"An Innovative Application from the DARPA Knowledge Bases Programs: Rapid Development of a Course-of-Action Critiquer," by Gheorghe Tecuci, Mihai Boicu, Mike Bowman, and Dorin Marcu, describes a critiquing agent for military courses of action, a challenge problem set by the Defense Advanced Research Projects Agency's (DARPA) High-Performance Knowledge Bases Program. Murray Burke, the DARPA manager for this program, introduces the article by setting the context for the application. Ontologies also play a key role in the creation and management of a web portal developed by Steffen Staab and his colleagues at the University of Karlsruhe, discussed in their article, "Knowledge Portals: Ontologies at Work." "L As in past years, papers were solicited in two categories: (1) deployed applications and (2) emerging applications and technologies. Deployed applications are systems that have been in use for at least several months by individuals or organizations other than their developers, have measurable benefits, and incorporate AI technologies. Emerging applications are systems that are close to deployment and clearly show an innovative implementation of AI technologies. Papers submitted in this track can also describe efforts that examine the utility of different AI techniques for specific applications. All these case studies are of value not only to other application developers looking for guidance in applying various techniques to their own applications but also to researchers who need to understand the technical challenges provided by real-world problems. Six deployed applications and 12 emerging application papers were presented plus 2 invited talks. Although no single theme emerges from this panoply of excellent applications, they served to demonstrate that the field continues to be fertile ground for innovation.

This article surveys previous work on combining planning techniques with expressive representations of knowledge in description logics to reason about tasks, plans, and goals. Description logics can reason about the logical definition of a class and automatically infer class-subclass subsumption relations as well as classify instances into classes based on their definitions. Descriptions of actions, plans, and goals can be exploited during plan generation, plan recognition, or plan evaluation. These techniques should be of interest to planning practitioners working on knowledge-rich application domains. Another emerging use of these techniques is the semantic web, where current ontology languages based on description logics need to be extended to reason about goals and capabilities for web services and agents.

Data integration is the problem of combining data residing at different autonomous, heterogeneous sources and providing the client with a unified, reconciled global view of the data. We discuss dataintegration systems, taking the abstract viewpoint that the global view is an ontology expressed in a class-based formalism. We resort to an expressive description logic, ALCQI, that fully captures classbased representation formalisms, and we show that query answering in data integration, as well as all other relevant reasoning tasks, is decidable. However, when we have to deal with large amounts of data, the high computational complexity in the size of the data makes the use of a fullfledged expressive description logic infeasible in practice. This leads us to consider DL-Lite, a specifically tailored restriction of ALCQI that ensures tractability of query answering in data integration while keeping enough expressive power to capture the most relevant features of class-based formalisms.

We propose the use of application semantics to enhance the process of semantic reconciliation. Application semantics involves those elements of business reasoning that affect the way concepts are presented to users: their layout, and so on. In particular, we pursue in this article the notion of precedence, in which temporal constraints determine the order in which concepts are presented to the user. Existing matching algorithms use either syntactic means (such as term matching and domain matching) or model semantic means, the use of structural information that is provided by the specific data model to enhance the matching process. The novelty of our approach lies in proposing a class of matching techniques that takes advantage of ontological structures and application semantics.

The Workshop on Applications of Ontologies and Problem-Solving Methods (PSMs), held in conjunction with the Thirteenth Biennial European Conference on Artificial Intelligence (ECAI '98), was held on 24 to 25 August 1998. Twenty-six people participated, and 16 papers were presented. Participants included scientists and practitioners from both the ontology and PSM communities. The first day was devoted to paper presentations and discussions. The second (half) day, a joint session was held with two other workshops: (1) Building, Maintaining, and Using Organizational Memories and (2) Intelligent Information Integration.

This article presents a learning agent shell and methodology for building knowledge bases and agents and their innovative application to the development of a critiquing agent for military courses of action, a challenge problem set by the Defense Advanced Research Projects Agency's High-Performance Knowledge Bases Program. The learning agent shell includes a general problemsolving engine and a general learning engine for a generic knowledge base structured into two main components: (1) an ontology that defines the concepts from an application domain and (2) a set of task-reduction rules expressed with these concepts. We believe success in this area will have an even greater impact on our society than the development of personal computers. Indeed, if personal computers allowed every person to become a computer user, without the need for special training in computer science, solutions to this AI challenge would allow any such person to become an agent developer. Agent development by typical computer users would lead to a large scale use of computers as personalized intelligent assistants, helping their users in a wide range of tasks.

Although a number of initiatives provide personalized context-aware guidance for niche use cases, a standard framework for context awareness remains lacking. This article explains how semantic technology has been exploited to generate a centralized repository of personal activity context. This data drives advanced features such as personal situation recognition and customizable rules for the context-sensitive management of personal devices and data sharing. As a proof of concept, we demonstrate how an innovative context-aware system has successfully adopted such an infrastructure. By treating these devices as part of a personal sensor network and analyzing the generated information collectively, valuable context information can be gathered and interpreted in an endless number of scenarios.

The orbital debris problem presents an opportunity for inter-agency and international cooperation toward the mutually beneficial goals of debris prevention, mitigation, remediation, and improved space situational awareness (SSA). Achieving these goals requires sharing orbital debris and other SSA data. Toward this, I present an ontological architecture for the orbital debris domain, taking steps in the creation of an orbital debris ontology (ODO). The purpose of this ontological system is to (I) represent general orbital debris and SSA domain knowledge, (II) structure, and standardize where needed, orbital data and terminology, and (III) foster semantic interoperability and data-sharing. In doing so I hope to (IV) contribute to solving the orbital debris problem, improving peaceful global SSA, and ensuring safe space travel for future generations.

A fast performing, dictionary-based tagger [1] constitutes EXTRACT's core. The tagger relies on a set of dictionaries that map biological names to corresponding terms in biological ontologies, or to pertinent records in public biological databases.

Click to learn more about author John Singer. When designing your Knowledge Maps it certainly helps to have some interesting questions that you are trying to answer. "Build it and they will come" approaches didn't work very well for data warehouse and BI efforts and the same will be true for your Knowledge Map. Of course, you can't connect the dots if you don't collect the dots so there will be some amount of loading data into the Knowledge Map that doesn't directly produce any high value results. However, the "network effect" of continually combining data together will lead to the ability to answer more difficult questions.