Semantic similarity measures play an important role in information retrieval and information integration. Traditional approaches to modeling semantic similarity compute the semantic distance between definitions within a single ontology. This single ontology is either a domain-independent ontology or the result of the integration of existing ontologies. We present an approach to computing semantic similarity that relaxes the requirement of a single ontology and accounts for differences in the levels of explicitness and formalization of the different ontology specifications. A similarity function determines similar entity classes by using a matching process over synonym sets, semantic neighborhoods, and distinguishing features that are classified into parts, functions, and attributes. Experimental results with different ontologies indicate that the model gives good results when ontologies have complete and detailed representations of entity classes. While the combination of word matching and semantic neighborhood matching is adequate for detecting equivalent entity classes, feature matching allows us to discriminate among similar, but not necessarily equivalent, entity classes.

Psychological studies on teamwork have shown that an effective team often can anticipate information needs of teammates based on a shared mental model. Existing multi-agent models for teamwork are limited in their ability to support proactive information exchange among teammates. To address this issue, we have developed and implemented a multi-agent architecture called CAST that simulates teamwork and supports proactive information exchange in a dynamic environment. We present a formal model for proactive information exchange. Knowledge regarding the structure and process of a team is described in a language called MALLET. Beliefs about shared team processes and their states are represented using Petri Nets. Based on this model, CAST agents offer information proactively to those who might need it using an algorithm called DIARG. Empirical evaluations using a multi-agent synthetic testbed application indicate that CAST enhances the effectiveness of teamwork among agents without sacrificing a high cost for communications. 1

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A semantic network or net is a graphic notation for representing knowledge in patterns of interconnected nodes and arcs. Computer implementations of semantic networks were first developed for artificial intelligence and machine translation, but earlier versions have long been used in philosophy, psychology, and linguistics. What is common to all semantic networks is a declarative graphic representation that can be used either to represent knowledge or to support automated systems for reasoning about knowledge. Some versions are highly informal, but other versions are formally defined systems of logic. Following are six of the most common kinds of semantic networks, each of which is discussed in detail in one section of this article. 1. Definitional networks emphasize the subtype or is-a relation between a concept type and a newly defined subtype. The resulting network, also called a generalization or subsumption hierarchy, supports the rule of inheritance for copying properties defined for a supertype to all of its subtypes. Since definitions are true by definition, the information in these networks is often assumed to be necessarily true.

. It is shown how Conceptual Graphs and Formal Concept Analysis may be combined to obtain a formalization of Elementary Logic which is useful for knowledge representation and processing. For this, a translation of conceptual graphs to formal contexts and concept lattices is described through an example. Using a suitable mathematization of conceptual graphs, basics of a unified mathematical theory for Elementary Logic are proposed. Contents 1. Formalization of Elementary Logic 2. From Conceptual Graphs to Formal Contexts 3. Mathematization of Conceptual Structures 1 Formalization of Elementary Logic Conceptual Graphs and Formal Concept Analysis have been used both for knowledge representation and processing in a large extent. This has caused the desire to combine the two approaches for deriving benefits from both disciplines and their experiences. There is even a fundamental reason for associating Conceptual Graphs and Formal Concept Analysis which lies in their far-back reaching root...

The paper presents a review of the ONIONS project. ONIONS is committed to developing a large­scale ontology library for medical terminology. The developed methodology exploits a description logic­based design for the modules in the library and makes extended use of generic theories, thus creating a stratification of the modules. Terminological knowledge is acquired by conceptual analysis and ontology integration over a set of authoritative sources. After addressing general issues about conceptual analysis and integration, the methodology is briefly described. The central part of the article presents the investigation we have made on the 476,000 medical concepts singled out by the National Library of Medicine as the Metathesaurus^TM in the UMLS project. This is followed by several case studies concerning lexical polysemy, the interface between ontologies and lexicon, and other special problems encountered in the specification of the ontologies. A section describing the current structure of the library and the generic theories reused is provided. Current results of our research include the integration of some top­level ontologies in the ON9.2 ontology library, and the formalization of the terminological knowledge in the UMLS Metathesaurus.

Abstract. The Fedora architecture is an extensible framework for the storage, management, and dissemination of complex objects and the relationships among them. Fedora accommodates the aggregation of local and distributed content into digital objects and the association of services with objects. This allows an object to have several accessible representations, some of them dynamically produced. The architecture includes a generic RDF-based relationship model that represents relationships among objects and their components. Queries against these relationships are supported by an RDF triple store. The architecture is implemented as a web service, with all aspects of the complex object architecture and related management functions exposed through REST and SOAP interfaces. The implementation is available as open-source software, providing the foundation for a variety of end-user applications for digital libraries, archives, institutional repositories, and learning object systems. 1

process semantics of its constellation of economic objects at multiple levels of abstraction, and the firm can use these semantics as an explicit domain conceptualization, both within the firm and between the firm and its trading partners. The extended REA structures also let a firm build a semantic enterprise model that links to other initiatives, such as the introduction of business-process reengineering and workflow management or the movement toward activity-based management. REA accounting as a script In starkly simple terms, all business enterprises operate in the same manner. Somebody has an idea about how to provide a new or improved service or product. This entrepreneur acquires some initial financing (debt or equity for the enterprise), then engages in a chain of economic exchanges with other parties (such as vendors and employees)---each time giving up an economic resource (perhaps money) in return for another resource of greater value. Value is defined as

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Ontology concerns itself with the representation of the objects in the universe and the web of their various connections. The traditional task of ontologists has been to extract from this tangle a single ordered structure, in the form of a tree or lattice. This structure consists of the terms that represent the objects, and the relationships that represent connections between objects. Recent work in ontology goes so far as to consider several distinct, superimposed structures, which each represent a classification of the universe according to a particular criterion. Our purpose is to defer the task of globally classifying terms and relationships. Instead, we focus on composing them for use as we need them. We define contexts to be our unit of encapsulation for ontologies, and use a rule-based algebra to compose novel ontological structures within them. We separate context from concept, the unit of ontological abstraction. Also, we distinguish composition from subsumption, or containment, the relationships that commonly provide structure to ontologies. Adding a formal notation of encapsulation and composition to ontologies leads to more dynamic and maintainable structures, and, we believe, greater computational efficiency for knowledge bases.