Tootfinder

LLM-Assisted Modeling of Semantic Web-Enabled Multi-Agents Systems with AJAN
Hacane Hechehouche, Andre Antakli, Matthias Klusch
https://arxiv.org/abs/2510.06911 https://

LLM-Assisted Modeling of Semantic Web-Enabled Multi-Agents Systems with AJAN
There are many established semantic Web standards for implementing multi-agent driven applications. The AJAN framework allows to engineer multi-agent systems based on these standards. In particular, agent knowledge is represented in RDF/RDFS and OWL, while agent behavior models are defined with Behavior Trees and SPARQL to access and manipulate this knowledge. However, the appropriate definition of RDF/RDFS and SPARQL-based agent behaviors still remains a major hurdle not only for agent modeler…

Die @… hat jetzt den #qlever-Endpunkt in eine eigene Subdomain geholt, über den der Datenbestand der DNB (und #GND) als

Modeling and Managing Temporal Obligations in GUCON Using SPARQL-star and RDF-star
Ines Akaichi, Giorgos Flouris, Irini Fundulaki, Sabrina Kirrane
https://arxiv.org/abs/2510.04652

Modeling and Managing Temporal Obligations in GUCON Using SPARQL-star and RDF-star
In the digital age, data frequently crosses organizational and jurisdictional boundaries, making effective governance essential. Usage control policies have emerged as a key paradigm for regulating data usage, safeguarding privacy, protecting intellectual property, and ensuring compliance with regulations. A central mechanism for usage control is the handling of obligations, which arise as a side effect of using and sharing data. Effective monitoring of obligations requires capturing usage trac…

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…

ARUQULA -- An LLM based Text2SPARQL Approach using ReAct and Knowledge Graph Exploration Utilities
Felix Brei, Lorenz B\"uhmann, Johannes Frey, Daniel Gerber, Lars-Peter Meyer, Claus Stadler, Kirill Bulert
https://arxiv.org/abs/2510.02200

ARUQULA -- An LLM based Text2SPARQL Approach using ReAct and Knowledge Graph Exploration Utilities
Interacting with knowledge graphs can be a daunting task for people without a background in computer science since the query language that is used (SPARQL) has a high barrier of entry. Large language models (LLMs) can lower that barrier by providing support in the form of Text2SPARQL translation. In this paper we introduce a generalized method based on SPINACH, an LLM backed agent that translates natural language questions to SPARQL queries not in a single shot, but as an iterative process of e…

SPARQL in N3: SPARQL CONSTRUCT as a rule language for the Semantic Web (Extended Version)
D\"orthe Arndt, William Van Woensel, Dominik Tomaszuk
https://arxiv.org/abs/2508.13041

SPARQL in N3: SPARQL CONSTRUCT as a rule language for the Semantic Web (Extended Version)
Reasoning in the Semantic Web (SW) commonly uses Description Logics (DL) via OWL2 DL ontologies, or SWRL for variables and Horn clauses. The Rule Interchange Format (RIF) offers more expressive rules but is defined outside RDF and rarely adopted. For querying, SPARQL is a well-established standard operating directly on RDF triples. We leverage SPARQL CONSTRUCT queries as logic rules, enabling (1) an expressive, familiar SW rule language, and (2) general recursion, where queries can act on the r…

@…
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 ;-)

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…

#SPARQL -Spielerei: Die Gemälde der Berliner Gemäldegalerie nach Fläche geordnet: https://qlever.cs.uni-freiburg.de/wikidata/DanBp9 (Ausführungszeit 166ms, im WDQS landet die Anfrage im …

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…

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…

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…

I had never used (or heard of) the 'DESCRIBE' form in #SPARQL. Sounds useful, though, if you don't know your data model well. So "DESCRIBE wd:Q42" provides a list of all triples where Q42 appears in any position (https://w.wiki/F6jK

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…

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…

Crosslisted article(s) found for cs.DL. https://arxiv.org/list/cs.DL/new
[1/1]:
- FIRESPARQL: A LLM-based Framework for SPARQL Query Generation over Scholarly Knowledge Graphs
Xueli Pan, Victor de Boer, Jacco van Ossenbruggen

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…