This paper presents an ontology-based application for mobile devices which is responsible for supporting groups of people with the management of their shared tasks. The ontology stores the domain knowledge about collaborative tasks, which is used to support task recognition and relocation. Such knowledge is used by a multi-agent system that consists of a group of agents representing each person in the group. The agents use plan recognition techniques to monitor the execution of tasks according to the schedules and negotiate task allocation when needed. Our techniques have been applied in a healthcare scenario which consists of a family group that takes care of an elderly person. This paper presents an ontology-based application for mobile devices which is responsible for supporting groups of people with the management of their shared tasks. % in a healthcare scenario.The ontology stores the domain knowledge about collaborative tasks, which is used to support task recognition and relocation.Such knowledge is used by a multi-agent system that consists of a group of agents representing each person in the group.The agents use plan recognition techniques to monitor the execution of tasks according to the schedules and negotiate task allocation when needed.Our techniques have been applied in a healthcare scenario which consists of a family group that takes care of an elderly person.

Instead of seeing each Machine Learning (ML) method as a "shiny new object", here is an attempt to create a unified picture. There is no consensus when it comes to an ontology for ML methods; organizational principles are simply ways to get our arms around knowledge so that we are not swamped by too many unconnected notions. A powerful organization of the concepts or Ontology of ML is based on conditional expectation. Conditional Expectation of Class'y' given input attributes, x, denoted by E[y x]. Implementation of estimation of the conditional expectation with various assumptions lead, one way or the other, to ALL the ML techniques that we have today in 2016.

As a long-term member of the Linked Data community, which has evolved from W3C's Semantic Web, the latest developments around Data Science have become more and more attractive to me due to its complementary perspectives on similar challenges. When taking a closer look to the approaches taken by those two'schools of advanced data management' one aspect becomes obvious: Both try to develop models in order to be able to'codify and to calculate the data soup'. While Linked Data technologies are built on top of knowledge models ('ontologies'), which try to describe first of all data in distributed environments like the web, are Data Science methods mainly based on statistical models. One could say: 'Causality and Reasoning over Distributed Data' meets'Correlation and Machine Learning on Big Data'. In contrast to this supposed contradiction, correlations and complementarities between those two disciplines prevail.

We investigate a higher-order extension of the description logic (DL) SROIQ that provides a fixedly interpreted role semantically coupled with instantiation. It is useful to express interesting meta-level constraints on the modelled ontology. We provide a model-theoretic characterization of the semantics, and we show the decidability by means of reduction.

In many scenarios where the integration of information into a knowledge base (KB) leads to inconsistencies there is a need to change the KB minimally. In belief revision, relevance postulates meet the minimality requirement by restricting the elimination of KB elements to those that are relevant for the incoming information. This paper focuses on two minimality postulates in an ontology revision scenario in which conflicts are caused by ambiguous use of symbols: a relevance postulate and a generalized inclusion postulate which limits the creativity of the operators. Both postulates exploit the (satisfiably) equivalent representation of a first-order logic KB by its prime implicates, which, intuitively, represent the most atomic logical components of the KB. The paper shows that reinterpretation operators (which are ontology revision operators) fulfill both postulates.

In this paper we show that the problem of deciding the consistency of a knowledge base in the Description Logic ALCM is ExpTime-complete. The M stands for meta-modelling as defined by Motz, Rohrer and Severi. To show our main result, we define an ExpTime Tableau algorithm as an extension of an algorithm for ALC by Nguyen and Szalas.

Inspired by recent work on higher-order Description Logics, we propose HOS, a new semantics for OWL 2 QL ontologies. We then consider SPARQL queries which are legal under the direct semantics entailment regime,we extend them with logical union, existential variables, and unrestricted use of variables so as to express meaningful meta-level queries. We show that both satisfiability checking and answering instance queries with metavariables have the same ABox complexity as under direct semantics.

Graphol is a visual language designed to help non-experts to understand and specify ontologies. Our language builds on the Entity-Relationship model, but has a formal semantics and higher expressiveness. Notably, OWL 2 can be completely encoded in Graphol. Thanks to the novel open-source Eddy ontology editor, designers can easily draw Graphol diagrams corresponding to OWL ontologies and export them into standard OWL 2 format. Both Graphol and Eddy have been used in several successful industrial projects and are currently under active development. This paper reports on our more recent progresses.

We consider the problem of obtaining coherence in a propositional knowledge base using techniques from Belief Change. Our motivation comes from the field of formal ontologies where coherence is interpreted to mean that a concept name has to be satisfiable. In the propositional case we consider here, this translates to a propositional formula being satisfiable. We define belief change operators in a framework of nonmonotonic preferential reasoning.We show how the introduction of defeasible information using contraction operators can be an effective means for obtaining coherence.

SPARQL, a query language for RDF graphs, is one of the key technologies for the Semantic Web. The expressivity and complexity of various fragments of SPARQL have been studied extensively. It is usually assumed that the optional matching operator OPTIONAL has only two graph patterns as arguments. The specification of SPARQL, however, defines it as a ternary operator, with an additional filter condition. We address the problem of expressibility of the full ternary OPTIONAL via the simplified binary version and show that it is possible, but only with an exponential blowup in the size of the query (under common complexity-theoretic assumptions). We also study expressibility of other non-monotone SPARQL operators via optional matching and each other.