We describe work on representing and using a rich notion ofcontext that goes beyond current networking applications focusingmostly on location. Our context model includes locationand surroundings, the presence of people and devices,inferred activities and the roles people fill in them. A keyelement of our work is the use of collaborative informationsharing where devices share and integrate knowledge abouttheir context. This introduces a requirement that users canset appropriate levels of privacy to protect the personal informationbeing collected and the inferences that can be drawnfrom it. We use Semantic Web technologies to model contextand to specify high-level, declarative policies specifying informationsharing constraints. The policies involve attributesof the subject (i.e., information recipient), target (i.e., the information)and their dynamic context (e.g., are the parties copresent).We discuss our ongoing work on context representationand inference and present a model for protecting andcontrolling the sharing of private data in context-aware mobileapplications.

We have been exploring the use of Web-derived knowledge bases through the development of Wikitology — a hybrid knowledge base of structured and unstructured information extracted from Wikipedia augmented by RDF data from DBpedia and other Linked Open Data resources. In this paper, we describe approaches that aid in enriching Wikipedia and thus the resources that derive from Wikipedia such as the Wikitology knowledge base, DBpedia, Freebase and Powerset.

Amongst the best practices that constitute linked data, one of the foremost is to use only HTTP-URIs as identifiers for RDF resources. This is so that the URI will resolve in a Linked Data browser to give information about the named resource. At the same time, Linked Data takes a resource-centric, as opposed to page-centric, approach to resolution. We argue that this approach can, in certain cases, obviate the need for insisting on HTTP-URIs. As a use of our “expanded” notion of Linked Data, we present as an example Life Science Identifiers.

The friend of a friend (FOAF) vocabulary is widely used on the Web to describe individual people and their properties. Since FOAF does not require a unique ID for a person, it is not clear when two FOAF agents should be linked as co-referent, i.e., denote the same person in the world. One approach is to use the presence of inverse functional properties (e.g., foaf:mbox) as evidence that two individuals are the same. Another applies heuristics based on the string similarity of values of FOAF properties such as name and school as evidence for or against co-reference. Performance is limited, however, by many factors: non-semantic string matching, noise, changes in the world, and the lack of more sophisticated graph analytics. We describe a supervised machine learning approach that uses features defined over pairs of FOAF individuals to produce a classifier for identifying co-referent FOAF instances. We present initial results using data collected from Swoogle and other sources and describe plans for additional analysis.

Citizens, both young and feeds, and semistructured metadata old, are also discovering how social media in the form of extensible markup language technology can improve their lives and (XML) and resource description give them more voice in the world. We they provide more useful, trustworthy, begin by describing an overarching task of and reliable. Pursuing this task uncovers It differs, however, in ways a number of problems that must be addressed, that affect how it should be modeled, analyzed, three of which we describe in and exploited. The first is recognizing spam model for the general web is as a directed graph of web pages with undifferentiated in the form of spam blogs (splogs) and links between pages. The second is developing has a much richer network structure more effective techniques to recognize in that there are more types of nodes the social structure of blog communities. For example, the abstract model for the underlying blog people who contribute to blogs and au-network structure and how it evolves. Figure 2 shows a hypothetical blog graph and its corresponding flow of information in the influence graph. Studies on influence in social networks and collaboration graphs have typically focused on the task of identifying key individuals who play an important role in propagating information. This is similar to finding authoritative pages on the web.

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.

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. Seven symposia were held. 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.

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.

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.