson’s hypothesis, but also decomposed them into an additional 16 detailed associations, formulated as chains of semantic predications. Altogether, 14 out of the 19 associations that can be attributed to Swanson were retrieved using our approach. To the best of our knowledge, such an in-depth recovery and decomposition of Swanson’s hypothesis has never been attempted. Conclusion In this work therefore, we presented a methodology to semi-automatically recover and decompose Swanson’s RS-DFO hypothesis using semantic representations and graph algorithms. Our methodology provides new insights into potential prerequisites for semantics-driven Literature-Based Discovery (LBD). Based on our observations, three critical aspects of LBD include: (1) the need for more expressive representations beyond Swanson’s ABC model; (2) an ability to accurately extract semantic information from text; and (3) the semantic integration of scientific literature and structured background knowledge.
Keyword: Background Knowledge; Bioinformatics; Communication; Communication Technology and New Media; Computer Sciences; Databases and Information Systems; Life Sciences; Literature-Based Discovery (LBD); OS and Networks; Physical Sciences and Mathematics; Science and Technology Studies; Semantic Associations; Semantic Predications; Social and Behavioral Sciences; Subgraph Creation; Swanson’s Hypothesis
URL: https://corescholar.libraries.wright.edu/knoesis/247
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19
iExplore: Interactive Browsing and Exploring Biomedical Knowledge
In: Kno.e.sis Publications (2012)
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