Query Containment Using a DLR ABox

Query containment under constraints is the problem of determining whether the result of one query is contained in the result of another query for every database satisfying a given set of constraints. This problem is of particular importance in information integration and warehousing where, in addition to the constraints derived from the source schemas and the global schema, inter-schema constraints can be used to specify relationships between objects in different schemas. A theoretical framework for tackling this problem using the DLR logic has been established, and in this paper we show how the framework can be extended to a practical decision procedure. The proposed technique is to extend DLR with an Abox (a set of assertions about named individuals and tuples), and to transform query subsumption problems into DLR Abox satisfiability problems. We then show how such problems can be decided, via a reification transformation, using a highly optimised reasoner for the SHIQ description logic

Quelo: an Ontology-Driven Query Interface

Abstract. In this paper we present a formal framework and tool supporting the user in the task of formulating a precise query – which best captures their information needs – even in the case of complete ignorance of the vocabulary of the underlying information system holding the data. Our intelligent interface is driven by means of appropriate automated reasoning techniques over an ontology describing the domain of the data in the information system. We will define what a query is and how it is internally represented, which operations are available to the user in order to modify the query and how contextual feedback is provided about it presenting only relevant pieces of information. We will then describe the elements that constitute the query interface available to the user, providing visual access to the underlying reasoning services and operations for query manipulation. Lastly, we will define a suitable representation in “linear form”, starting from which the query can be more easily expressed in natural language.

Process Discovery on Deviant Traces and Other Stranger Things

As the need to understand and formalise business processes into a model has grown over the last years, the process discovery research field has gained more and more importance, developing two different classes of approaches to model representation: procedural and declarative. Orthogonally to this classification, the vast majority of works envisage the discovery task as a one-class supervised learning process guided by the traces that are recorded into an input log. In this work instead, we focus on declarative processes and embrace the less-popular view of process discovery as a binary supervised learning task, where the input log reports both examples of the normal system execution, and traces representing a “stranger” behaviour according to the domain semantics. We therefore deepen how the valuable information brought by both these two sets can be extracted and formalised into a model that is “optimal” according to user-defined goals. Our approach, namely NegDis, is evaluated w.r.t. other relevant works in this field, and shows promising results regarding both the performance and the quality of the obtained solution

Discovering Business Processes models expressed as DNF or CNF formulae of Declare constraints

In the field of Business Process Management, the Process Discovery task is one of the most important and researched topics. It aims to automatically learn process models starting from a given set of logged execution traces. The majority of the approaches employ procedural languages for describing the discovered models, but declarative languages have been proposed as well. In the latter category there is the Declare language, based on the notion of constraint, and equipped with a formal semantics on LTLf. Also, quite common in the field is to consider the log as a set of positive examples only, but some recent approaches pointed out that a binary classification task (with positive and negative examples) might provide better outcomes. In this paper, we discuss our preliminary work on the adaptation of some existing algorithms for Inductive Logic Programming, to the specific setting of Process Discovery: in particular, we adopt the Declare language with its formal semantics, and the perspective of a binary classification task (i.e., with positive and negative examples

Specification of a common framework for characterizing alignment

bouquet2004aNo abstract available

D2.2.1 Specification of a common framework for characterizing alignment

Definition of a common semantic framework for characterizing alignment of heterogeneous information