Paola Velardi

Learning Domain Ontologies from Document Warehouses and Dedicated Web Sites

We present a method and a tool, OntoLearn, aimed at the extraction of domain ontologies from Web sites, and more generally from documents shared among the members of virtual organizations. OntoLearn first extracts a domain terminology from available documents. Then, complex domain terms are semantically interpreted and arranged in a hierarchical fashion. Finally, a general-purpose ontology, WordNet, is trimmed and enriched with the detected domain concepts. The major novel aspect of this approach is semantic interpretation, that is, the association of a complex concept with a complex term. This involves finding the appropriate WordNet concept for each word of a terminological string and the appropriate conceptual relations that hold among the concept components. Semantic interpretation is based on a new word sense disambiguation algorithm, called structural semantic interconnections.

Ontology learning and its application to automated terminology translation

Our OntoLearn system is an infrastructure for automated ontology learning from domain text. It is the only system, as far as we know, that uses natural language processing and machine learning techniques, and is part of a more general ontology engineering architecture. We describe the system and an experiment in which we used a machine-learned tourism ontology to automatically translate multiword terms from English to Italian. The method can apply to other domains without manual adaptation.

Structural semantic interconnections: a knowledge-based approach to word sense disambiguation

Word sense disambiguation (WSD) is traditionally considered an AI-hard problem. A break-through in this field would have a significant impact on many relevant Web-based applications, such as Web information retrieval, improved access to Web services, information extraction, etc. Early approaches to WSD, based on knowledge representation techniques, have been replaced in the past few years by more robust machine learning and statistical techniques. The results of recent comparative evaluations of WSD systems, however, show that these methods have inherent limitations. On the other hand, the increasing availability of large-scale, rich lexical knowledge resources seems to provide new challenges to knowledge-based approaches. In this paper, we present a method, called structural semantic interconnections (SSI), which creates structural specifications of the possible senses for each word in a context and selects the best hypothesis according to a grammar G, describing relations between sense specifications. Sense specifications are created from several available lexical resources that we integrated in part manually, in part with the help of automatic procedures. The SSI algorithm has been applied to different semantic disambiguation problems, like automatic ontology population, disambiguation of sentences in generic texts, disambiguation of words in glossary definitions. Evaluation experiments have been performed on specific knowledge domains (e.g., tourism, computer networks, enterprise interoperability), as well as on standard disambiguation test sets.

Using text processing techniques to automatically enrich a domain ontology

Though the utility of domain Ontologies is now widely acknowledgedin an increasing number of domains, several barriers must beovercome before Ontologies become practical and useful tools. Acritical issue is the task of identifying, defining, and enteringthe concept definitions. In case of large and complex applicationdomains this task can be lengthy, costly, and controversial (sincedifferent persons may have different points of view about the sameconcept). To reduce time, cost (and, sometimes, harsh discussions)it is highly advisable to refer, in constructing or updating anontology, to the documents available in the field. In this paper wedescribe OntoLearn, a text-mining tool devised to improvehuman productivity during the process of ontology construction.

The OntoWordNet project: Extension and axiomatization of conceptual relations in WordNet

In this paper we present a progress report of the OntoWordNet project, a research program aimed at achieving a formal specification of WordNet. Within this program, we developed a hybrid bottom-up top-down methodology to automatically extract association relations from WordNet, and to interpret those associations in terms of a set of conceptual relations, formally defined in the DOLCE foundational ontology. Preliminary results provide us with the conviction that a research program aiming to obtain a consistent, modularized, and axiomatized ontology from WordNet can be completed in acceptable time with the support of semi-automatic techniques.

Integrated approach to Web ontology learning and engineering

Developing the Semantic Web, seeking to improve the semantic awareness of computers connected via the Internet, requires a systematic, computer-oriented world representation. Researchers often refer to such a model as an ontology. Despite the work done on them in recent years, ontologies have yet to be widely applied and used. Research has devoted only limited attention to such practical issues as techniques and tools aimed at an ontologys actual construction and content. The authors have built a software environment, centered around the OntoLearn tool, which can build and assess a domain ontology for intelligent information integration within a virtual user community. OntoLearn has already been tested in two European projects, where it functioned as the basis for a semantic interoperability platform used by small- and medium-sized tourism enterprises. Further, developers can easily adapt OntoLearn to work with other general-purpose ontologies.

A graph-based algorithm for inducing lexical taxonomies from scratch

In this paper we present a graph-based approach aimed at learning a lexical taxonomy automatically starting from a domain corpus and the Web. Unlike many taxonomy learning approaches in the literature, our novel algorithm learns both concepts and relations entirely from scratch via the automated extraction of terms, definitions and hypernyms. This results in a very dense, cyclic and possibly disconnected hypernym graph. The algorithm then induces a taxonomy from the graph. Our experiments show that we obtain high-quality results, both when building brand-new taxonomies and when reconstructing WordNet sub-hierarchies.

Ontolearn reloaded: A graph-based algorithm for taxonomy induction

In 2004 we published in this journal an article describing OntoLearn, one of the first systems to automatically induce a taxonomy from documents and Web sites. Since then, OntoLearn has continued to be an active area of research in our group and has become a reference work within the community. In this paper we describe our next-generation taxonomy learning methodology, which we name OntoLearn Reloaded. Unlike many taxonomy learning approaches in the literature, our novel algorithm learns both concepts and relations entirely from scratch via the automated extraction of terms, definitions, and hypernyms. This results in a very dense, cyclic and potentially disconnected hypernym graph. The algorithm then induces a taxonomy from this graph via optimal branching and a novel weighting policy. Our experiments show that we obtain high-quality results, both when building brand-new taxonomies and when reconstructing sub-hierarchies of existing taxonomies.

Identification of relevant terms to support the construction of domain ontologies

Though the utility of domain Ontologies is now widely acknowledged in the IT (Information Technology) community, several barriers must be overcome before Ontologies become practical and useful tools. One important achievement would be to reduce the cost of identifying and manually entering several thousand-concept descriptions. This paper describes a text mining technique to aid an Ontology Engineer to identify the important concepts in a Domain Ontology.

A Taxonomy Learning Method and Its Application to Characterize a Scientific Web Community

The need to extract and manage domain-specific taxonomies has become increasingly relevant in recent years. A taxonomy is a form of business intelligence used to integrate information, reduce semantic heterogeneity, describe emergent communities and interest groups, and facilitate communication between information systems. We present a semiautomated strategy to extract domain-specific taxonomies from Web documents and its application to model a network of excellence in the emerging research field of enterprise interoperability