Semantic integration focuses on discovering, representing, and manipulating correspondences between entities in disparate data sources. 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. In recent years, however, semantic integration over text has also received increasing attention. 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. We present a machine-learning study of this problem. The first approach is a discriminative approach -- a pairwise local classifier is trained in a supervised way to determine whether two given mentions represent the same real-world entity. This is followed, potentially, by a global clustering algorithm that uses the classifier as its similarity metric. Our second approach is a global generative model, at the heart of which is a view on how documents are generated and how names (of different entity types) are "sprinkled" into them. 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. We show that both approaches perform very accurately, in the range of 90-95 percent. F1 measure for different entity types, much better than previous approaches to some aspects of this problem. 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.

Caesarea Edmond Benjamin de Rothschild Email: patrick.hung@uoit.ca (MDAI 2005).

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.

AAAI presented the AAAI-04 workshop program on July 25-26, 2004 in San Jose, California. This program included twelve workshops covering a wide range of topics in artificial intelligence. The titles of the workshops were as follows: (1) Adaptive Text Extraction and Mining; (2) Agent Organizations: Theory and Practice; (3) Anchoring Symbols to Sensor Data; (4) Challenges in Game AI; (5) Fielding Applications of Artificial Intelligence; (6) Forming and Maintaining Coalitions in Adaptive Multiagent Systems; (7) Intelligent Agent Architectures: Combining the Strengths of Software Engineering and Cognitive Systems; (8) Learning and Planning in Markov Processes -- Advances and Challenges; (9) Semantic Web Personalization; (10) Sensor Networks; (11) Spatial and Temporal Reasoning; and (12) Supervisory Control of Learning and Adaptive Systems.

XML, web services, and the semantic web have opened the door for new and exciting informationintegration applications. Information sources on the web are controlled by different organizations or people, utilize different text formats, and have varying inconsistencies. Therefore, any system that integrates information from different data sources must identify common entities from these sources. Data from many data sources on the web does not contain enough information to link the records accurately using state-of-the-art record-linkage systems. However, it is possible to exploit secondary data sources on the web to improve the recordlinkage process. We present an approach to accurately and automatically match entities from various data sources by utilizing a state-of-the-art record-linkage system in conjunction with a data-integration system. The data-integration system is able to automatically determine which secondary sources need to be queried when linking records from various data sources. In turn, the record-linkage system is then able to utilize this additional information to improve the accuracy of the linkage between datasets.

Posters, and the AAAI/ SIGART consist of two phases: a qualification Doctoral Consortium. Please visit the round and a runoff competition. A AAAI is pleased to announce the AAAI-05 web site periodically for upto-date $10,000 prize will be awarded to the launch of the First Annual Artificial information. We hope you will winning entrant. The competition is Intelligence for Interactive Digital join us in Pittsburgh!

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.

The First Conference on E-mail and Anti- Spam was held from July 30 to July 31, 2004 in Mountain View, California. The conference, attended by 180 researchers, featured 29 papers that covered a number of topics, including e-mail in general, nonstatistical techniques for stopping spam, machine learning techniques, issues of identity in e-mail, as well as law and policy. The 2005 conference will be held at Stanford University from July 21 to 22.

Systems Lab at the UWE, told CNN that of Automation. News" collection that can be found--complete is becoming a reality as adaptive Man and the Machines--It's time to start with links to the item's source and technologies revolutionize the way businesses "At some point in the nottoo-distant reflect the overall tenor of the item, nor for adaptive technologies. The that moment might not be far off. And as years ago when the mechanical tomato more pertinent question is how these automated their creations begin to display a growing harvester revolutionized California agriculture. The Christian computers write poems and serve question?

AI'2004 was held at the University of AI'2004 had a large program committee were affiliated with AI'2004: The Third Canada (NRC) (who also hosted the hosted one of the longest-running and the First Causality and preconference workshops) cosponsored conference series in the AI field. The Causal Discovery workshop, organized the AI'2004 Graduate Symposium. CSCSI conference started in 1976 as a by Kamran Karimi (University of Regina). The Eighteenth Canadian Conference the conference became an annual forum, In addition, the AI'2004 graduate on Artificial Intelligence thus making AI'2004 the seventeenth symposium attracted many graduate (AI'2005) will be held in Victoria, conference in this series. These events turned the city British Columbia, from May 9 to 11, the years, the conference has attracted of London into a Mecca for AI and 2005.