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  • 1.
    Alisade, Hubert
    et al.
    Department of American Studies, University of Innsbruck, Austria.
    Calvanese, Diego
    Umeå University, Faculty of Science and Technology, Department of Computing Science. Faculty of Engineering, Free University of Bozen-Bolzano, Italy.
    Klarer, Mario
    Department of American Studies, University of Innsbruck, Austria.
    Mosca, Alessandro
    Faculty of Engineering, Free University of Bozen-Bolzano, Italy.
    Ndefo, Nonyelum
    Faculty of Engineering, Free University of Bozen-Bolzano, Italy.
    Rangger, Bernadette
    Department of American Studies, University of Innsbruck, Austria.
    Tratter, Aaron
    Department of American Studies, University of Innsbruck, Austria.
    Exploration of medieval manuscripts through keyword spotting in the MENS project2023In: Proceedings of the AIxIA 2023 discussion papers (AIxIA 2023 DP), Rome, Italy, November 6-9, 2023 / [ed] Roberto Basili; Domenico Lembo; Carla Limongelli; AndreA Orlandini, CEUR-WS , 2023, p. 67-74Conference paper (Refereed)
    Abstract [en]

    In-depth searching for specific content in medieval manuscripts requires labor-intensive, hence time-consuming manual manuscript screening. Using existing IT tools to carry out this task has not been possible, since state-of-the-art keyword spotting lacks the necessary metaknowledge or larger ontology that scholars intuitively apply in their investigations. This problem is being addressed in the “Research Südtirol/Alto Adige” 2019 project “MENS – Medieval Explorations in Neuro-Science (1050–1450): Ontology-Based Keyword Spotting in Manuscript Scans,” whose goal is to build a paradigmatic case study for compiling and subsequent screening of large collections of manuscript scans by using AI techniques for natural language processing and data management based on formal ontologies. We report here on the ongoing work and the results achieved so far in the MENS project.

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  • 2.
    Calvanese, Diego
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science. Free University of Bozen-Bolzano, Italy.
    Corman, Julien
    Free University of Bozen-Bolzano, Italy.
    Lanti, Davide
    Free University of Bozen-Bolzano, Italy.
    Razniewski, Simon
    Max-Planck-Institut für Informatik, Germany.
    Counting query answers over a DL-Lite knowledge base2021In: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence: Yokohama / [ed] Christian Bessiere, International Joint Conferences on Artificial Intelligence , 2021, p. 1658-1666Conference paper (Refereed)
    Abstract [en]

    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.

  • 3.
    Calvanese, Diego
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science. Free University of Bozen-Bolzano, Italy.
    Corman, Julien
    Free University of Bozen-Bolzano, Italy.
    Lanti, Davide
    Free University of Bozen-Bolzano, Italy.
    Razniewski, Simon
    Max-Planck-Institut für Informatik, Saarbrücken, Germany.
    Rewriting count queries over DL-lite TBoxes with number restrictions2020In: CEUR Workshop Proceedings / [ed] Stefan Borgwardt; Thomas Meyer, CEUR-WS , 2020, article id 162804Conference paper (Refereed)
    Abstract [en]

    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.

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  • 4.
    Calvanese, Diego
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science. Free University of Bozen-Bolzano, Italy.
    De Giacomo, Giuseppe
    Sapienza University of Rome, Italy.
    Montali, Marco
    Free University of Bozen-Bolzano, Italy.
    Patrizi, Fabio
    Sapienza University of Rome, Italy.
    Verification and Monitoring for First-Order LTL with Persistence-Preserving Quantification over Finite and Infinite Traces2022In: IJCAI International Joint Conference on Artificial Intelligence, International Joint Conferences on Artificial Intelligence , 2022, p. 2553-2560Conference paper (Refereed)
    Abstract [en]

    We address the problem of model checking first-order dynamic systems where new objects can be injected in the active domain during execution. Notable examples are systems induced by a first-order action theory expressed, e.g., in the situation calculus. Recent results show that, under state-boundedness, such systems, in spite of having a first-order representation of the state, admit decidable model checking for full first-order mu-calculus. However, interestingly, model checking remains undecidable in the case of first-order LTL (LTL-FO). In this paper, we show that in LTL-FOp, the fragment of LTL-FO where quantification ranges only over objects that persist along traces, model checking state-bounded systems becomes decidable over infinite and finite traces. We then employ this result to show how to handle monitoring of LTL-FOp properties against a trace stemming from an unknown state-bounded dynamic system, simultaneously considering the finite trace up to the current point, and all its possibly infinite future continuations.

  • 5.
    Calvanese, Diego
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science. Free University of Bozen-Bolzano, Italy.
    De Giacomo, Giuseppe
    Sapienza University of Rome, Italy.
    Montali, Marco
    Free University of Bozen-Bolzano, Italy.
    Patrizi, Fabio
    Sapienza University of Rome, Italy.
    Verification of generic, relational transition systems2022In: PMAI 2022. Process management in the AI era 2022: Proceedings of the workshop on process management in the AI era (PMAI 2022) / [ed] Giuseppe De Giacomo; Antonella Guzzo; Marco Montali; Lior Limonad; Fabiana Fournier; Tagatha Chakraborti, CEUR-WS , 2022, p. 93-96Conference paper (Refereed)
    Abstract [en]

    Generic, relational transition systems form an interesting class of infinite-state transition systems that naturally captures the execution semantics of a variety of formalisms expressing processes operating over (relational) data. Examples of such data-aware processes include action theories in the situation calculus in AI and data-centric business processes in BPM. In this extended abstract, we summarize the main body of results produced in a decade-long research program focused on the verification of generic, relational transition systems against properties specified using variants of first-order temporal logics.

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  • 6.
    Calvanese, Diego
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science. Faculty of Computer Science, Free University of Bozen-Bolzano, Bolzano, Italy.
    Ding, Linfang
    Faculty of Computer Science, Free University of Bozen-Bolzano, Bolzano, Italy.
    Mosca, Alessandro
    Faculty of Computer Science, Free University of Bozen-Bolzano, Bolzano, Italy.
    Xiao, Guohui
    Faculty of Computer Science, Free University of Bozen-Bolzano, Bolzano, Italy.
    Realizing ontology-based reusable interfaces for data access via virtual knowledge graphs2021In: Proceedings of the 14th Biannual Conference of the Italian SIGCHI Chapter / [ed] Antonella De Angeli, Luca Chittaro, Rosella Gennari, Maria De Marsico, Alessandra Melonio, Cristina Gena, Luigi De Russis, Lucio Davide Spano, Association for Computing Machinery , 2021Conference paper (Refereed)
    Abstract [en]

    In this paper, we present a comprehensive framework, which we call VKG-UI, for realizing ontology-based reusable user interfaces (UIs) for data access via virtual knowledge graphs (VKGs). The VKG approach uses an ontology to model the domain of interest and to hide the heterogeneity of the underlying data sources. Reusable UIs can be built by relying on queries that are issued to the VKG system and that use the high level vocabulary from the ontology layer. This use of VKGs allows for decoupling the data from the UIs, and brings great reusability in designing the latter. To illustrate our approach, we introduce significant use cases with various types of UIs, including programming, graphic, natural language, and voice interfaces.

  • 7.
    Calvanese, Diego
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science. Free University of Bozen-Bolzano, Bolzano, Italy.
    Gal, Avigdor
    Technion – Israel Institute of Technology, Haifa, Israel.
    Haba, Naor
    Technion – Israel Institute of Technology, Haifa, Israel.
    Lanti, Davide
    Free University of Bozen-Bolzano, Bolzano, Italy.
    Montali, Marco
    Free University of Bozen-Bolzano, Bolzano, Italy.
    Mosca, Alessandro
    Free University of Bozen-Bolzano, Bolzano, Italy.
    Shraga, Roee
    Technion – Israel Institute of Technology, Haifa, Israel.
    ADaMaP: Automatic Alignment of Relational Data Sources Using Mapping Patterns2021In: Advanced Information Systems Engineering: 33rd International Conference, CAiSE 2021 Melbourne, VIC, Australia, June 28 – July 2, 2021 Proceedings / [ed] Marcello La Rosa; Shazia Sadiq; Ernest Teniente, Cham: Springer, 2021, p. 193-209Conference paper (Refereed)
    Abstract [en]

    We propose a method for automatically extracting semantics from data sources. The availability of multiple data sources on the one hand and the lack of proper semantic documentation of such data sources on the other hand call for new strategies in integrating data sources by extracting semantics from the data source itself rather than from its documentation. In this work we focus on relational databases, observing they are created from semantically-rich designs such as ER diagrams, which are often not conveyed together with the database itself. While the relational model may be semantically-poor with respect to ontological models, the original semantically-rich design of the application domain leaves recognizable footprints that can be converted into ontology mapping patterns. In this work, we offer an algorithm to automatically detect and map a relational schema to ontology mapping patterns and offer an empirical evaluation using two benchmark datasets.

  • 8.
    Calvanese, Diego
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science. Free-University of Bozen-Bolzano, Bolzano, Italy.
    Gal, Avigdor
    Technion - Israel Institute of Technology, Haifa, Israel.
    Lanti, Davide
    Free-University of Bozen-Bolzano, Bolzano, Italy.
    Montali, Marco
    Free-University of Bozen-Bolzano, Bolzano, Italy.
    Mosca, Alessandro
    Free-University of Bozen-Bolzano, Bolzano, Italy.
    Shraga, Roee
    Khoury College of Computer Science, Northeastern University, MA, Boston, United States.
    Conceptually-grounded Mapping Patterns for Virtual Knowledge Graphs2022In: SEBD 2022: Italian Symposium on Advanced Database Systems: Proceedings of the 30th Italian Symposium on Advanced Database Systems / [ed] Giuseppe Amato; Valentina Bartalesi; Devis Bianchini; Claudio Gennaro; Riccardo Torlone, 2022, p. 85-92Conference paper (Refereed)
    Abstract [en]

    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.

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  • 9.
    Calvanese, Diego
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science. Free-University of Bozen-Bolzano, Bolzano, Italy.
    Gal, Avigdor
    Technion – Israel Institute of Technology, Haifa, Israel.
    Lanti, Davide
    Free-University of Bozen-Bolzano, Bolzano, Italy.
    Montali, Marco
    Free-University of Bozen-Bolzano, Bolzano, Italy.
    Mosca, Alessandro
    Free-University of Bozen-Bolzano, Bolzano, Italy.
    Shraga, Roee
    Khoury College of Computer Science, Northeastern University, MA, Boston, United States.
    Conceptually-grounded mapping patterns for Virtual Knowledge Graphs2023In: Data & Knowledge Engineering, ISSN 0169-023X, E-ISSN 1872-6933, Vol. 145, article id 102157Article in journal (Refereed)
    Abstract [en]

    Virtual Knowledge Graphs (VKGs) constitute one of the most promising paradigms for integrating and accessing legacy data sources. A critical bottleneck in the integration process involves the definition, validation, and maintenance of mapping assertions that link data sources to a domain ontology. To support the management of mappings throughout their entire lifecycle, we identify a comprehensive catalog of sophisticated mapping patterns that emerge when linking databases to ontologies. To do so, we build on well-established methodologies and patterns studied in data management, data analysis, and conceptual modeling. These are extended and refined through the analysis of concrete VKG benchmarks and real-world use cases, and considering the inherent impedance mismatch between data sources and ontologies. We validate our catalog on the considered VKG scenarios, showing that it covers the vast majority of mappings present therein.

  • 10.
    Calvanese, Diego
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science. Free-University of Bozen-Bolzano, Bolzano, Italy.
    Gal, Avigdor
    Technion – Israel Institute of Technology, Haifa, Israel.
    Lanti, Davide
    Free-University of Bozen-Bolzano, Bolzano, Italy.
    Montali, Marco
    Free-University of Bozen-Bolzano, Bolzano, Italy.
    Mosca, Alessandro
    Free-University of Bozen-Bolzano, Bolzano, Italy.
    Shraga, Roee
    Technion – Israel Institute of Technology, Haifa, Israel.
    Mapping patterns for virtual knowledge graphs (A report on ongoing research)2020In: Proceedings of the 33rd International Workshop on Description Logics (DL 2020)co-located with the 17th International Conference on Principles of Knowledge Representation and Reasoning (KR 2020) / [ed] Stefan Borgwardt; Thomas Meyer, CEUR-WS , 2020, article id 162804Conference paper (Refereed)
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  • 11.
    Calvanese, Diego
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science. Faculty of Engineering, Free University of Bozen-Bolzano, Italy.
    Gianola, Alessandro
    Faculty of Engineering, Free University of Bozen-Bolzano, Italy.
    Mazzullo, Andrea
    Faculty of Engineering, Free University of Bozen-Bolzano, Italy.
    Montali, Marco
    Faculty of Engineering, Free University of Bozen-Bolzano, Italy.
    SMT safety verification of ontology-based processes2023In: Proceedings of the 37th AAAI conference on artificial intelligence, AAAI2023, AAAI Press, 2023, Vol. 37, p. 6271-6279Conference paper (Refereed)
    Abstract [en]

    In the context of verification of data-aware processes, a formal approach based on satisfiability modulo theories (SMT) has been considered to verify parameterised safety properties. This approach requires a combination of model-theoretic notions and algorithmic techniques based on backward reachability. We introduce here Ontology-Based Processes, which are a variant of one of the most investigated models in this spectrum, namely simple artifact systems (SASs), where, instead of managing a database, we operate over a description logic (DL) ontology. We prove that when the DL is expressed in (a slight extension of) RDFS, it enjoys suitable model-theoretic properties, and that by relying on such DL we can define Ontology-Based Processes to which backward reachability can still be applied. Relying on these results we are able to show that in this novel setting, verification of safety properties is decidable in PSPACE.

  • 12.
    Calvanese, Diego
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science. KRDB Research Centre for Knowledge, Data Free University of Bozen-Bolzano, Italy.
    Gianola, Alessandro
    KRDB Research Centre for Knowledge, Data Free University of Bozen-Bolzano, Italy.
    Mazzullo, Andrea
    KRDB Research Centre for Knowledge, Data Free University of Bozen-Bolzano, Italy.
    Montali, Marco
    KRDB Research Centre for Knowledge, Data Free University of Bozen-Bolzano, Italy.
    SMT-Based Safety Verification of Data-Aware Processes under Ontologies (Preliminary Results)2021In: Proceedings of the 34th International Workshop on Description Logics (DL 2021)part of Bratislava Knowledge September (BAKS 2021) / [ed] Martin Homola; Vladislav Ryzhikov; Renate Schmidt, CEUR-WS , 2021Conference paper (Refereed)
    Abstract [en]

    In the context of verification of data-aware processes, a formal approach based on satisfiability modulo theories (SMT) has been considered to verify parameterised safety properties of so-called artifactcentric systems. This approach requires a combination of model-theoretic notions and algorithmic techniques based on backward reachability. We introduce here a variant of one of the most investigated models in this spectrum, namely simple artifact systems (SASs), where, instead of managing a database, we operate over a description logic (DL) ontology expressed in (a slight extension of) RDFS. This DL, enjoying suitable model-theoretic properties, allows us to define DL-based SASs to which backward reachability can still be applied, leading to decidability in PSpace of the corresponding safety problems.

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  • 13.
    Calvanese, Diego
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science. Free University of Bozen-Bolzano, Bolzano, Italy.
    Jans, Mieke
    Hasselt University, Hasselt, Belgium; Maastricht University, MD, Maastricht, Netherlands.
    Kalayci, Tahir Emre
    Virtual Vehicle Research GmbH, Graz, Austria.
    Montali, Marco
    Free University of Bozen-Bolzano, Bolzano, Italy.
    Extracting event data from document-driven enterprise systems2023In: Advanced information systems engineering: 35th International conference, CAiSE 2023, Zaragoza, Spain, June 12–16, 2023, proceedings / [ed] Marta Indulska; Iris Reinhartz-Berger; Carlos Cetina; Oscar Pastor, Springer, 2023, p. 193-209Conference paper (Refereed)
    Abstract [en]

    The preparation of input event data is one of the most critical phases in process mining projects. Different frameworks have been developed to offer methodologies and/or supporting toolkits for data preparation. One of these frameworks, called OnProm, relies on sophisticated semantic technologies to extract event logs from relational databases. The toolkit consists of a series of general steps, meant to work on arbitrary, legacy databases. However, in many settings, the input database is not a legacy one but is structured with conceptually understandable object types and relationships that can be effectively employed to support business users in the extraction process. This is, for example, the case for document-driven enterprise systems. In this paper, we focus on this class of systems and propose a guided approach, erprep, to support a group of business and technical users in setting up OnProm with minimal effort. We demonstrate the approach in a real-life use case.

  • 14.
    Calvanese, Diego
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science. Faculty of Computer Science, Free University of Bozen-Bolzano, Italy; Ontopic S.R.L., Bolzano, Italy.
    Lanti, Davide
    De Farias, Tarcisio Mendes
    Mosca, Alessandro
    Xiao, Guohui
    Accessing scientific data through knowledge graphs with Ontop2021In: Patterns, ISSN 2666-3899, Vol. 2, no 10, article id 100346Article in journal (Refereed)
    Abstract [en]

    Knowledge graphs (KGs) have recently gained attention due to their flexible data model, which reduces the effort needed for integration across different, possibly heterogeneous, data sources. In this tutorial, we learn how to access scientific data stored in a relational database through the virtual knowledge graph (VKG) approach. In such an approach, the data are exposed as a KG and enriched with semantic information coming from a domain ontology. The KG is “virtual” in the sense that the data are not replicated but stay within the data sources and are accessed at query time.

    We demonstrate the approach over scientific data coming from the biomedical domain and using the open-source VKG system Ontop. Since legacy data are exposed as a KG, users can access the data by means of a more convenient vocabulary provided by the domain ontology, benefit from automated reasoning capabilities, and do not need to focus on how the data are actually stored. Furthermore, the virtual approach allows for the use of KGs even in those contexts where the user does not own the data nor is granted the rights to make a copy of them.

    By relying on existing federation tools, the approach described here for accessing scientific data can also be used to integrate multiple, heterogeneous, and possibly semi-structured and unstructured data sources.

    Summary: In this tutorial, we learn how to set up and exploit the virtual knowledge graph (VKG) approach to access data stored in relational legacy systems and to enrich such data with domain knowledge coming from different heterogeneous (biomedical) resources. The VKG approach is based on an ontology that describes a domain of interest in terms of a vocabulary familiar to the user and exposes a high-level conceptual view of the data. Users can access the data by exploiting the conceptual view, and in this way they do not need to be aware of low-level storage details. They can easily integrate ontologies coming from different sources and can obtain richer answers thanks to the interaction between data and domain knowledge.

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  • 15.
    Calvanese, Diego
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science. Free University of Bozen-Bolzano, Bolzano, Italy.
    Lukumbuzya, Sanja
    Montali, Marco
    Simkus, Mantas
    Process mining with common sense2021In: BPM Problems to Solve Before We Die 2021: Proceedings of the International Workshop on BPM Problems to Solve Before We Die (PROBLEMS 2021) / [ed] Iris Beerepoot; Claudio Di Ciccio; Andrea Marrella; Hajo A. Reijers; Stefanie Rinderle-Ma; Barbara Weber, CEUR-WS , 2021, Vol. 2938, p. 45-50Conference paper (Refereed)
    Abstract [en]

    We argue that, with the growth of process mining in breadth (variety of covered tasks) and depth (sophistication of the considered pro- cess models), event logs need to be augmented by commonsense knowl- edge to provide a better input for process mining algorithms. This is crucial to infer key facts that are not explicitly recorded in the logs, but are necessary in a variety of tasks, such as understanding the event data, assessing their compliance and quality, identifying outliers and clusters, computing statistics, and discovering decisions, ultimately empowering process mining as a whole.

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  • 16.
    Calvanese, Diego
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science. Free University of Bozen-Bolzano, Italy.
    Okulmus, Cem
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Ortiz, Magdalena
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Šimkus, Mantas
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    On the way to temporal OBDA systems2023In: Proceedings of the 15th Alberto Mendelzon International Workshop on Foundations of Data Management (AMW 2023), CEUR-WS , 2023Conference paper (Refereed)
    Abstract [en]

    Extending the OBDA approach - where multiple data sources are exposed to users via a unified conceptual schema based on description logics - to also cover temporal reasoning has been a long standing goal, with many proposals over the last decades. To the best of our knowledge, these have yet to yield results in the form of systems or prototypes. As part of our ongoing work towards practical applicability, we identify here a number of key problems, which we believe have not been addressed suitably by previous works. Among these is the ability to deal with heterogeneous representations of time, the ability to deal with temporal inconsistencies, either due to missing value samples or conflicting values for a given time point and finally we also seek a suitable query language, where we in particular want compositionality - the ability to use the output of queries to form new temporal views on the data. We present here our initial ideas on how to meet these challenges.

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  • 17.
    Calvanese, Diego
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science. Free University of Bozen-Bolzano, Bolzano, Italy.
    Simkus, Mantas
    Interview with Diego Calvanese2020In: Künstliche Intelligenz, ISSN 0933-1875, E-ISSN 1610-1987, Vol. 34, p. 551-555Article in journal (Other academic)
  • 18.
    Confalonieri, Roberto
    et al.
    Free University of Bozen-Bolzano, Faculty of Computer Science, Italy.
    Kutz, Oliver
    Free University of Bozen-Bolzano, Faculty of Computer Science, Italy.
    Calvanese, Diego
    Umeå University, Faculty of Science and Technology, Department of Computing Science. Free University of Bozen-Bolzano, Faculty of Computer Science, Italy.
    Preface2021In: CEUR Workshop Proceedings, E-ISSN 1613-0073, Vol. 2998Article in journal (Other academic)
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  • 19.
    D'Auria, Daniela
    et al.
    Faculty of Engineering, Free University of Bozen-Bolzano, Bolzano, Italy.
    Russo, Raffaele
    Pineta Grande Hospital, Caserta, Italy.
    Fedele, Alfonso
    University Riuniti Hospital, Ancona, Italy.
    Addabbo, Federica
    Kronosan Srl, Montevergine Hospital, Mercogliano, Italy.
    Calvanese, Diego
    Umeå University, Faculty of Science and Technology, Department of Computing Science. Faculty of Engineering, Free University of Bozen-Bolzano, Bolzano, Italy.
    An intelligent telemonitoring application for coronavirus patients: reCOVeryaID2023In: Frontiers in Big Data, E-ISSN 2624-909X, Vol. 6, article id 1205766Article in journal (Refereed)
    Abstract [en]

    The COVID-19 emergency underscored the importance of resolving crucial issues of territorial health monitoring, such as overloaded phone lines, doctors exposed to infection, chronically ill patients unable to access hospitals, etc. In fact, it often happened that people would call doctors/hospitals just out of anxiety, not realizing that they were clogging up communications, thus causing problems for those who needed them most; such people, often elderly, have often felt lonely and abandoned by the health care system because of poor telemedicine. In addition, doctors were unable to follow up on the most serious cases or make sure that others did not worsen. Thus, uring the first pandemic wave we had the idea to design a system that could help people alleviate their fears and be constantly monitored by doctors both in hospitals and at home; consequently, we developed reCOVeryaID, a telemonitoring application for coronavirus patients. It is an autonomous application supported by a knowledge base that can react promptly and inform medical doctors if dangerous trends in the patient's short- and long-term vital signs are detected. In this paper, we also validate the knowledge-base rules in real-world settings by testing them on data from real patients infected with COVID-19.

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  • 20. Ding, Linfang
    et al.
    Xiao, Guohui
    Calvanese, Diego
    Umeå University, Faculty of Science and Technology, Department of Computing Science. KRDB Research Centre, Faculty of Computer Science, Free University of Bozen-Bolzano, Bolzano, Italy; Ontopic S.r.L, Bolzano, Italy.
    Meng, Liqiu
    A Framework Uniting Ontology-Based Geodata Integration and Geovisual Analytics2020In: ISPRS International Journal of Geo-Information, ISSN 2220-9964, Vol. 9, no 8, article id 474Article in journal (Refereed)
    Abstract [en]

    In a variety of applications relying on geospatial data, getting insights into heterogeneous geodata sources is crucial for decision making, but often challenging. The reason is that it typically requires combining information coming from different sources via data integration techniques, and then making sense out of the combined data via sophisticated analysis methods. To address this challenge we rely on two well-established research areas: data integration and geovisual analytics, and propose to adopt an ontology-based approach to decouple the challenges of data access and analytics. Our framework consists of two modules centered around an ontology: (1) an ontology-based data integration (OBDI) module, in which mappings specify the relationship between the underlying data and a domain ontology; (2) a geovisual analytics (GeoVA) module, designed for the exploration of the integrated data, by explicitly making use of standard ontologies. In this framework, ontologies play a central role by providing a coherent view over the heterogeneous data, and by acting as a mediator for visual analysis tasks. We test our framework in a scenario for the investigation of the spatiotemporal patterns of meteorological and traffic data from several open data sources. Initial studies show that our approach is feasible for the exploration and understanding of heterogeneous geospatial data.

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  • 21.
    Ding, Linfang
    et al.
    Department of Civil and Environmental Engineering, Norwegian University of Science and Technology, Trondheim, Norway.
    Xiao, Guohui
    Department of Information Science and Media Studies, University of Bergen, Bergen, Norway; Ontopic S.r.l, Bolzano, Italy.
    Pano, Albulen
    Faculty of Engineering, Free University of Bozen-Bolzano, Bolzano, Italy.
    Fumagalli, Mattia
    Faculty of Engineering, Free University of Bozen-Bolzano, Bolzano, Italy.
    Chen, Dongsheng
    Chair of Cartography and Visual Analytics, Technical University of Munich, Munich, Germany.
    Feng, Yu
    Chair of Cartography and Visual Analytics, Technical University of Munich, Munich, Germany.
    Calvanese, Diego
    Umeå University, Faculty of Science and Technology, Department of Computing Science. Ontopic S.r.l, Bolzano, Italy; Faculty of Engineering, Free University of Bozen-Bolzano, Bolzano, Italy.
    Fan, Hongchao
    Department of Civil and Environmental Engineering, Norwegian University of Science and Technology, Trondheim, Norway.
    Meng, Liqiu
    Chair of Cartography and Visual Analytics, Technical University of Munich, Munich, Germany.
    Integrating 3D city data through knowledge graphs2024In: Geo-Spatial Information Science, ISSN 1009-5020Article in journal (Refereed)
    Abstract [en]

    CityGML is a widely adopted standard for representing and exchanging 3D city models. The representation of semantic and topological properties in CityGML makes it possible to query such 3D city data for analysis in various applications. Nevertheless, the potential of querying CityGML data has not been fully exploited. The official GML encoding of CityGML is mainly an information model used for data storage and exchange, but not suitable for performing complex queries. The most common way of dealing with CityGML data is to store them as tables in the 3DCityDB system. However, it remains a challenging task for end users to formulate SQL queries over 3DCityDB directly for their ad-hoc analytical tasks because of the gap between the semantics of CityGML and the relational schema adopted in 3DCityDB. The technology of Knowledge Graphs (KGs), where an ontology is at the core, is a good solution to bridge such a gap. Moreover, embracing KGs makes it easier to integrate with other spatial data sources, e.g. OpenStreetMap, and to perform queries combining information from multiple data sources. In this work, we describe a CityGML-KG framework to expose the CityGML data in 3DCityDB as a KG. To evaluate our approach, we use CityGML data from the city of Munich as a test area and integrate OpenStreetMap data.

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  • 22.
    Ding, Linfang
    et al.
    KRDB Research Centre, Free-University of Bozen-Bolzano, Piazza Domenicani 3, Bolzano, Italy.
    Xiao, Guohui
    KRDB Research Centre, Free-University of Bozen-Bolzano, Piazza Domenicani 3, Bolzano, Italy; Ontopic s.r.l, via A. Volta 13/A, Bolzano, Italy.
    Pano, Albulen
    KRDB Research Centre, Free-University of Bozen-Bolzano, Piazza Domenicani 3, Bolzano, Italy; Ontopic s.r.l, via A. Volta 13/A, Bolzano, Italy.
    Stadler, Claus
    Institute for Applied Informatics, University of Leipzig, Leipzig, Germany.
    Calvanese, Diego
    Umeå University, Faculty of Science and Technology, Department of Computing Science. KRDB Research Centre, Free-University of Bozen-Bolzano, Piazza Domenicani 3, Bolzano, Italy; Ontopic s.r.l, via A. Volta 13/A, Bolzano, Italy.
    Towards the next generation of the LinkedGeoData project using virtual knowledge graphs2021In: Journal of Web Semantics, ISSN 1570-8268, E-ISSN 1873-7749, Vol. 71, article id 100662Article in journal (Refereed)
    Abstract [en]

    With the advancement of Semantic Technologies, large geospatial data sources have been increasingly published as Linked data on the Web. The LinkedGeoData project is one of the most prominent such projects to create a large knowledge graph from OpenStreetMap (OSM) with global coverage and interlinking of other data sources. In this paper, we report on the ongoing effort of exposing the relational database in LinkedGeoData as a SPARQL endpoint using Virtual Knowledge Graph (VKG) technology. Specifically, we present two realizations of VKGs, using the two systems Sparqlify and Ontop. In order to improve compliance with the OGC GeoSPARQL standard, we have implemented GeoSPARQL support in Ontop v4. Moreover, we have evaluated the VKG-powered LinkedGeoData in the test areas of Italy and Germany. Our experiments demonstrate that such system supports complex GeoSPARQL queries, which confirms that query answering in the VKG approach is efficient.

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  • 23.
    Ghosh, Arka
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Šimkus, Mantas
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Calvanese, Diego
    Umeå University, Faculty of Science and Technology, Department of Computing Science. Research Centre for Knowledge and Data, Faculty of Engineering, Free University of Bozen-Bolzano, Bolzano, Italy.
    Semantic querying of integrated raster and relational data: a virtual knowledge graph approach2023In: Proceedings of the 17th International Rule Challenge and 7th Doctoral Consortium @ RuleML+RR 2023 (RuleML+RR-Companion 2023), Oslo, Norway, 18 - 20 September, 2023 / [ed] Jan Vanthienen; Tomáš Kliegr; Paul Fodor; Davide Lanti; Dörthe Arndt; Egor V. Kostylev; Theodoros Mitsikas; Ahmet Soylu, CEUR-WS , 2023, article id 8240Conference paper (Refereed)
    Abstract [en]

    Ontology-based data access (OBDA) facilitates access to heterogeneous data sources through the mediation of an ontology (e.g. OWL), which captures the domain of interest and is connected to data sources through a declarative mapping. In our study, large, heterogeneous earth observational (EO) data, known as raster data, and geometrical data, known as vector data, are considered as (heterogeneous) data sources. Raster data represent, e.g., Earth's natural phenomena, such as surface temperature, elevation, or air pollution, as multidimensional arrays. In contrast, vector data depict, e.g., locations, networks, or regions on Earth, using geometries. Domain experts, such as earth scientists and GIS practitioners, still struggle to undertake advanced studies by querying large raster and vector data in an integrated way because, unlike relational data, they come in diverse formats and different data structures. In our approach to integration, we use a geospatial extension of an RDBMS to represent vector data as relational data, and a domain-agnostic array DBMS to handle raster data. Our aim is to extend the OBDA paradigm to effectively deal with relational, vector, and raster data in a combined way, while leveraging the built-in capabilities of data management tools relevant to each type of data. We also plan to develop techniques to calculate on the fly for each user query posed over the ontology an optimal query plan that exploits, at best, the query processing capabilities of each tool, while limiting costly data transfer operations between tools.

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  • 24.
    Gu, Zhenzhen
    et al.
    Free University of Bozen-Bolzano, Bolzano, Italy.
    Calvanese, Diego
    Umeå University, Faculty of Science and Technology, Department of Computing Science. Free University of Bozen-Bolzano, Bolzano, Italy.
    Di Panfilo, Marco
    Free University of Bozen-Bolzano, Bolzano, Italy.
    Lanti, Davide
    Free University of Bozen-Bolzano, Bolzano, Italy.
    Mosca, Alessandro
    Free University of Bozen-Bolzano, Bolzano, Italy.
    Xiao, Guohui
    University of Bergen, Bergen, Norway.
    Ontology-based data federation: a framework proposal2023In: CEUR Workshop Proceedings / [ed] Diego Calvanese; Claudia Diamantini; Guglielmo Faggioli; Nicola Ferro; Stefano Marchesin; Gianmaria Silvello; Letizia Tanca, CEUR-WS , 2023, p. 210-219Conference paper (Refereed)
    Abstract [en]

    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].

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  • 25.
    Gu, Zhenzhen
    et al.
    KRDB Research Centre, Faculty of Computer Science, Free University of Bozen-Bolzano, Italy.
    Corcoglioniti, Francesco
    KRDB Research Centre, Faculty of Computer Science, Free University of Bozen-Bolzano, Italy.
    Lanti, Davide
    KRDB Research Centre, Faculty of Computer Science, Free University of Bozen-Bolzano, Italy.
    Mosca, Alessandro
    KRDB Research Centre, Faculty of Computer Science, Free University of Bozen-Bolzano, Italy.
    Xiao, Guohui
    Department of Information Science and Media Studies, University of Bergen, Norway; Department of Informatics, University of Oslo, Norway; Ontopic S.r.l, Italy.
    Xiong, Jing
    KRDB Research Centre, Faculty of Computer Science, Free University of Bozen-Bolzano, Italy.
    Calvanese, Diego
    Umeå University, Faculty of Science and Technology, Department of Computing Science. KRDB Research Centre, Faculty of Computer Science, Free University of Bozen-Bolzano, Italy; Ontopic S.r.l, Italy.
    A systematic overview of data federation systems2024In: Semantic Web, ISSN 1570-0844, E-ISSN 2210-4968, Vol. 15, no 1, p. 107-165Article in journal (Refereed)
    Abstract [en]

    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.

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  • 26.
    Gu, Zhenzhen
    et al.
    KRDB Research Centre for Knowledge and Data, Free University of Bozen-Bolzano, Bolzano, Italy.
    Lanti, Davide
    KRDB Research Centre for Knowledge and Data, Free University of Bozen-Bolzano, Bolzano, Italy.
    Mosca, Alessandro
    KRDB Research Centre for Knowledge and Data, Free University of Bozen-Bolzano, Bolzano, Italy.
    Xiao, Guohui
    Faculty of Social Sciences, University of Bergen, Bergen, Norway; Ontopic S.R.L., Bolzano, Italy.
    Xiong, Jing
    KRDB Research Centre for Knowledge and Data, Free University of Bozen-Bolzano, Bolzano, Italy.
    Calvanese, Diego
    Umeå University, Faculty of Science and Technology, Department of Computing Science. KRDB Research Centre for Knowledge and Data, Free University of Bozen-Bolzano, Bolzano, Italy; Ontopic S.R.L., Bolzano, Italy.
    Ontology-based data federation2022In: Proceedings of the 35th international workshop on description logics (dl 2022) / [ed] Ofer Arieli; Martin Homola; Jean Christoph Jung; Marie-Laure Mugnier, 2022, article id 11Conference paper (Refereed)
    Abstract [en]

    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.

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  • 27.
    Gu, Zhenzhen
    et al.
    Faculty of Computer Science, Free University of Bozen-Bolzano, Bolzano, Italy.
    Lanti, Davide
    Faculty of Computer Science, Free University of Bozen-Bolzano, Bolzano, Italy.
    Mosca, Alessandro
    Faculty of Computer Science, Free University of Bozen-Bolzano, Bolzano, Italy.
    Xiao, Guohui
    Department of Information Science and Media Studies, University of Bergen, Norway; Department of Informatics, University of Oslo, Norway; Ontopic S.r.l., Bolzano, Italy.
    Xiong, Jing
    Faculty of Computer Science, Free University of Bozen-Bolzano, Bolzano, Italy.
    Calvanese, Diego
    Umeå University, Faculty of Science and Technology, Department of Computing Science. Faculty of Computer Science, Free University of Bozen-Bolzano, Bolzano, Italy; Ontopic S.r.l., Bolzano, Italy.
    Ontology-based data federation2022In: Proceedings of the 11th International Joint Conference on Knowledge Graphs: IJCKG 2022 / [ed] Alessandro Artale; Diego Calvanese; Haofen Wang; Xiaowang Zhang, Association for Computing Machinery (ACM), 2022, p. 10-19Conference paper (Refereed)
    Abstract [en]

    Ontology-based data access (OBDA) is a well-established approach to information management which facilitates the access to a (single) relational data source through the mediation of a high-level ontology, and the use of a declarative mapping linking the data layer to 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, possibly heterogeneous sources are virtually exposed as a single relational database. We discuss opportunities and challenges of OBDF, and provide techniques to deliver efficient query answering in an OBDF setting. Such techniques are validated through an extensive experimental evaluation based on the Berlin SPARQL Benchmark.

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  • 28.
    Hamdani, Younes
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Xiao, Guohui
    Department of Information Science and Media Studies, University of Bergen, Bergen, Norway; Department of Informatics, University of Oslo, Olso, Norway; Ontopic S.r.l, Bolzano, Italy.
    Ding, Linfang
    Department of Civil and Environmental Engineering, Norwegian University of Science and Technology, Trondheim, Norway.
    Calvanese, Diego
    Umeå University, Faculty of Science and Technology, Department of Computing Science. Ontopic S.r.l, Bolzano, Italy; KRDB Research Centre for Knowledge and Data, Free University of Bozen-Bolzano, Bolzano, Italy.
    An ontology-based framework for geospatial integration and querying of raster data cube using virtual knowledge graphs2023In: ISPRS International Journal of Geo-Information, ISSN 2220-9964, Vol. 12, no 9, article id 375Article in journal (Refereed)
    Abstract [en]

    The integration of the raster data cube alongside another form of geospatial data (e.g., vector data) raises considerable challenges when it comes to managing and representing it using knowledge graphs. Such integration can play an invaluable role in handling the heterogeneity of geospatial data and linking the raster data cube to semantic technology standards. Many recent approaches have been attempted to address this issue, but they often lack robust formal elaboration or solely concentrate on integrating raster data cubes without considering the inclusion of semantic spatial entities along with their spatial relationships. This may constitute a major shortcoming when it comes to performing advanced geospatial queries and semantically enriching geospatial models. In this paper, we propose a framework that can enable such semantic integration and advanced querying of raster data cubes based on the virtual knowledge graph (VKG) paradigm. This framework defines a semantic representation model for raster data cubes that extends the GeoSPARQL ontology. With such a model, we can combine the semantics of raster data cubes with features-based models that involve geometries as well as spatial and topological relationships. This could allow us to formulate spatiotemporal queries using SPARQL in a natural way by using ontological concepts at an appropriate level of abstraction. We propose an implementation of the proposed framework based on a VKG system architecture. In addition, we perform an experimental evaluation to compare our framework with other existing systems in terms of performance and scalability. Finally, we show the potential and the limitations of our implementation and we discuss several possible future works.

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  • 29.
    Kalaycı, Elem Güzel
    et al.
    Free University of Bozen-Bolzano, Bolzano, Italy; Virtual Vehicle Research GmbH, Graz, Austria.
    Grangel González, Irlan
    Robert Bosch GmbH, Corporate Research, Stuttgart, Germany.
    Lösch, Felix
    Robert Bosch GmbH, Corporate Research, Stuttgart, Germany.
    Xiao, Guohui
    Free University of Bozen-Bolzano, Bolzano, Italy; Ontopic S.r.L., Bolzano, Italy.
    ul-Mehdi, Anees
    Robert Bosch GmbH, Corporate Research, Stuttgart, Germany.
    Kharlamov, Evgeny
    Robert Bosch GmbH, Bosch Center for Artificial Intelligence, Renningen, Germany; University of Oslo, Blindern, Oslo, Norway.
    Calvanese, Diego
    Umeå University, Faculty of Science and Technology, Department of Computing Science. Free University of Bozen-Bolzano, Bolzano, Italy; Ontopic S.r.L., Bolzano, Italy.
    Semantic Integration of Bosch Manufacturing Data Using Virtual Knowledge Graphs2020In: The Semantic Web – ISWC 2020: 19th International Semantic Web Conference, Athens, Greece, November 2–6, 2020, Proceedings, Part II / [ed] Jeff Z. Pan; Valentina Tamma; Claudia d’Amato; Krzysztof Janowicz; Bo Fu; Axel Polleres; Oshani Seneviratne; Lalana Kagal, Springer, 2020, p. 464-481Conference paper (Refereed)
    Abstract [en]

    Analyses of products during manufacturing are essential to guarantee their quality. In complex industrial settings, such analyses require to use data coming from many different and highly heterogeneous machines, and thus are affected by the data integration challenge. In this work, we show how this challenge can be addressed by relying on semantic data integration, following the Virtual Knowledge Graph approach. For this purpose, we propose the SIB Framework, in which we semantically integrate Bosch manufacturing data, and more specifically the data necessary for the analysis of the Surface Mounting Process (SMT) pipeline. In order to experiment with our framework, we have developed an ontology for SMT manufacturing data, and a set of mappings that connect the ontology to data coming from a Bosch plant. We have evaluated SIB using a catalog of product quality analysis tasks that we have encoded as SPARQL queries. The results we have obtained are promising, both with respect to expressivity (i.e., the ability to capture through queries relevant analysis tasks) and with respect to performance.

  • 30.
    Lanti, Davide
    et al.
    Free-University of Bozen-Bolzano, Bolzano, Italy.
    Mosca, Alessandro
    Free-University of Bozen-Bolzano, Bolzano, Italy.
    Calvanese, Diego
    Umeå University, Faculty of Science and Technology, Department of Computing Science. Free-University of Bozen-Bolzano, Bolzano, Italy.
    Montali, Marco
    Free-University of Bozen-Bolzano, Bolzano, Italy.
    Direct mappings under the lens of information capacity2023In: Proceedings of the 36th international workshop on Description Logics (DL 2023) / [ed] Oliver Kutz; Carsten Lutz; Ana Ozaki, CEUR-WS , 2023Conference paper (Refereed)
    Abstract [en]

    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.

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  • 31.
    Romanenko, Elena
    et al.
    Free University of Bozen-Bolzano, Bolzano, Italy.
    Calvanese, Diego
    Umeå University, Faculty of Science and Technology, Department of Computing Science. Free University of Bozen-Bolzano, Bolzano, Italy.
    Guizzardi, Giancarlo
    Free University of Bozen-Bolzano, Bolzano, Italy; University of Twente, Enschede, Netherlands.
    Abstracting Ontology-Driven Conceptual Models: Objects, Aspects, Events, and Their Parts2022In: International Conference on Research Challenges in Information Science, RCIS 2022 / [ed] Renata Guizzardi; Jolita Ralyté; Xavier Franch, Springer Science+Business Media B.V., 2022, p. 372-388Conference paper (Refereed)
    Abstract [en]

    Ontology-driven conceptual models are widely used to capture information about complex and critical domains. Therefore, it is essential for these models to be comprehensible and cognitively tractable. Over the years, different techniques for complexity management in conceptual models have been suggested. Among these, a prominent strategy is model abstraction. This work extends an existing strategy for model abstraction of OntoUML models that proposes a set of graph-rewriting rules leveraging on the ontological semantics of that language. That original work, however, only addresses a set of the ontological notions covered in that language. We review and extend that rule set to cover more generally types of objects, aspects, events, and their parts.

  • 32.
    Romanenko, Elena
    et al.
    Free University of Bozen-Bolzano, Bolzano, Italy.
    Calvanese, Diego
    Umeå University, Faculty of Science and Technology, Department of Computing Science. Free University of Bozen-Bolzano, Bolzano, Italy.
    Guizzardi, Giancarlo
    University of Twente, Enschede, Netherlands; Stockholm University, Stockholm, Sweden.
    ExpO: towards explaining ontology-driven conceptual models2024In: Research Challenges in Information Science: 18th International Conference, RCIS 2024, Guimarães, Portugal, May 14–17, 2024, Proceedings, Part II / [ed] João Araújo; Jose Luis de la Vara; Maribel Yasmina Santos; Saïd Assar, Springer Nature, 2024, p. 20-28Conference paper (Refereed)
    Abstract [en]

    Ontology-driven conceptual models play an explanatory role in complex and critical domains. However, since those models may consist of a large number of elements, including concepts, relations and sub-diagrams, their reuse or adaptation requires significant efforts. While conceptual model engineers tend to be biased against the removal of information from the models, general users struggle to fully understand them. The paper describes ExpO—a prototype that addresses this trade-off by providing three components: (1) an API that implements model transformations, (2) a software plugin aimed at modelers working with the language OntoUML, and (3) a web application for model exploration mostly designed for domain experts. We describe characteristics of every component and specify scenarios of possible usages.

  • 33.
    Romanenko, Elena
    et al.
    Free University of Bozen-Bolzano, Bolzano, Italy.
    Calvanese, Diego
    Umeå University, Faculty of Science and Technology, Department of Computing Science. Free University of Bozen-Bolzano, Bolzano, Italy.
    Guizzardi, Giancarlo
    Free University of Bozen-Bolzano, Bolzano, Italy; University of Twente, Enschede, Netherlands.
    Towards pragmatic explanations for domain ontologies2022In: Knowledge engineering and knowledge management: 23rd International Conference, EKAW 2022, Bolzano, Italy, September 26–29, 2022, proceedings / [ed] Oscar Corcho; Laura Hollink; Oliver Kutz; Nicolas Troquard; Fajar J. Ekaputra, Springer Nature, 2022, p. 201-208Conference paper (Refereed)
    Abstract [en]

    Ontologies have gained popularity in a wide range of research fields, in the domains where possible interpretations of terms have to be narrowed and there is a need for explicit inter-relations of concepts. Although reusability has always been claimed as one of the main characteristics of ontologies, it has been shown that reusing domain ontologies is not a common practice. Perhaps this is due to the fact that despite a large number of works towards complexity management of ontologies, popular systems do not incorporate enough functionality for ontology explanation. We analyse the state of the art and substantiate a minimal functionality that the system should provide in order to make domain ontologies better understandable for their users.

  • 34.
    Romanenko, Elena
    et al.
    Free University of Bozen-Bolzano, Bolzano, Italy.
    Calvanese, Diego
    Umeå University, Faculty of Science and Technology, Department of Computing Science. Free University of Bozen-Bolzano, Bolzano, Italy.
    Guizzardi, Giancarlo
    University of Twente, Enschede, Netherlands.
    What do users think about abstractions of ontology-driven conceptual models?2023In: Research Challenges in Information Science: Information Science and the Connected World: 17th International Conference, RCIS 2023, Corfu, Greece, May 23–26, 2023, Proceedings, Springer, 2023, p. 53-68Conference paper (Refereed)
    Abstract [en]

    In a previous paper, we proposed an algorithm for ontology-driven conceptual model abstractions [18]. We have implemented and tested this algorithm over a FAIR Catalog of such models represented in the OntoUML language. This provided evidence for the correctness of the algorithm’s implementation, i.e., that it correctly implements the model transformation rules prescribed by the algorithm, and its effectiveness, i.e., it is able to achieve high compression (summarization) rates over these models. However, in addition to these properties, it is fundamental to test the validity of this algorithm, i.e., that it achieves what it is intended to do, namely provide summarizing abstractions over these models whilst preserving the gist of the conceptualization being represented. We performed three user studies to evaluate the usefulness of the resulting abstractions as perceived by modelers. This paper reports on the findings of these user studies and reflects on how they can be exploited to improve the existing algorithm.

  • 35.
    Romanenko, Elena
    et al.
    Free University of Bozen-Bolzano, Bolzano, Italy.
    Kutz, Oliver
    Free University of Bozen-Bolzano, Bolzano, Italy.
    Calvanese, Diego
    Umeå University, Faculty of Science and Technology, Department of Computing Science. Free University of Bozen-Bolzano, Bolzano, Italy.
    Guizzardi, Giancarlo
    University of Twente, Enschede, Netherlands.
    Towards semantics for abstractions in ontology-driven conceptual modeling2023In: Advances in conceptual modeling: ER 2023 Workshops, CMLS, CMOMM4FAIR, EmpER, JUSMOD, OntoCom, QUAMES, and SmartFood, Lisbon, Portugal, November 6–9, 2023, proceedings / [ed] Tiago Prince Sales; João Araújo; José Borbinha; Giancarlo Guizzardi, Springer Science+Business Media B.V., 2023, p. 199-209Conference paper (Refereed)
    Abstract [en]

    Ontology-driven conceptual models are precise and semantically transparent domain descriptions that enable the development of information systems. As symbolic artefacts, such models are usually considered to be self-explanatory. However, the complexity of a system significantly correlates with the complexity of the conceptual model that describes it. Abstractions of both conceptual models and ontology-driven conceptual models are thus considered to be a promising way to improve the understandability and comprehensibility of those models. Although algorithms for providing abstractions of such models already exist, they still lack precisely formulated formal semantics. This paper aims to provide an approach towards the formalization of the abstraction process. We specify in first-order modal logic one of the graph-rewriting rules for ontology-driven conceptual model abstractions, in order to verify the correctness of the corresponding abstraction step. We also assess the entire network of abstractions of ontology-driven conceptual models and discuss existing drawbacks.

  • 36.
    Wandji, Romuald Esdras
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Šimkus, Mantas
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Calvanese, Diego
    Umeå University, Faculty of Science and Technology, Department of Computing Science. Research Centre for Knowledge and Data, Faculty of Engineering, Free University of Bozen-Bolzano, Bolzano, Italy.
    Towards techniques for updating virtual knowledge graphs2023In: Proceedings of the 17th International Rule Challenge and 7th Doctoral Consortium @ RuleML+RR 2023 (RuleML+RR-Companion 2023), Oslo, Norway, 18 - 20 September, 2023 / [ed] Jan Vanthienen; Tomáš Kliegr; Paul Fodor; Davide Lanti; Dörthe Arndt; Egor V. Kostylev; Theodoros Mitsikas; Ahmet Soylu, CEUR-WS , 2023, article id 9284Conference paper (Refereed)
    Abstract [en]

    The field of Virtual Knowledge Graphs (VKGs) continues to grow in both academic and applied contexts. Yet, the issue of updates in VKG systems has not yet received adequate attention, although it is crucial to manage data modifications at the data source level through the lens of an ontology. In this paper, we focus on VKGs whose ontology is specified in the lightweight ontology language DL-LiteA, and we propose diverse settings and research directions we intend to explore to address the challenge of translating ontology-based updates into updates at the level of data sources. We also pay attention to the important problem of automated analysis of mappings, which plays a major role when it comes to reformulating ontology-based update requests into update requests over the data sources.

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  • 37.
    Xiao, Guohui
    et al.
    Free University of Bozen-Bolzano, Bolzano, Italy; Ontopic s.r.l., Bolzano, Italy.
    Lanti, Davide
    Free University of Bozen-Bolzano, Bolzano, Italy.
    Kontchakov, Roman
    Birkbeck, University of London, London, United Kingdom.
    Komla-Ebri, Sarah
    Ontopic s.r.l., Bolzano, Italy.
    Güzel-Kalaycı, Elem
    Virtual Vehicle Research GmbH, Graz, Austria.
    Ding, Linfang
    Free University of Bozen-Bolzano, Bolzano, Italy.
    Corman, Julien
    Free University of Bozen-Bolzano, Bolzano, Italy.
    Cogrel, Benjamin
    Ontopic s.r.l., Bolzano, Italy.
    Calvanese, Diego
    Umeå University, Faculty of Science and Technology, Department of Computing Science. Free University of Bozen-Bolzano, Bolzano, Italy; Ontopic s.r.l., Bolzano, Italy.
    Botoeva, Elena
    Imperial College London, London, United Kingdom.
    The Virtual Knowledge Graph System Ontop2020In: The Semantic Web – ISWC 2020: 19th International Semantic Web Conference, Athens, Greece, November 2–6, 2020, Proceedings, Part II / [ed] Jeff Z. Pan; Valentina Tamma; Claudia d’Amato; Krzysztof Janowicz; Bo Fu; Axel Polleres; Oshani Seneviratne; Lalana Kagal, Springer, 2020, p. 259-277Conference paper (Refereed)
    Abstract [en]

    Ontop is a popular open-source virtual knowledge graph system that can expose heterogeneous data sources as a unified knowledge graph. Ontop has been widely used in a variety of research and industrial projects. In this paper, we describe the challenges, design choices, new features of the latest release of Ontop v4, summarizing the development efforts of the last 4 years.

  • 38.
    Xiao, Guohui
    et al.
    Free University of Bozen-Bolzano, Italy; Ontopic s.r.l., Bolzano, Italy.
    Lanti, Davide
    Free University of Bozen-Bolzano, Italy.
    Kontchakov, Roman
    Birkbeck, University of London, United Kingdom.
    Komla-Ebri, Sarah
    Ontopic s.r.l., Bolzano, Italy.
    Güzel-Kalaycı, Elem
    Virtual Vehicle Research GmbH, Graz, Austria.
    Ding, Linfang
    Free University of Bozen-Bolzano, Italy.
    Corman, Julien
    Free University of Bozen-Bolzano, Italy.
    Cogrel, Benjamin
    Ontopic s.r.l., Bolzano, Italy.
    Calvanese, Diego
    Umeå University, Faculty of Science and Technology, Department of Computing Science. Free University of Bozen-Bolzano, Italy; Ontopic s.r.l., Bolzano, Italy.
    Botoeva, Elena
    Imperial College London, United Kingdom.
    The virtual knowledge graph system ontop2020In: DL 2020. Description Logics 2020: Proceedings of the 33rd International Workshop on Description Logics (DL 2020)co-located with the 17th International Conference on Principles of Knowledge Representation and Reasoning (KR 2020) / [ed] Stefan Borgwardt; Thomas Meyer, 2020Conference paper (Refereed)
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  • 39.
    Xiong, Jing
    et al.
    Free University of Bozen-Bolzano, Bolzano, Italy.
    Xiao, Guohui
    University of Bergen, Bergen, Norway; University of Oslo, Oslo, Norway; Ontopic S.R.L, Bolzano, Italy.
    Kalayci, Tahir Emre
    Virtual Vehicle Research GmbH, Graz, Austria.
    Montali, Marco
    Free University of Bozen-Bolzano, Bolzano, Italy.
    Gu, Zhenzhen
    Free University of Bozen-Bolzano, Bolzano, Italy.
    Calvanese, Diego
    Umeå University, Faculty of Science and Technology, Department of Computing Science. Free University of Bozen-Bolzano, Bolzano, Italy; Ontopic S.R.L, Bolzano, Italy.
    A virtual knowledge graph based approach for object-centric event logs extraction2023In: Process Mining Workshops: ICPM 2022 International Workshops, Bozen-Bolzano, Italy, October 23–28, 2022, Revised Selected Papers / [ed] Marco Montali; Arik Senderovich; Matthias Weidlich, Springer Science and Business Media Deutschland GmbH , 2023, p. 466-478Conference paper (Refereed)
    Abstract [en]

    Process mining is a family of techniques that support the analysis of operational processes based on event logs. Among the existing event log formats, the IEEE standard eXtensible Event Stream (XES) is the most widely adopted. In XES, each event must be related to a single case object, which may lead to convergence and divergence problems. To solve such issues, object-centric approaches become promising, where objects are the central notion and one event may refer to multiple objects. In particular, the Object-Centric Event Logs (OCEL) standard has been proposed recently. However, the crucial problem of extracting OCEL logs from external sources is still largely unexplored. In this paper, we try to fill this gap by leveraging the Virtual Knowledge Graph (VKG) approach to access data in relational databases. We have implemented this approach in the OnProm system, extending it to support both XES and OCEL standards. We have carried out an experiment with OnProm over the Dolibarr system. The evaluation results confirm that OnProm can effectively extract OCEL logs and the performance is scalable.

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  • 40.
    Xiong, Jing
    et al.
    KRDB Research Centre for Knowledge and Data, Free University of Bozen-Bolzano, Bolzano, Italy.
    Xiao, Guohui
    Department of Information Science and Media Studies, University of Bergen, Bergen, Norway; Ontopic S.R.L., Bolzano, Italy.
    Kalayci, Tahir Emre
    Virtual Vehicle Research GmbH, Graz, Austria.
    Montali, Marco
    KRDB Research Centre for Knowledge and Data, Free University of Bozen-Bolzano, Bolzano, Italy; Ontopic S.R.L., Bolzano, Italy.
    Gu, Zhenzhen
    KRDB Research Centre for Knowledge and Data, Free University of Bozen-Bolzano, Bolzano, Italy.
    Calvanese, Diego
    Umeå University, Faculty of Science and Technology, Department of Computing Science. KRDB Research Centre for Knowledge and Data, Free University of Bozen-Bolzano, Bolzano, Italy; Ontopic S.R.L., Bolzano, Italy.
    Extraction of object-centric event logs through virtual knowledge graphs2022In: Proceedings of the 35th International Workshop on Description Logics (DL 2022) / [ed] Ofer Arieli; Martin Homola; Jean Christoph Jung; Marie-Laure Mugnier, 2022, article id 15Conference paper (Refereed)
    Abstract [en]

    Process mining is a family of techniques that supports the analysis of operational processes based on event logs. Among the existing event log formats, the IEEE standard eXtensible Event Stream (XES) is the most widely adopted. In XES, each event must be related to a single case object, which may lead to convergence and divergence problems. To solve such issues, object-centric approaches become promising, where objects are the central notion, and one event may refer to multiple objects. In particular, the Object-Centric Event Logs (OCEL) standard has been proposed recently. However, the crucial problem of extracting OCEL logs from external sources is still largely unexplored. In this paper, we try to fill this gap by leveraging the Virtual Knowledge Graph (VKG) approach to access data in relational databases. We have implemented this approach in the OnProm system, extending it from XES to OCEL support. The full version of this article has been submitted to an international conference.

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