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. 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.

The topic has been widely studied in the context of structured data, with problems being considered including ontology and schema matching, matching relational tuples, and reconciling inconsistent data values. This article studies a key challenge in semantic integration over text: identifying whether different mentions of real-world entities, such as "JFK" and "John Kennedy," within and across natural language text documents, actually represent the same concept. In its most general form, our model assumes (1) a joint distribution over entities (for example, a document that mentions "President Kennedy" is more likely to mention "Oswald" or "White House" than "Roger Clemens"), and (2) an "author" model that assumes that at least one mention of an entity in a document is easily identifiable and then generates other mentions via (3) an "appearance" model that governs how mentions are transformed from the "representative" mention. Finally, we discuss how our solution for mention matching in text can be potentially applied to matching relational tuples, as well as to linking entities across databases and text.

Although this is to be based on use of shared ontologies published on the semantic web, services produced and described by different developers may well use different, perhaps partly overlapping, sets of ontologies. Interoperability will depend on ontology mappings and architectures supporting the associated translation processes. This article reviews some of the processing assumptions that were made in the development of the semantic web service modeling ontology OWL-S and argues that, as a practical matter, the translation function cannot always be isolated in mediators. The translation for service discovery, service process model interpretation, task negotiation, service invocation, and response interpretation may then be distributed to various places in the architecture so that translation can be done in the specific goal-oriented informational contexts of the agents performing these processes.

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 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.

The Association for the Advancement of Artificial Intelligence, in cooperation with Stanford University's Department of Computer Science, presented the 2004 Spring Symposium Series, Monday through Wednesday, March 22-24, at Stanford University. The titles of the eight symposia were (1) Accessible Hands-on Artificial Intelligence and Robotics Education; (2) Architectures for Modeling Emotion: Cross-Disciplinary Foundations; (3) Bridging the Multiagent and Multirobotic Research Gap; (4) Exploring Attitude and Affect in Text: Theories and Applications; (5) Interaction between Humans and Autonomous Systems over Extended Operation; (6) Knowledge Representation and Ontologies for Autonomous Systems; (7) Language Learning: An Interdisciplinary Perspective; and (8) Semantic Web Services. Most symposia chairs elected to create AAAI technical reports of their symposium, which are available as paperbound reports or (for AAAI members) are downloadable on the AAAI members-only Web site. This report includes summaries of the eight symposia, written by the symposia chairs.

For the last two decades, configuration systems relying on AI techniques have successfully been applied in industrial environments. These systems support the configuration of complex products and services in shorter time with fewer errors and, therefore, reduce the costs of a mass-customization business model. The European Union-funded project entitled CUSTOMER-ADAPTIVE WEB INTERFACE FOR THE CONFIGURATION OF PRODUCTS AND SERVICES WITH MULTIPLE SUPPLIERS (CAWICOMS) aims at the next generation of web-based configuration applications that cope with two challenges of today's open, networked economy: (1) the support for heterogeneous user groups in an open-market environment and (2) the integration of configurable subproducts provided by specialized suppliers. This article describes the CAWICOMS WORKBENCH for the development of configuration services, offering personalized user interaction as well as distributed configuration of products and services in a supply chain.

In this article, we discuss the general problems related to the semantic interpretation of WORDNET taxonomy in light of rigorous ontological principles inspired by the philosophical tradition. Then we introduce the DOLCE upper-level ontology, which is inspired by such principles but with a clear orientation toward language and cognition. We report the results of an experimental effort to align WORDNET's upper level with DOLCE. We suggest that such alignment could lead to an "ontologically sweetened" WORDNET, meant to be conceptually more rigorous, cognitively transparent, and efficiently exploitable in several applications.

This article presents the methodology that has been successfully used over the past seven years by an interdisciplinary team to create the International Committee for Documentation of the International Council of Museums (CIDOC) CONCEPTUAL REFERENCE MODEL (CRM), a high-level ontology to enable information integration for cultural heritage data and their correlation with library and archive information. The CIDOC CRM is now in the process to become an International Organization for Standardization (ISO) standard. The CIDOC CRM analyzes the common conceptualizations behind data and metadata structures to support data transformation, mediation, and merging. It is assumed that the presented methodology and the upper level of the ontology are applicable in a far wider domain.

In this article, we discuss some issues that arise when ontologies are used to support corporate application domains such as electronic commerce (ecommerce) and some technical problems in deploying ontologies for real-world use. In particular, we focus on issues of ontology integration and the related problem of semantic mapping, that is, the mapping of ontologies and taxonomies to reference ontologies to preserve semantics. Along the way, we discuss what typically constitutes an ontology architecture. By its very nature, B2B e-commerce must try to interlink buyers and sellers from multiple companies with disparate product-description terminologies and meanings, thus serving as a paradigmatic case for the use of ontologies to support corporate applications.

The Association for the Advancement of Artificial Intelligence, in cooperation with Stanford University's Department of Computer Science, presented the 2002 Spring Symposium Series, held Monday through Wednesday, 25 to 27 March 2002, at Stanford University. The nine symposia were entitled (1) Acquiring (and Using) Linguistic (and World) Knowledge for Information Access; (2) Artificial Intelligence and Interactive Entertainment; (3) Collaborative Learning Agents; (4) Information Refinement and Revision for Decision Making: Modeling for Diagnostics, Prognostics, and Prediction; (5) Intelligent Distributed and Embedded Systems; (6) Logic-Based Program Synthesis: State of the Art and Future Trends; (7) Mining Answers from Texts and Knowledge Bases; (8) Safe Learning Agents; and (9) Sketch Understanding.