A lot of alignment systems providing mappings between the concepts of two ontologies rely on an additional source, called background knowledge, represented most of the time by a third ontology. The objective is to complement others current matching techniques. In this paper, we present the difficulties encountered when using WordNet as background knowledge and we show how the TaxoMap system we ...

Scientific discourse leaves implicit a vast amount of knowledge, assumes that this background knowledge is taken into account – even taken for granted – and treated as undisputed. In particular, the terminology in the empirical sciences is treated as antecedently understood. The background knowledge surrounding a theory is usually assumed to be true or approximately true. This is in sharp contr...

Ontology matching is a critical operation in many well-known metadata intensive applications, such as data integration and peer-to-peer information sharing. Typically, heterogeneity in these applications is reduced in two steps: (i) matching ontologies to determine correspondences and (ii) executing correspondences according to an application needs (e.g., data translation). In this position sta...

Intelligent systems capable of automatically understanding natural language text are important for many artificial intelligence applications including mobile phone voice assistants, computer vision, and robotics. Understanding language often constitutes fitting new information into a previously acquired view of the world. However, many machine reading systems rely on the text alone to infer its...

Traditional logic can be understood as the investigation of the three main essential functions of thinking – concepts, judgements and conclusions. In the last years, in a new research field termed Contextual Logic, a mathematical theory of this logic is elaborated. Concepts have already been mathematically elaborated by Formal Concept Analysis. Judgements and Conclusions can be expressed by so-...

The aim of collaborative clustering is to make different clustering methods collaborate, in order to reach at an agreement on the partitioning of a common dataset. As different clustering methods can produce different partitioning of the same dataset, finding a consensual clustering from these results is often a hard task. The collaboration aims to make the methods agree on the partitioning thr...

This paper presents a new sequential algorithm to answer the question about the existence of a causal explanation for a set of independence statements (a dependency model), which is consistent with a given set of background knowledge. Emphasis is placed on generality, efficiency and ease of parallelization of the algorithm. From this sequential algorithm, an efficient, scalable, and easy to imp...

Ontology matching is an integral part for establishing semantic interoperability. One of the main challenges within ontology operation heterogeneity, i.e. modeling differences between two ontologies that are to be integrated. The semantics most or schemas are, however, typically incomplete because they designed a certain context which not explicitly modeled. Therefore, external background knowl...