Virtual Knowledge Graph (VKG) is known as a data integration paradigm used to eficiently manage the heterogeneity of richly structured data that is common inside several organizations, in interorganizational settings, and more openly on the Web. Although such a paradigm continues to gain importance in both foundational and applied research, updates in VKG systems remain an open challenge that has received less attention. Yet, a solution to such a problem would be of great importance, as it would allow VKG systems to be full-fledged, thus allowing end-users to fully manage source data through the lens of the ontology they are exposed to. This research aims to propose a comprehensive framework for instance-level updates in VKGs, where updates posed over the ontology have to be translated into source-level updates and, more importantly, how the side efects related to the propagation of ontology-based updates to the underlying data source can be minimized.
As a rapidly growing field, Virtual Knowledge Graphs (VKGs) are robust tools for integrating
heterogeneous data sources with the help of ontologies. VKG systems are virtual approaches
that allow users to issue high-level ontological queries, which are automatically translated into
equivalent low-level queries (like SQL in a relational setting) that the database engine at the
underlying data source can execute. Formerly known as ontology-based data Access (OBDA),
a VKG system consists of three main components: an ontology, a set of data sources, and a
declarative mapping between the two [
Problem Statement and Contribution. VKGs provide the ability to query information
stored in data sources using Semantic Web technologies, such as Resource Description
Framework (RDF) [
This research aims at introducing in VKGs the notion of ontology-based update and evolution and to study foundational and applied issues related to this extension. In particular, it would be possible to: (a) Insert new objects into a class of the ontology and populate the corresponding relations that are mapped to this class. (b) Add a new data property instance to an object in a class and populate the corresponding attribute(s) that are mapped to this class. (c) Connect two objects in two classes of the ontology through an object property instance and populate the corresponding attributes and relations that are mapped to these classes. (d) Remove an object, an instance of a data property, or an instance of an object property by deleting the corresponding data from the underlying mapped relations. (e) Perform a combination of multiple operations of the types above. Overall, this research aims at extending the capabilities of the VKG framework from “read-only” to “write-also”, so as to dynamically manage and evolve data through ontology-based operations.
Importance. Enriching VKGs with update and evolution capabilities while maintaining consistency represents an important step toward the practical usefulness of the VKG paradigm, as it will impact how modern information systems handle data, making them more flexible and responsive to changes. Using low-level languages like SQL to manage complex and large data can be challenging and time consuming as it requires domain experts for maintenance. However, by leveraging ontologies specified in user-friendly languages, organizations could simplify data management, reducing reliance on domain experts, lowering operational costs, and increasing organizational agility.
We observe that in the typical context of incomplete information provided by an ontology, each
of the insertion operations and their combination may generate an inconsistency in the data
with respect to the axioms contained in the ontology. In order to characterize the semantics
of such a system it becomes therefore necessary to rely on a suitably-defined
consistencytolerant semantics, e.g., based on the notion of repair. A second challenge in VKG systems
comes from the presence of mappings, due to the inherent ambiguity that such mappings
introduce when there is the need to propagate an ontology-based update (even one that does
not generate an inconsistency, such as a delete operation) to the source data. Indeed, a VKG
mapping is essentially a view that defines an ontology element (class or property) in terms of a
query over the data source. Hence, each update over the ontology element translates into an
update over the view that combines all queries that correspond to mappings for that ontology
element, and thus faces the view-update problem that has a long history in relational database
management [
This scenario poses a set of challenges and research questions that we aim to investigate: RQ1 Under which conditions can update operations over a (virtual) knowledge graph in the presence of an ontology be rewritten into update operations performed directly over the (virtual) objects that constitute the knowledge graph (without the need to take into account the ontology axioms)? RQ2 Under which conditions can update operations over a VKG defined by a data source and a set of declarative mappings, be faithfully realized through update operations over the data source? Under which conditions is the translation uniquely determined? RQ3 How can one find the source update operation that realizes a given update operation over a VKG? When the VKG update is not realizable, how can one find the best approximation (within the space of all possible source updates)? How can one eficiently search over the space of all source updates? RQ4 When an update over a VKG is either not realizable or not uniquely determined, which additional information is it necessary to maintain in order to find an efective solution to the view-update problem for VKG mappings? RQ5 How can update operations over a VKG be implemented efectively in a state-of-the art
VKG system that supports query rewriting?
This work has been partially supported by the Wallenberg AI, Autonomous Systems and Software Program (WASP) funded by the Knut and Alice Wallenberg Foundation, by the Province of Bolzano and DFG through the project D2G2 (DFG grant n. 500249124), and by the HEU project CyclOps (under GA n. 101135513).