In the field of Explainable AI, multiples evaluation metrics have been proposed in order to assess the quality of explanation methods w.r.t. a set of desired properties. In this work, we study the articulation between the stability, correctness and plausibility of explanations based on feature importance for image classifiers. We show that the existing metrics for evaluating these properties do not always agree, raising the issue of what constitutes a good evaluation metric for explanations. Finally, in the particular case of stability and correctness, we show the possible limitations of some evaluation metrics and propose new ones that take into account the local behaviour of the model under test.

In this work, we perform an in-depth analysis of the visualisation methods implemented in two popular self-explaining models for visual classification based on prototypes - ProtoPNet and ProtoTree. Using two fine-grained datasets (CUB-200-2011 and Stanford Cars), we first show that such methods do not correctly identify the regions of interest inside of the images, and therefore do not reflect the model behaviour. Secondly, using a deletion metric, we demonstrate quantitatively that saliency methods such as Smoothgrads or PRP provide more faithful image patches. We also propose a new relevance metric based on the segmentation of the object provided in some datasets (e.g. CUB-200-2011) and show that the imprecise patch visualisations generated by ProtoPNet and ProtoTree can create a false sense of bias that can be mitigated by the use of more faithful methods. Finally, we discuss the implications of our findings for other prototype-based models sharing the same visualisation method.

Today, the main applications of OBDA SQL) defines a broad collection of languages. Keyvalue can be found in data integration as well as in querying the stores are NOSQL systems adopting the data model of Semantic Web. The interest of OBDA is to allow the users to key-value records (also called JSON records). These records ask queries on high-level ontology vocabularies and to delegate are processed on distributed systems, but also increasingly to algorithms (1) the reformulation of these high-level exchanged on the Web thereby replacing semistructured queries into a set of low-level databases queries, (2) the efficient XML data and many RDF formats (see JSON-LD (Sporny computation of their answers by native data management et al. 2004)). Key-value records are non-first normal forms systems in which data is stored and indexed, and (3) where values are not only atomic (in contrast with relational the combination of these answers in order to obtain the final databases) and nesting is possible (Abiteboul, Hull, answers to the users' query. The advantage of OBDA is and Vianu 1995).

We present a novel semantics for extracting bounded-level modules from RDF ontologies and databases augmented with safe inference rules, a la Datalog. Dealing with a recursive rule language poses challenging issues for defining the module semantics, and also makes module extraction algorithmically unsolvable in some cases. Our results include a set of module extraction algorithms compliant with the novel semantics. Experimental results show that the resulting framework is effective in extracting expressive modules from RDF datasets with formal guarantees, whilst controlling their succinctness.

In this paper we model the problem of data linkage in Linked Data as a reasoning problem on possibly decentralized data. We describe a novel import-by-query algorithm that alternates steps of sub-query rewriting and of tailored querying the Linked Data cloud in order to import data as specific as possible for inferring or contradicting given target same-as facts. Experiments conducted on a real-world dataset have demonstrated the feasibility of this approach and its usefulness in practice for data linkage and disambiguation.

The Workshop on Intelligent Information Integration (III), organized in conjunction with the Sixteenth International Joint Conference on Artificial Intelligence, was held on 31 July 1999 in Stockholm, Sweden. Approximately 40 people participated, and nearly 20 papers were presented. This packed workshop schedule resulted from a large number of submissions that made it difficult to reserve discussion time without rejecting an unproportionately large number of papers. Participants included scientists and practitioners from industry and academia.

The Workshop on Intelligent Information Integration (III), organized in conjunction with the Sixteenth International Joint Conference on Artificial Intelligence, was held on 31 July 1999 in Stockholm, Sweden. Approximately 40 people participated, and nearly 20 papers were presented. This packed workshop schedule resulted from a large number of submissions that made it difficult to reserve discussion time without rejecting an unproportionately large number of papers. Participants included scientists and practitioners from industry and academia. Topics included query planning, applications of III, mediator architectures, and the use of ontologies for III.