Many challenges of bringing communicating multiagent systems to the Web require ontologies. The integration of agent technology and ontologies could significantly affect the use of Web services and the ability to extend programs to perform tasks for users more efficiently and with less human intervention.

Ontologies are often seen as basic building blocks for the Semantic Web, as they provide a reusable piece of knowledge about a specific domain. However, those pieces of knowledge are not static, but evolve over time. Domain changes, adaptations to different tasks, or changes in the conceptualization require modifications of the ontology. The evolution of ontologies causes operability problems, which will hamper their effective reuse. A versioning mechanism might help to reduce those problems, as it will make the relations between different revisions of an ontology explicit. This paper will discuss the problem of ontology versioning. Inspired by the work done in database schema versioning and program interface versioning, it will also propose building blocks for the most important aspects of a versioning mechanism, i.e., ontology identification and change specification.

The combined efforts of human volunteers have recently extracted numerous facts from Wikipedia, storing them as machine-harvestable object-attribute-value triples in Wikipedia infoboxes. Machine learning systems, such as Kylin, use these infoboxes as training data, accurately extracting even more semantic knowledge from natural language text. But in order to realize the full power of this information, it must be situated in a cleanly-structured ontology. This paper introduces KOG, an autonomous system for refining Wikipedia’s infobox-class ontology towards this end. We cast the problem of ontology refinement as a machine learning problem and solve it using both SVMs and a more powerful joint-inference approach expressed in Markov Logic Networks. We present experiments demonstrating the superiority of the joint-inference approach and evaluating other aspects of our system. Using these techniques, we build a rich ontology, integrating Wikipedia’s infobox-class schemata with WordNet. We demonstrate how the resulting ontology may be used to enhance Wikipedia with improved query processing and other features.

Despite some successes, the lack of tools to allow subject matter experts to directly enter, query, and debug formal domain knowledge in a knowledge-base still remains a major obstacle to their deployment. Our goal is to create such tools, so that a trained knowledge engineer is no longer required to mediate the interaction. This paper presents our work on the knowledge entry part of this overall knowledge capture task, which is based on several claims: that users can construct representations by connecting pre-fabricated, representational components, rather than writing low-level axioms; that these components can be presented to users as graphs; and the user can then perform composition through graph manipulation operations. To operationalize this, we have developed a novel technique of graphical dialog using examples of the component concepts, followed by an automated process for generalizing the user's graphically-entered assertions into axioms. We present these claims, our approach, the system (called SHAKEN) that we are developing, and an evaluation of our progress based on having users encode knowledge using the system. Keywords Graphical knowledge entry, knowledge acquisition, components, composition, knowledge-based systems.

One very successful approach to developing knowledge acquisition tools use expectations of what the user has to add or may want to add, based on how new knowledge fits within a knowledge base that already exists. When a knowledge base is first created or undergoes significant extensions and changes, these tools cannot provide much support. This paper presents an approach to creating expectations when a new knowledge base is built, and describes a knowledge acquisition tool that we implemented using this approach that supports users in creating problem-solving knowledge. As the knowledge base grows, the knowledge acquisition tool derives more frequent and more reliable expectations that result from enforcing constraints in the knowledge representation system, looking for missing pieces of knowledge in the knowledge base, and working out incrementally the inter-dependencies among the different components of the knowledge base. Our preliminary evaluations show a thirty percent time...

Our long-term research goal is to create systems capable of answering a wide variety of questions, including questions which were unanticipated at the time the system was constructed, and questions tailored to novel scenarios which the user is interested in. Our approach is to augment on-line text with a knowledge-based question-answering component, capable of reasoning about a scenario which the user provides, and synthesizing customized answers at run-time to his/her questions. To answer a question, the system creates a model of the user's scenario of interest, infers the facts about it required for the answer, assembles those facts into a single structure, and presents it to the user. This process is guided by "answer schemata" (one for each di#erent question type), specifying what information should be included in the answer and how it should be organized. In this paper, we describe an implemented system based on this approach, which has been applied to several di#erent application...

Many AI tasks require determining whether two knowledge representations encode the same knowledge. Solving this matching problem is hard because representations may encode the same content but differ substantially in form. Previous approaches to this problem have used either syntactic measures, such as graph edit distance, or semantic knowledge to determine the “distance ” between two representations. Although semantic approaches outperform syntactic ones, previous research has focused primarily on the use of taxonomic knowledge. We show that this is not enough because mismatches between representations go largely unaddressed. In this paper, we describe how transformations can augment existing semantic approaches to further improve matching. We also describe the application of our approach to the task of critiquing military Courses of Action and compare its performance to other leading algorithms.

Helping end users build and check process models is a challenge for many science and engineering fields. Many AI researchers have investigated useful ways of verifying and validating knowledge bases for ontologies and rules, but it is not easy to directly apply them to checking process models. Other techniques developed for checking and refining planning knowledge tend to focus on automated plan generation rather than helping users author process information. In this paper, we propose a complementary approach which helps users author and check process models. Our system, called KANAL, relates pieces of information in process models among themselves and to the existing KB, analyzing how different pieces of input are put together to achieve some effect. It builds interdependency models from this analysis and uses them to find errors and propose fixes. Our initial evaluation shows that KANAL was able to find most of the errors in the process models and suggest useful fixes including the fixes that directly point to the sources of the errors. 1

This paper describes an approach to ontology negotiation between information agents. Ontologies are declarative (data driven) expressions of an agent’s “world”: the objects, operations, facts, and rules that constitute the logical space within which an agent performs. Ontology negotiation enables agents to cooperate in performing a task, even if they are based on different ontologies. The process allows agents to discover ontology conflicts and then, though incremental interpretation, clarification, and explanation, establish a common basis for communicating with each other. 1.

Abstract. This paper discusses the advantages of the Common Algebraic Specification Language (Casl) for the development of modular ontologies. Casl not only offers logics with a limited expressivity like description logic, but also e.g. first-order logic and modal logic. The central part of Casl is its powerful structuring mechanism, which is orthogonal to the logical formalisms. Hence the modularization applies uniformly to various logics and its extension Heterogeneous Casl (HetCasl) has even constructs for the combination of different logics. Additionally, the Heterogeneous Tool Set (Hets) is presented which enables reasoning and manipulation of Casl specifications. By presenting a detailed example ontology used for spatial knowledge representation the benefits of specification in Casl are discussed. Furthermore, a comparison with the OWL DL import mechanism is provided. 1