Capability Modeling of Knowledge-Based Agents for Commonsense Knowledge Integration
Robust intelligent systems require commonsense knowledge. While significant progress has been made in building large commonsense knowledge bases, they are intrinsically incomplete. It is difficult to combine multiple knowledge bases due to their different choices of representation and inference mechanisms, thereby limiting users to one knowledge base and its reasonable methods for any specific task. This paper presents a multi-agent framework for commonsense knowledge integration, and proposes an approach to capability modeling of knowledge bases without a common ontology. The proposed capability model provides a general description of large heterogeneous knowledge bases, such that contents accessible by the knowledge-based agents may be matched up against specific requests. The concept correlation matrix of a knowledge base is transformed into a k-dimensional vector space using low-rank approximation for dimensionality reduction. Experiments are performed with the matchmaking mechanism for commonsense knowledge integration framework using the capability models of ConceptNet, WordNet, and Wikipedia. In the user study, the matchmaking results are compared with the ranked lists produced by online users to show that over 85% of them are accurate and have positive correlation with the user-produced ranked lists.
Keywordsmulti-agent system common sense commonsense knowledge integration capability model agent description
Unable to display preview. Download preview PDF.
- 2.Etzioni, O., Cafarella, M., Downey, D., Popescu, A.M., Shaked, T., Soderland, S., Weld, D.S., Yates, A.: Methods for domain-independent information extraction from the web: an experimental comparison. In: Proceedings of AAAI 2004 (2004)Google Scholar
- 3.Havasi, C., Speer, R., Alonso, J.: ConceptNet 3: A flexible, multilingual semantic network for common sense knowledge. In: Recent Advances in Natural Language Processing, Borovets, Bulgaria (September 2007)Google Scholar
- 4.Kuo, Y.L., Lee, J.C., Chiang, K.Y., Wang, R., Shen, E., Chan, C.W., Hsu, J.Y.j.: Community-based game design: experiments on social games for commonsense data collection. In: Proceedings of the ACM SIGKDD Workshop on Human Computation (2009)Google Scholar
- 8.Minsky, M.: The Society of Mind. Simon and Schuster (1988)Google Scholar
- 10.Pedersen, T., Patwardhan, S., Michelizzi, J.: WordNet: Similarity - measuring the relatedness of concepts. In: Proceedings of AAAI 2004 (2004)Google Scholar
- 11.Preece, A., Hui, K., Gray, A., Bench-capon, T., Joes, D., Cui, Z.: The KRAFT architecture for knowledge fusion and transformation. In: Proceedings of the 19th SGES International Conference on Knowledge-Based Systems and Applied Artificial Intelligence (1999)Google Scholar
- 12.Schubert, L., Tong, M.: Extracting and evaluating general world knowledge from the brown corpus. In: Proceedings of the HLT-NAACL Workshop on Text Meaning (2003)Google Scholar
- 13.Singh, M.P., Huhns, M.N.: Service-Oriented Computing: Semantics, Processes, Agents. Wiley (2005)Google Scholar
- 14.Singh, P.: The public acquisition of commonsense knowledge. In: Proceedings of AAAI Spring Symposium (2002)Google Scholar
- 17.Speer, R., Havasi, C., Lieberman, H.: AnalogySpace: Reducing the dimensionality of common sense knowledge. In: Proceedings of AAAI 2008 (2008)Google Scholar
- 18.Strube, M., Ponzetto, S.P.: WikiRelate! computing semantic relatedness using wikipedia. In: Proceedings of the 21st National Conference on Artificial Intelligence, AAAI 2006 (2006)Google Scholar