Indexing and retrieval in Web catalogs can benefit from using faceted taxonomies. A faceted taxonomy consists of a set of facets, where each facet consists of a predefined set of terms structured by a subsumption relation. We propose two extensions of faceted taxonomies, which allow inferring conjunctions of terms that are valid in the underlying domain. We give a model-theoretic interpretation to these extended faceted taxonomies and we provide mechanisms for inferring the valid conjunctions of terms. This inference service can be exploited for preventing errors during the indexing process and for dynamically generating navigation trees that are suitable for browsing through the Web. The proposed scheme has several advantages by comparison to the hierarchical classification schemes that are currently employed by Web catalogs, namely: (a) conceptual clarity: it is easier to understand; (b) compactness: it takes less space; and (c) scalability: the update operations can be formulated easier and be performed more efficiently.

We propose a model for providing integrated and unified access to multiple information sources of the kind of Web catalogs. We assume that each source comprises two parts: (a) an ontology i.e. a set of terms structured by a subsumption relation, and (b) a database that stores objects of interest under the terms of the ontology. We assume that these objects belong to an underlying domain that is common to all sources (e.g. a set of Web pages of interest), and that different sources may use different ontologies with terms that correspond to different natural languages or to different levels of granularity. Information integration is obtained through a mediator comprising two parts: (a) an ontology, and (b) a set of articulations to the sources. Here, by articulation to a source we mean a set of relationships between terms of the mediator and terms of that source. Information requests (queries) are addressed to the mediator whose task is to analyze each query into sub-queries, translate them into queries to the appropriate sources, then merge the results to answer the original query. We study the querying and answering process in such a model and present algorithms for handling the main tasks of the mediator, namely, query translation between the mediator and the sources, source selection and result merging to produce the final answer.

Although semantic data models provide expressive conceptual modeling mechanisms, they do not support context, i.e. providing controlled partial information on conceptual entities by viewing them from different viewpoints or in different situations. The authors present a model for representing contexts in information bases along with a set of operations for manipulating contexts. These operations support creating, updating, combining, and comparing contexts. The model contributes to the efficient handling of information, especially in distributed, cooperative environments, as it enables (i) representing (possibly overlapping) partitions of an information base; (ii) partial representations of objects, (iii) flexible naming (e.g. relative names, synonyms and homonyms), (iv) focusing attention, and (v) combining and comparing different partial representations. The work advances towards the development of a formal framework intended to clarify several theoretical and practical issues related to the notion of context. The use of context in a cooperative environment is illustrated through a detailed example.

We elaborate on the semantics of an enhanced object-oriented semantic network, where multiple instantiation, multiple specialization, and meta-classes are supported for both kinds of objects: entities and properties. By semantics of a semantic network, we mean the information (both explicit and derived) that the semantic network carries. Several data models use semantic networks to organize information. However, many of these models do not have a formalism defining what the semantics of the semantic network is. In our data model, in addition to the Isa relation, we consider a stronger form of specialization for properties, that we call restriction isa, or Risa for short. The Risa relation expresses property value refinement. A distinctive feature of our data model is that it supports the interaction between Isa and Risa relations. The combination of Isa and Risa provides a powerful conceptual modeling mechanism. The user declares objects and relations between objects through a program. Reasoning is done through a number of (built-in) inference rules that allow for derivations both at instance and schema level. Through the inference rules, new objects and new relations between objects are derived. In our data model, inherited properties are considered to be derived objects. In addition to the inference rules, a number of (built-in) system constraints exist for checking the validity of a program.

In information bases following semantic and object-oriented data models logical names are used for the external identification of objects. Yet the naming schemes employed are not "natural" enough and several problems often arise: logical names can be ambiguous, excessively long, unrelated to or unable to follow the changes of the environment of named object. In natural language, similar problems are resolved by the context within which words are used. An approach to introducing a notion of context in an information base is to provide structuring mechanisms for decomposing it into possibly overlapping parts. The paper focuses on developing a context mechanism for an information base and, in particular, exploiting this mechanism for naming purposes. Rules are developed for generating meaningful names for objects by taking their context into account. This context-based naming enhances name readability, resolves name ambiguities, saves a lot of redundant name substrings, and it localizes and thus facilitates consistency checking, query processing and update operations. In modeling, it supports systematic naming of objects, and thus enhances cooperation between the designers and the end-users in the sense that the contents of the information base are more understandable by both of them.

Thesauri merging is the activity of consolidating a set of thesauri into a thesaurus which accommodates the vocabularies and the structure of all thesauri being merged. In this paper, we introduce a general framework for monolingual thesauri merging. We also present a domain independent set-theoretic model for the representation of terms, relationships, and integrity constraints. Finally, we present a method for the merging of monolingual thesauri focusing on its mechanisms for the detection of equivalent terms among the thesauri being merged. Our method expands previous work on the problem; we introduce equivalence assumptions that express similarity between terms and we propose a term distance model which can be used to guide the confirmation or rejection of equivalence assumptions.

In information bases following semantic and object-oriented data models, logical names are used for the external identification of objects. Yet the naming schemes employed are not "natural" enough and several problems often arise: logical names can be ambiguous, excessively long, unrelated to or unable to follow the changes of the environment of the named object. In natural language, similar problems are resolved by the context within which words are used. An approach to introducing a notion of context in an information base is to provide structuring mechanisms for decomposing it into possibly overlapping parts. This paper focuses on developing a context mechanism for an information base and, in particular, exploiting this mechanism for naming purposes. Rules are developed for generating meaningful names for objects by taking their context into account. This context-based naming enhances name readability, resolves name ambiguities, saves a lot of redundant name substrings, and it localizes and thus facilitates consistency checking, query processing and update operations. In modeling, it supports systematic naming of objects, and thus enhances cooperation between the designers and the end-users in the sense that the contents of the information base are more understandable by both of them.

This article defines and analyzes a computational model of similarity which detects analogies between objects based on conceptual descriptions of them, constructed from classification, generalization relations, and attributes. Analogies are detected (elaborated) by functions which measure conceptual distances between objects with respect to these semantic modeling abstractions. The model is domain independent and operational upon objects described in nonuniform ways. It does not require any special forms of knowledge for identifying analogies and distinguishes the importance of distinct object elements. Also, it has a polynomial complexity. Due to these characteristics, it may be used in complex tasks involving intra- or interdomain analogical reasoning. So far the similarity model has been applied in the domain of software engineering. First, to support the specification of software requirements by analogical reuse and second, to enable the integration of requirements specifications, generated by the multiple agents involved in information system development. Details of these applications can be found in cited references. Also, we have conducted an empirical evaluation of: (i) the consistency of the estimates generated by the model against human intuition about similarity and (ii) its recall performance in tasks of analogical retrieval, the results of which are presented in this article. © 1996 John Wiley & Sons, Inc.