Tootfinder

FIRESPARQL: A LLM-based Framework for SPARQL Query Generation over Scholarly Knowledge Graphs
Xueli Pan, Victor de Boer, Jacco van Ossenbruggen
https://arxiv.org/abs/2508.10467 …

FIRESPARQL: A LLM-based Framework for SPARQL Query Generation over Scholarly Knowledge Graphs
Question answering over Scholarly Knowledge Graphs (SKGs) remains a challenging task due to the complexity of scholarly content and the intricate structure of these graphs. Large Language Model (LLM) approaches could be used to translate natural language questions (NLQs) into SPARQL queries; however, these LLM-based approaches struggle with SPARQL query generation due to limited exposure to SKG-specific content and the underlying schema. We identified two main types of errors in the LLM-generat…

At the Semantic Digital Humanities 2025 Workshop, Jose Maldonado-Rodríguez is presenting "Natural Language Querying for Humanities #KnowledgeGraphs A case study on the GOLEM KG". Main contribution is a bilingual dataset (English-Spanish) specifically designed to evaluate automatic text-to-SPARQL translation systems for GOLEM, a specialized humanities KG.
paper:

Difference Views for Visual Graph Query Building
Benedikt Kantz, Stefan Lengauer, Peter Waldert, Tobias Schreck
https://arxiv.org/abs/2508.05314 https://ar…

Difference Views for Visual Graph Query Building
Knowledge Graphs (KGs) contain vast amounts of linked resources that encode knowledge in various domains, which can be queried and searched for using specialized languages like SPARQL, a query language developed to query KGs. Existing visual query builders enable non-expert users to construct SPARQL queries and utilize the knowledge contained in these graphs. Query building is, however, an iterative and, often, visual process where the question of the user can change and differ throughout the p…

Fuzzy Ontology Embeddings and Visual Query Building for Ontology Exploration
Vladimir Zhurov, John Kausch, Kamran Sedig, Mostafa Milani
https://arxiv.org/abs/2508.08128 https://…

Fuzzy Ontology Embeddings and Visual Query Building for Ontology Exploration
Ontologies play a central role in structuring knowledge across domains, supporting tasks such as reasoning, data integration, and semantic search. However, their large size and complexity, particularly in fields such as biomedicine, computational biology, law, and engineering, make them difficult for non-experts to navigate. Formal query languages such as SPARQL offer expressive access but require users to understand the ontology's structure and syntax. In contrast, visual exploration tools and…

Text-to-SPARQL Goes Beyond English: Multilingual Question Answering Over Knowledge Graphs through Human-Inspired Reasoning
Aleksandr Perevalov, Andreas Both
https://arxiv.org/abs/2507.16971

Text-to-SPARQL Goes Beyond English: Multilingual Question Answering Over Knowledge Graphs through Human-Inspired Reasoning
Accessing knowledge via multilingual natural-language interfaces is one of the emerging challenges in the field of information retrieval and related ones. Structured knowledge stored in knowledge graphs can be queried via a specific query language (e.g., SPARQL). Therefore, one needs to transform natural-language input into a query to fulfill an information need. Prior approaches mostly focused on combining components (e.g., rule-based or neural-based) that solve downstream tasks and come up wi…

I wouldn't have thought that quantitative analyses of retrospective national bibliographies would be that painful: data access via SRU, OAI, and REST API; another resource has a JSON dump, another one again consists of various ttl for which you have to set up your own sparql endpoint. And I've not even arrived at formats, metadata standards and cataloguing peculiarities 🤯 so everything's #FAIR

Mehr Quizzes für Metadatenmenschen, um spielerisch #Metadatenㅤㅤㅤ-dienste und ihre Möglichkeiten kennenzulernen! Ich habe mal mit einer Frage zu #lobid-gnd angefangen:

Eat your own KR: a KR-based approach to index Semantic Web Endpoints and Knowledge Graphs
Pierre Maillot (WIMMICS), Catherine Faron (UniCA, I3S, WIMMICS), Fabien Gandon (WIMMICS), Franck Michel (Laboratoire I3S - SPARKS, WIMMICS), Pierre Monnin (WIMMICS)
https://arxiv.org/abs/2508.08713

Eat your own KR: a KR-based approach to index Semantic Web Endpoints and Knowledge Graphs
Over the last decade, knowledge graphs have multiplied, grown, and evolved on the World Wide Web, and the advent of new standards, vocabularies, and application domains has accelerated this trend. IndeGx is a framework leveraging an extensible base of rules to index the content of KGs and the capacities of their SPARQL endpoints. In this article, we show how knowledge representation (KR) and reasoning methods and techniques can be used in a reflexive manner to index and characterize existing kn…

@…
Actually, this is WIkipedia and only structured data, i.e. no RDF.
However, use this SPARQL query on DBpedia to get vitamins and minerals of a cucumber ;-)

Service-Link zur heutigen @… -Veranstaltung anlässlich des #Digitaltag2025 (vgl. https://

When I'm reading pages on Wikidata like Christian Frates (Q47691741) I'm thinking somebody should make a "social media version" of WikiBase/Wikidata and let people on social media sites like Mastodon and the larger fediverse put themselves into 100% data to be searchable in sparql.
Wikidata would be thankful!
Then I could add "shoppingtonz" Aka "100%/0% TAX" Aka "Lord Gold"(just came up with it)
and add
interests = neur…

LLMs are starving for knowledge graphs. Raphael Troncy was pointing out that many LLM company crawlers are constantly visiting their KGs. Some crawlers even perform explicit SPARQL queries on the KGs.
#knowledgegraphs #eswc2025

SPARQL Query Generation with LLMs: Measuring the Impact of Training Data Memorization and Knowledge Injection
Aleksandr Gashkov, Aleksandr Perevalov, Maria Eltsova, Andreas Both
https://arxiv.org/abs/2507.13859

SPARQL Query Generation with LLMs: Measuring the Impact of Training Data Memorization and Knowledge Injection
Nowadays, the importance of software with natural-language user interfaces cannot be underestimated. In particular, in Question Answering (QA) systems, generating a SPARQL query for a given natural-language question (often named Query Building) from the information retrieved from the same question is the central task of QA systems working over Knowledge Graphs (KGQA). Due to the rise of Large Language Models (LLMs), they are considered a well-suited method to increase the quality of the questio…

Enhancing Manufacturing Knowledge Access with LLMs and Context-aware Prompting
Sebastian Monka, Irlan Grangel-Gonz\'alez, Stefan Schmid, Lavdim Halilaj, Marc Rickart, Oliver Rudolph, Rui Dias
https://arxiv.org/abs/2507.22619

Enhancing Manufacturing Knowledge Access with LLMs and Context-aware Prompting
Knowledge graphs (KGs) have transformed data management within the manufacturing industry, offering effective means for integrating disparate data sources through shared and structured conceptual schemas. However, harnessing the power of KGs can be daunting for non-experts, as it often requires formulating complex SPARQL queries to retrieve specific information. With the advent of Large Language Models (LLMs), there is a growing potential to automatically translate natural language queries into…

Has anybody here ever successfully used the #cerl apis to download larger datasets? In theory, the service looks great (https://data.cerl.org/all ); they even offer sparql endpoints. However, I can't use t…

In today's ISE 2025 lecture,, we will introduce SPARQL as a query language for knowledge graphs. Again, I'm trying out 'Dystopian Novels' as example knowledge graph playground. Let's see, if the students might know any of them. Wtat do you think? ;-)
#dystopia #literature