Explainable Recommendation Systems (RS) enhance the user experience on online platforms by recommending personalized content, as well as explanations for the given recommendations to add transparency and build up trust in the platforms. Extending the notion of explainable RS, in this paper we define Why-Not explanations for recommendations that were expected but not returned, and propose and implement a technique for computing Why-Not explanations in a post-hoc manner for a graph-based RS. Our approach builds on the notion of counterfactual explanations in the means of a set of user-rooted edges to add or remove in the graph, in order to place the missing recommendation to the top of the recommendation list, and provides in this way actionable insights on the source data and their interrelations. Our experimental evaluation on a real-world data set demonstrates the feasibility of our proposal and reveals interesting directions for future work.

Abstract In recent years, we have witnessed the proliferation of knowledge graphs (KG) in various domains, aiming to support applications like question answering, recommendations, etc. A frequent task when integrating knowledge from different KGs is to find which subgraphs refer to the same real-world entity, a task largely known as the Entity Alignment. Recently, embedding methods have been used for entity alignment tasks, that learn a vector-space representation of entities which preserves their similarity in the original KGs. A wide variety of supervised, unsupervised, and semi-supervised methods have been proposed that exploit both factual (attribute based) and structural information (relation based) of entities in the KGs. Still, a quantitative assessment of their strengths and weaknesses in real-world KGs according to different performance metrics and KG characteristics is missing from the literature. In this work, we conduct the first meta-level analysis of popular embedding methods for entity alignment, based on a statistically sound methodology. Our analysis reveals statistically significant correlations of different embedding methods with various meta-features extracted by KGs and rank them in a statistically significant way according to their effectiveness across all real-world KGs of our testbed. Finally, we study interesting trade-offs in terms of methods’ effectiveness and efficiency.

In this article we introduce a Data and Knowledge Integration Model and a Collaborative Platform for fact-oriented Agricultural Biodiversity Management that is inspired by the conservation and sustainable use of biodiversity within agricultural landscapes, which is essential for the future of agriculture and food security. We demonstrate and validate our proposal in a realistic case study that was carried out with stakeholders from educational institutes including several government agencies from five Ministries, i.e. Ministry of Agricultural and Cooperative, Ministry of Natural Resources and Environment, Ministry of Public Health, Ministry of Commerce and Ministry of Higher Education, Science, Research and Innovation. Key challenges are how to make data inter-operation across these agencies when re-engineering the existing information system and how to make trustworthy platform for data collecting, integrating and sharing, especially, how to keep these agencies engaged throughout the project. The resulting Syntax-Semantic-Organizational Inter-operability model was proposed to provide a candidate best practice for engineering data and knowledge integration through a community-shared and reusable Data Reference Model. The resulting Data Governance Implementation across government agencies, by using BIO-AGRI-WATCH as a case study, has significant consequences regarding communication and engagement with stakeholders and dedicated team for increasing their trust in digital data sharing platform.

International audience

With the increasing use of Web 2.0 to create, disseminate, and consume large volumes of data, more and more information is published and becomes available for potential data consumers, that is, applications/services, individual users and communities, outside their production site. The most representative example of this trend is Linked Open Data (LOD), a set of interlinked data and knowledge bases. The main challenge in this context is data governance within loosely coordinated organizations that are publishing added-value interlinked data on the Web, bringing together issues related to data management and data quality, in order to support the full lifecycle of data production, consumption, and management. In this article, we are interested in curation issues for RDF(S) data, which is the default data model for LOD. In particular, we are addressing change management for RDF(S) data maintained by large communities (scientists, librarians, etc.) which act as curators to ensure high quality of data. Such curated Knowledge Bases (KBs) are constantly evolving for various reasons, such as the inclusion of new experimental evidence or observations, or the correction of erroneous conceptualizations. Managing such changes poses several research problems, including the problem of detecting the changes (delta) between versions of the same KB developed and maintained by different groups of curators, a crucial task for assisting them in understanding the involved changes. This becomes all the more important as curated KBs are interconnected (through copying or referencing) and thus changes need to be propagated from one KB to another either within or across communities. This article addresses this problem by proposing a change language which allows the formulation of concise and intuitive deltas. The language is expressive enough to describe unambiguously any possible change encountered in curated KBs expressed in RDF(S), and can be efficiently and deterministically detected in an automated way. Moreover, we devise a change detection algorithm which is sound and complete with respect to the aforementioned language, and study appropriate semantics for executing the deltas expressed in our language in order to move backwards and forwards in a multiversion repository, using only the corresponding deltas. Finally, we evaluate through experiments the effectiveness and efficiency of our algorithms using real ontologies from the cultural, bioinformatics, and entertainment domains.

In this paper, we measure and analyze the graph features of semantic Web (SW) schemas with focus on power-law degree distributions. Our main finding is that the majority of SW schemas with a significant number of properties (respectively, classes) approximate a power law for total-degree (respectively, the number of subsumed classes) distribution. Moreover, our analysis revealed some emerging conceptual modeling practices of SW schema developers: (1) each schema has a few focal classes that have been analyzed in detail (that is, they have numerous properties and subclasses), which are further connected with focal classes defined in other schemas, (2) class subsumption hierarchies are mostly unbalanced (that is, some branches are deep and heavy, while others are shallow and light), (3) most properties have as domain/range classes that are located high at the class subsumption hierarchies, and (4) the number of recursive/multiple properties is significant. The knowledge of these features is essential for guiding synthetic SW schema generation, which is an important step toward benchmarking SW repositories and query language implementations.

Online Curriculum Portals aim to support networks of instructors and learners by providing a space of convergence for enhancing peer-to-peer learning interactions among individuals of an educational institution. To this end, effective, open and scalable e-learning systems are required to acquire, store, and share knowledge under the form of learning objects (LO). In this paper, we are interested in exploiting the semantic relationships that characterize these LOs (e.g., prerequisite, part-of or see-also) in order to capture and access individual and group knowledge in conjunction with the learning processes supported by educational institutions. To achieve this functionality, Semantic Web (e.g., RDF/s) and declarative query languages (e.g., RQL) are employed to represent LOs and their relationships (e.g., LOM), as well as, to support navigation at the conceptual e-learning Portal space. In this way, different LOs could be presented to the same learners, according to the traversed schema navigation paths (i.e., learning paths). Using the Apache Jetspeed framework we are able to generate and assemble at run-time portlets (i.e., pluggable web components) for visualizing personalized views as dynamic web pages. Last but not least, both learners and instructors can employ the same Portal GUI for updating semantically described LOs and thus support an open-ended continuum of learning. To the best of our knowledge, the work presented in this paper is the first Online Curriculum Portal platform supporting the aforementioned functionality.