Despite the recent broad adoption of Large Language Models (LLMs) across various domains, their potential for enriching information systems in extracting and exploring Linked Data (LD) and Resource Description Framework (RDF) triplestores has not been extensively explored. This paper examines the integration of LLMs within existing systems, emphasising the enhancement of conversational user interfaces (UIs) and their capabilities for data extraction by producing more accurate SPARQL queries without the requirement for model retraining. Typically, conversational UI models necessitate retraining with the introduction of new datasets or updates, limiting their functionality as general-purpose extraction tools. Our approach addresses this limitation by incorporating LLMs into the conversational UI workflow, significantly enhancing their ability to comprehend and process user queries effectively. By leveraging the advanced natural language understanding capabilities of LLMs, our method improves RDF entity extraction within web systems employing conventional chatbots. This integration facilitates a more nuanced and context-aware interaction model, critical for handling the complex query patterns often encountered in RDF datasets and Linked Open Data (LOD) endpoints. The evaluation of this methodology shows a marked enhancement in system expressivity and the accuracy of responses to user queries, indicating a promising direction for future research in this area. This investigation not only underscores the versatility of LLMs in enhancing existing information systems but also sets the stage for further explorations into their potential applications within more specialised domains of web information systems.

In this paper, we introduce AutoRDF2GML, a framework designed to convert RDF data into data representations tailored for graph machine learning tasks. AutoRDF2GML enables, for the first time, the creation of both content-based features -- i.e., features based on RDF datatype properties -- and topology-based features -- i.e., features based on RDF object properties. Characterized by automated feature extraction, AutoRDF2GML makes it possible even for users less familiar with RDF and SPARQL to generate data representations ready for graph machine learning tasks, such as link prediction, node classification, and graph classification. Furthermore, we present four new benchmark datasets for graph machine learning, created from large RDF knowledge graphs using our framework. These datasets serve as valuable resources for evaluating graph machine learning approaches, such as graph neural networks. Overall, our framework effectively bridges the gap between the Graph Machine Learning and Semantic Web communities, paving the way for RDF-based machine learning applications.

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.

SPARQL is the de facto language for querying RDF data, since its standardization in 2008. A new version, called SPARQL 1.1, was released in 2013, with the aim of enriching the 2008 language with reasoning capabilities to deal with RDFS and OWL vocabularies, and a mechanism to express navigation patterns through regular expressions. However, SPARQL 1.1 is not powerful enough for expressing some relevant navigation patterns, and it misses a general form of recursion. In this work, we focus on OWL 2 QL and we propose TriQ-Lite 1.0, a tractable rule-based formalism that supports the above functionalities, and thus it can be used for querying RDF data. Unlike existing composite approaches, our formalism has simple syntax and semantics in the same spirit as good old Datalog.

The popularization and quick growth of Linked Open Data (LOD) has led to challenging aspects regarding quality assessment and data exploration of the RDF triples that shape the LOD cloud.Particularly, we are interested in the completeness of data and its potential to provide concept definitions in terms of necessary and sufficient conditions.In this work we propose a novel technique based on Formal Concept Analysis which organizes RDF data into a concept lattice.This allows data exploration as well as the discovery of implications, which are used to automatically detect missing information and then to complete RDF data.Moreover, this is a way of reconciling syntax and semantics in the LOD cloud.Finally, experiments on the DBpedia knowledge base show that the approach is well-founded and effective.

After a short summer break, I have prepared a new blog series. In this first part, we will construct a knowledge graph of monuments located in Spain. As you might know, I have lately gained a lot of interest and respect for the wealth of knowledge that is available through the WikiData API. We will continue honing our SPARQL syntax knowledge and fetch the information regarding the monuments located in Spain from the WikiData API. I wasn't aware of this before, but scraping the RDF data available online and importing it into Neo4j is such a popular topic that Dr. Jesus Barrasa developed a Neosemantics library to help us with this process.

Due to the openness and decentralization of the Web, mechanisms to represent and reason about the reliability of RDF data become essential. This paper embarks on a formal analysis of RDF data enriched with trust information by focusing on the characterization of its model-theoretic semantics and on the study of relevant reasoning problems. The impact of trust values on the computational complexity of well-known concepts related to the entailment of RDF graphs is studied. In particular, islands of tractability are identified for classes of acyclic and nearly-acyclic graphs. Moreover, an implementation of the framework and an experimental evaluation on real data are discussed.

Without Linked Data, transport data is limited to applications exclusively around transport. In this paper, we present a workflow for publishing and linking transport data on the Web. So we will be able to develop transport applications and to add other features which will be created from other datasets. This will be possible because transport data will be linked to these datasets. We apply this workflow to two datasets: NEPTUNE, a French standard describing a transport line, and Passim, a directory containing relevant information on transport services, in every French city.

In recent years, the significant growth of RDF data used in numerous applications has made its efficient and scalable manipulation an important issue. In this paper, we present RDFViewS, a system capable of choosing the most suitable views to materialize, in order to minimize the query response time for a specific SPARQL query workload, while taking into account the view maintenance cost and storage space constraints. Our system employs practical algorithms and heuristics to navigate through the search space of potential view configurations, and exploits the possibly available semantic information - expressed via an RDF Schema - to ensure the completeness of the query evaluation.