Journal of the American Society for Information Science
An interactive system for finding complementary literatures: a stimulus to scientific discovery
Artificial Intelligence - Special issue on scientific discovery
Using latent semantic indexing for literature based discovery
Journal of the American Society for Information Science
Literature-based discovery by lexical statistics
Journal of the American Society for Information Science
Journal of the American Society for Information Science and Technology
The ρ operator: discovering and ranking associations on the semantic web
ACM SIGMOD Record
Ρ-Queries: enabling querying for semantic associations on the semantic web
WWW '03 Proceedings of the 12th international conference on World Wide Web
LitLinker: capturing connections across the biomedical literature
Proceedings of the 2nd international conference on Knowledge capture
Text mining: generating hypotheses from MEDLINE
Journal of the American Society for Information Science and Technology
Journal of Biomedical Informatics - Special issue: Unified medical language system
SemRank: ranking complex relationship search results on the semantic web
WWW '05 Proceedings of the 14th international conference on World Wide Web
On six degrees of separation in DBLP-DB and more
ACM SIGMOD Record
Using statistical and knowledge-based approaches for literature-based discovery
Journal of Biomedical Informatics
SPARQ2L: towards support for subgraph extraction queries in rdf databases
Proceedings of the 16th international conference on World Wide Web
Semantics-empowered text exploration for knowledge discovery
Proceedings of the 48th Annual Southeast Regional Conference
Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium
Semantic Predications for Complex Information Needs in Biomedical Literature
BIBM '11 Proceedings of the 2011 IEEE International Conference on Bioinformatics and Biomedicine
Literature-based discovery: Beyond the ABCs
Journal of the American Society for Information Science and Technology
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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.