This paper provides a formal and practical framework for sound abstraction of probabilistic actions. We start by precisely defining the concept of sound abstraction within the context of finite-horizon planning (where each plan is a finite sequence of actions). Next we show that such abstraction cannot be performed within the traditional probabilistic action representation, which models a world with a single probability distribution over the state space. We then present the constraint mass assignment representation, which models the world with a set of probability distributions and is a generalization of mass assignment representations. Within this framework, we present sound abstraction procedures for three types of action abstraction. We end the paper with discussions and related work on sound and approximate abstraction. We give pointers to papers in which we discuss other sound abstraction-related issues, including applications, estimating loss due to abstraction, and automatically generating abstraction hierarchies.

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).

In numerous distributed environments, including today's World Wide Web, enterprise data management systems, large science projects, and the emerging semantic web, applications will inevitably use the information described by multiple ontologies and schemas. We organized the Workshop on Semantic Integration at the Second International Semantic Web Conference to bring together different communities working on the issues of enabling integration among different resources. The workshop generated a lot of interest and attracted more than 70 participants.