Minh-Hoang Dang

1. 2025

   Dang, M.-H., Pham, T. H. T., Molli, P., Skaf-Molli, H., & Gaignard, A. (2025). LLM4SchemaOrg: Generating Schema.org Markups with Large Language Models. Semantic Web Journal.

   DOI BIB Abstract

   @article{dang2025llm4schema,
     title = {{LLM4SchemaOrg}: Generating {Schema.org} Markups with Large Language Models},
     author = {Dang, Minh-Hoang and Pham, Thi Hoang Thi and Molli, Pascal and Skaf-Molli, Hala and Gaignard, Alban},
     journal = {Semantic Web Journal},
     year = {2025},
     month = jun,
     doi = {10.1177/22104968251382172}
   }
   

   Integrating Schema.org markup into web pages has resulted in the generation of billions of RDF triples. However, around 75% of web pages still lack this critical markup. Large language models (LLMs) present a promising solution by automatically generating the missing Schema.org markup. Despite this potential, there is currently no benchmark to evaluate the markup quality produced by LLMs. This article introduces LLM4Schema.org, an innovative approach for assessing the performance of LLMs in generating Schema.org markup. Unlike traditional methods, LLM4Schema.org does not require a predefined ground truth. Instead, it compares the quality of LLM-generated markup against human-generated markup. Our findings reveal that 40%–50% of the markup produced by GPT-3.5 and GPT-4 is invalid, non-factual, or non-compliant with the Schema.org ontology. These errors underscore the limitations of LLMs …

2. 2024

   Aimonier-Davat, J., Nédelec, B., Dang, M.-H., Molli, P., & Skaf-Molli, H. (2024). FedUP: Querying Large-Scale Federations of SPARQL Endpoints. Proceedings of the ACM on Web Conference 2024, 2315–2324.

   DOI BIB Abstract

   @inproceedings{aimonier2024fedup,
     title = {{FedUP}: Querying Large-Scale Federations of {SPARQL} Endpoints},
     author = {Aimonier-Davat, Julien and N{\'e}delec, Brice and Dang, Minh-Hoang and Molli, Pascal and Skaf-Molli, Hala},
     booktitle = {Proceedings of the ACM on Web Conference 2024},
     pages = {2315--2324},
     year = {2024},
     month = may,
     doi = {10.1145/3589334.3645704}
   }
   

   Processing SPARQL queries over large federations of SPARQL endpoints is crucial for keeping the Semantic Web decentralized. Despite the existence of hundreds of SPARQL endpoints, current federation engines only scale to dozens. One major issue comes from the current definition of the source selection problem, i.e., finding the minimal set of SPARQL endpoints to contact per triple pattern. Even if such a source selection is minimal, only a few combinations of sources may return results. Consequently, most of the query processing time is wasted evaluating combinations that return no results. In this paper, we introduce the concept of Result-Aware query plans. This concept ensures that every subquery of the query plan effectively contributes to the result of the query. To compute a Result-Aware query plan, we propose FedUP, a new federation engine able to produce Result-Aware query plans by tracking the …

3. 2023

   Dang, M.-H., Aimonier-Davat, J., Molli, P., Hartig, O., Skaf-Molli, H., & Le Crom, Y. (2023). FedShop: A Benchmark for Testing the Scalability of SPARQL Federation Engines. ISWC’23: 22nd International Semantic Web Conference.

   DOI BIB Abstract

   @inproceedings{dang2023fedshop,
     title = {{FedShop}: A Benchmark for Testing the Scalability of {SPARQL} Federation Engines},
     author = {Dang, Minh-Hoang and Aimonier-Davat, Julien and Molli, Pascal and Hartig, Olaf and Skaf-Molli, Hala and Le Crom, Yotlan},
     booktitle = {ISWC'23: 22nd International Semantic Web Conference},
     year = {2023},
     doi = {10.1007/978-3-031-47243-5_16}
   }
   

   While several approaches to query a federation of SPARQL endpoints have been proposed in the literature, very little is known about the effectiveness of these approaches and the behavior of the resulting query engines for cases in which the number of federation members increases. The existing benchmarks that are typically used to evaluate SPARQL federation engines do not consider such a form of scalability. In this paper, we set out to close this knowledge gap by investigating the behavior of 4 state-of-the-art SPARQL federation engines using a novel benchmark designed for scalability experiments. Based on the benchmark, we show that scalability is a challenge for each of these engines, especially with respect to the effectiveness of their source selection & query decomposition approaches. FedShop is freely available online at: https://github.com/GDD-Nantes/FedShop.

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