We describe an approach for identifying fine-grained entity types in heterogeneous data graphs that is effective for unstructured data or when the underlying ontologies or semantic schemas are unknown. Identifying fine-grained entity types, rather than a few high-level types, supports coreference resolution in heterogeneous graphs by reducing the number of possible coreference relations that must be considered. For such cases, we use supervised machine learning to map entity attributes and relations to a known set of attributes and relations from appropriate background knowledge bases to predict instance entity types. We evaluated this approach in experiments on data from DBpedia, Freebase, and Arnetminer using DBpedia as the background knowledge base.
Blisard, Sam (Naval Research Laboratory) | Carmichael, Ted (University of North Carolina at Charlotte) | Ding, Li (University of Maryland, Baltimore County) | Finin, Tim (University of Maryland, Baltimore County) | Frost, Wende (Naval Research Laboratory) | Graesser, Arthur (University of Memphis) | Hadzikadic, Mirsad (University of North Carolina at Charlotte) | Kagal, Lalana (Massachusetts Institute of Technology) | Kruijff, Geert-Jan M. (German Research Center for Artificial Intelligence) | Langley, Pat (Arizona State University) | Lester, James (North Carolina State University) | McGuinness, Deborah L. (Rensselaer Polytechnic Institute) | Mostow, Jack (Carnegie Mellon University) | Papadakis, Panagiotis (University of Sapienza, Rome) | Pirri, Fiora (Sapienza University of Rome) | Prasad, Rashmi (University of Wisconsin-Milwaukee) | Stoyanchev, Svetlana (Columbia University) | Varakantham, Pradeep (Singapore Management University)
The Association for the Advancement of Artificial Intelligence was pleased to present the 2011 Fall Symposium Series, held Friday through Sunday, November 4–6, at the Westin Arlington Gateway in Arlington, Virginia. The titles of the seven symposia are as follows: (1) Advances in Cognitive Systems; (2) Building Representations of Common Ground with Intelligent Agents; (3) Complex Adaptive Systems: Energy, Information and Intelligence; (4) Multiagent Coordination under Uncertainty; (5) Open Government Knowledge: AI Opportunities and Challenges; (6) Question Generation; and (7) Robot-Human Teamwork in Dynamic Adverse Environment. The highlights of each symposium are presented in this report.
Bongard, Joshua, Brock, Derek, Collins, Samuel G., Duraiswami, Ramani, Finin, Tim, Harrison, Ian, Honavar, Vasant, Hornby, Gregory S., Jonsson, Ari, Kassoff, Mike, Kortenkamp, David, Kumar, Sanjeev, Murray, Ken, Rudnicky, Alexander I., Trajkovski, Goran
The American Association for Artificial Intelligence was pleased to present the AAAI 2006 Fall Symposium Series, held Friday through Sunday, October 13-15, at the Hyatt Regency Crystal City in Washington, DC. The titles were (1) Aurally Informed Performance: Integrating Ma- chine Listening and Auditory Presentation in Robotic Systems; (2) Capturing and Using Patterns for Evidence Detection; (3) Developmental Systems; (4) Integrating Reasoning into Everyday Applications; (5) Interaction and Emergent Phenomena in Societies of Agents; (6) Semantic Web for Collaborative Knowledge Acquisition; and (7) Spacecraft Autonomy: Using AI to Expand Human Space Exploration.
Lesperance, Yves, Wagnerg, Gerd, Birmingham, William, Bollacke, Kurt r, Nareyek, Alexander, Walser, J. Paul, Aha, David, Finin, Tim, Grosof, Benjamin, Japkowicz, Nathalie, Holte, Robert, Getoor, Lise, Gomes, Carla P., Hoos, Holger H., Schultz, Alan C., Kubat, Miroslav, Mitchell, Tom, Denzinger, Joerg, Gil, Yolanda, Myers, Karen, Bettini, Claudio, Montanari, Angelo
The AAAI-2000 Workshop Program was held Sunday and Monday, 3031 July 2000 at the Hyatt Regency Austin and the Austin Convention Center in Austin, Texas. The 15 workshops held were (1) Agent-Oriented Information Systems, (2) Artificial Intelligence and Music, (3) Artificial Intelligence and Web Search, (4) Constraints and AI Planning, (5) Integration of AI and OR: Techniques for Combinatorial Optimization, (6) Intelligent Lessons Learned Systems, (7) Knowledge-Based Electronic Markets, (8) Learning from Imbalanced Data Sets, (9) Learning Statistical Models from Rela-tional Data, (10) Leveraging Probability and Uncertainty in Computation, (11) Mobile Robotic Competition and Exhibition, (12) New Research Problems for Machine Learning, (13) Parallel and Distributed Search for Reasoning, (14) Representational Issues for Real-World Planning Systems, and (15) Spatial and Temporal Granularity.
Building new knowledge-based systems today usually entails constructing new knowledge bases from scratch. System developers would then only need to worry about creating the specialized knowledge and reasoners new to the specific task of their system. This approach would facilitate building bigger and better systems cheaply. This article presents a vision of the future in which knowledge-based system development and operation is facilitated by infrastructure and technology for knowledge sharing.