In this paper, we argue that a core ontology is one of the key building blocks necessary to enable the scalable assimilation of information from diverse sources. A complete and extensible ontology that expresses the basic concepts that are common across a variety of domains and can provide the basis for specialization into domain-specific concepts and vocabularies, is essential for well-defined mappings between domain-specific knowledge representations (i.e., metadata vocabularies) and the subsequent building of a variety of services such as cross-domain searching, browsing, data mining and knowledge extraction. This paper describes the results of a series of three workshops held in 2001 and 2002 which brought together representatives from the cultural heritage and digital library communities with the goal of harmonizing their knowledge perspectives and producing a core ontology. The knowledge perspectives of these two communities were represented by the CIDOC/CRM [31], an ontology for information exchange in the cultural heritage and museum community, and the ABC ontology [33], a model for the exchange and integration of digital library information. This paper describes the mediation process between these two different knowledge biases and the results of this mediation – the harmonization of the ABC and CIDOC/CRM ontologies, which we believe may provide a useful basis for information integration in the wider scope of the involved communities.

Most of the data stored in the Semantic Web are organized in schema models, that can be represented as labeled graphs where labels are short natural language expressions. Examples of schema models are ER-schema automata, ontologies, taxonomies, and Web Directories. The semantics of schema

The next generation of online reference works will require structured representations of their contents in order to support scholarly functions such as semantic search, automated generation of cross-references, tables of contents, and ontology-driven conceptual navigation. Many of these works can be expected to contain massive amounts of data and be updated dynamically, which limits the feasibility of “manually ” coded ontologies to keep up with changes in content. However, relationships relevant to inferring an ontology can be recovered from statistical text processing, and these estimates can be verified with carefully-solicited expert feedback. In this paper, we explain a method by which we have used answer set programming on such expert feedback to dynamically populate and partially infer an ontology for a well-established, open-access reference work, the Stanford Encyclopedia of Philosophy.

Anaphora occur commonly in natural language text, and resolving them is essential for capturing the knowledge encoded in text. Indirect anaphora are especially challenging to resolve because the referring expression and the antecedent are related by unstated background knowledge. Such anaphora need to be resolved properly in order to automatically capture the knowledge expressed in natural language. Resolving indirect anaphora has been treated as a unique problem that requires special-purpose methods, and these methods have had limited success in precision and recall. In this study, we used a generic tool for finding semantic paths between two concepts to resolve these anaphora, and it achieved approximately twice the recall of the best previous system without loss of precision. A series of ablation study showed that the biggest increase in recall came from an abductive stopping criterion of the search.

The paper argues that Guarino is right that ontologies are different from thesauri and similar objects, but not in the ways he believes: they are distinguished from essentially linguistic objects like thesauri and hierarchies of conceptual relations because they unpack, ultimately, in terms of sets of objects and individuals. However this is a lonely status, and without much application outside strict scientific and engineering disciplines, and of no direct relevance to language processing (NLP). More interesting structures, of NLP relevance, that encode conceptual knowledge, cannot be subjected to the “cleaning up ” techniques that Guarino advocates, because his conditions are too strict to be applicable, and because the terms used in such structures retain their language-like features of ambiguity and vagueness, and in a way that cannot be eliminated by reference to sets of objects, as it can be in ontologies in the narrow sense. Wordnet is a structure that remains useful to NLP, and has within it features of both types (ontologies and conceptual hierarchies) and its function and usefulness will remain, properly, resistant to Guarino’s techniques, because those rest on a misunderstanding about concepts. The ultimate way out of such disputes can only come from automatic construction and evaluation procedures for conceptual and ontological structures from data, which is to say, corpora. Key words: ontologies, thesauri, necessary and sufficient conditions, identity criteria, evaluation.

Abstract. In recent years, there is an increasing interest in the Semantic Web and the relevant technologies, which can have a significant impact in the context of information and knowledge management. An important observation is that the entity identification problem lies at the core of many semantic web applications and the intrinsic difficulties of this problem have hindered progress in this area. In this paper, we argue for an infrastructure responsible for assigning and managing unique identifiers for entities in the semantic web, and we propose a conceptual model for the storage and management of these entities. The proposed model is generic and flexible and it allows for efficient and effective retrieval and analysis of the stored entities. We discuss the requirements with respect to creating and modifying these entities, as well as to managing their evolution over time. Finally, we study some enhancements of the entity representation, and we discuss the beneficial impact they can have on the performance of the system. 1

In this article we describe results of our research on the disambiguation of user queries using ontologies for categorization. We present an approach to cluster search results by using classes or "Sense Folders" (prototype categories) derived from the concepts of an assigned ontology, in our case WordNet. Using the semantic relations provided from such a resource, we can assign categories to prior, not annotated documents. The disambiguation of query terms in documents with respect to a user-specific ontology is an important issue in order to improve the retrieval performance for the user. Furthermore, we show that a clustering process can enhance the semantic classification of documents, and we discuss how this clustering process can be further enhanced using only the most descriptive classes of the ontology.

Consistent ontologies are vital for the growth of the Semantic Web. We describe and appraise the OntoClean methodology and the di#erent implementations available to evaluate taxonomic relationships in ontologies. We propose a new system, CleanONTO, which uses definitions to describe each concept, where definitions are paths from the concept to the root node of the ontology. In the current study, these definitions (paths) have been extracted from WordNet.

Abstract. This paper presents an automatic method for differentiating between instances and classes in a large scale taxonomy induced from the Wikipedia category network. The method exploits characteristics of the category names and the structure of the network. The approach we present is the first attempt to make this distinction automatically in a large scale resource. In contrast, this distinction has been made in WordNet and Cyc based on manual annotations. The result of the process is evaluated against ResearchCyc. On the subnetwork shared by our taxonomy and ResearchCyc we report 84.52 % accuracy. 1

We assume that the ontological structure of the common-sense world, and thus of human knowledge about this world, is organized in networks rather than in hierarchies. Thus, using the taxonomies that semantic relations generate in WordNet as the only source for the reconstruction of ontological information must fail at some point. Comparing the ontological structures underlying roles to WordNet representations, we demonstrate that the power of lexical semantics to abstract over contexts distorts the taxonomic order of a conceivable ontology. Approaches trying to adjust the semantics of WordNet relations, in order to reach a higher ontological adequacy, unintentionally produce artifacts deriving from differences between the frequency of contexts, and from metonymy-like reference to ontological relations.