Method for Reusing and Re-engineering Non-ontological Resources for Building Ontologies

This thesis is focused on the reuse and possible subsequent re-engineering of knowledge resources, as opposed to custom-building new ontologies from scratch. The deep analysis of the state of the art has revealed that there are some methods and tools in the literature for transforming non-ontological resources into ontologies, but with some limitations: _ Most of the methods presented are based on ad-hoc transformations for the resource type, and the resource implementation. _ Only a few take advantage of the resource data model, an important artifact for the re-engineering process [GGPSFVT08]. _ There is no any integrated framework, method or corresponding tool, that considers the resources types, data models and implementations identified in an unified way. _ With regard to the transformation approach, the majority of the methods perform a TBox transformation, many others perform an ABox transformation and some perform a population. However, no method includes the possibility to perform the three transformation approaches. _ Regarding to the degree of automation, almost all the methods perform a semi-automatic transformation of the resource. _ According to the explicitation of the hidden semantics in the relations of the resource components, we can state that the methods that perform a TBox transformation make explicit the semantics in the relations of the resource components. Most of those methods identify subClassOf relations, others identify ad-hoc relations, and some identify partOf relations. However, only a few methods make explicit the three types of relations. _ With respect to how the methods make explicit the hidden semantics in the relations of the resource terms, we can say that three methods rely on the domain expert for making explicit the semantics, and two rely on an external resource, e.g., DOLCE ontology. Moreover, there are two methods that rely on external resources but not for making explicit the hidden semantics, but for finding out a proper ontology for populating it. _ According to the provision of the methodological guidelines, almost all the methods provide methodological guidelines for the transformation. However these guidelines are not finely detailed; for instance, they do not provide information about who is in charge of performing a particular activity/task, nor when that activity/task has to be carried out. _ With regard to the techniques employed, most of the methods do not mention them at all. Only a few methods specify techniques as transformation rules, lexico-syntactic patterns, mapping rules and natural language techniques. In this thesis we have provided a method and its technological support that rely on re-engineering patterns in order to speed up the ontology development process by reusing and re-engineering as much as possible available non-ontological resources. To achieve this overall goal, we have decomposed it in the following objectives: (1) the definition of methodological aspects related with the reuse of non-ontolo-gical resource for building ontologies; (2) the definition of methodological aspects related with the re-engineering of non-ontological resources for building ontologies; (3) the creation of a library of patterns for re-engineering nonontological resources into ontologies; and (4) the development of a software library that implements the suggestions given by the re-engineering patterns. Having in mind these goals, in this chapter we present how the open research problems identified in Chapter 2 are solved by the main thesis contributions. Then, we discuss the verification of our hypotheses, and finally we provide an outlook for the future work in those topics

Benchmarking Ontologies: Bigger or Better?

A scientific ontology is a formal representation of knowledge within a domain, typically including central concepts, their properties, and relations. With the rise of computers and high-throughput data collection, ontologies have become essential to data mining and sharing across communities in the biomedical sciences. Powerful approaches exist for testing the internal consistency of an ontology, but not for assessing the fidelity of its domain representation. We introduce a family of metrics that describe the breadth and depth with which an ontology represents its knowledge domain. We then test these metrics using (1) four of the most common medical ontologies with respect to a corpus of medical documents and (2) seven of the most popular English thesauri with respect to three corpora that sample language from medicine, news, and novels. Here we show that our approach captures the quality of ontological representation and guides efforts to narrow the breach between ontology and collective discourse within a domain. Our results also demonstrate key features of medical ontologies, English thesauri, and discourse from different domains. Medical ontologies have a small intersection, as do English thesauri. Moreover, dialects characteristic of distinct domains vary strikingly as many of the same words are used quite differently in medicine, news, and novels. As ontologies are intended to mirror the state of knowledge, our methods to tighten the fit between ontology and domain will increase their relevance for new areas of biomedical science and improve the accuracy and power of inferences computed across them

Could we automatically reproduce semantic relations of an information retrieval thesaurus?

A well constructed thesaurus is recognized as a valuable source of semantic information for various applications, especially for Information Retrieval. The main hindrances to using thesaurus-oriented approaches are the high complexity and cost of manual thesauri creation. This paper addresses the problem of automatic thesaurus construction, namely we study the quality of automatically extracted semantic relations as compared with the semantic relations of a manually crafted thesaurus. The vector-space model based on syntactic contexts was used to reproduce relations between the terms of a manually constructed thesaurus. We propose a simple algorithm for representing both single word and multiword terms in the distributional space of syntactic contexts. Furthermore, we propose a method for evaluation quality of the extracted relations. Our experiments show significant difference between the automatically and manually constructed relations: while many of the automatically generated relations are relevant, just a small part of them could be found in the original thesaurus

Validation Framework for RDF-based Constraint Languages

In this thesis, a validation framework is introduced that enables to consistently execute RDF-based constraint languages on RDF data and to formulate constraints of any type. The framework reduces the representation of constraints to the absolute minimum, is based on formal logics, consists of a small lightweight vocabulary, and ensures consistency regarding validation results and enables constraint transformations for each constraint type across RDF-based constraint languages

Un environnement de spécification et de découverte pour la réutilisation des composants logiciels dans le développement des logiciels distribués

Notre travail vise à élaborer une solution efficace pour la découverte et la réutilisation des composants logiciels dans les environnements de développement existants et couramment utilisés. Nous proposons une ontologie pour décrire et découvrir des composants logiciels élémentaires. La description couvre à la fois les propriétés fonctionnelles et les propriétés non fonctionnelles des composants logiciels exprimées comme des paramètres de QoS. Notre processus de recherche est basé sur la fonction qui calcule la distance sémantique entre la signature d'un composant et la signature d'une requête donnée, réalisant ainsi une comparaison judicieuse. Nous employons également la notion de " subsumption " pour comparer l'entrée-sortie de la requête et des composants. Après sélection des composants adéquats, les propriétés non fonctionnelles sont employées comme un facteur distinctif pour raffiner le résultat de publication des composants résultats. Nous proposons une approche de découverte des composants composite si aucun composant élémentaire n'est trouvé, cette approche basée sur l'ontologie commune. Pour intégrer le composant résultat dans le projet en cours de développement, nous avons développé l'ontologie d'intégration et les deux services " input/output convertor " et " output Matching ".Our work aims to develop an effective solution for the discovery and the reuse of software components in existing and commonly used development environments. We propose an ontology for describing and discovering atomic software components. The description covers both the functional and non functional properties which are expressed as QoS parameters. Our search process is based on the function that calculates the semantic distance between the component interface signature and the signature of a given query, thus achieving an appropriate comparison. We also use the notion of "subsumption" to compare the input/output of the query and the components input/output. After selecting the appropriate components, the non-functional properties are used to refine the search result. We propose an approach for discovering composite components if any atomic component is found, this approach based on the shared ontology. To integrate the component results in the project under development, we developed the ontology integration and two services " input/output convertor " and " output Matching "

Applying Wikipedia to Interactive Information Retrieval

There are many opportunities to improve the interactivity of information retrieval systems beyond the ubiquitous search box. One idea is to use knowledge bases—e.g. controlled vocabularies, classification schemes, thesauri and ontologies—to organize, describe and navigate the information space. These resources are popular in libraries and specialist collections, but have proven too expensive and narrow to be applied to everyday webscale search. Wikipedia has the potential to bring structured knowledge into more widespread use. This online, collaboratively generated encyclopaedia is one of the largest and most consulted reference works in existence. It is broader, deeper and more agile than the knowledge bases put forward to assist retrieval in the past. Rendering this resource machine-readable is a challenging task that has captured the interest of many researchers. Many see it as a key step required to break the knowledge acquisition bottleneck that crippled previous efforts. This thesis claims that the roadblock can be sidestepped: Wikipedia can be applied effectively to open-domain information retrieval with minimal natural language processing or information extraction. The key is to focus on gathering and applying human-readable rather than machine-readable knowledge. To demonstrate this claim, the thesis tackles three separate problems: extracting knowledge from Wikipedia; connecting it to textual documents; and applying it to the retrieval process. First, we demonstrate that a large thesaurus-like structure can be obtained directly from Wikipedia, and that accurate measures of semantic relatedness can be efficiently mined from it. Second, we show that Wikipedia provides the necessary features and training data for existing data mining techniques to accurately detect and disambiguate topics when they are mentioned in plain text. Third, we provide two systems and user studies that demonstrate the utility of the Wikipedia-derived knowledge base for interactive information retrieval