The Association for the Advancement of Artificial Intelligence, in cooperation with Stanford University's Computer Science Department, was pleased to present its 2006 Spring Symposium Series held March 27-29, 2006, at Stanford University, California. The titles of the eight symposia were (1) Argumentation for Consumers of Health Care (chaired by Nancy Green); (2) Between a Rock and a Hard Place: Cognitive Science Principles Meet AI Hard Problems (chaired by Christian Lebiere); (3) Computational Approaches to Analyzing Weblogs (chaired by Nicolas Nicolov); (4) Distributed Plan and Schedule Management (chaired by Ed Durfee); (5) Formalizing and Compiling Background Knowledge and Its Applications to Knowledge Representation and Question Answering (chaired by Chitta Baral); (6) Semantic Web Meets e-Government (chaired by Ljiljana Stojanovic); (7) To Boldly Go Where No Human-Robot Team Has Gone Before (chaired by Terry Fong); and (8) What Went Wrong and Why: Lessons from AI Research and Applications (chaired by Dan Shapiro).

In a peer-to-peer inference system, each peer can reason locally but can also solicit some of its acquaintances, which are peers sharing part of its vocabulary. In this paper, we consider peer-to-peer inference systems in which the local theory of each peer is a set of propositional clauses defined upon a local vocabulary. An important characteristic of peer-to-peer inference systems is that the global theory (the union of all peer theories) is not known (as opposed to partition-based reasoning systems). The main contribution of this paper is to provide the first consequence finding algorithm in a peer-to-peer setting: DeCA. It is anytime and computes consequences gradually from the solicited peer to peers that are more and more distant. We exhibit a sufficient condition on the acquaintance graph of the peer-to-peer inference system for guaranteeing the completeness of this algorithm. Another important contribution is to apply this general distributed reasoning setting to the setting of the Semantic Web through the Somewhere semantic peer-to-peer data management system. The last contribution of this paper is to provide an experimental analysis of the scalability of the peer-to-peer infrastructure that we propose, on large networks of 1000 peers.

Additional initial ontologies include: search was used, queries were expanded to include (4) single part issues--relate to only one more words to search for, and thus, relevant vehicle component, such as a ding in a fender; documents could be found. Since the documents (5) multiple part issues--relate to two or more being searched were in a limited parts, especially misalignments, unsatisfactory domain, there were few problems with multiple gaps, malformations of joints between parts; senses of words introducing problems that (6) data analysis--results of analysis of measurement hurt precision. In our database, case entries are data generated by optical and mechanical similar--the textual fields do not contain long gages; and (7) plant locations--zones descriptions, and the content is limited to and stations organized topologically or functionally.

We present a novel approach to the automatic acquisition of taxonomies or concept hierarchies from a text corpus. The approach is based on Formal Concept Analysis (FCA), a method mainly used for the analysis of data, i.e. for investigating and processing explicitly given information. We follow Harris' distributional hypothesis and model the context of a certain term as a vector representing syntactic dependencies which are automatically acquired from the text corpus with a linguistic parser. On the basis of this context information, FCA produces a lattice that we convert into a special kind of partial order constituting a concept hierarchy. The approach is evaluated by comparing the resulting concept hierarchies with hand-crafted taxonomies for two domains: tourism and finance. We also directly compare our approach with hierarchical agglomerative clustering as well as with Bi-Section-KMeans as an instance of a divisive clustering algorithm. Furthermore, we investigate the impact of using different measures weighting the contribution of each attribute as well as of applying a particular smoothing technique to cope with data sparseness.

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. 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.

A semantics-preserving exchange of information between two software applications requires mappings between logically equivalent concepts in the ontology of each application. The challenge of semantic integration is therefore equivalent to the problem of generating such mappings, determining that they are correct, and providing a vehicle for executing the mappings, thus translating terms from one ontology into another. This article presents an approach toward this goal using techniques that exploit the model-theoretic structures underlying ontologies. With these as inputs, semiautomated and automated components may be used to create mappings between ontologies and perform translations.

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.

Sharing data across disparate sources requires solving many problems of semantic integration, such as matching ontologies or schemas, detecting duplicate tuples, reconciling inconsistent data values, modeling complex relations between concepts in different sources, and reasoning with semantic mappings. This issue of AI Magazine includes papers that discuss various methods on establishing mappings between ontology elements or data fragments. The collection includes papers that discuss semantic-integration issues in such contexts as data integration and web services. The issue also includes a brief survey of semantic-integration research in the database community.

Semantic integration has been a long-standing challenge for the database community. It has received steady attention over the past two decades, and has now become a prominent area of database research. In this article, we first review database applications that require semantic integration and discuss the difficulties underlying the integration process. We then describe recent progress and identify open research issues. We focus in particular on schema matching, a topic that has received much attention in the database community, but also discuss data matching (for example, tuple deduplication) and open issues beyond the match discovery context (for example, reasoning with matches, match verification and repair, and reconciling inconsistent data values). For previous surveys of database research on semantic integration, see Rahm and Bernstein (2001); Ouksel and Seth (1999); and Batini, Lenzerini, and Navathe (1986).

Research on semantic web services promises greater interoperability among software agents and web services by enabling content-based automated service discovery and interaction and by utilizing . 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. The question we ask is, does the traditional approach of introducing mediator agents to translate messages between requestors and services work in such an open environment? 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. Ontology mappings need to be published on the semantic web just as ontologies themselves are. 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. We present arguments for assigning translation responsibility to particular agents in the cases of service invocation, response translation, and matchmaking.