Background: Relational concept analysis (RCA) is an extension of formal concept analysis dealing with several related formal contexts simultaneously. It can learn description logic theories from data and has been used within various applications. However, RCA returns a single family of concept lattices, though, when the data feature circular dependencies, other solutions may be considered acceptable. The semantics of RCA, provided in an operational way, does not shed light on this issue. Objectives: This paper aims at defining precisely the semantics of RCA and identifying alternative solutions. Methods: We first characterise the acceptable solutions as those families of concept lattices which belong to the space determined by the initial contexts (well-formed), which cannot scale new attributes (saturated), and which refer only to concepts of the family (self-supported). We adopt a functional view on the RCA process by defining the space of well-formed solutions and two functions on that space: one expansive and the other contractive. In this context, the acceptable solutions are the common fixed points of both functions. Results: We show that RCA returns the least element of the set of acceptable solutions. In addition, it is possible to build dually an operation that generates its greatest element. The set of acceptable solutions is a complete sublattice of the interval between these two elements. Its structure, and how the defined functions traverse it, are studied in detail.

Reproducibility is a desirable property of scientific research. On the one hand, it increases confidence in results. On the other hand, reproducible results can be extended on a solid basis. In rapidly developing fields such as machine learning, the latter is particularly important to ensure the reliability of research. In this paper, we present a systematic approach to reproducing (using the available implementation), replicating (using an alternative implementation) and reevaluating (using different datasets) state-of-the-art experiments. This approach enables the early detection and correction of deficiencies and thus the development of more robust and transparent machine learning methods. We detail the independent reproduction, replication, and reevaluation of the initially published experiments with a method that we want to extend. For each step, we identify issues and draw lessons learned. We further discuss solutions that have proven effective in overcoming the encountered problems. This work can serve as a guide for further reproducibility studies and generally improve reproducibility in machine learning.

Link keys express conditions under which instances of two classes of different RDF data sets may be considered as equal. As such, they can be used for data interlinking. There exist algorithms to extract link key candidates from RDF data sets and different measures have been defined to evaluate the quality of link key candidates individually. For certain data sets, however, it may be necessary to use more than one link key on a pair of classes to retrieve a more complete set of links. To this end, in this paper, we define disjunction of link keys, propose strategies to extract disjunctions of link key candidates from RDF data, and apply existing quality measures to evaluate them. We also report on experiments with these strategies.

Agents may use ontology alignments to communicate when they represent knowledge with different ontologies: alignments help reclassifying objects from one ontology to the other. These alignments may not be perfectly correct, yet agents have to proceed. They can take advantage of their experience in order to evolve alignments: upon communication failure, they will adapt the alignments to avoid reproducing the same mistake. Such repair experiments had been performed in the framework of networks of ontologies related by alignments. They revealed that, by playing simple interaction games, agents can effectively repair random networks of ontologies. Here we repeat these experiments and, using new measures, show that previous results were underestimated. We introduce new adaptation operators that improve those previously considered. We also allow agents to go beyond the initial operators in two ways: they can generate new correspondences when they discard incorrect ones, and they can provide less precise answers. The combination of these modalities satisfy the following properties: (1) Agents still converge to a state in which no mistake occurs. (2) They achieve results far closer to the correct alignments than previously found. (3) They reach again 100\% precision and coherent alignments.

Linked data technologies make it possible to publish and link structured data on the Web. Although RDF is not about text, many RDF data providers publish their data in their own language. Cross-lingual interlinking aims at discovering links between identical resources across knowledge bases in different languages. In this paper, we present a method for interlinking RDF resources described in English and Chinese using the BabelNet multilingual lexicon. Resources are represented as vectors of identifiers and then similarity between these resources is computed. The method achieves an F-measure of 88%. The results are also compared to a translation-based method.

Links are important for the publication of RDF data on the web. Yet, establishing links between data sets is not an easy task. We develop an approach for that purpose which extracts weak linkkeys. Linkkeys extend the notion of a key to the case of different data sets. They are made of a set of pairs of properties belonging to two different classes. A weak linkkey holds between two classes if any resources having common values for all of these properties are the same resources. An algorithm is proposed to generate a small set of candidate linkkeys. Depending on whether some of the, valid or invalid, links are known, we define supervised and non supervised measures for selecting the appropriate linkkeys. The supervised measures approximate precision and recall, while the non supervised measures are the ratio of pairs of entities a linkkey covers (coverage), and the ratio of entities from the same data set it identifies (discrimination). We have experimented these techniques on two data sets, showing the accuracy and robustness of both approaches.

After years of research on ontology matching, it is reasonable to consider several questions: is the field of ontology matching still making progress? Is this progress significant enough to pursue further research? If so, what are the particularly promising directions? To answer these questions, we review the state of the art of ontology matching and analyze the results of recent ontology matching evaluations. These results show a measurable improvement in the field, the speed of which is albeit slowing down. We conjecture that significant improvements can be obtained only by addressing important challenges for ontology matching. We present such challenges with insights on how to approach them, thereby aiming to direct research into the most promising tracks and to facilitate the progress of the field.

SPARQL query containment under schema axioms is the problem of determining whether, for any RDF graph satisfying a given set of schema axioms, the answers to a query are contained in the answers of another query. This problem has major applications for verification and optimization of queries. In order to solve it, we rely on the mu-calculus. Firstly, we provide a mapping from RDF graphs into transition systems. Secondly, SPARQL queries and RDFS and SHI axioms are encoded into mu-calculus formulas. This allows us to reduce query containment and equivalence to satisfiability in the mu-calculus. Finally, we prove a double exponential upper bound for containment under SHI schema axioms.

Semantic social networks are social networks using ontologies for characterising resources shared within the network. It has been postulated that, in such networks, it is possible to discover social affinities between network members through measuring the similarity between the ontologies or part of ontologies they use. Using similar ontologies should reflect the cognitive disposition of the subjects. The main concern of this paper is the methodological aspect of experimenting in order to validate or invalidate such an hypothesis. Indeed, given the current lack of broad semantic social networks, it is difficult to rely on available data and experiments have to be designed from scratch. For that purpose, we first consider experimental settings that could be used and raise practical and methodological issues faced with analysing their results. We then describe a full experiments carried out according to some identified modalities and report the obtained results. The results obtained seem to invalidate the proposed hypothesis. We discuss why this may be so.

Presence management, i.e., the ability to automatically identify the status and availability of communication partners, is becoming an invaluable tool for collaboration in enterprise contexts. In this paper, we argue for efficient presence management by means of a holistic view of both physical cont

This demo presents the Cupboard online system for sharing and reusing ontologies linked together with alignments, and that are attached to rich metadata and reviews.

Solving problems raised by heterogeneous ontologies can be achieved by matching the ontologies and processing the resulting alignments. This is typical of data mediation in which the data must be translated from one knowledge source to another. We propose to solve the data translation problem, i.e. the processing part, using the SPARQL query language. Indeed, such a language is particularly adequate for extracting data from one ontology and, through its CONSTRUCT statement, for generating new data. We present examples of such transformations, but we also present a set of example correspondences illustrating the needs for particular representation constructs, such as aggregates, value generating built-in functions and paths, which are missing from SPARQL. Hence, we advocate the use of two SPARQL extensions providing these missing features.

Temporal and spatial phenomena can be seen at a more or less precise granularity, depending on the kind of perceivable details. As a consequence, the relationship between two objects may differ depending on the granularity considered. When merging representations of different granularity, this may raise problems. This paper presents general rules of granularity conversion in relation algebras. Granularity is considered independently of the specific relation algebra, by investigating operators for converting a representation from one granularity to another and presenting six constraints that they must satisfy. The constraints are shown to be independent and consistent and general results about the existence of such operators are provided. The constraints are used to generate the unique pairs of operators for converting qualitative temporal relationships (upward and downward) from one granularity to another. Then two fundamental constructors (product and weakening) are presented: they permit the generation of new qualitative systems (e.g. space algebra) from existing ones. They are shown to preserve most of the properties of granularity conversion operators.