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Data federation addresses the problem of uniformly accessing multiple, possibly heterogeneous data sources, by mapping them into a unified schema, such as an RDF(S)/OWL ontology or a relational schema, and by supporting the execution of queries, like SPARQL or SQL queries, over that unified schema. Data explosion in volume and variety has made data federation increasingly popular in many application domains. Hence, many data federation systems have been developed in industry and academia, and it has become challenging for users to select suitable systems to achieve their objectives. In order to systematically analyze and compare these systems, we propose an evaluation framework comprising four dimensions: (i) federation capabilities, i.e., query language, data source, and federation techniques; (ii) data security, i.e., authentication, authorization, auditing, encryption, and data masking; (iii) interface, i.e., graphical interface, command line interface, and application programming interface; and (iv) development, i.e., main development language, deployment, commercial support, open source, and release. Using this framework, we thoroughly studied 51 data federation systems from the Semantic Web and Database communities. This paper shares the results of our investigation and aims to provide reference material and insights for users, developers and researchers selecting or further developing data federation systems.

With the rising popularity of graph-based approaches to data management, exposing the content of traditional, often relational, sources as (knowledge) graphs becomes more and more relevant. In such scenarios, Direct Mapping approaches are often used to automatically transformsuch sources into graph-like formats. A "fundamental" property of these transformations is to be information preserving, that is, it should be always possible to (algorithmically) reconstruct the content of the original database. Information preservation, along with other "fundamental" or "desirable" properties proposed in the Semantic Web literature, has never been put into correspondence with over 40 years of extended literature coming from the traditional database perspective. In particular, to the best of our knowledge, it is unknown how classical results on information capacity, dominance, and equivalence, tailored towards specific tasks such as query answering or data update, relate to the results and definitions from the Semantic Web world., Extended abstract.

Ontology-based data access (OBDA) is a well established approach to information management that facilitates the access to relational data sources through the mediation of a conceptual domain view, given in terms of an ontology, and the use of a declarative mapping between the data layer and the ontology. We formally introduce here the notion of ontology-based data federation (OBDF) to denote a framework that combines OBDA with a data federation layer where multiple heterogeneous sources are virtually exposed as a single relational database. We discuss opportunities and challenges of OBDF, and propose novel techniques to make query answering in the OBDF setting more efficient. Our techniques are validated through an extensive experimental evaluation based on the Berlin SPARQL Benchmark. This work is an abridged version of [1].

As Large Language Models gain prominence in the AI landscape, it is essential to understand their capabilities and limitations, among others in terms of reasoning. This paper is a first step towards understanding the capabilities in terms of defeasible rule-based reasoning. It presents results of initial experiments and discussed future research directions.

Knowledge Graphs (KGs) have been gaining momentum recently in both academia and industry, due to the flexibility of their data model, allowing one to access and integrate collections of data of different forms. Virtual Knowledge Graphs (VKGs), a variant of KGs originating from the field of Ontology-based Data Access (OBDA), are a promising paradigm for integrating and accessing legacy data sources. The main idea of VKGs is that the KG remains virtual: the end-user interacts with a KG, but queries are reformulated on-the-fly as queries over the data source(s). To enable the paradigm, one needs to define declarative mappings specifying the link between the data sources and the elements in the VKG. In this work, we try to investigate common patterns that arise when specifying such mappings, building on well-established methodologies from the area of conceptual modeling and database design.

We formally introduce ontology-based data federation (OBDF), to denote a framework combining ontology-based data access (OBDA) with a data federation layer, which virtually exposes multiple heterogeneous sources as a single relational database. In this setting, the SQL queries generated by the OBDA component by translating user SPARQL queries are further transformed by the data federation layer so as to be efficiently executed over the data sources. The structure of these SQL queries directly affects their execution time in the data federation layer and their optimization is crucial for performance. We propose here novel optimizations specific for OBDF, which are based on “hints” about existing data redundancies in the sources, empty join operations, and the need for materialized views. Such hints can be systematically inferred by analyzing the OBDA mappings and ontology and exploited to simplify the query structure. We also carry out an experimental evaluation in which we show the effectiveness of our optimizations.

A full-fledged data exploration system must combine different access modalities with a powerful concept of guiding the user in the exploration process, by being reactive and anticipative both for data discovery and for data linking. Such systems are a real opportunity for our community to cater to users with different domain and data science expertise. We introduce INODE - an end-to-end data exploration system - that leverages, on the one hand, Machine Learning and, on the other hand, semantics for the purpose of Data Management (DM). Our vision is to develop a classic unified, comprehensive platform that provides extensive access to open datasets, and we demonstrate it in three significant use cases in the fields of Cancer Biomarker Research, Research and Innovation Policy Making, and Astrophysics. INODE offers sustainable services in (a) data modeling and linking, (b) integrated query processing using natural language, (c) guidance, and (d) data exploration through visualization, thus facilitating the user in discovering new insights. We demonstrate that our system is uniquely accessible to a wide range of users from larger scientific communities to the public. Finally, we briefly illustrate how this work paves the way for new research opportunities in DM.

Counting answers to a query is an operation supported by virtually all database management systems. In this paper we focus on counting answers over a Knowledge Base (KB), which may be viewed as a database enriched with background knowledge about the domain under consideration. In particular, we place our work in the context of Ontology-Mediated Query Answering/Ontology-based Data Access (OMQA/OBDA), where the language used for the ontology is a member of the DL-Lite family and the data is a (usually virtual) set of assertions. We study the data complexity of query answering, for different members of the DL-Lite family that include number restrictions, and for variants of conjunctive queries with counting that differ with respect to their shape (connected, branching, rooted). We improve upon existing results by providing P and coNP lower bounds, and upper bounds in P and LogSpace. For the LogSpace case, we have devised a novel query rewriting technique into first-order logic with counting.

We propose a query rewriting algorithm for a restricted class of conjunctive queries evaluated under count semantics over a DL-Lite knowledge base. The target query language is an extension of relational algebra with aggregation and arithmetic functions, which can be translated into SQL. The algorithm supports number restrictions on the RHS of axioms in the input TBox, which can be used to encode statistics. The size of the output query remains linear in the binary encoding of these numbers, which improves upon previously proposed approaches.