Muthiah, Sathappan (Virginia Polytechnic Institute and State University.) | Huang, Bert (Virginia Polytechnic Institute and State University.) | Arredondo, Jaime (University of California, San Diego) | Mares, David (University of California, San Diego) | Getoor, Lise (University of California, Santa Cruz) | Katz, Graham (IBM, Inc.) | Ramakrishnan, Naren (Virginia Polytechnic Institute and State University.)
Civil unrest events (protests, strikes, and "occupy" events) are common occurrences in both democracies and authoritarian regimes. In countries where civil unrest is lawful, qualitative analysis has revealed that more than 75 percent of the protests are planned, organized, or announced in advance; therefore detecting references to future planned events in relevant news and social media is a direct way to develop a protest forecasting system. It uses a combination of keyphrase learning to identify what to look for, probabilistic soft logic to reason about location occurrences in extracted results, and time normalization to resolve future time mentions. We also study the selective superiorities of news media versus social media (Twitter, Facebook) to identify relevant trade-offs.
Many information extraction and knowledge base construction systems are addressing the challenge of deriving knowledge from text. In this article, we represent the desired knowledge base as a knowledge graph and introduce the problem of knowledge graph identification, collectively resolving the entities, labels, and relations present in the knowledge graph. Knowledge graph identification requires reasoning jointly over millions of extractions simultaneously, posing a scalability challenge to many approaches. We use probabilistic soft logic (PSL), a recently-introduced statistical relational learning framework, to implement an efficient solution to knowledge graph identification and present state-of-the-art results for knowledge graph construction while performing an order of magnitude faster than competing methods.
Many real-world applications produce networked data such as the world-wide web (hypertext documents connected via hyperlinks), social networks (for example, people connected by friendship links), communication networks (computers connected via communication links) and biological networks (for example, protein interaction networks). A recent focus in machine learning research has been to extend traditional machine learning classification techniques to classify nodes in such networks. In this article, we provide a brief introduction to this area of research and how it has progressed during the past decade. We introduce four of the most widely used inference algorithms for classifying networked data and empirically compare them on both synthetic and real-world data.
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.