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Scientific Knowledge Engineering: a conceptual delineation and overview of the state of the art

Published online by Cambridge University Press:  29 March 2016

Paulo Sérgio M. Dos Santos Open the ORCID record for Paulo Sérgio M. Dos Santos [Opens in a new window]  and
Guilherme H. Travassos Open the ORCID record for Guilherme H. Travassos [Opens in a new window]
Paulo Sérgio M. Dos Santos
Affiliation:
Ilha do Fundão, Centro de Tecnologia, Bloco H, Sala 317, Rio de Janeiro, RJ, Brazil e-mail: pasemes@cos.ufrj.br, ght@cos.ufrj.br
Guilherme H. Travassos
Affiliation:
Ilha do Fundão, Centro de Tecnologia, Bloco H, Sala 317, Rio de Janeiro, RJ, Brazil e-mail: pasemes@cos.ufrj.br, ght@cos.ufrj.br
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Abstract

As a community work, scientific contributions are usually built incrementally, involving some transformation, expansion or refutation of existing conceptual and propositional networks. As the body of knowledge increases, scientists concentrate more effort on ensuring that new hypotheses and observations are needed and consistent with previous findings. In this paper, we will characterize Knowledge Engineering as an important groundwork for structuring scientific knowledge. We argue that knowledge-based computational infrastructures can support researchers in organizing and making explicit the main aspects needed to make inferences or extract conclusions from an existing body of knowledge. This view is also comparatively built, contrasting it with alternatives for manipulating scientific knowledge, namely data-intensive approaches and the computational discovery of scientific knowledge. The current state of the art is presented with 22 knowledge representations and computational infrastructure implementations, with their main relevant properties analyzed and compared. Based on this review and on the theoretical foundations of Knowledge Engineering, a high level step-by-step approach for specifying and constructing scientific computational environments is described. The paper concludes by indicating paths for further development of the view initiated here, especially related to the technical specificities that originates from applying Knowledge Engineering to scientific knowledge.

Type
Articles
Information
The Knowledge Engineering Review , Volume 31 , Issue 2 , March 2016 , pp. 167 - 199
Copyright
© Cambridge University Press, 2016 

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References

References

Atkins, D., Best, D., Briss, P. A., Eccles, M., Falck-Ytter, Y., Flottorp, S., Guyatt, G. H., Harbour, R. T., Haugh, M. C., Henry, D., Hill, S., Jaeschke, R., Leng, G., Liberati, A., Magrini, N., Mason, J., Middleton, P., Mrukowicz, J., O’Connell, D., Oxman, A. D., Phillips, B., Schünemann, H. J., Edejer, T. T.-T., Varonen, H., Vist, G. E.,Williams, J. W. Jr & Zaza, S., GRADE Working Group 2004. Grading quality of evidence and strength of recommendations. BMJ 328(7454), 1490.Google Scholar
Bairoch, A. 2009. The future of annotation/biocuration, Nature Precedings.Google Scholar
Barga, R. & Gannon, D. 2007. Scientific versus business workows. In Workows for e-Science, Taylor I. J., Deelman E., Gannon D. B. & Shields M. (eds). Springer, 9–16.CrossRefGoogle Scholar
Bauer-Mehren, A., Furlong, L. I. & Sanz, F. 2009. Pathway databases and tools for their exploitation: benefits, current limitations and challenges. Molecular Systems Biology 5(290).Google Scholar
Bechhofer, S., Buchan, I., De Roure, D., Missier, P., Ainsworth, J., Bhagat, J., Couch, P., Cruickshank, D., Delderfield, M., Dunlop, I., Gamble, M., Michaelides, D., Owen, S., Newman, D., Sufi, S. & Goble, C. 2013. Why linked data is not enough for scientists. Future Generation Computer Systems 29(2), 599–611.CrossRefGoogle Scholar
Biolchini, J., Mian, P., Natali, A. & Travassos, G. H. 2005. Systematic review in software engineering. Technical report No. RT-ES 679/05, Federal University of Rio de Janeiro (UFRJ/COPPE).Google Scholar
Booth, A. 2011. Evidence-based practice: triumph of style over substance? Health Information & Libraries Journal 28(3), 237–241.Google Scholar
Budgen, D., Turner, M., Brereton, P. & Kitchenham, B. 2008. Using mapping studies in software engineering. In Proceedings of PPIG Psychology of Programming Interest Group, 195–204. Lancaster University.Google Scholar
Bunge, M. 2004. How does it work? The search for explanatory mechanisms. Philosophy of the Social Sciences 34(2), 182–210.Google Scholar
Bylander, T. & Chandrasekaran, B. 1987. Generic tasks for knowledge-based reasoning: the ‘right’ level of abstraction for knowledge acquisition. International Journal of Man-Machine Studies 26(2), 231–243.Google Scholar
Callahan, A., Dumontier, M. & Shah, N. H. 2011. HyQue: evaluating hypotheses using semantic web technologies. Journal of Biomedical Semantics 2(2), 1–17.Google Scholar
Chua, C. E. H., Storey, V. C. & Chiang, R. H. 2012. Deriving knowledge representation guidelines by analyzing knowledge engineer behavior. Decision Support Systems 54(1), 304–315.CrossRefGoogle Scholar
Cohen, A. M. & Hersh, W. R. 2005. A survey of current work in biomedical text mining. Briefings in Bioinformatics 6(1), 57–71.Google Scholar
Cooper, H. M., Hedges, L. V. & Valentine, J. C. 2009. The Handbook of Research Synthesis and Meta-Analysis. Russell Sage Foundation.Google Scholar
Craver, C. F. & Darden, L. 2005. Introduction. Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences 36(2), 233–244.CrossRefGoogle ScholarPubMed
da Cruz, S., Campos, M. & Mattoso, M. 2009. Towards a taxonomy of provenance in scientific workow management systems. In 2009 World Conference on Services – I, 259–266.Google Scholar
Deelman, E., Gannon, D., Shields, M. & Taylor, I. 2009. Workows and e-science: an overview of workow system features and capabilities. Future Generation Computer Systems 25(5), 528–540.CrossRefGoogle Scholar
Dennis, C. 2002. Biology databases: information overload. Nature 417(6884), 14.CrossRefGoogle ScholarPubMed
Dibble, D. & Bostrom, R. P. 1987. Managing expert systems projects: factors critical for successful implementation. In Proceedings of the Conference on the 1987 ACM SIGBDP-SIGCPR Conference, SIGCPR’ 87, 96–128. ACM.Google Scholar
Dinakarpandian, D., Lee, Y., Vishwanath, K. & Lingambhotla, R. 2006. MachineProse: an ontological framework for scientific assertions. Journal of the American Medical Informatics Association 13(2), 220–232.Google Scholar
Dixon-Woods, M., Agarwal, S., Jones, D., Young, B. & Sutton, A. 2005. Synthesising qualitative and quantitative evidence: a review of possible methods. Journal of Health Services Research & Policy 10(1), 45–53.Google Scholar
Dung, P. M. 1995. On the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming and n-person games. Artificial Intelligence 77(2), 321–357.Google Scholar
Dyba, T., Dingsoyr, T. & Hanssen, G. 2007. Applying systematic reviews to diverse study types: an experience report. In International Symposium on Empirical Software Engineering and Measurement, 225–234.Google Scholar
Džeroski, S., Langley, P. & Todorovski, L. 2007. Computational discovery of scientific knowledge. In Computational Discovery of Scientific Knowledge, Džeroski S. & Todorovski L. (eds), Lecture Notes in Computer Science 4660, 1–14. Springer.Google Scholar
Easterbrook, S., Singer, J., Storey, M.-A. & Damian, D. 2008. Selecting empirical methods for software engineering research. In Guide to Advanced Empirical Software Engineering, Shull F., Singer J. & Sjøberg D. I. K. (eds). Springer, 285–311.Google Scholar
Eriksson, H. 1992. A survey of knowledge acquisition techniques and tools and their relationship to software engineering. Journal of Systems and Software 19(1), 97–107.Google Scholar
Fayyad, U. & Stolorz, P. 1997. Data mining and KDD: promise and challenges. Future Generation Computer Systems 13(2–3), 99–115.Google Scholar
Fellers, J. 1987. Key factors in knowledge acquisition. SIGCPR Computer Personnel 11(1), 10–24.Google Scholar
Fiore, S. & Aloisio, G. 2011. Special section: data management for eScience. Future Generation Computer Systems 27(3), 290–291.Google Scholar
Forbus, K. D. & DeKleer, J. 1993. Building Problem Solvers. MIT Press.Google Scholar
Ford, K. M. 1993. Knowledge Acquisition as Modeling. Wiley.Google Scholar
Freiling, M., Alexande, J., Messick, S., Rehfuss, S. & Shulman, S. 1985. Starting a knowledge engineering project: a step-by-step approach. AI Magazine 6(3), 150.Google Scholar
Goertz, G. & Mahoney, J. 2012. A Tale of Two Cultures: Qualitative and Quantitative Research in the Social Sciences. Princeton University Press.Google Scholar
Hars, A. 2001. Designing scientific knowledge infrastructures: the contribution of epistemology. Information Systems Frontiers 3(1), 63–73.Google Scholar
Hey, T. & Trefethen, A. 2003. The data deluge: an e-science perspective. In Grid Computing, Berman F., Fox G. & Hey T. (eds). John Wiley & Sons Ltd, 809–824.Google Scholar
Hunter, A. & Liu, W. 2010. A survey of formalisms for representing and reasoning with scientific knowledge. The Knowledge Engineering Review 25(2), 199–222.Google Scholar
Hunter, J. 2008. Scientific publication packages—a selective approach to the communication and archival of scientific output. International Journal of Digital Curation 1(1), 33–52.Google Scholar
Ivarsson, M. & Gorschek, T. 2012. Tool support for disseminating and improving development practices. Software Quality Journal 20(1), 173–199.Google Scholar
Khatri, P., Sirota, M. & Butte, A. J. 2012. Ten years of pathway analysis: current approaches and outstanding challenges. PLoS Computational Biology 8(2), e1002375.Google Scholar
Kiritchenko, S., Bruijn, B. D., Carini, S., Martin, J. & Sim, I. 2010. ExaCT: automatic extraction of clinical trial characteristics from journal publications. BMC Medical Informatics and Decision Making 10(1), 56.Google Scholar
Kitchenham, B. & Charters, S. 2007. Guidelines for performing systematic literature reviews in software engineering. Technical Report No. EBSE 2007-001, Keele University and Durham University Joint Report.Google Scholar
Langley, P. 1987. Scientific Discovery: Computational Explorations of the Creative Processes. MIT Press.Google Scholar
Langley, P., Zytkow, J. M., Bradshaw, G. L. & Simon, H. A. 1983. Three facets of scientific discovery. In Proceedings of the Eighth International Joint Conference on Artificial Intelligence – Volume 1, IJCAI’83, 465–468. Morgan Kaufmann Publishers, Inc.Google Scholar
Lenat, D. B. & Feigenbaum, E. A. 1991. On the thresholds of knowledge. Artificial Intelligence 47(1–3), 185–250.Google Scholar
Lewis-Beck, M., Bryman, A. & Liao, T. F. 2004. Encyclopedia of Social Science Research Methods. SAGE Publications, Inc.Google Scholar
Lin, C., Lu, S., Fei, X., Chebotko, A., Pai, D., Lai, Z., Fotouhi, F. & Hua, J. 2009. A reference architecture for scientific workflow management systems and the VIEW SOA solution. IEEE Transactions on Services Computing 2(1), 79–92.Google Scholar
Lord, P., Macdonald, A., Lyon, L. & Giaretta, D. 2004. From data deluge to data curation, In Proceeding of the 3th UK e-Science All Hands Meeting, 371–375.Google Scholar
Maccagnan, A., Riva, M., Feltrin, E., Simionati, B., Vardanega, T., Valle, G. & Cannata, N. 2010. Combining ontologies and workflows to design formal protocols for biological laboratories. Automated Experimentation 2(1), 1–14.Google Scholar
Martinez-Fernandez, S., Santos, P., Ayala, C., Franch, X. & Travassos, G. 2015. Aggregating Empirical Evidence about the Benefits and Drawbacks of Software Reference Architectures, 2015 ACM/IEEE. International Symposium, on Empirical Software Engineering and Measurement (ESEM), pp. 1–10.Google Scholar
Mcdermott, J. 1988. Preliminary steps toward a taxonomy of problem-solving methods. In Automating Knowledge Acquisition for Expert Systems, number 57 in The Kluwer International Series in Engineering and Computer Science, Marcus S. (ed.). Springer, 225–256.Google Scholar
Mons, B. 2005. Which gene did you mean? BMC Bioinformatics 6(1), 142.Google Scholar
Mons, B. & Velterop, J. 2009. Nano-publication in the e-science era. In Workshop on Semantic Web Applications in Scientific Discourse.Google Scholar
Moody, D. 2009. The ‘physics’ of notations: toward a scientific basis for constructing visual notations in software engineering. IEEE Transactions on Software Engineering 35(6), 756–779.Google Scholar
Motta, E., Rajan, T. & Eisenstadt, M. 1990. Knowledge acquisition as a process of model refinement. Knowledge Acquisition 2(1), 21–49.Google Scholar
Newman, H. B., Ellisman, M. H. & Orcutt, J. A. 2003. Data-intensive e-science frontier research. Communication of the ACM 46(11), 68–77.CrossRefGoogle Scholar
Noblit, G. W. & Hare, R. D. 1988. Meta-Ethnography: Synthesizing Qualitative Studies. SAGE.Google Scholar
Novère, N. L., Hucka, M., Mi, H., Moodie, S., Schreiber, F., Sorokin, A., Demir, E., Wegner, K., Aladjem, M. I., Wimalaratne, S. M., Bergman, F. T., Gauges, R., Ghazal, P., Kawaji, H., Li, L., Matsuoka, Y., Villéger, A., Boyd, S. E., Calzone, L., Courtot, M., Dogrusoz, U., Freeman, T. C., Funahashi, A., Ghosh, S., Jouraku, A., Kim, S., Kolpakov, F., Luna, A., Sahle, S., Schmidt, E., Watterson, S., Wu, G., Goryanin, I., Kell, D. B., Sander, C., Sauro, H., Snoep, J. L., Kohn, K. & Kitano, H. 2009. The systems biology graphical notation. Nature Biotechnology 27(8), 735–741.CrossRefGoogle ScholarPubMed
Petersen, K., Feldt, R., Mujtaba, S. & Mattsson, M. 2008. Systematic mapping studies in software engineering. In Proceedings of the 12th International Conference on Evaluation and Assessment in Software Engineering, EASE’08, 68–77. British Computer Society.Google Scholar
Plant, R. T. 1991. Rigorous approach to the development of knowledge-based systems. Knowledge-Based Systems 4(4), 186–196.Google Scholar
Rainer, A., Jagielska, D. & Hall, T. 2005. Software engineering practice versus evidence-based software engineering research. In Proceedings of the 2005 Workshop on Realising Evidence-Based Software Engineering, REBSE’05, 1–5. ACM.Google Scholar
Rook, F. & Croghan, J. 1989. The knowledge acquisition activity matrix: a systems engineering conceptual framework. IEEE Transactions on Systems, Man and Cybernetics 19(3), 586–597.Google Scholar
Rzhetsky, A., Iossifov, I., Koike, T., Krauthammer, M., Kra, P., Morris, M., Yu, H., Duboué, P. A., Weng, W., Wilbur, W. J., Hatzivassiloglou, V. & Friedman, C. 2004. GeneWays: a system for extracting, analyzing, visualizing, and integrating molecular pathway data. Journal of Biomedical Informatics 37(1), 43–53.CrossRefGoogle ScholarPubMed
Sackett, D. L., Rosenberg, W. M., Gray, J. A., Haynes, R. B. & Richardson, W. S. 1996. Evidence based medicine: what it is and what it isn’t. BMJ 312(7023), 71–72.Google Scholar
Sanders, T. J. M., Spooren, W. P. M. & Noordman, L. G. M. 1993. Coherence relations in a cognitive theory of discourse representation. Cognitive Linguistics 4(2), 93–134.Google Scholar
Santos, P. & Travassos, G. 2013. On the representation and aggregation of evidence in software engineering: a theory and belief-based perspective. Electronic Notes in Theoretical Computer Science 292, 95–118.Google Scholar
Santos, P. & Travassos, G. 2015. Aggregating empirical evidence about the benefits and drawbacks of software reference architectures. In International Symposium on Empirical Software Engineering and Measurement (in press).Google Scholar
Santos, P. S., Nascimento, I. & Travassos, G. H. 2015. A computational infrastructure for research synthesis in software engineering. In XVIII Ibero-American Conference on Software Engineering, 309–322. URP, SPC, UCSP, UCSP.Google Scholar
Schreiber, G. 2000. Knowledge Engineering and Management: The CommonKADS Methodology. MIT Press.Google Scholar
Shafer, G. 1976. A Mathematical Theory of Evidence. Princeton University Press.Google Scholar
Shotton, D. 2009. Semantic publishing: the coming revolution in scientific journal publishing. Learned Publishing 22(2), 85–94.Google Scholar
Shrager, J. 1990. Computational Models of Scientific Discovery and Theory Formation. Morgan Kaufmann Publisher.Google Scholar
Shull, F., Feldmann, R. & Shaw, M. 2006. Building decision support in an imperfect world. In International Symposium on Empirical Software Engineering ISESE, 33–35.Google Scholar
Shull, F., Singer, J. & Sjøberg, D. I. K. 2007. Guide to Advanced Empirical Software Engineering, 2008 edition. Springer.Google Scholar
Simon, H. A. 1977. Scientific discovery and the psychology of problem solving. In Models of Discovery, Number 54 in Boston Studies in the Philosophy of Science, Simon H. A. (ed.). Springer, 286–303.Google Scholar
Sjøberg, D. I. K., Dybå, T., Anda, B. C. D. & Hannay, J. E. 2008. Building theories in software engineering. In Guide to Advanced Empirical Software Engineering, Shull F., Singer J. & Sjøberg D. I. K. (eds). Springer, 312–336.Google Scholar
Slater, T., Bouton, C. & Huang, E. S. 2008. Beyond data integration. Drug Discovery Today 13(13–14), 584–589.Google Scholar
Stock, K., Robertson, A., Reitsma, F., Stojanovic, T., Bishr, M., Medyckyj-Scott, D. & Ortmann, J. 2009. eScience for sea science: a semantic scientific knowledge infrastructure for marine scientists. In Fifth IEEE International Conference on e-Science. e-Science’ 09, 110–117.Google Scholar
Studer, R., Benjamins, V. & Fensel, D. 1998. Knowledge engineering: principles and methods. Data & Knowledge Engineering 25(1–2), 161–197.Google Scholar
Travassos, G., Santos, P., Neto, P. & Biolchini, J. 2008. An environment to support large scale experimentation in software engineering. In 13th IEEE International Conference on Engineering of Complex Computer Systems, 2008. ICECCS 2008, 193–202.Google Scholar
Valdés-Pérez, R. E. 1996. Computer science research on scientific discovery. The Knowledge Engineering Review 11(1), 57–66.Google Scholar
Vorms, M. 2011. Representing with imaginary models: formats matter. Studies in History and Philosophy of Science Part A 42(2), 287–295.Google Scholar
Wallace, D. & Fujii, R. 1989. Software verification and validation: an overview. IEEE Software 6(3), 10–17.Google Scholar
Wielinga, B., Schreiber, A. & Breuker, J. 1992. KADS: a modelling approach to knowledge engineering. Knowledge Acquisition 4(1), 5–53.Google Scholar

Literature Survey References

Bölling, C., Weidlich, M. & Holzhüutter, H.-G. 2014. SEE: structured representation of scientific evidence in the biomedical domain using semantic web techniques. Journal of Biomedical Semantics 5(Suppl 1), S1.Google Scholar
Boyce, R., Collins, C., Horn, J. & Kalet, I. 2007. Modeling drug mechanism knowledge using evidence and truth maintenance. IEEE Transactions on Information Technology in Biomedicine 11(4), 386–397.Google Scholar
Brodaric, B., Reitsma, F. & Qiang, Y. 2008. SKIing with DOLCE: toward an e-science knowledge infrastructure. In Proceedings of the Fifth International Conference on Formal Ontology in Information Systems (FOIS 2008), 208–219. IOS Press.Google Scholar
Ciccarese, P., Wu, E., Wong, G., Ocana, M., Kinoshita, J., Ruttenberg, A. & Clark, T. 2008. The SWAN biomedical discourse ontology. Journal of Biomedical Informatics 41(5), 739–751.Google Scholar
Clare, A., Croset, S., Grabmueller, C., Kafkas, S., Liakata, M., Oellrich, A. & Rebholz-Schuhmann, D. 2011. Exploring the generation and integration of publishable scientific facts using the concept of nano-publications. In Workshop on Semantic Publishing at ESWC2011, 13–17.Google Scholar
Croft, D., O’Kelly, G., Wu, G., Haw, R., Gillespie, M., Matthews, L., Caudy, M., Garapati, P., Gopinath, G., Jassal, B., Jupe, S., Kalatskaya, I., Mahajan, S., May, B., Ndegwa, N., Schmidt, E., Shamovsky, V., Yung, C., Birney, E., Hermjakob, H., D’Eustachio, P. & Stein, L. 2011. Reactome: a database of reactions, pathways and biological processes. Nucleic Acids Research 39(Suppl 1), D691–D697.Google Scholar
de Waard, A., Buckingham Shum, S., Carusi, A., Park, J., Samwald, M. & Sándor, Á. 2009. Hypotheses, evidence and relationships: the HypER approach for representing scientific knowledge claims.Google Scholar
de Waard, A. & Schneider, J. 2012. Formalising uncertainty: an ontology of reasoning, certainty and attribution (ORCA). In Workshop on Semantic Technologies Applied to Biomedical Informatics and Individualized Medicine (SATBI+SWIM)..Google Scholar
Dinakarpandian, D., Lee, Y., Vishwanath, K., Lingambhotla, R. 2006. MachineProse: an ontological framework for scientific assertions. Journal of the American Medical Informatics Association 13(2), 220–232.CrossRefGoogle Scholar
Ekaputra, F., Sabou, M., Serral, E. & Biffl, S. 2014. Supporting information sharing for reuse and analysis of scientific research publication data. In Proceedings of the 4th Workshop on Semantic Publishing, SePublica ‘14.Google Scholar
Groth, P., Gibson, A. & Velterop, J. 2010. The anatomy of a nanopublication. Information Services and Use 30(1), 51–56.Google Scholar
Groza, T., Möller, K., Handschuh, S., Trif, D. & Decker, S. 2007. SALT: weaving the claim web. In The Semantic Web, Aberer K., Choi K.-S., Noy N., Allemang D., Lee K.-I., Nixon L., Golbeck J., Mika P., Maynard D., Mizoguchi R., Schreiber G. & Cudré-Mauroux P. (eds), Lecture Notes in Computer Science 4825, 197–210. Springer.Google Scholar
Hunter, A. & Williams, M. 2012. Aggregating evidence about the positive and negative effects of treatments. Artificial Intelligence in Medicine 56(3), 173–190.Google Scholar
Kraines, S. & Guo, W. 2011. A system for ontology-based sharing of expert knowledge in sustainability science. Data Science Journal 9, 107–123.Google Scholar
Kuhn, T., Barbano, P. E., Nagy, M. L. & Krauthammer, M. 2013. Broadening the scope of nanopublications. In The Semantic Web: Semantics and Big Data, Cimiano P., Corcho O., Presutti V., Hollink L. & Rudolph S. (eds), Lecture Notes in Computer Science 7882, 487–501. Springer.Google Scholar
Mancini, C. & Buckingham Shum, S. J. 2006. Modelling discourse in contested domains: a semiotic and cognitive framework. International Journal of Human-Computer Studies 64(11), 1154–1171.Google Scholar
Marcondes, C. H. 2011. Knowledge network of scientific claims derived from a semantic publication system. Information Services and Use 31(3), 167–176.Google Scholar
Pike, W. & Gahegan, M. 2007. Beyond ontologies: toward situated representations of scientific knowledge. International Journal of Human-Computer Studies 65(7), 674–688.Google Scholar
Russ, T. A., Ramakrishnan, C., Hovy, E. H., Bota, M. & Burns, G. A. 2011. Knowledge engineering tools for reasoning with scientific observations and interpretations: a neural connectivity use case. BMC Bioinformatics 12(1), 351.Google Scholar
Santos, P. & Travassos, G. 2013. On the representation and aggregation of evidence in software engineering: a theory and belief-based perspective. Electronic Notes in Theoretical Computer Science 292, 95–118.Google Scholar
Sharma, R., Poole, D. & Smyth, C. 2010. A framework for ontologically-grounded probabilistic matching. International Journal of Approximate Reasoning 51(2), 240–262.Google Scholar
van Valkenhoef, G., Tervonen, T., Zwinkels, T., de Brock, B. & Hillege, H. 2013. ADDIS: a decision support system for evidence-based medicine. Decision Support Systems 55(2), 459–475.Google Scholar