Blending in the Hub: Towards a Collaborative Concept Invention Platform
Inhaltsverzeichnis
Reference
Oliver Kutz, Fabian Neuhaus, Till Mossakowski and Mihai Codescu: Blending in the Hub: Towards a Collaborative Concept Invention Platform. In: Computational Creativity 2014 ICCC 2014.
DOI
Abstract
Conceptual blending has been employed very success- fully to understand the process of concept invention, studied particularly within cognitive psychology and linguistics. However, despite this influential research, within computational creativity little effort has been devoted to fully formalise these ideas and to make them amenable to computational techniques. We here present the basic formalisation of conceptual blending, as sketched by the late Joseph Goguen, and show how the Distributed Ontology Language DOL can be used to declaratively specify blending diagrams. Moreover, we discuss in detail how the workflow and creative act of generating and evaluating a new, blended concept can be managed and computationally supported within On- tohub, a DOL-enabled theory repository with support for a large number of logical languages and formal link- ing constructs.
Extended Abstract
Bibtex
Used References
Adámek, J.; Herrlich, H.; and Strecker, G. 1990. Abstract and Concrete Categories. Wiley, New York.
Codescu, M., and Mossakowski, T. 2008. Heterogeneous colimits. In Boulanger, F.; Gaston, C.; and Schobbens, P.-Y., eds., MoVaH’08.
Fauconnier, G., and Turner, M. 1998. Conceptual integra- tion networks. Cognitive Science 22(2):133—187.
Fauconnier, G., and Turner, M. 2003. The Way We Think: Conceptual Blending and the Mind’s Hidden Complexi- ties. Basic Books.
Forbus, K.; Falkenhainer, B.; and Gentner, D. 1989. The structure-mapping engine. Artificial Intelligence 41:1–63. Gärdenfors, P. 2000. Conceptual Spaces - The Geometry of Thought. Bradford Books. MIT Press.
Gentner, D. 1983. Structure-mapping: A theoretical frame- work for analogy. Cognitive Science 7:155–170.
Goguen, J. A., and Burstall, R. M. 1992. Institutions: Abstract Model Theory for Specification and Program- ming. Journal of the Association for Computing Machin- ery 39(1):95–146. Predecessor in: LNCS 164, 221–256, 1984.
Goguen, J. A., and Harrell, D. F. 2009. Style: A Com- putational and Conceptual Blending-Based Approach. In The Structure of Style: Algorithmic Approaches to Under- standing Manner and Meaning. Springer.
Goguen, J., and Malcolm, G. 1996. Algebraic Semantics of Imperative Programs. MIT Press.
Goguen, J. 1999. An introduction to algebraic semiotics, with applications to user interface design. In Nehaniv, C. L., ed., Computation for Metaphors, Analogy, and Agents, volume 1562 of Lecture Notes in Computer Sci- ence. Springer. 242–291.
Grüninger, M., and Fox, M. S. 1995. The role of competency questions in enterprise engineering. In Benchmarking— Theory and Practice. Springer. 22–31.
Guarino, N., and Welty, C. 2002. Evaluating ontological decisions with OntoClean. Commun. ACM 45(2):61–65.
Hois, J.; Kutz, O.; Mossakowski, T.; and Bateman, J. 2010. Towards Ontological Blending. In Proc. of the The 14th International Conference on Artificial Intelligence: Methodology, Systems, Applications (AIMSA-2010).
Jaszczolt, K. M. 2003. On Translating ‘What Is Said’: Ter- tium Comparationis in Contrastive Semantics and Prag- matics. In Meaning Through Language Contrast Vol. 2. J. Benjamins. 441–462.
Kuhn, W. 2002. Modeling the Semantics of Geographic Categories through Conceptual Integration. In Proc. of GIScience 2002, 108–118. Springer.
Kutz, O., and Normann, I. 2009. Context Discovery via Theory Interpretation. In Proc. of the IJCAI Workshop on Automated Reasoning about Context and Ontology Evo- lution, ARCOE-09, Pasadena, California.
Kutz, O.; Lücke, D.; Mossakowski, T.; and Normann, I. 2008. The OWL in the C ASL—Designing Ontologies Across Logics. In Proc. of OWLED-08, volume 432. CEUR.
Kutz, O.; Mossakowski, T.; Hois, J.; Bhatt, M.; and Bate- man, J. 2012. Ontological Blending in DOL. In Besold, T.; Kuehnberger, K.-U.; Schorlemmer, M.; and Smaill, A., eds., Computational Creativity, Concept In- vention, and General Intelligence, Proc. of the 1st Int. Workshop C3GI@ECAI, volume 01-2012. Montpellier, France: Publications of the Institute of Cognitive Science, Osnabrück.
Kutz, O.; Mossakowski, T.; and Lücke, D. 2010. Carnap, Goguen, and the Hyperontologies: Logical Pluralism and Heterogeneous Structuring in Ontology Design. Logica Universalis 4(2):255–333. Special Issue on ‘Is Logic Uni- versal?’.
Martinez, M.; Besold, T. R.; Abdel-Fattah, A.; Kühnberger, K.-U.; Gust, H.; Schmidt, M.; and Krumnack, U. 2011. Towards a Domain-Independent Computational Frame- work for Theory Blending. In Proc. of the AAAI Fall 2011 Symposium on Advances in Cognitive Systems.
Mossakowski, T.; Kutz, O.; Codescu, M.; and Lange, C. 2013. The Distributed Ontology, Modeling and Specifi- cation Language. In et al., C. D. V., ed., Proceedings of the 7th International Workshop on Modular Ontologies (WoMO-13), volume 1081. CEUR-WS.
Neuhaus, F.; Vizedom, A.; Baclawski, K.; Bennett, M.; Dean, M.; Denny, M.; Grüninger, M.; Hashemi, A.; Longstreth, T.; Obrst, L.; et al. 2013. Towards ontology evaluation across the life cycle: The Ontology Summit 2013. Applied Ontology 8(3):179–194.
Normann, I. 2008. Automated Theory Interpretation. Ph.D. Dissertation, Department of Computer Science, Jacobs University, Bremen.
Núñez, R. E. 2005. Creating mathematical infinities: Metaphor, blending, and the beauty of transfinite cardi- nals. Journal of Pragmatics 37:1717–1741.
Pereira, F. C., and Cardoso, A. 2003. Optimality Princi- ples for Conceptual Blending: A First Computational Ap- proach. AISB Journal 1(4).
Pereira, F. C. 2007. Creativity and Artificial Intelligence, volume 4 of Applications of Cognitive Linguistics. Mou- ton de Bruyter.
Plotkin, G. D. 1970. A note on inductive generalization. Machine Intelligence 5:153–163.
Poveda-Villalón, M.; Suárez-Figueroa, M. C.; and Gómez- Pérez, A. 2012. Validating Ontologies with OOPS! In Knowledge Engineering and Knowledge Management. Springer. 267–281.
Ritze, D.; Meilicke, C.; Šváb Zamazal, O.; and Stucken- schmidt, H. 2009. A Pattern-based Ontology Matching Approach for Detecting Complex Correspondences. In OM-09, volume 551 of CEUR.
Schwering, A.; Krumnack, U.; Kühnberger, K.-U.; and Gust, H. 2009. Syntactic Principles of Heuristic- Driven Theory Projection. Cognitive Systems Research 10(3):251–269.
Turner, M. 2007. The Way We Imagine. In Roth, I., ed., Imaginative Minds - Proc. of the British Academy. Ox- ford: OUP. 213–236.
Veale, T. 1997. Creativity as pastiche: A computational treatment of metaphoric blends, with special reference to cinematic “borrowing”. In Proc. of Mind II: Computa- tional Models of Creative Cognition.
Walshe, B. 2012. Identifying complex semantic matches. In The Semantic Web: Research and Applications. Springer. 849–853.
Links
Full Text
http://computationalcreativity.net/iccc2014/wp-content/uploads/2014/06//14.2_Kutz.pdf
