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Emerging approaches in literature-based discovery: techniques and performance review

Published online by Cambridge University Press:  16 May 2017

Yakub Sebastian
Affiliation:
Faculty of Engineering, Computing and Science, Swinburne University of Technology Sarawak Campus, Jalan Simpang Tiga, 93350 Kuching, Sarawak, Malaysia e-mail: ysebastian@swinburne.edu.my
Eu-Gene Siew
Affiliation:
School of Business, Monash University Malaysia, Jalan Lagoon Selatan, Bandar Sunway, 47500 Subang Jaya, Selangor, Malaysiae-mail: siew.eu-gene@monash.edu
Sylvester O. Orimaye
Affiliation:
School of Information Technology, Monash University Malaysia, Jalan Lagoon Selatan, Bandar Sunway, 47500 Subang Jaya, Selangor, Malaysiae-mail: sylvester.orimaye@monash.edu

Abstract

Literature-based discovery systems aim at discovering valuable latent connections between previously disparate research areas. This is achieved by analyzing the contents of their respective literatures with the help of various intelligent computational techniques. In this paper, we review the progress of literature-based discovery research, focusing on understanding their technical features and evaluating their performance. The present literature-based discovery techniques can be divided into two general approaches: the traditional approach and the emerging approach. The traditional approach, which dominate the current research landscape, comprises mainly of techniques that rely on utilizing lexical statistics, knowledge-based and visualization methods in order to address literature-based discovery problems. On the other hand, we have also observed the births of new trends and unprecedented paradigm shifts among the recently emerging literature-based discovery approach. These trends are likely to shape the future trajectory of the next generation literature-based discovery systems.

Type
Survey Article
Copyright
© Cambridge University Press, 2017 

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References

Andronis, C., Sharma, A., Deftereos, S., Virvilis, V., Konstanti, O., Persidis, A. & Persidis, A. 2012. Mining scientific and clinical databases to identify novel uses for existing drugs. In Drug Repositioning: Bringing New Life to Shelved Assets and Existing Drugs, Michael J. Barrat & Donald E. Frail (eds). Wiley, 137.Google Scholar
Bassecoulard, E. & Zitt, M. 2004. Patents and publications. In Handbook of Quantitative Science and Technology Research, Henk F. Moed, Wolfgang Glänzel, & Ulrich Schmoch (eds). Springer, 665694.Google Scholar
Bekhuis, T. 2006. Conceptual biology, hypothesis discovery, and text mining: Swanson’s legacy. Biomedical Digital Libraries 3(1), 1.CrossRefGoogle ScholarPubMed
Berry, M. W. & Castellanos, M. 2004. Survey of text mining. Computing Reviews 45(9), 548.Google Scholar
Blei, D. M., Ng, A. Y. & Jordan, M. I. 2003. Latent dirichlet allocation. Journal of Machine Learning Research 3, 9931022.Google Scholar
Bornmann, L. & Mutz, R. 2015. Growth rates of modern science: a bibliometric analysis based on the number of publications and cited references. Journal of the Association for Information Science and Technology 66(11), 22152222.Google Scholar
Boyack, K. W. & Klavans, R. 2010. Co-citation analysis, bibliographic coupling, and direct citation: which citation approach represents the research front most accurately? Journal of the American Society for Information Science and Technology 61(12), 23892404.Google Scholar
Boyack, K. W., Small, H. & Klavans, R. 2013. Improving the accuracy of co-citation clustering using full text. Journal of the American Society for Information Science and Technology 64(9), 17591767.Google Scholar
Brin, S. & Page, L. 2012. Reprint of: the anatomy of a large-scale hypertextual web search engine. Computer Networks 56(18), 38253833.CrossRefGoogle Scholar
Callon, M., Courtial, J.-P., Turner, W. A. & Bauin, S. 1983. From translations to problematic networks: an introduction to co-word analysis. Social Science Information 22(2), 191235.CrossRefGoogle Scholar
Cameron, D., Bodenreider, O., Yalamanchili, H., Danh, T., Vallabhaneni, S., Thirunarayan, K., Sheth, A. P. & Rindflesch, T. C. 2013. A graph-based recovery and decomposition of Swanson’s hypothesis using semantic predications. Journal of Biomedical Informatics 46(2), 238251.CrossRefGoogle ScholarPubMed
Cameron, D. H. 2014. A Context-Driven Subgraph Model for Literature-Based Discovery. PhD thesis, Wright State University.CrossRefGoogle Scholar
Cameron, D., Kavuluru, R., Rindflesch, T. C., Sheth, A. P., Thirunarayan, K. & Bodenreider, O. 2015. Context-driven automatic subgraph creation for literature-based discovery. Journal of Biomedical Informatics 54, 141157.Google Scholar
Chang, J. & Blei, D. M. 2010. Hierarchical relational models for document networks. The Annals of Applied Statistics 4(1), 124150.Google Scholar
Chen, C. 2012. Predictive effects of structural variation on citation counts. Journal of the American Society for Information Science and Technology 63(3), 431449.CrossRefGoogle Scholar
Chen, C., Chen, Y., Horowitz, M., Hou, H., Liu, Z. & Pellegrino, D. 2009. Towards an explanatory and computational theory of scientific discovery. Journal of Informetrics 3(3), 191209.Google Scholar
Chen, H.-H., Gou, L., Zhang, X. L. & Giles, C. L. 2013. Towards the discovery of diseases related by genes using vertex similarity measures. In 2013 IEEE International Conference on Healthcare Informatics (ICHI), 505–510. IEEE.Google Scholar
Cohen, A. M. & Hersh, W. R. 2005. A survey of current work in biomedical text mining. Briefings in Bioinformatics 6(1), 5771.Google Scholar
Cohen, P. R. 2015. Darpa’s big mechanism program. Physical Biology 12(4), 045008.Google Scholar
Cohen, T., Schvaneveldt, R. & Widdows, D. 2010. Reflective random indexing and indirect inference: a scalable method for discovery of implicit connections. Journal of Biomedical Informatics 43(2), 240256.Google Scholar
Cohen, T., Widdows, D. & Rindflesch, T. 2015. Expansion-by-analogy: a vector symbolic approach to semantic search. In Quantum Interaction: 8th International Conference, QI 2014, Filzbach, Switzerland, June 30–July 3, Atmanspacher, H., Bergomi, C., Filk, T. & Kitto, K. (eds). Springer International Publishing, 54–66.Google Scholar
Cohen, T., Widdows, D., Schvaneveldt, R. W., Davies, P. & Rindflesch, T. C. 2012. Discovering discovery patterns with predication-based semantic indexing. Journal of Biomedical Informatics 45(6), 10491065.Google Scholar
Cohen, T., Widdows, D., Stephan, C., Zinner, R., Kim, J., Rindflesch, T. & Davies, P. 2014. Predicting high-throughput screening results with scalable literature-based discovery methods. CPT: Pharmacometrics & Systems Pharmacology 3(10), 19.Google ScholarPubMed
Cory, K. A. 1997. Discovering hidden analogies in an online humanities database. Computers and the Humanities 31(1), 112.Google Scholar
Davies, R. 1989. The creation of new knowledge by information retrieval and classification. Journal of Documentation 45(4), 273301.Google Scholar
Deerwester, S., Dumais, S. T., Furnas, G. W., Landauer, T. K. & Harshman, R. 1990. Indexing by latent semantic analysis. Journal of the American Society for Information Science 41(6), 391.3.0.CO;2-9>CrossRefGoogle Scholar
DiGiacomo, R. A., Kremer, J. M. & Shah, D. M. 1989. Fish-oil dietary supplementation in patients with Raynaud’s phenomenon: a double-blind, controlled, prospective study. The American Journal of Medicine 86(2), 158164.Google Scholar
Ding, Y., Song, M., Han, J., Yu, Q., Yan, E., Lin, L. & Chambers, T. 2013. Entitymetrics: measuring the impact of entities. PloS One 8(8), e71416.Google Scholar
Eronen, L. & Toivonen, H. 2012. Biomine: predicting links between biological entities using network models of heterogeneous databases. BMC Bioinformatics 13(1), 1.Google Scholar
Feller, I. & Stern, P. C. 2007. A Strategy for Assessing Science: Behavioral and Social Research on Aging. National Academies Press.Google Scholar
Freeman, L. C. 1978. Centrality in social networks conceptual clarification. Social Networks 1(3), 215239.Google Scholar
Frijters, R., Van Vugt, M., Smeets, R., Van Schaik, R., De Vlieg, J. & Alkema, W. 2010. Literature mining for the discovery of hidden connections between drugs, genes and diseases. PLoS Computational Biology 6(9), e1000943.Google Scholar
Fujita, K. 2012. Finding linkage between sustainability science and technologies based on citation network analysis. In 2012 Fifth IEEE International Conference on Service-Oriented Computing and Applications (SOCA), 1–6. IEEE.Google Scholar
Ganiz, M., Pottenger, W. M. & Janneck, C. D. 2005. Recent Advances in Literature Based Discovery. Technical report, Lehigh University.Google Scholar
Getoor, L. & Diehl, C. P. 2005. Link mining: a survey. ACM SIGKDD Explorations Newsletter 7(2), 312.Google Scholar
Goodwin, J. C., Cohen, T. & Rindflesch, T. 2012. Discovery by scent: discovery browsing system based on the information foraging theory. In Proceedings of the 2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW), 232–239. IEEE.CrossRefGoogle Scholar
Gordon, M. D. & Dumais, S. 1998. Using latent semantic indexing for literature based discovery. Journal of the American Society for Information Science 49(8), 674685.Google Scholar
Gordon, M. D. & Lindsay, R. K. 1996. Toward discovery support systems: a replication, re-examination, and extension of Swanson’s work on literature-based discovery of a connection between Raynaud’s and fish oil. Journal of the American Society for Information Science 47(2), 116128.Google Scholar
Gordon, M., Lindsay, R. K. & Fan, W. 2002. Literature-based discovery on the world wide web. ACM Transactions on Internet Technology 2(4), 261275.CrossRefGoogle Scholar
Hahn, U., Cohen, K. B., Garten, Y. & Shah, N. H. 2012. Mining the pharmacogenomics literature: a survey of the state of the art. Briefings in Bioinformatics 13(4), 460494.Google Scholar
Hristovski, D., Džeroski, S., Peterlin, B. & Rožić, A. 2000. Supporting discovery in medicine by association rule mining of bibliographic databases. In Principles of Data Mining and Knowledge Discovery: 4th European Conference, PKDD 2000 Lyon, France, September 13–16, 2000 Proceedings, Zighed, D. A., Komorowski, J, Żytkow, J. (eds). Springer Berlin Heidelberg, 149159.Google Scholar
Hristovski, D., Friedman, C., Rindflesch, T. C. & Peterlin, B. 2006. Exploiting semantic relations for literature-based discovery. In Proceedings of the 2006 AMIA Symposium, 349–353.Google Scholar
Hu, X., Yoo, I., Song, M., Zhang, Y. & Song, I.-Y. 2005. Mining undiscovered public knowledge from complementary and non-interactive biomedical literature through semantic pruning. In Proceedings of the 14th ACM International Conference on Information and Knowledge Management, CIKM ’05, 249–250. ACM.CrossRefGoogle Scholar
Ittipanuvat, V., Fujita, K., Sakata, I. & Kajikawa, Y. 2014. Finding linkage between technology and social issue: a literature based discovery approach. Journal of Engineering and Technology Management 32, 160184.Google Scholar
Janssens, F., Glänzel, W. & De Moor, B. 2008. A hybrid mapping of information science. Scientometrics 75(3), 607631.CrossRefGoogle Scholar
Jensen, L. J., Saric, J. & Bork, P. 2006. Literature mining for the biologist: from information retrieval to biological discovery. Nature Reviews Genetics 7(2), 119129.Google Scholar
Juršič, M., Sluban, B., Cestnik, B., Grčar, M. & Lavrač, N. 2012. Bridging concept identification for constructing information networks from text documents. In Bisociative Knowledge Discovery: An Introduction to Concept, Algorithms, Tools, and Applications, M. R. Berthold (ed.). Springer Berlin Heidelberg, 6690.CrossRefGoogle Scholar
Kastrin, A., Rindflesch, T. C. & Hristovski, D. 2013. Link prediction in a mesh co-occurrence network: preliminary results. Studies in Health Technology and Informatics 205, 579583.Google Scholar
Kessler, M. M. 1963. Bibliographic coupling between scientific papers. American Documentation 14(1), 1025.Google Scholar
Kleinberg, J. M. 1999. Authoritative sources in a hyperlinked environment. Journal of the ACM 46(5), 604632.Google Scholar
Kostoff, R. N. 2007. Validating discovery in literature-based discovery. Journal of Biomedical Informatics 40(4), 448450.Google Scholar
Kostoff, R. N. 2008. Literature-related discovery (LRD): potential treatments for cataracts. Technological Forecasting and Social Change 75(2), 215225.Google Scholar
Kostoff, R. N. 2012. Literature-related discovery and innovation update. Technological Forecasting and Social Change 79(4), 789800.Google Scholar
Kostoff, R. N. 2014. Literature-related discovery: common factors for Parkinson’s disease and Crohn’s disease. Scientometrics 100(3), 623657.Google Scholar
Kostoff, R. N., Block, J. A., Solka, J. L., Briggs, M. B., Rushenberg, R. L., Stump, J. A., Johnson, D., Lyons, T. J. & Wyatt, J. R. 2009. Literature-related discovery. Annual Review of Information Science and Technology 43(1), 171.CrossRefGoogle Scholar
Kostoff, R. N. & Briggs, M. B. 2008. Literature-related discovery (LRD): potential treatments for Parkinson’s disease. Technological Forecasting and Social Change 75(2), 226238.CrossRefGoogle Scholar
Kostoff, R. N., Briggs, M. B. & Lyons, T. J. 2008. Literature-related discovery (LRD): potential treatments for multiple sclerosis. Technological Forecasting and Social Change 75(2), 239255.Google Scholar
Kostoff, R. N., Solka, J. L., Rushenberg, R. L. & Wyatt, J. A. 2008. Literature-related discovery (LRD): water purification. Technological Forecasting and Social Change 75(2), 256275.Google Scholar
Kraines, S. B., Guo, W., Hoshiyama, D., Makino, T., Mizutani, H., Okuda, Y., Shidahara, Y. & Takagi, T. 2010. Literature-based knowledge discovery from relationship associations based on a DL ontology created from mesh. In Proceedings of the International Joint Conference on Knowledge Discovery, Knowledge Engineering, and Knowledge Management, 87–106. Springer.Google Scholar
Larsen, P. O. & Von Ins, M. 2010. The rate of growth in scientific publication and the decline in coverage provided by science citation index. Scientometrics 84(3), 575603.Google Scholar
Leskovec, J., Kleinberg, J. & Faloutsos, C. 2005. Graphs over time: densification laws, shrinking diameters and possible explanations. In Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, 177–187. ACM.CrossRefGoogle Scholar
Leskovec, J., Lang, K. J. & Mahoney, M. 2010. Empirical comparison of algorithms for network community detection. In Proceedings of the 19th International Conference on World Wide Web, 631–640. ACM.Google Scholar
Li, C., Liakata, M. & Rebholz-Schuhmann, D. 2014. Biological network extraction from scientific literature: state of the art and challenges. Briefings in Bioinformatics 15(5), 856877.Google Scholar
Li, J., Zhu, X. & Chen, J. Y. 2010. Discovering breast cancer drug candidates from biomedical literature. International Journal of Data Mining and Bioinformatics 4(3), 241255.Google Scholar
Lindsay, R. K. & Gordon, M. D. 1999. Literature-based discovery by lexical statistics. Journal of the Association for Information Science and Technology 50(7), 574.Google Scholar
Lytras, M., Sicilia, M.-A., Davies, J., Kashyap, V. & Hu, X. 2005. Mining novel connections from large online digital library using biomedical ontologies. Library Management 26(4/5), 261270.Google Scholar
Manning, C. D., Raghavan, P. & Schütze, H. 2008. Introduction to Information Retrieval. Cambridge University Press.Google Scholar
Marsi, E. & Öztürk, P. 2015. Extraction and generalisation of variables from scientific publications. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP 2015).Google Scholar
Marsi, E., Oztürk, P., Aamot, E., Sizov, G. & Ardelan, M. V. 2014. Towards text mining in climate science: extraction of quantitative variables and their relations. In Proceedings of the Fourth Workshop on Building and Evaluating Resources for Health and Biomedical Text Processing, Reykjavik, Iceland.Google Scholar
Meyer, H. S. & Lundberg, G. D. 1985. Fifty-One Landmark Articles in Medicine: The JAMA Centennial Series. Chicago Review Press.Google Scholar
Miller, C. M., Rindflesch, T. C., Fiszman, M., Hristovski, D., Shin, D., Rosemblat, G., Zhang, H. & Strohl, K. P. 2012. A closed literature-based discovery technique finds a mechanistic link between hypogonadism and diminished sleep quality in aging men. Sleep 35(2), 279285.Google Scholar
Mostafa, J., Seki, K. & Ke, W. 2009. Beyond information retrieval: literature mining for biomedical knowledge discovery. In J. Y. Chen & S. Lonardi (eds). Biological Data Mining. CRC Press, 449485.Google Scholar
Nakamura, H., Ii, S., Chida, H., Friedl, K., Suzuki, S., Mori, J. & Kajikawa, Y. 2014. Shedding light on a neglected area: a new approach to knowledge creation. Sustainability Science 9(2), 193204.Google Scholar
Narayanasamy, V., Mukhopadhyay, S., Palakal, M. & Potter, D. A. 2004. Transminer: Mining transitive associations among biological objects from text. Journal of Biomedical Science 11(6), 864873.Google Scholar
Newman, M. E. 2001. The structure of scientific collaboration networks. Proceedings of the National Academy of Sciences 98(2), 404409.Google Scholar
Newman, M. E. 2003. The structure and function of complex networks. SIAM Review 45(2), 167256.Google Scholar
Newman, M. E. 2004. Fast algorithm for detecting community structure in networks. Physical Review E 69(6), 066133.Google Scholar
Novacek, V. 2015. Formalising hypothesis virtues in knowledge graphs: a general theoretical framework and its validation in literature-based discovery experiments. arXiv preprint arXiv:1503.09137.Google Scholar
Perez-Iratxeta, C., Bork, P. & Andrade, M. A. 2002. Association of genes to genetically inherited diseases using data mining. Nature Genetics 31(3), 316319.Google Scholar
Perez-Iratxeta, C., Wjst, M., Bork, P. & Andrade, M. A. 2005. G2d: a tool for mining genes associated with disease. BMC Genetics 6(1), 1.CrossRefGoogle ScholarPubMed
Petrič, I., Cestnik, B., Lavrač, N. & Urbančič, T. 2010. Outlier detection in cross-context link discovery for creative literature mining 55(1). The Computer Journal, 4761.Google Scholar
Piatetsky-Shapiro, G., Djeraba, C., Getoor, L., Grossman, R., Feldman, R. & Zaki, M. 2006. What are the grand challenges for data mining?: Kdd-2006 panel report. ACM SIGKDD Explorations Newsletter 8(2), 7077.Google Scholar
Pratt, W. & Yetisgen-Yildiz, M. 2003. Litlinker: capturing connections across the biomedical literature. In Proceedings of the 2nd International Conference on Knowledge Capture, K-CAP ’03, 105–112. ACM.Google Scholar
Preiss, J. & Stevenson, R. 2016. The effect of word sense disambiguation accuracy on literature based discovery. BMC Medical Informatics and Decision Making 16(Suppl 1), 57.Google Scholar
Preiss, J., Stevenson, M. & Gaizauskas, R. 2015. Exploring relation types for literature-based discovery, Journal of the American Medical Informatics Association 22(5), 987992.Google Scholar
Salton, G. & McGill, M. J. 1986. Introduction to Modern Information Retrieval. McGraw-Hill.Google Scholar
Sebastian, Y. 2014. Cluster links prediction for literature based discovery using latent structure and semantic features. In Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval, 1275–1275. ACM.Google Scholar
Sebastian, Y., Siew, E.-G. & Orimaye, S. O. 2015. Predicting future links between disjoint research areas using heterogeneous bibliographic information network. In Advances in Knowledge Discovery and Data Mining: 19th Pacific-Asia Conference, PAKDD 2015, Ho Chi Minh City, Vietnam, May 19-22, T. Cao, E.-P. Lim, Z.-H. Zhou, T.-B. Ho, D. Cheung H. Motoda (eds). Springer International Publishing, 610621.Google Scholar
Sebastian, Y., Siew, E.-G. & Orimaye, S. O. 2017. Learning the heterogeneous bibliographic information network for literature-based discovery. Knowledge-Based Systems 115, 6679.CrossRefGoogle Scholar
Seki, K. 2015. Hypothesis discovery exploiting closed chains of relation. In A. Hameurlain, J. Küng & R. Wagner (eds). Transactions on Large-Scale Data- and Knowledge-Centered Systems XXII. Springer Berlin Heidelberg, 145164.Google Scholar
Shang, N., Xu, H., Rindflesch, T. C. & Cohen, T. 2014. Identifying plausible adverse drug reactions using knowledge extracted from the literature. Journal of Biomedical Informatics 52, 293310.Google Scholar
Smalheiser, N. R. 2012. Literature-based discovery: beyond the ABCs. Journal of the American Society for Information Science and Technology 63(2), 218224.Google Scholar
Smalheiser, N. R. & Swanson, D. R. 1996a. Indomethacin and Alzheimer’s disease. Neurology 46(2), 583583.Google Scholar
Smalheiser, N. R. & Swanson, D. R. 1996b. Linking estrogen to Alzheimer’s disease an informatics approach. Neurology 47(3), 809810.CrossRefGoogle ScholarPubMed
Smalheiser, N. R. & Torvik, V. I. 2008. The place of literature-based discovery in contemporary scientific practice. In P. Bruza & M. Weeber (eds). Literature-Based Discovery. Springer Berlin Heidelberg, 1322.Google Scholar
Small, H. 2010. Maps of science as interdisciplinary discourse: co-citation contexts and the role of analogy. Scientometrics 83(3), 835849.Google Scholar
Sneed, W. A. 2003. Knowledge Synthesis in the Biomedical Literature: Nordihydroguaiaretic Acid and Breast Cancer. PhD thesis, University of North Texas.Google Scholar
Song, M., Han, N.-G., Kim, Y.-H., Ding, Y. & Chambers, T. 2013. Discovering implicit entity relation with the gene-citation-gene network. PloS One 8(12), e84639.Google Scholar
Song, M., Heo, G. E. & Ding, Y. 2015. SemPathFinder: semantic path analysis for discovering publicly unknown knowledge. Journal of Informetrics 9(4), 686703.Google Scholar
Srinivasan, P. 2004. Text mining: generating hypotheses from medline. Journal of the American Society for Information Science and Technology 55(5), 396413.Google Scholar
Srinivasan, P. & Libbus, B. 2004. Mining medline for implicit links between dietary substances and diseases. Bioinformatics 20(Suppl 1), i290i296.Google Scholar
Srinivasan, P., Libbus, B. & Sehgal, A. K. 2004. Mining medline: postulating a beneficial role for curcumin longa in retinal diseases. In Workshop BioLINK, Linking Biological Literature, Ontologies and Databases at HLT NAACL, 33–40.Google Scholar
Stegmann, J. & Grohmann, G. 2003. Hypothesis generation guided by co-word clustering. Scientometrics 56(1), 111135.Google Scholar
Sun, Y. & Han, J. 2012. Mining heterogeneous information networks: principles and methodologies. Synthesis Lectures on Data Mining and Knowledge Discovery 3(2), 1159.Google Scholar
Swanson, D. 2008. Literature-based discovery? The very idea. In Literature-Based Discovery, Peter Bruza & Marc Weeber (eds.). Springer, 311.CrossRefGoogle Scholar
Swanson, D. R. 1979. Libraries and the growth of knowledge. The Library Quarterly 49(1), 325.Google Scholar
Swanson, D. R. 1986a. Fish oil, Raynaud’s syndrome, and undiscovered public knowledge. Perspectives in Biology and Medicine 30(1), 718.Google Scholar
Swanson, D. R. 1986b. Undiscovered public knowledge. The Library Quarterly 56(2), 103118.Google Scholar
Swanson, D. R. 1987. Two medical literatures that are logically but not bibliographically connected. Journal of the American Society for Information Science 38(4), 228.3.0.CO;2-G>CrossRefGoogle Scholar
Swanson, D. R. 1988. Migraine and magnesium: eleven neglected connections. Perspectives in Biology and Medicine 31(4), 526557.Google Scholar
Swanson, D. R. 1990. The absence of co-citation as a clue to undiscovered causal connections. Scholarly Communication and Bibliometrics, 129137.Google Scholar
Swanson, D. R. 1993. Intervening in the life cycles of scientific knowledge. Library Trends 41(4), 606631.Google Scholar
Swanson, D. R. & Smalheiser, N. R. 1997. An interactive system for finding complementary literatures: a stimulus to scientific discovery. Artificial Intelligence 91(2), 183203.Google Scholar
Symonds, M., Bruza, P. & Sitbon, L. 2014. The efficiency of corpus-based distributional models for literature-based discovery on large data sets. In Proceedings of the Second Australasian Web Conference – Volume 155, AWC ’14, 49–57.Google Scholar
Tarjan, R. 1972. Depth-first search and linear graph algorithms. SIAM Journal on Computing 1(2), 146160.CrossRefGoogle Scholar
Torvik, V. I. & Smalheiser, N. R. 2007. A quantitative model for linking two disparate sets of articles in medline. Bioinformatics 23(13), 16581665.Google Scholar
Uzzi, B., Mukherjee, S., Stringer, M. & Jones, B. 2013. Atypical combinations and scientific impact. Science 342(6157), 468472.Google Scholar
Valdés-Pérez, R. E. 1999. Principles of human-computer collaboration for knowledge discovery in science. Artificial Intelligence 107(2), 335346.Google Scholar
van Haagen, H.H., AC’t Hoen, P., Bovo, A.B., de Morrée, A., van Mulligen, E.M., Chichester, C., Kors, J.A., den Dunnen, J.T., van Ommen, G.J.B., van der Maarel, S.M. & Kern, V.M. 2009. Novel protein-protein interactions inferred from literature context. PLoS One 4(11), e7894.Google Scholar
van Haagen, H. H., ’t Hoen, P. A., de Morree, A., van Roon-Mom, W., Peters, D. J., Roos, M., Mons, B., van Ommen, G.-J. & Schuemie, M. J. 2011. In silico discovery and experimental validation of new protein–protein interactions. Proteomics 11(5), 843853.Google Scholar
van Mulligen, E. M., van Der Eijk, C., Kors, J. A., Schijvenaars, B. J. & Mons, B. 2002. Research for research: tools for knowledge discovery and visualization. In Proceedings of the 2002 AMIA Symposium, 835. American Medical Informatics Association.Google Scholar
Waltman, L. & Eck, N. J. 2012. A new methodology for constructing a publication-level classification system of science. Journal of the American Society for Information Science and Technology 63(12), 23782392.Google Scholar
Weeber, M., Klein, H., de Jong-van den Berg, L. & Vos, R. 2001. Using concepts in literature-based discovery: simulating Swanson’s Raynaud–fish oil and migraine–magnesium discoveries. Journal of the American Society for Information Science and Technology 52(7), 548557.Google Scholar
Weeber, M., Kors, J. A. & Mons, B. 2005. Online tools to support literature-based discovery in the life sciences. Briefings in Bioinformatics 6(3), 277286.Google Scholar
Weeber, M., Vos, R., Klein, H., Aronson, A. R. & Molema, G. 2003. Generating hypotheses by discovering implicit associations in the literature: a case report of a search for new potential therapeutic uses for thalidomide. Journal of the American Medical Informatics Association 10(3), 252259.Google Scholar
Wei, C.-P., Chen, K.-A. & Chen, L.-C. 2014. Mining biomedical literature and ontologies for drug repositioning discovery. In Advances in Knowledge Discovery and Data Mining: 18th Pacific-Asia Conference, PAKDD 2014, Tainan, Taiwan, May 13-16, V. S. Tseng, T. B. Ho, Z.-H. Zhou, A. L. P. Chen & H.-Y. Kao (eds). Springer International Publishing, 373384.Google Scholar
White, H. D. & Griffith, B. C. 1981. Author cocitation: a literature measure of intellectual structure. Journal of the American Society for Information Science 32(3), 163171.Google Scholar
Wilkowski, B., Fiszman, M., Miller, C. M., Hristovski, D., Arabandi, S., Rosemblat, G. & Rindflesch, T. C. 2011. Graph-based methods for discovery browsing with semantic predications. In Proceedings of the 2011 AMIA Symposium, 2011, 1514. American Medical Informatics Association.Google Scholar
Witten, I. H. & Frank, E. 2005. Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann.Google Scholar
Wren, J. D. 2004. Extending the mutual information measure to rank inferred literature relationships. BMC Bioinformatics 5(1), 1.Google Scholar
Wren, J. D. 2008. The ‘open discovery’ challenge. In Literature-Based Discovery, P. Bruza & M. Weeber (eds). Springer Berlin Heidelberg, 3955.CrossRefGoogle Scholar
Wren, J. D., Bekeredjian, R., Stewart, J. A., Shohet, R. V. & Garner, H. R. 2004. Knowledge discovery by automated identification and ranking of implicit relationships. Bioinformatics 20(3), 389398.Google Scholar
Yamamoto, Y. & Takagi, T. 2007. Biomedical knowledge navigation by literature clustering. Journal of Biomedical Informatics 40(2), 114130.Google Scholar
Yetisgen-Yildiz, M. 2006. Litlinker: a system for searching potential discoveries in biomedical literature. In Proceedings of 29th Annual International ACM SIGIR Conference on Research & Development on Information Retrieval (SIGIR’06) Doctoral Consortium, Seattle, WA.Google Scholar
Yetisgen-Yildiz, M. & Pratt, W. 2006. Using statistical and knowledge-based approaches for literature-based discovery. Journal of Biomedical Informatics 39(6), 600611.Google Scholar
Yetisgen-Yildiz, M. & Pratt, W. 2008. Evaluation of literature-based discovery systems. In Literature-Based Discovery, P. Bruza & M. Weeber (eds). Springer Berlin Heidelberg, 101113.Google Scholar
Yetisgen-Yildiz, M. & Pratt, W. 2009. A new evaluation methodology for literature-based discovery systems. Journal of Biomedical Informatics 42(4), 633643.Google Scholar
Youn, H., Strumsky, D., Bettencourt, L. M. & Lobo, J. 2015. Invention as a combinatorial process: evidence from US patents. Journal of The Royal Society Interface 12(106), 20150272.Google Scholar