We study the evaluation of ontology-mediated queries (OMQs) on databases of bounded cliquewidth from the viewpoint of parameterized complexity theory. As the ontology language, we consider the description logics $\mathcal{ALC}$ and $\mathcal{ALCI}$ as well as the guarded two-variable fragment GF$_2$ of first-order logic. Queries are atomic queries (AQs), conjunctive queries (CQs), and unions of CQs. All studied OMQ problems are fixed-parameter linear (FPL) when the parameter is the size of the OMQ plus the cliquewidth. Our main contribution is a detailed analysis of the dependence of the running time on the parameter, exhibiting several interesting effects.

Resource Description Framework (RDF) and Property Graph (PG) are the two most commonly used data models for representing, storing, and querying graph data. We present Expressive Reasoning Graph Store (ERGS) -- a graph store built on top of JanusGraph (a Property Graph store) that also allows storing and querying of RDF datasets. First, we describe how RDF data can be translated into a Property Graph representation and then describe a query translation module that converts SPARQL queries into a series of Gremlin traversals. The converters and translators thus developed can allow any Apache Tinkerpop compliant graph database to store and query RDF datasets. We demonstrate the effectiveness of our proposed approach using JanusGraph as the base Property Graph store and compare its performance with standard RDF systems.

Knowledge Graphs (KGs) have proven to be a reliable way of structuring data. They can provide a rich source of contextual information about cultural heritage collections. However, cultural heritage KGs are far from being complete. They are often missing important attributes such as geographical location, especially for sculptures and mobile or indoor entities such as paintings. In this paper, we first present a framework for ingesting knowledge about tangible cultural heritage entities from various data sources and their connected multi-hop knowledge into a geolocalized KG. Secondly, we propose a multi-view learning model for estimating the relative distance between a given pair of cultural heritage entities, based on the geographical as well as the knowledge connections of the entities.

Semantic Web technology has successfully facilitated many RDF models with rich data representation methods. It also has the potential ability to represent and store multimodal knowledge bases such as multimodal scene graphs. However, most existing query languages, especially SPARQL, barely explore the implicit multimodal relationships like semantic similarity, spatial relations, etc. We first explored this issue by organizing a large-scale scene graph dataset, namely Visual Genome, in the RDF graph database. Based on the proposed RDF-stored multimodal scene graph, we extended SPARQL queries to answer questions containing relational reasoning about color, spatial, etc. Further demo (i.e., VGStore) shows the effectiveness of customized queries and displaying multimodal data.

In our pursuit of generic criteria for decidable ontology-based querying, we introduce 'finite-cliquewidth sets' (FCS) of existential rules, a model-theoretically defined class of rule sets, inspired by the cliquewidth measure from graph theory. By a generic argument, we show that FCS ensures decidability of entailment for a sizable class of queries (dubbed 'DaMSOQs') subsuming conjunctive queries (CQs). The FCS class properly generalizes the class of finite-expansion sets (FES), and for signatures of arity at most 2, the class of bounded-treewidth sets (BTS). For higher arities, BTS is only indirectly subsumed by FCS by means of reification. Despite the generality of FCS, we provide a rule set with decidable CQ entailment (by virtue of first-order-rewritability) that falls outside FCS, thus demonstrating the incomparability of FCS and the class of finite-unification sets (FUS). In spite of this, we show that if we restrict ourselves to single-headed rule sets over signatures of arity at most 2, then FCS subsumes FUS.

The blockchain technology provides integrity and reliability of the information, thus offering a suitable solution to guarantee trustability in a multi-stakeholder scenario that involves actors defining business agreements. The Ride2Rail project investigated the use of the blockchain to record as smart contracts the agreements between different stakeholders defined in a multimodal transportation domain. Modelling an ontology to represent the smart contracts enables the possibility of having a machine-readable and interoperable representation of the agreements. On one hand, the underlying blockchain ensures trust in the execution of the contracts, on the other hand, their ontological representation facilitates the retrieval of information within the ecosystem. The paper describes the development of the Ride2Rail Ontology for Agreements to showcase how the concept of an ontological smart contract, defined in the OASIS ontology, can be applied to a specific domain. The usage of the designed ontology is discussed by describing the modelling as ontological smart contracts of business agreements defined in a ride-sharing scenario.

This paper demonstrates our vision for knowledge graphs that assist machines to reason about the cause of signals observed by sensors. We show how the approach allows for constructing smarter surveillance systems that reason about the most likely cause (e.g., an attacker breaking a window) of a signal rather than acting directly on the received signal without consideration for how it was produced.

Road accidents have become the eight leading cause of death all over the world. Lots of these accidents are due to a driver's inattention or lack of focus, due to fatigue. Various factors cause driver's fatigue. This paper considers all the measureable data that manifest driver's fatigue, namely those manifested in the vehicle measureable data while driving as well as the driver's physical and physiological data. Each of the three main factors are further subdivided into smaller details. For example, the vehicle's data is composed of the values obtained from the steering wheel's angle, yaw angle, the position on the lane, and the speed and acceleration of the vehicle while moving. Ontological knowledge and rules for driver fatigue detection are to be integrated into an intelligent system so that on the first sign of dangerous level of fatigue is detected, a warning notification is sent to the driver. This work is intended to contribute to safe road driving.

The development of domain ontological models, though being a mature research arena backed by well-established methodologies, still suffer from two key shortcomings. Firstly, the issues concerning the semantic persistency of ontology concepts and their flexible reuse in domain development employing existing approaches. Secondly, due to the difficulty in understanding and reusing top-level concepts in existing foundational ontologies, the obfuscation regarding the semantic nature of domain representations. The paper grounds the aforementioned shortcomings in representation diversity and proposes a three-fold solution - (i) a pipeline for rendering concepts reuse-ready, (ii) a first characterization of a minimalistic foundational knowledge model, named foundational teleology, semantically explicating foundational distinctions enforcing the static as well as dynamic nature of domain representations, and (iii) a flexible, reuse-native methodology for diversity-aware domain development exploiting solutions (i) and (ii). The preliminary work reported validates the potentiality of the solution components.

Among a variety of other considerations, energy efficiency is a major focus for the Union's ultimate decarbonization. This makes high energy efficiency a critical priority for all energy sectors, particularly the residential sector [2], which occupies more than a quarter of the Union's total final energy consumption. Energy decentralization has emerged as one of the most popular contemporary research topic in this domain as a mean for increasing energy efficiency [3]. With the growing usage of Information and Communication Technologies (ICT) in the Internet of Things (IoT) sector, data on household energy consumption and production (HECP) may now be generated in a decentralized manner, for example, from an electric vehicle, a heat pump, or home appliances. Due to the range and granularity of data-generating devices, a new generation of smart household energy systems is geared toward decentralization and has the potential to considerably assist in the transition to a sustainable energy future [4, 5]. On the other hand, evaluating household energy data is getting increasingly difficult as a result of various smart devices interacting and forming a complex energy flow data network [6, 7]. Decentralized energy systems are often paired with research into data-driven technologies (e.g. machine learning) for opti-2 mizing the systems based on the massive ocean of incoming data in order to manage the inherent risk associated with energy usage's intermittent and unpredictable nature and achieve energy sustainability, including cost reduction, emission reduction, and energy efficiency. However, most of those technologies are developed for project-specific decentralized data (i.e.