Elias Zavitsanos

Ontology population and enrichment: state of the art

Ontology learning is the process of acquiring (constructing or integrating) an ontology (semi-) automatically. Being a knowledge acquisition task, it is a complex activity, which becomes even more complex in the context of the BOEMIE project1, due to the management of multimedia resources and the multi-modal semantic interpretation that they require. The purpose of this chapter is to present a survey of the most relevant methods, techniques and tools used for the task of ontology learning. Adopting a practical perspective, an overview of the main activities involved in ontology learning is presented. This breakdown of the learning process is used as a basis for the comparative analysis of existing tools and approaches. The comparison is done along dimensions that emphasize the particular interests of the BOEMIE project. In this context, ontology learning in BOEMIE is treated and compared to the state of the art, explaining how BOEMIE addresses problems observed in existing systems and contributes to issues that are not frequently considered by existing approaches.

Gold Standard Evaluation of Ontology Learning Methods through Ontology Transformation and Alignment

This paper presents a method along with a set of measures for evaluating learned ontologies against gold ontologies. The proposed method transforms the ontology concepts and their properties into a vector space representation to avoid the common string matching of concepts and properties at the lexical layer. The proposed evaluation measures exploit the vector space representation and calculate the similarity of the two ontologies (learned and gold) at the lexical and relational levels. Extensive evaluation experiments are provided, which show that these measures capture accurately the deviations from the gold ontology. The proposed method is tested using the Genia and the Lonely Planet gold ontologies, as well as the ontologies in the benchmark series of the Ontology Alignment Evaluation Initiative.

Learning subsumption hierarchies of ontology concepts from texts

Abstract. This paper proposes a method for learning ontologies given a corpus of text documents. The method identifies conceptsin documents and organizes them into a subsumption hierarchy, without presupposing the existence of a seed ontology. Themethod uncovers latent topics for generating document text. The discovered topics form the concepts of the new ontology.Concept discovery is done in a language neutral way, using probabilistic space reduction techniques over the original term spaceof the corpus. Furthermore, the proposed method constructs a subsumption hierarchy of the concepts by performing conditionalindependence tests among pairs of latent topics, given a third one. The paper provides experimental results on the Genia and theLonely Planet corpora from the domains of molecular biology and tourism respectively.Keywords: Ontology Learning, Concept Discovery, Subsumption Hierarchy Construction, Latent Dirichlet Allocation, ConditionalIndependence 1. IntroductionOntologies have been proposed as the key ele-ment to shape, manage and further process knowledge.However, the engineering of ontologies is a costly,time-consuming and error-prone task when done man-ually. Furthermore, in quickly evolving domains ofknowledge, or in cases where information is constantlybeing updated, possibly making prior knowledge obso-lete, the continuous maintenance and evolution of on-tologies are tasks that require significant human effort.Thus, there is a strong need to automate the ontologydevelopment/maintenance tasks in order to minimizethe cost of ontology creation and evolution.For this reason, ontology learning has emerged as afield of research, aiming to help knowledge engineersto build and further extend ontologies with the help ofautomated or semi-automated machine learning tech-niques, exploiting several sources of information. On-tology learning is commonly viewed ([1], [10], [30],[35]) as the task of extending or enriching an exist-ing ontology with new ontology elements mined fromtext corpora. Depending on the ontology elements be-ing discovered, existing approaches deal with the iden-tification of concepts, subsumption relations amongconcepts, instances of concepts, or concept proper-ties/relations. Linguistic, statistical, or machine learn-ing techniques are used for these tasks.The seed ontology used in ontology enrichmentis usually a hierarchical backbone of concepts, re-lated via subsumption relations, or a generic ontologythat formalizes some of the concepts in a documentcollection. Linguistic approaches additionally sufferfrom language dependence, as they rely on language-specific lexico-syntactic patterns.In contrast to the majority of the existing work, thispaper proposes an automated approach to ontologylearning, without presupposing the existence of a seedontology, or any other type of external resource, exceptthe corpus of training text documents. The proposed

Scalable semantic annotation of text using lexical and web resources

In this paper we are dealing with the task of adding domain-specific semantic tags to a document, based solely on the domain ontology and generic lexical and Web resources In this manner, we avoid the need for trained domain-specific lexical resources, which hinder the scalability of semantic annotation More specifically, the proposed method maps the content of the document to concepts of the ontology, using the WordNet lexicon and Wikipedia The method comprises a novel combination of measures of semantic relatedness and word sense disambiguation techniques to identify the most related ontology concepts for the document We test the method on two case studies: (a) a set of summaries, accompanying environmental news videos, (b) a set of medical abstracts The results in both cases show that the proposed method achieves reasonable performance, thus pointing to a promising path for scalable semantic annotation of documents.

Semantic Web Services and Mobile Agents Integration for Efficient Mobile Services

The requirement for ubiquitous service access in wireless environments presents a great challenge in light of well-known problems like high error rate and frequent disconnections. In order to satisfy this requirement, we propose the integration of two modern service technologies: Web Services and Mobile Agents. This integration allows wireless users to access and invoke semantically enriched Web Services without the need for simultaneous, online presence of the service requestor. Moreover, in order to improve the capabilities of Service registries, we exploit the advantages offered by the Semantic Web framework. Specifically, we use enhanced registries enriched with semantic information that provide semantic matching to service queries and published service descriptions. Finally, we discuss the implementation of the proposed framework and present our performance assessment findings.

Wireless Web Services using Mobile Agents and Ontologies

We discuss the integration of two contemporary service technologies: Web services and mobile agents. We exploit the capabilities offered by mobile agents to query and invoke semantically enriched Web services without the need for simultaneous, online presence of the service requestor. Such service setting is ideal for wireless/mobile computing, where user terminals are not necessarily online during their entire session. To improve the capabilities of service registries met in the Web services reference architecture, we exploit the advantages of the semantic Web framework. Specifically, we use enhanced registries enriched with semantic information that provide semantic matching to service queries and published service descriptions

Learning Ontologies of Appropriate Size

Determining the size of an ontology that is automatically learned from text corpora is an open issue. In this paper, we study the similarity between ontology concepts at different levels of a taxonomy, quantifying in a natural manner the quality of the ontology attained. Our approach is integrated in a recently proposed method for language-neutral learning of ontologies of thematic topics from text corpora. Evaluation results over the Genia and the Lonely Planet corpora demonstrate the significance of our approach.

A Case Based Reasoning Framework for Service Selection and Adaptation in Mobile Networks

Service selection and adaptation is of paramount importance in contemporary mobile networks. Many diverse parameters should be taken into account (e.g., user context, terminal and network capabilities) for the selection of the appropriate service or the required service adaptations. In this paper we propose a framework for service selection and adaptation. A case based reasoning system (CBRS) is used to select the most appropriate service. Services are modelled using formal semantics. The CBRS retrieves the most appropriate service by comparing previous cases with the current service request. This comparison is performed using similarity metrics. We elaborate on the different aspects of the discussed architecture and provide indicative examples to illustrate the versatility of the proposed scheme.