Tarcisio Mendes de Farias

Panayiotis Smeros, Vincent Emonet, Ruijie Wang et al.

The advent of large language models is contributing to the emergence of novel approaches that promise to better tackle the challenge of generating structured queries, such as SPARQL queries, from natural language. However, these new approaches mostly focus on response accuracy over a single source while ignoring other evaluation criteria, such as federated query capability over distributed data stores, as well as runtime and cost to generate SPARQL queries. Consequently, they are often not production-ready or easy to deploy over (potentially federated) knowledge graphs with good accuracy. To mitigate these issues, in this paper, we extend our previous work and describe and systematically evaluate SPARQL-LLM, an open-source and triplestore-agnostic approach, powered by lightweight metadata, that generates SPARQL queries from natural language text. First, we describe its architecture, which consists of dedicated components for metadata indexing, prompt building, and query generation and execution. Then, we evaluate it based on a state-of-the-art challenge with multilingual questions, and a collection of questions from three of the most prevalent knowledge graphs within the field of bioinformatics. Our results demonstrate a substantial increase of 24% in the F1 Score on the state-of-the-art challenge, adaptability to high-resource languages such as English and Spanish, as well as ability to form complex and federated bioinformatics queries. Furthermore, we show that SPARQL-LLM is up to 36x faster than other systems participating in the challenge, while costing a maximum of $0.01 per question, making it suitable for real-time, low-cost text-to-SPARQL applications. One such application deployed over real-world decentralized knowledge graphs can be found at https://www.expasy.org/chat.

Ana-Claudia Sima, Tarcisio Mendes de Farias

In this paper, we present work in progress on the role of artificial intelligence (AI) chatbots, such as ChatGPT, in facilitating data access to federated knowledge graphs. In particular, we provide examples from the field of bioinformatics, to illustrate the potential use of Conversational AI to describe datasets, as well as generate and explain (federated) queries across datasets for the benefit of domain experts.

Julio C. Rangel, Tarcisio Mendes de Farias, Ana Claudia Sima et al.

The recent success of Large Language Models (LLM) in a wide range of Natural Language Processing applications opens the path towards novel Question Answering Systems over Knowledge Graphs leveraging LLMs. However, one of the main obstacles preventing their implementation is the scarcity of training data for the task of translating questions into corresponding SPARQL queries, particularly in the case of domain-specific KGs. To overcome this challenge, in this study, we evaluate several strategies for fine-tuning the OpenLlama LLM for question answering over life science knowledge graphs. In particular, we propose an end-to-end data augmentation approach for extending a set of existing queries over a given knowledge graph towards a larger dataset of semantically enriched question-to-SPARQL query pairs, enabling fine-tuning even for datasets where these pairs are scarce. In this context, we also investigate the role of semantic "clues" in the queries, such as meaningful variable names and inline comments. Finally, we evaluate our approach over the real-world Bgee gene expression knowledge graph and we show that semantic clues can improve model performance by up to 33% compared to a baseline with random variable names and no comments included.

Sihem Amer-Yahia, Georgia Koutrika, Frederic Bastian et al.

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

Tarcisio Mendes de Farias, Kurt Stockinger, Christophe Dessimoz

Semantic heterogeneity remains a problem when interoperating with data from sources of different scopes and knowledge domains. Causes for this challenge are context-specific requirements (i.e. no "one model fits all"), different data modelling decisions, domain-specific purposes, and technical constraints. Moreover, even if the problem of semantic heterogeneity among different RDF publishers and knowledge domains is solved, querying and accessing the data of distributed RDF datasets on the Web is not straightforward. This is because of the complex and fastidious process needed to understand how these datasets can be related or linked, and consequently, queried. To address this issue, we propose to extend the existing Vocabulary of Interlinked Datasets (VoID) by introducing new terms such as the Virtual Link Set concept and data model patterns. A virtual link is a connection between resources such as literals and IRIs (Internationalized Resource Identifier) with some commonality where each of these resources is from a different RDF dataset. The links are required in order to understand how to semantically relate datasets. In addition, we describe several benefits of using virtual links to improve interoperability between heterogenous and independent datasets. Finally, we exemplify and apply our approach to multiple world-wide used RDF datasets.

Tarcisio Mendes de Farias, Ana Roxin, Christophe Nicolle

In the context of Building Information Modelling, ontologies have been identified as interesting in achieving information interoperability. Regarding the construction and facility management domains, several IFC (Industry Foundation Classes) based ontologies have been developed, such as IfcOWL. In the context of ontology modelling, the constraint of optimizing the size of IFC STEP-based files can be leveraged. In this paper, we propose an adaptation of the IFC model into OWL which leverages from all modelling constraints required by the object-oriented structure of IFC schema. Therefore, we do not only present a syntactic but also a semantic adaptation of the IFC model. Our model takes into consideration the meaning of entities, relationships, properties and attributes defined by the IFC standard. Our approach presents several advantages compared to other initiatives such as the optimization of query execution time. Every advantage is defended by means of practical examples and benchmarks.