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A survey of formalisms for representing and reasoning with scientific knowledge

Published online by Cambridge University Press:  01 June 2010

Anthony Hunter*
Affiliation:
Department of Computer Science, University College London, London, WC1E 6BT, UK
Weiru Liu*
Affiliation:
School of Electronics, Electrical Engineering and Computer Science, Queen’s University Belfast, Belfast, BT9 5BN, UK

Abstract

With the rapid growth in the quantity and complexity of scientific knowledge available for scientists, and allied professionals, the problems associated with harnessing this knowledge are well recognized. Some of these problems are a result of the uncertainties and inconsistencies that arise in this knowledge. Other problems arise from heterogeneous and informal formats for this knowledge. To address these problems, developments in the application of knowledge representation and reasoning technologies can allow scientific knowledge to be captured in logic-based formalisms. Using such formalisms, we can undertake reasoning with the uncertainty and inconsistency to allow automated techniques to be used for querying and combining of scientific knowledge. Furthermore, by harnessing background knowledge, the querying and combining tasks can be carried out more intelligently. In this paper, we review some of the significant proposals for formalisms for representing and reasoning with scientific knowledge.

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Articles
Copyright
Copyright © Cambridge University Press 2010

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References

Andersen, S., Olesen, K., Jensen, F. V., Jensen, F. 1989. Hugin—a shell for building bayesian belief universes for expert systems. In Proceedings of the 10th International Joint Conference on Artificial Intelligence, 10801085. Morgan Kaufmann.Google Scholar
Andreassen, S., Woldbye, M., Falck, B., Andersen, S. 1987. MUNIN—a causal probabilistic network for interpretation of electromyographic findings. In Proceedings of the 10th International Joint Conference on Artificial Intelligence (IJCAI’87), 366–372.Google Scholar
Antoniou, G., Bikakis, A. 2007. DR-prolog: a system for defeasible reasoning with rules and ontologies on the semantic web. IEEE Transactions on Knowledge and Data Engineering 19(2), 233246.CrossRefGoogle Scholar
Baader, F., Calvanese, D., McGuinness, D., Nardi, D., Patel-Schneider, P. (eds) 2003. The Description Logic Handbook: Theory, Implementation and Applications. Cambridge University Press.Google Scholar
Bacchus, F. 1990. Representing and Reasoning with Probabilisitic Knowledge: A Logical Approach to Probabilities. MIT Press.Google Scholar
Bada, M., Turi, D., McEntire, R., Stevens, R. 2004. Using reasoning to guide annotation with gene ontology terms in goat. SIGMOD Record 33(2), 2732.CrossRefGoogle Scholar
Baker, P. G., Brass, A., Bechhofer, S., Goble, C., Paton, N., Stevens, R. 1998. Tambis: transparent access to multiple bioinformatics information sources. An overview. In Proceedings of the 6th International Conference on Intelligent Systems for Molecular Biology (ISMB’98), 2534. AAAI Press.Google Scholar
Baral, C., Hunsaker, M. 2007. Using the probabilistic logic programming language p-log for causal and counterfactual reasoning and non-naive conditioning. In Proceeding of 20th International Joint Conference on Artificial Intelligence (IJCAI’07), 243–249.Google Scholar
Baral, C., Kraus, S., Minker, J. 1991. Combining multiple knowledge bases. IEEE Transactions on Knowledge and Data Engineering 3, 208220.CrossRefGoogle Scholar
Baral, C., Chancellor, K., Tran, N., Tran, N., Joy, A., Berens, M. 2004. A knowledge based approach for representing and reasoning about signaling networks. Bioinformatics 20, Supplement 1, i15i20.CrossRefGoogle ScholarPubMed
Belnap, N. 1977. A useful four-valued logic. In Modern Uses of Multiple-valued Logic, Epstein, G (ed), 837. Reidel.Google Scholar
Bench-Capon, T., Dunne, P. 2007. Argumentation in artificial intelligence. Artificial Intelligence 171, 619641.CrossRefGoogle Scholar
Bergamaschi, S., Castano, S., Vincini, M., Beneventano, D. 2001. Semantic integration of heterogeneous information sources. Data and Knowledge Engineering 36, 215249.CrossRefGoogle Scholar
Bertossi, L., Chomicki, J. 2003. Query answering in inconsistent databases. In Logics for Emerging Applications of Databases, Chomicki G. S. J. & van der Meyden R. (eds). Springer.Google Scholar
Besnard, P., Hunter, A. 2001. A logic-based theory of deductive arguments. Artificial Intelligence 128, 203235.CrossRefGoogle Scholar
Besnard, P., Hunter, A. 2005. Practical first-order argumentation. In Proceedings of the 20th National Conference on Artificial Intelligence (AAAI 2005), 590595. MIT Press.Google Scholar
Besnard, P., Hunter, A. 2008. Elements of Argumentation. MIT Press.CrossRefGoogle Scholar
Bratko, I. 2000. Prolog Programming for Artificial Intelligence. Addison Wesley.Google Scholar
Bryant, D., Krause, P. 2008. A review of current defeasible reasoning implementations. Knowledge Engineering Review 23(2), 134.CrossRefGoogle Scholar
Bryant, D., Krause, P., Vreeswijk, G. 2006. Argue tuprolog: a lightweight argumentation engine for agent applications. In Computational Models of Argument (Comma’06), 2732. IOS Press.Google Scholar
Buckingham Shum, S. 2007. Modelling naturalistic argumentation in research literatures: representation and interaction design issues. Intelligent Systems 22(1), 1747. Special Issue on Computational Modelling of Naturalistic Argumentation.Google Scholar
Burger, A., Davidson, D., Baldock, R. 2004. Formalization of mouse embryo anatomy. Bioinformatics 200(2), 259267.CrossRefGoogle Scholar
Calvanese, D., Giacomo, G. D., Lenzerini, M., Nardi, D., Rosati, R. 1998a. Source integration in data warehousing. In Proceedings of the 9th International Workshop on Database and Expert Systems (DEXA’98), 192197. IEEE Computer Society Press.Google Scholar
Calvanese, D., Giacomo, G. D., Lenzerini, M., Nardi, D., Rosati, R. 1998b. Description logic framework for information integration. In Proceedings of the 6th Conference on the Principles of Knowledge Representation and Reasoning (KR’98), 213. Morgan Kaufmann.Google Scholar
Calvanese, D., Lembo, G. D., Lenzerin, M., Rosati, R. 2008. Inconsistency tolerance in P2P data integration: an epistemic logic approach. Information Systems 33(4–5), 360384.CrossRefGoogle Scholar
Chesñevar, C., Maguitman, A., Loui, R. 2000. Logical models of argument. ACM Computing Surveys 32, 337383.CrossRefGoogle Scholar
Chesñevar, C., McGinnis, J., Modgil, S., Rahwan, I., Reed, C., Simari, G., South, M., Vreeswijk, G., Willmott, S. 2006. Towards an argument interchange format. The Knowledge Engineering Review 21(4), 293316.CrossRefGoogle Scholar
Choi, N., Song, I., Han, H. 2006. A survey on ontology mapping. Sigmod Record 35(3), 3441.CrossRefGoogle Scholar
Cholvy, L. 1998. Reasoning with data provided by federated databases. Journal of Intelligent Information Systems 10, 4980.CrossRefGoogle Scholar
Cholvy, L., Moral, S. 2001. Merging databases: problems and examples. International Journal of Intelligent Systems 10, 11931221.CrossRefGoogle Scholar
Clegg, A., Shepherd, A. 2007. Benchmarking natural-language parsers for biological applications using dependency graphs. BMC Bioinformatics 8, 24.CrossRefGoogle ScholarPubMed
Cowie, J., Lehnert, W. 1996. Information extraction. Communications of ACM 39, 8191.CrossRefGoogle Scholar
Cunningham, H., Maynard, D., Bontcheva, K., Tablan, V. 2002. Gate: a framework and graphical development environment for robust nlp tools and applications. In Proceedings of the 40th Anniversary Meeting of the Association for Computational Linguistics (ACL’02).Google Scholar
Dal Palu, A., Dovier, A., Fogolari, F. 2004. Constraint logic programming approach to protein structure prediction. BMC Bioinformatics 5, 186.CrossRefGoogle ScholarPubMed
de Bruijn, J., Martin-Recuerda, F., Manov, D., Ehrig, M. 2004. State-of-the-art survey on ontology merging and aligning v1. Technical report, SEKT: Semantically Enabled Knowledge Technologies, 2004. EU-IST Integrated Project (IP) IST-2003-506826.Google Scholar
Dung, P. 1995. On the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming, and n-person games. Artificial Intelligence 77, 321357.CrossRefGoogle Scholar
Dung, P., Kowalski, R., Toni, F. 2006. Dialectical proof procedures for assumption-based admissible argumentation. Artificial Intelligence 17, 114159.CrossRefGoogle Scholar
Dworschak, S., Grell, S., Nikiforova, V., Schaub, T., Selbig, J. 2008. Modeling biological networks by action languages via answer set programming. Constraints 13(1–2), 2165.CrossRefGoogle Scholar
Efstathiou, V., Hunter, A. 2008. Algorithms for effective argumentation in classical propositional logic: a connection graph approach. In Foundations of Information and Knowledge Systems, 5th International Symposium (FoIKS’08), Lecture Notes in Computer Science 4932, 272–290.Google Scholar
Finn, P., Muggleton, S., Page, D., Srinivasan, A. 1998. Pharmacophore discovery using the inductive logic programming system progol. Machine Learning 30, 241271.CrossRefGoogle Scholar
Fox, J., Das, S. 2000. Safe and Sound: Artificial Intelligence in Hazardous Applications. MIT Press.Google Scholar
Franconi, E., Sattler, U. 1999. A data warehouse conceptual data model for multidimensional aggregation. In Proceedings of the Workshop in Design and Management of Data Warehouses, Gatziu, S., Jeusfeld, M., Staudt, M. & Vassiliou, Y. (eds). CEUR Workshop Proceedings.Google Scholar
Friedman, C., Kra, P., Yu, H., Krauthammer, M., Rzhetsky, A. 2001. GENIES: a natural-language processing system for the extraction of molecular pathways from journal articles. Bioinformatics 17, Supplement 1, S74S82.CrossRefGoogle ScholarPubMed
Fuhr, N. 2000. Probabilistic datalog: implementing logical information retrieval for advanced applications. Journal of the American Society for Information Science 51(2), 95110.3.0.CO;2-H>CrossRefGoogle Scholar
Fukuda, K., Takagi, T. 2001. Knowledge representation of signal transduction pathways. Bioinformatics 17(9), 829837.CrossRefGoogle ScholarPubMed
Gaertner, D., Toni, F. 2007. Computing arguments and attacks in assumption-based argumentation. IEEE Intelligent Systems 22(6), 2433.CrossRefGoogle Scholar
García, A., Simari, G. 2004. Defeasible logic programming: an argumentative approach. Theory and Practice of Logic Programming 4, 95138.CrossRefGoogle Scholar
Gorogiannis, N., Hunter, A. 2008a. Implementing semantic merging operators using binary decision diagrams. International Journal of Approximate Reasoning 49, 234251.CrossRefGoogle Scholar
Gorogiannis, N., Hunter, A. 2008b. Merging first-order knowledge using dilation operators. In Foundations of Information and Knowledge Systems (FOIKS’08), Lecture Notes in Computer Science, 4932, 132150. Springer.CrossRefGoogle Scholar
Grahne, G., Mendelzon, A. 1999. Tableau techniques for querying information sources through global schemas. In Proceedings of the 7th International Conference on Database Theory (ICDT’99), Lecture Notes in Computer Science, 1540, 332347. Springer.Google Scholar
Hahn, U., Wermter, J., Blasczyk, R., Horn, P. 2007. Text mining: powering the database revolution. Nature 448, 130.CrossRefGoogle ScholarPubMed
Halpern, J. 1990. An analysis of first-order logics of probability. Artificial Intelligence 46, 311350.CrossRefGoogle Scholar
Harmon, J. 2007. The Scientific Literature: A Guided Tour. University of Chicago Press.Google Scholar
Heckerman, D., Wellman, M. 1995. Bayesian networks. Communications of the ACM 38, 2730.CrossRefGoogle Scholar
Hui, K., Gray, P. 2000. Developing finite domain constraints—a data model approach. In Proceedings of Computation Logic 2000 Conference, Lecture Notes in Computer Science, 1861, 448462. Springer.CrossRefGoogle Scholar
Hunter, A., Liu, W. 2006. Fusion rules for merging uncertain information. Information Fusion 70(1), 97134.CrossRefGoogle Scholar
Hunter, A., Summerton, R. 2006. A knowledge-based approach to merging information. Knowledge-Based Systems 19(8), 647674.CrossRefGoogle Scholar
Jefferys, B., Kelley, L., Sergot, M., Fox, J., Sternberg, M. 2006. Capturing expert knowledge with argumentation:a case study in bioinformatics. Bioinformatics 22, 924933.CrossRefGoogle Scholar
Jensen, F. 1996. Introduction to Bayesian networks. UCL Press.Google Scholar
Jonker, C., Snoep, J., Treur, J., Westerhoff, H., Winjgaards, W. 2002. Putting intentions into cell biochemistry: an artificial intelligence perspective. Journal of Theoretical Biology 214, 105134.CrossRefGoogle ScholarPubMed
Kakas, A., Miller, R., Toni, F. 2000. E-RES: a system for reasoning about actions, events and observations. In Proccedings of the International Workshop on Non-monotonic Reasoning (NMR’00).Google Scholar
King, R., Muggleton, S., Srinivasan, A., Sternberg, M. 1996. Structure-activity relationships derived by machine learning: the use of atoms and their bond connectives to predict mutagenicity by inductive logic programming. Proceedings of the National Academy of Sciences 93, 438442.CrossRefGoogle ScholarPubMed
King, R., Whelan, K., Jones, F., Reiser, P., Bryant, C., Muggleton, S., Kell, D., Oliver, S. 2004. Functional genomic hypothesis generation and experimentation by a robot scientist. Nature 427, 247252.CrossRefGoogle ScholarPubMed
Konieczny, S., Lang, J., Marquis, P. 2004. DA2 merging operator. Artificial Intelligence 157, 4979.CrossRefGoogle Scholar
Krause, P., Clark, D. 1993. Representing Uncertain Knowledge: An Artificial Intelligence Approach. Intellect.Google Scholar
Krause, P., Fox, J., Judson, P. 1993. An argumentation-based approach to risk assessment. IMA Journal of Mathmematics in Business and Industry 5, 249263.Google Scholar
Krause, P., Ambler, S., Elvang-Gorannson, M., Fox, J. 1995a. A logic of argumentation for reasoning under uncertainty. Computational Intelligence 11, 113131.CrossRefGoogle Scholar
Krause, P., Ambler, S., Elvang-Gorannson, M., Fox, J. 1995b. Is there a role for qualitative risk assessment. In Proceedings of the 11th Conference on Uncertainty in Artificial Intelligence. Morgan Kaufmann.Google Scholar
Krause, P., Fox, J., Judson, P., Patel, M. 1998. Qualitative risk assessment fulfils a need. In Applications of Uncertainty Formalisms, Lecture Notes in Computer Science, 1455, 138156. Springer.CrossRefGoogle Scholar
Krötzsch, M., Hitzler, P., Vrandecic, D., Sintek, M. 2006. How to reason with OWL in a logic programming system. In Proceedings of the 2nd International Conference on Rules and Rule Markup Languages for the Semantic Web (RuleML2006), 1726. IEEE Computer Society.Google Scholar
Laera, L., Tamma, V., Bench-Capon, T., Semeraro, G. 2004. Sweetprolog: a system to integrate ontologies and rules. In Rules and Rule Mark-Up Languages for the Semantic Web, Lecture Notes in Computer Science, 3323, 188193. Springer.CrossRefGoogle Scholar
Laera, L., Blacoe, I., Tamma, V., Payne, T., Euzenat, J., Bench-Capon, T. 2007. Argumentation over ontology correspondences in mas. In Proceedings of the 6th International Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS 2007).Google Scholar
Lauritzen, S., Siegelhalter, D. 1988. Local computations with probabilities on graphical structures and their application to expert systems. Journal of the Royal Statistical Society B 50, 157224.Google Scholar
Loyer, Y., Spyratos, N., Stamate, D. 2000. Integration of information in four-valued logics under non-uniform assumptions. In Proceedings of 30th IEEE International Symposium on Multiple-Valued Logic (ISMVL2000). IEEE Press.Google Scholar
Lukasiewicz, T. 1998. Probabilistic logic programming. In Proceedings of the European Conference on Artificial Intelligence (ECAI’98), 388392. John Wiley.Google Scholar
Manning, C., Schutze, H. 2000. Foundations of Statistical Natural Language Processing. MIT Press.Google Scholar
Ma, J., Liu, W., Hunter, A. 2007. Incomplete statistical information fusion and its application to clinical trials data. In Scalable Uncertainty Management (SUM’07), Lecture Notes in Computer Science 4772, 89103. Springer.CrossRefGoogle Scholar
Ma, J., Liu, W., Hunter, A., Zhang, W. 2008. Performing meta-analysis with incomplete statistical information in clinical trials. BMC Medical Research Methodology 8, 56.CrossRefGoogle ScholarPubMed
McBurney, P., Parsons, S. 2001. Dialectical argumentation for reasoning about chemical carcinogenicity. Logic Journal of the IGPL 9(2), 191203.CrossRefGoogle Scholar
Meyer, T., Lee, K., Booth, R. 2006. Knowledge integration for description logics. In Proceedings of the 20th National Conference on Artificial Intelligence (AAAI’06), 645650.Google Scholar
Muggleton, S. 1991. Inductive logic programming. New Generation Computing 8(4), 295318.CrossRefGoogle Scholar
Muggleton, S. 1999. Scientific knowledge discovery using inductive logic programming. Communications of the ACM 42, 4246.CrossRefGoogle Scholar
Nardi, D., Brachman, R. 2003. An introduction to description logics. In The Description Logic Handbook: Theory, Implementation and Applications, Baader, F., Calvanese, D., McGuinness, D., Nardi, D., Patel-Schneider, P. (eds). chapter 1, Cambridge University Press.Google Scholar
Nilsson, N. 1986. Probabilistic logic. Artificial Intelligence 28, 7187.CrossRefGoogle Scholar
Noy, N., Musen, M. 2000. Prompt: algorithm and tool for automated ontology merging and alignment. In Proceeding AAAI’00, 450455.Google Scholar
Oda, K., Kim, J., Ohta, T., Okanohara, D., Matsuzaki, T., Tateisi, Y., Tsujii, J. 2008. New challenges for text mining: mapping between text and manually curated pathways. BMC Bioinformatics 9, 114; (Supplement 3), S5.CrossRefGoogle ScholarPubMed
Parsons, S. 1998. Qualitative Approaches to Reasoning Under Uncertainty. MIT Press.Google Scholar
Parsons, S., Hunter, A. 1998. A review of uncertainty handling formalisms. In Applications of Uncertainty Formalisms, Lecture Notes in Computer Science 1455, 837. Springer.CrossRefGoogle Scholar
Pearl, J. 1988. Probabilistic Reasoning in Intelligent Systems: Networks of plausible inference. Morgan Kaufmann.Google Scholar
Poulovassilis, A., McBrien, P. 1998. A general formal framework for schema transformation. Data and Knowledge Engineering 28, 4771.CrossRefGoogle Scholar
Prakken, H., Vreeswijk, G. 2002. Logical systems for defeasible argumentation. In Handbook of Philosophical Logic D. Gabbay (ed), 219318. Kluwer.Google Scholar
Preece, A., Hui, K., Gray, A., Marti, P., Bench-Capon, T., Jeans, D., Cui, Z. 1999. The KRAFT architecture for knowledge fusion and transformation. In Expert Systems. Springer.Google Scholar
Raedt, L. D., Kimmig, A., Toivonen, H. 2007. Problog: a probabilistic prolog and its application in link discovery. In Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI’07), 24622467.Google Scholar
Rahwan, I., McBurney, P. 2007. Argumentation technology. IEEE Intelligent Systems 22(6), 2123.CrossRefGoogle Scholar
Rector, A. 2003. Medical informatics. In The Description Logics Handbook, Baader, F., Calvanese, D., McGuinness, D., Nardi, D. & Patel-Schneider, P. (eds), 406426. Cambridge University Press.Google Scholar
South, M., Vreeswijk, G., Fox, J. 2008. Dungine: a Java Dung reasoner. In Computational Models of Argument (COMMA’08), 360368.Google Scholar
Stevens, R., Goble, C., Horrocks, I., Bechhofer, S. 2001a. OILing the way to machine understandable bioinformatics resources. Special issue: IEEE Information Technology in Biomedicine 6(2), 129134.Google Scholar
Stevens, R., Goble, C., Horrocks, I., Bechhofer, S. 2001b. Building a bioinformatics ontology using OIL. Special issue: IEEE Information Technology in Biomedicine 6(2), 135141.Google Scholar
Szalay, A., Gray, J. 2007. 2020 Computing: science in an exponential world. Nature 44, 413414.Google Scholar
Tamaddoni-Nezhad, A., Chaleil, R., Kakas, A., Muggleton, S. 2004. Abduction and induction for modelling inhibition in metabolic networks. In Proceedings of the International Workshop on the Integration of Abduction and Induction in Artificial Intelligence, Flach, P., Kakas, A. & Ray, O. (eds).Google Scholar
Tamaddoni-Nezhad, A., Chaleil, R., Kakas, A., Sternberg, M., Nicholson, J., Muggleton, S. 2007. Modeling the effects of toxins in metabolic networks. IEEE Engineering in Medicine and Biology 26, 3746.CrossRefGoogle ScholarPubMed
Teufel, S., Moens, M. 2000. What’s yours and what’s mine: determining intellectual attribution in scientific text. In Proceedings of the 2000 Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora.Google Scholar
Teufel, S., Moens, M. 2002. Summarising scientific articles: experiments with relevance and rhetorical status. Computational Linguistics 28, 409445.CrossRefGoogle Scholar
Tran, N., Baral, C. 2007. Reasoning about non-immediate triggers in biochemical networks. Annals of Mathematics and Artificial Intelligence 51(2–4), 267293.CrossRefGoogle Scholar
Tran, N., Baral, C. 2009. Hypothesizing about signaling networks. Journal of Applied Logic 7(3), 253274.CrossRefGoogle Scholar
Tran, N., Baral, C., Shankland, C. 2005. Issues in reasoning about interaction networks in cells: necessity of event ordering knowledge. In Proceedings of the National Conference on Artificial Intelligence (AAAI’05), 676681. MIT Press.Google Scholar
Walton, D. 2006. Fundamentals of Critical Argumentation. Cambridge University Press.Google Scholar
Walton, R., Gierl, C., Yudkin, P., Mistry, H., Vessey, M., Fox, J. 1997. Evaluation of computer support for prescribing (CAPSULE). British Medical Journal 315, 791795.CrossRefGoogle ScholarPubMed
Williams, M., Hunter, A. 2007. Harnessing ontologies for argument-based decision-making in breast cancer. In Proceedings of the International Conference on Tools with AI (ICTAI’07), 254261. IEEE Press.Google Scholar
Zupan, B., Bratko, I., Demsar, J., Beck, J., Kuspa, A., Shaulsky, G. 2001. Abductive inference of genetic networks. In Artificial Intelligence in Medicine, Lecture Notes in Computer Science 2101, 304313. Springer.CrossRefGoogle Scholar
Zupan, B., Bratko, I., Demsar, J., Juvan, P., Halter, J., Kuspa, A., Shaulsky, G. 2003. Genepath: a system for automated construction of genetic networks from mutant data. Bioinformatics 19(3), 383389.CrossRefGoogle ScholarPubMed