We describe an ongoing project to develop technology that will support collaborative construction and effective use of distributed large-scale repositories of highly expressive reusable ontologies. We are focusing on developing a distributed server architecture for ontology construction and use, representation formalisms that remove key barriers to expressing essential knowledge in and about ontologies, ontology construction tools, and tools for obtaining domain models for use in applications from large-scale ontology repositories. We are building on the results of the DARPA Knowledge Sharing Effort, specifically by using the Knowledge Interchange Format (KIF) as a core representation language and the Ontolingua system as a core ontology development environment. In order to enable distributed ontology repositories and services, we are developing a distributed server architecture for ontology construction and use based on ontology servers which provide access via a network API to the ...

A central theme of the semantic web is that programs should be able to easily aggregate data from di#erent sources. Unfortunately, even if two sites provide their data using the same data model and vocabulary, subtle differences in their use of terms and in the assumptions they make pose challenges for aggregation. Experiences with the TAP project reveal some of the phenomena that pose obstacles to a simplistic model of aggregation. Similar experiences have been reported by AI projects such as Cyc, which has lead to the development and use of various context mechanisms. In this paper we report on some of the problems with aggregating independently published data and propose a context mechanism to handle some of these problems. We briefly survey the context mechanisms developed in in AI and contrast them with the requirements of a context mechanism for the semantic web. Finally, we present a context mechanism for the semantic web that is adequate to handle the aggregation tasks, yet simple from both computational and model theoretic perspectives.

Abstract. Ontology translation is one of the most difficult problems that webbased agents must cope with. An ontology is a formal specification of a vocabulary, including axioms relating its terms. Ontology translation is best thought of in terms of ontology merging. The merge of two related ontologies is obtained by taking the union of the terms and the axioms defining them. We add bridging axioms not only as “bridges ” between terms in two related ontologies but also to make this merge into a complete new ontology for further merging with other ontologies. Translation is implemented using an inference engine (OntoEngine), running in either a demand-driven (backwardchaining) or data-driven (forward chaining) mode. We illustrate our method by describing its application in an online ontology translation system, OntoMerge, which translates a dataset in the DAML notation to a new DAML dataset that captures the same information, but in a different ontology. A uniform internal representation, Web-PDDL is used for representing merged ontologies and datasets for automated reasoning. 1.

ion of Representation for Interoperation David A. Maluf and Gio Wiederhold Department of Computer Science Stanford University Stanford, CA 94305 Abstract. When combining data from distinct sources, there is a need to share meta-data and other knowledge about various source domains. Due to semantic inconsistencies, problems arise when combining knowledge across domains and the knowledge is simply merged. Also, knowledge that is irrelevant to the task of interoperation will be included, making the result unnecessarily complex. An algebra over ontologies has been proposed to support disciplined manipulation of domain knowledge resources. However, if one tries to interoperate directly with the knowledge bases, semantic problems arise due to heterogeneity of representations. This heterogeneity problem can be eliminated by using an intermediate model that controls the knowledge translation from a source knowledge base. The intermediate model we have developed is based on the concept of abst...

Controlled medical vocabularies are useful in application areas such as medical information systems and decision-support systems. However, such vocabularies are large and complex, and working with them can be daunting. It is important to provide a means for orienting vocabulary designers and users to the vocabulary's contents. We describe a methodology for partitioning a vocabulary based on an IS-A hierarchy into small meaningful pieces. The methodology uses our disciplined modeling framework to refine the IS-A hierarchy according to prescribed rules in a process carried out by a user in conjunction with the computer. The partitioning of the hierarchy implies a partitioning of the vocabulary. We demonstrate the methodology with respect to a complex sample of the MED, an existing medical vocabulary. Keywords: Controlled Medical Vocabulary, Object-Oriented Modeling, Ontology, Semantic Network, Partitioning 1

Our work is motivated by the desire to develop methods to comprehend large vocabularies and large schemas of Object-Oriented Databases. The ability of a user of a database participating in a federated system to retrieve information from the other database systems will be greatly enhanced by acquiring a better comprehension of these systems. We are trying to develop both a theoretical paradigm and a methodology to analyze existing large schemas. Our approach to achieve comprehension is based on combining two concepts: informational thinning (i.e. concentration on the specialization hierarchy of the schema) and partitioning. In this paper we present a new technique for modeling which is called disciplined modeling. Based on the rules of disciplined modeling we develop a theoretical paradigm to support the existence of a meaningful forest hierarchy within the specialization hierarchy. Such a hierarchy functions as a skeleton of the schema and supports comprehension and partitioning effort...

Ontologies represent rich semantics in a lexical way. Lexical labels are used to identify concepts and relationships, though there is no bijective mapping between them. Phenomenons such as synonyms and homonyms exemplify this, and can result in frustrating misunderstanding and ambiguity. In the elicitation and application of ontologies, the meaning of the ontological knowledge is dependent on the context. We consider the role of context in ontology elicitation by introducing context in a concept definition server for ontology representation. We also adopt other features of context found in literature, such as packaging of knowledge, aligning elements of different contexts, and reasoning about contexts. Finally, we illustrate context-driven ontology elicitation with a real world case study.

Abstract. A viable ontology engineering methodology requires supporting domain experts in gradually building and managing increasingly complex versions of ontological elements and their converging and diverging interrelationships. Contexts are necessary to formalise and reason about such a dynamic wealth of knowledge. However, context dependencies introduce many complexities. In this article, we introduce a formal framework for supporting context dependency management processes, based on the DOGMA framework and methodology for scalable ontology engineering. Key notions are a set of context dependency operators, which can be combined to manage complex context dependencies like articulation, application, specialisation, and revision dependencies. In turn, these dependencies can be used in context-driven ontology engineering processes tailored to the specific requirements of collaborative communities. This is illustrated by a real-world case of interorganisational competency ontology engineering.

We focus on how we should define the relevance of information to a context for information processing agents, such as oracles. We build our formalization of relevance upon works in pragmatics which refer to contextual information without giving any explicit representation of context. We use a formalization of context (due to us) in Situation Theory, and demonstrate its power in this task. We also discuss some computational aspects of this formalization.

The modeling, representation and use of context is the challenge for the coming years in Artificial Intelligence, especially when we now face very large knowledge bases, complex problems and multimedia means. The notion of context is important since it can capture many of the interesting aspects of the way we understand the world, such as relativity, locality, partiality, and contextdependence.