As recently as the last decade or two, data-driven science workflows have become increasingly popular and semantic technology has been relied on to help align often parallel research efforts in the different domains and foster interoperability and data sharing. However, a key challenge is the size of the data and the pace at which it is being generated, so much that manual procedures lag behind. Thus, eliciting automation of most workflows. In this study, the effort is to continue investigating ways by which some tasks performed by experts in the nanotechnology domain, specifically in ontology engineering, could benefit from automation. An approach, featuring phrase-based topic modelling and formal topical concept analysis is further motivated, together with formal implication rules, to uncover new concepts and axioms relevant to two nanotechnology-related ontologies. A corpus of 2,715 nanotechnology research articles helps showcase that the approach can scale, as seen in a number of experiments conducted. The usefulness of document text ranking as an alternative form of input to topic models is highlighted as well as the benefit of implication rules to the task of concept discovery. In all, a total of 203 new concepts are uncovered by the approach to extend the referenced ontologies

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.

A simple search problem is studied in which a binary n-tuple is to be found in a list, by sequential bit comparisons with cost. The problem can be solved (for small n) using dynamic programming. We show how the “bottom up” part of the algorithm can be organized by means of Formal Concept Analysis.

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A joining implication is a restricted form of an implication where it is explicitly specified which attributesmay occur in the premise and in the conclusion, respectively. A technique for sound and complete axiomatization of joining implications valid in a given formal context is provided. In particular, a canonical base for the joining implications valid in a given formal context is proposed, which enjoys the property of being of minimal cardinality among all such bases. Background knowledge in form of a set of valid joining implications can be incorporated. Furthermore, an application to inductive learning in a Horn description logic is proposed, that is, a procedure for sound and complete axiomatization of Horn-M concept inclusions from a given interpretation is developed. A complexity analysis shows that this procedure runs in deterministic exponential time.

The notion of a most specific consequence with respect to some terminological box is introduced, conditions for its existence in the description logic EL and its variants are provided, and means for its computation are developed. Algebraic properties of most specific consequences are explored. Furthermore, several applications that make use of this new notion are proposed and, in particular, it is shown how given terminological knowledge can be incorporated in existing approaches for the axiomatization of observations. For instance, a procedure for an incremental learning of concept inclusions from sequences of interpretations is developed.

Description logic knowledge bases can be used to represent knowledge about a particular domain in a formal and unambiguous manner. Their practical relevance has been shown in many research areas, especially in biology and the semantic web. However, the tasks of constructing knowledge bases itself, often performed by human experts, is difficult, time-consuming and expensive. In particular the synthesis of terminological knowledge is a challenge every expert has to face. Because human experts cannot be omitted completely from the construction of knowledge bases, it would therefore be desirable to at least get some support from machines during this process. To this end, we shall investigate in this work an approach which shall allow us to extract terminological knowledge in the form of general concept inclusions from factual data, where the data is given in the form of vertex and edge labeled graphs. As such graphs appear naturally within the scope of the Semantic Web in the form of sets of RDF triples, the presented approach opens up the possibility to extract terminological knowledge from the Linked Open Data Cloud. We shall also present first experimental results showing that our approach has the potential to be useful for practical applications.

Dans cette thèse, nous présentons les problèmes d’échange de documents d'affaires et proposons une méthode pour y remédier. Nous proposons une méthodologie pour adapter les standards d’affaires basés sur XML aux technologies du Web sémantique en utilisant la transformation des documents définis en DTD ou XML Schema vers une représentation ontologique en OWL 2. Ensuite, nous proposons une approche basée sur l'analyse formelle de concept pour regrouper les classes de l'ontologie partageant une certaine sémantique dans le but d'améliorer la qualité, la lisibilité et la représentation de l'ontologie. Enfin, nous proposons l’alignement d'ontologies pour déterminer les liens sémantiques entre les ontologies d'affaires hétérogènes générés par le processus de transformation pour aider les entreprises à communiquer fructueusement. / In this thesis, we present the problems of business document exchanges. We propose a methodology to adapt the XML-based business standards for the Semantic Web technologies by mapping documents defined on DTD or XML Schema to an ontological representation in OWL 2. Next, we propose an approach based on formal concept analysis techniques to regroup the ontology classes sharing some semantics to improve the quality, readability and the representation of the ontology. Finally, we propose ontology alignment to determine the semantic links between heterogeneous business ontologies generated by the transformation process to help entreprises to communicate fruitfully.

Na atualidade, o interesse pelo uso de ontologias tem sido incrementado. No entanto, o processo de construção pode ser custoso em termos de tempo. Para uma ontologia ser construída, precisa-se de um especialista com conhecimentos de um editor de ontologias. Com a finalidade de reduzir tal processo de construção pelo especialista, analisamos e propomos um método para realizar aprendizado de ontologias (AO) de forma supervisionada. O presente trabalho consiste em uma abordagem combinada de diferentes técnicas no AO. Primeiro, usamos uma técnica estatística chamada C/NC-values, acompanhada da ferramenta Cogroo, para extrair os termos mais representativos do texto. Esses termos são considerados por sua vez como conceitos. Projetamos também uma gramática de restrições (GR), com base na informação linguística do Português, com o objetivo de reconhecer e estabelecer relações entre conceitos. Para poder enriquecer a informação na ontologia, usamos a análise de conceitos formais (ACF) com o objetivo de identificar possíveis superconceitos entre dois conceitos. Finalmente, extraímos ontologias para os textos de três temas, submetendo-as à avaliação dos especialistas na área. Um web site foi feito para tornar o processo de avaliação mais amigável para os avaliadores e usamos o questionário de marcos de características proposto pelo método OntoMetrics. Os resultados mostram que nosso método provê um ponto de partida aceitável para a construção de ontologias. / Nowadays, the interest in the use of ontologies has increased, nevertheless, the process of ontology construction can be very time consuming. To build an ontology, we need a domain expert with knowledge in an ontology editor. In order to reduce the time needed by the expert, we propose and analyse a supervised ontology learning (OL) method. The present work consists of a combined approach of different techniques in OL. First, we use a statistic technique called C/NC-values, with the help of the Cogroo tool, to extract the most significant terms. These terms are considered as concepts consequently. We also design a constraint grammar (CG) based in linguistic information of Portuguese to recognize relations between concepts. To enrich the ontology information, we use the formal concept analysis (FCA) in order to discover a parent for a set of concepts. In order to evaluate the method, we have extracted ontologies from text on three different domains and tested them with corresponding experts. A web site was built to make the evaluation process friendlier for the experts and we used an evaluation framework proposed in the OntoMetrics method. The results show that our method provides an acceptable starting point for the construction of ontologies.

This work presents mainly two contributions to Description Logics (DLs) research by means of Formal Concept Analysis (FCA) methods: supporting bottom-up construction of DL knowledge bases, and completing DL knowledge bases. Its contribution to FCA research is on the computational complexity of computing generators of closed sets.