Dans le contexte du Web sémantique, les ontologies OWL représentent des connaissances explicites sur un domaine sur la base d'une conceptualisation des domaines d'intérêt, tandis que la connaissance correspondante sur les individus est donnée par les données RDF qui s'y réfèrent. Dans cette thèse, sur la base d'idées dérivées de l'ILP, nous visons à découvrir des motifs de connaissance cachés sous la forme de règles d'association multi-relationnelles en exploitant l'évidence provenant des assertions contenues dans les bases de connaissances ontologiques. Plus précisément, les règles découvertes sont codées en SWRL pour être facilement intégrées dans l'ontologie, enrichissant ainsi son pouvoir expressif et augmentant les connaissances sur les individus (assertions) qui en peuvent être dérivées. Deux algorithmes appliqués aux bases de connaissances ontologiques peuplées sont proposés pour trouver des règles à forte puissance inductive : (i) un algorithme de génération et test par niveaux et (ii) un algorithme évolutif. Nous avons effectué des expériences sur des ontologies accessibles au public, validant les performances de notre approche et les comparant avec les principaux systèmes de l'état de l'art. En outre, nous effectuons une comparaison des métriques asymétriques les plus répandues, proposées à l'origine pour la notation de règles d'association, comme éléments constitutifs d'une fonction de fitness pour l'algorithme évolutif afin de sélectionner les métriques qui conviennent à la sémantique des données. Afin d'améliorer les performances du système, nous avons proposé de construire un algorithme pour calculer les métriques au lieu d'interroger viaSPARQL-DL. / In the Semantic Web context, OWL ontologies represent explicit domain knowledge based on the conceptualization of domains of interest while the corresponding assertional knowledge is given by RDF data referring to them. In this thesis, based on ideas derived from ILP, we aim at discovering hidden knowledge patterns in the form of multi-relational association rules by exploiting the evidence coming from the assertional data of ontological knowledge bases. Specifically, discovered rules are coded in SWRL to be easily integrated within the ontology, thus enriching its expressive power and augmenting the assertional knowledge that can be derived. Two algorithms applied to populated ontological knowledge bases are proposed for finding rules with a high inductive power: (i) level-wise generated-and-test algorithm and (ii) evolutionary algorithm. We performed experiments on publicly available ontologies, validating the performances of our approach and comparing them with the main state-of-the-art systems. In addition, we carry out a comparison of popular asymmetric metrics, originally proposed for scoring association rules, as building blocks for a fitness function for evolutionary algorithm to select metrics that are suitable with data semantics. In order to improve the system performance, we proposed to build an algorithm to compute metrics instead of querying via SPARQL-DL.

Un nombre croissant de sources de données est publié sur le Web, décrites dans les langages proposés par le W3C tels que RDF, RDF(S) et OWL. Une quantité de données sans précédent est ainsi disponible pour les utilisateurs et les applications, mais l'exploitation pertinente de ces sources constitue encore un défi : l'interrogation des sources est en effet limitée d'abord car elle suppose la maîtrise d'un langage de requêtes tel que SPARQL, mais surtout car elle suppose une certaine connaissance de la source de données qui permet de cibler les ressources et les propriétés pertinentes pour les besoins spécifiques des applications. Le travail présenté ici s'intéresse à l'exploration de sources de données RDF, et ce selon deux axes complémentaires : découvrir d'une part les thèmes sur lesquels porte la source de données, fournir d'autre part un support pour l'interrogation d'une source sans l'utilisation de langage de requêtes, mais au moyen de mots clés. L'approche d'exploration proposée se compose ainsi de deux stratégies complémentaires : l'exploration thématique et la recherche par mots clés. La découverte de thèmes dans une source de données RDF consiste à identifier un ensemble de sous-graphes, non nécessairement disjoints, chacun représentant un ensemble cohérent de ressources sémantiquement liées et définissant un thème selon le point de vue de l'utilisateur. Ces thèmes peuvent être utilisés pour permettre une exploration thématique de la source, où les utilisateurs pourront cibler les thèmes pertinents pour leurs besoins et limiter l'exploration aux seules ressources composant les thèmes sélectionnés. La recherche par mots clés est une façon simple et intuitive d'interroger les sources de données. Dans le cas des sources de données RDF, cette recherche pose un certain nombre de problèmes, comme l'indexation des éléments du graphe, l'identification des fragments du graphe pertinents pour une requête spécifique, l'agrégation de ces fragments pour former un résultat, et le classement des résultats obtenus. Nous abordons dans cette thèse ces différents problèmes, et nous proposons une approche qui permet, en réponse à une requête mots clés, de construire une liste de sous-graphes et de les classer, chaque sous-graphe correspondant à un résultat pertinent pour la requête. Pour chacune des deux stratégies d'exploration d'une source RDF, nous nous sommes intéressés à prendre en compte de la connaissance externe, permettant de mieux répondre aux besoins des utilisateurs. Cette connaissance externe peut représenter des connaissances du domaine, qui permettent de préciser le besoin exprimé dans le cas d'une requête, ou de prendre en compte des connaissances permettant d'affiner la définition des thèmes. Dans notre travail, nous nous sommes intéressés à formaliser cette connaissance externe et nous avons pour cela introduit la notion de pattern. Ces patterns représentent des équivalences de propriétés et de chemins dans le graphe représentant la source. Ils sont évalués et intégrés dans le processus d'exploration pour améliorer la qualité des résultats. / An increasing number of datasets is published on the Web, expressed in languages proposed by the W3C to describe Web data such as RDF, RDF(S) and OWL. The Web has become a unprecedented source of information available for users and applications, but the meaningful usage of this information source is still a challenge. Querying these data sources requires the knowledge of a formal query language such as SPARQL, but it mainly suffers from the lack of knowledge about the source itself, which is required in order to target the resources and properties relevant for the specific needs of the application. The work described in this thesis addresses the exploration of RDF data sources. This exploration is done according to two complementary ways: discovering the themes or topics representing the content of the data source, and providing a support for an alternative way of querying the data sources by using keywords instead of a query formulated in SPARQL. The proposed exploration approach combines two complementary strategies: thematic-based exploration and keyword search. Theme discovery from an RDF dataset consists in identifying a set of sub-graphs which are not necessarily disjoints, and such that each one represents a set of semantically related resources representing a theme according to the point of view of the user. These themes can be used to enable a thematic exploration of the data source where users can target the relevant theme and limit their exploration to the resources composing this theme. Keyword search is a simple and intuitive way of querying data sources. In the case of RDF datasets, this search raises several problems, such as indexing graph elements, identifying the relevant graph fragments for a specific query, aggregating these relevant fragments to build the query results, and the ranking of these results. In our work, we address these different problems and we propose an approach which takes as input a keyword query and provides a list of sub-graphs, each one representing a candidate result for the query. These sub-graphs are ordered according to their relevance to the query. For both keyword search and theme identification in RDF data sources, we have taken into account some external knowledge in order to capture the users needs, or to bridge the gap between the concepts invoked in a query and the ones of the data source. This external knowledge could be domain knowledge allowing to refine the user's need expressed by a query, or to refine the definition of themes. In our work, we have proposed a formalization to this external knowledge and we have introduced the notion of pattern to this end. These patterns represent equivalences between properties and paths in the dataset. They are evaluated and integrated in the exploration process to improve the quality of the result.

I dagens samhälle är energieffektivisering en viktig parameter när man pratar om hållbar-het. Många fastigheter saknar tekniska lösningar för att effektivt övervaka och hantera en-ergiförbrukning. För att möta detta behov strävar företag som iquest efter att digitalisera och automatisera energiövervakning. Iquest har i nuläget problem med ineffektivitet och dödläge vid uppladdning av stora datamängder i deras nuvarande grafdatabas. Genom en noggrann utvärdering har examensarbetet identifierat lämpliga alternativ för iquest att överväga.Under undersökningen presenterades grafdatabaserna Neo4j, Stardog, Allegrograph, Ama-zon Neptune, GraphDB, BlazingGraph och OrientDB. Baserat på grafdatabasernas egen-skaper och funktioner kunde det konstateras att Neo4j, Stardog, Allegrograph, Amazon Neptune och GraphDB uppfyllde kraven som ställdes för en lämplig grafdatabas.Implementeringen av grafdatabaserna begränsades av tidsbegränsningar och endast Neo4j, Stardog, Allegrograph och GraphDB kunde implementeras och genomgå tester. Trots att testerna utfördes med reducerade datamängder och gratisversionen av data-baserna, visade resultaten att två av de implementerade databaserna klarade alla testerna. / In today's society, energy efficiency is an important parameter when discussing sustaina-bility. Many buildings lack technical solutions to effectively monitor and manage energy consumption. To address this need, companies like iquest strive to digitize and automate energy monitoring. Currently, iquest faces issues of inefficiency and bottlenecks when up-loading large amounts of data into their current graph database. Through a thorough eval-uation, the thesis project has identified suitable alternatives for iquest to consider.During the investigation, the graph databases Neo4j, Stardog, Allegrograph, Amazon Nep-tune, GraphDB, BlazingGraph, and OrientDB were presented. Based on the characteristics and features of these graph databases, it was determined that Neo4j, Stardog, Allegro-graph, Amazon Neptune, and GraphDB meet the requirements for a suitable graph data-base.The implementation of the graph databases was limited by time constraints, and only Neo4j, Stardog, Allegrograph, and GraphDB could be implemented and subjected to test-ing. Despite conducting tests with reduced data volumes and using the free versions of the databases, the results showed that two of the implemented databases successfully passed all the tests.

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Lappugglans utbredningsområde kan vara påväg att förändras som respons till det moderna skogsbruket. I den här studien har jag fokuserat på lappugglors abundans vid kalhyggen av olika storlek med hypotesen att de föredrar hyggen av mindre storlek. Eftersom kalhyggena i södra Sverige i snitt är mindre så skulle det kunna vara en del i förklaringen att lappugglor börjat synas allt oftare söderut. Data laddades ned från artdatabanken och skogsstyrelsen och bearbetades i QGiS för att undersöka lappugglornas utbredning i relation till kalhyggen. Resultatet indikerar att ugglorna föredrar små framför stora kalhyggen och även kan dras till små hyggen. Resultaten visar också en trend söderut för ugglornas utbrednings mellan 2005 och 2020. Dessa resultat bekräftar att lappugglornas utbredningsmönster rör sig söderut och stödjer hypotesen att det är skogsbruk och kalhyggens storlek som driver denna utveckling. Mindre kalhyggen gynnar även andra arter än Lappugglor och kan därför generellt vara en god idé för att förena ekologi och ekonomi inom modernt skogsbruk. / The great grey owl may be adjusting its distribution area in Sweden in response to altered forestry practices. Here i focused on the abundance of owls in clear cut areas of different sizes with the hypothesis that owls prefer smaller clear cut areas. As clear cut areas are smaller in the south than in the north, such a preference could explain a trend of changing distribution southward. Data from artdatabanken and skogsstyrelsen were processed and analyzed in QGiS to investigate the spatial pattern of owl reports in relation to the locations of clear cuts. The results indicate that the owls prefer small over large clear cuts, and are indeed attracted to small clear cuts. The results also show a southward change in the distribution area from 2005 to 2020. These results confirm that the distribution of great grey owls moves southwards in Sweden and support the idea that this trend may be driven by the species being attracted to smaller clear cuts. Since smaller clear cuts benefit more species than owls it is generally a good idea to combine economy with ecology in modern forestry.

We present an FCA-based axiomatization method that produces a complete EL TBox (the terminological part of an OWL 2 EL ontology) from a graph dataset in at most exponential time. We describe technical details that allow for efficient implementation as well as variations that dispense with the computation of extremely large axioms, thereby rendering the approach applicable albeit some completeness is lost. Moreover, we evaluate the prototype on real-world datasets. / This is an extended version of an article accepted at AAAI 2024.

ITC/USA 2010 Conference Proceedings / The Forty-Sixth Annual International Telemetering Conference and Technical Exhibition / October 25-28, 2010 / Town and Country Resort & Convention Center, San Diego, California / The complexity of modern test and evaluation (T&E) processes has resulted in an explosion of the quantity and diversity of metadata used to describe end-to-end T&E processes. Ideally, it would be possible to integrate metadata in such a way that disparate systems can seamlessly access the metadata and easily interoperate with other systems. Unfortunately, there are several barriers to achieving this goal: metadata is often designed for use with specific tools or specific purposes; metadata exists in a variety of formats (legacy, non-legacy, structured and unstructured metadata); and the same information is represented in multiple ways across different metadata formats.

ITC/USA 2009 Conference Proceedings / The Forty-Fifth Annual International Telemetering Conference and Technical Exhibition / October 26-29, 2009 / Riviera Hotel & Convention Center, Las Vegas, Nevada / The vision of the semantic web is to unleash the next generation of information sharing and interoperability by encoding meaning into the symbols that are used to describe various computational capabilities within the World Wide Web or other networks. This paper describes the application of semantic web technologies to Test and Evaluation (T&E) metadata verification and validation. Verification is a quality process that is used to evaluate whether or not a product, service, or system complies with a regulation, specification, or conditions imposed at the start of a development phase or which exists in the organization. Validation is the process of establishing documented evidence that provides a high degree of assurance that a product, service, or system accomplishes its intended requirements. While this often involves acceptance and suitability with external customers, automation provides significant assistance to the customers.

This report describes the outcomes of a recent workshop, building on a series of workshops from the last three years with the goal if integrating genomics and biodiversity research, with a more specific goal here to express terms in Darwin Core and Audubon Core, where class constructs have been historically underspecified, into a Biological Collections Ontology (BCO) framework. For the purposes of this workshop, the BCO provided the context for fully defining classes as well as object and data properties, including domain and range information, for both the Darwin Core and Audubon Core. In addition, the workshop participants reviewed technical specifications and approaches for annotating instance data with BCO terms. Finally, we laid out proposed activities for the next 3 to 18 months to continue this work.

Organizational project knowledge is not being captured, consolidated, and organized, making it difficult to learn from past projects, expose the knowledge of the most experienced people, or share experience across geographic project locations. The lack of an ontology for representing this comprehensive project store inhibits its creation and the development of tools to operate on it. Process-orientation links organizational resources or artifacts with process phases and workflow. A process-orientation in knowledge management can be used to add contextual metadata to knowledge artifacts. Context can be used to improve information retrieval precision. Therefore, the study proposed a process-oriented ontology to improve the transfer of software engineering project knowledge. Four questions guided the research: What knowledge about projects should be captured? Are all project artifacts necessary and are they all equally valuable? How can process-orientation be applied to a software engineering project knowledge ontology? Are current knowledge representation languages appropriate for the task? Can software development project knowledge, as represented by this ontology, be captured and retrieved effectively in a KMS? Literature research and an empirical laboratory study answered all of the questions: Four areas of project knowledge are particularly valuable in terms of their impact on project success; requirements, revisions, risks, and resolutions. These areas also cover a meaningful breadth of software engineering project knowledge. A process abstraction was created that breaks a project down into eleven phases. These phases were the basis for a class definition that was added as a peer class to the knowledge artifacts. Using Protégé, the Process-Oriented Ontology for Software Engineering (POSE) was successfully implemented in OWL-DL. Project knowledge from a software organization was used to construct two knowledgebases: one using Google Desktop and the other using Protégé and POSE. Results demonstrated that software engineering project knowledge, as represented by POSE, can be effectively captured and retrieved. POSE-enhanced search was superior to keyword search. Google was comparable in broad text search. But the benefits of metadata and semantics proved to have significant advantages for ontologies. Process-orientation was also validated as a contributor to improved classification and retrieval.