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- Definition/Description: Disambiguation: given mentions and contexts, need a function which maps them to entities in KG
- Oftentimes also part of IE pipelines (e.g. KG Completion)
- has typical steps: candidate generation, entity ranking
- Direct LLM assistance: Verbalized description of link candidates compared against a text snippet that contains the entity that should be linked/disambiguated + formulated as a classification task
- Indirect LLM assistance: E.g., using LLM-assisted KG IE from texts and compare the found entities and relationships against the neighborhood of the link candidates
- References: Golnaz Shapurian.Large language models and knowledge graphs for astronomical entity disambiguation.arXiv preprint arXiv:https://doi.org/10.48550/arXiv.2406.11400, 2024; Shaojun Liu and Yanfeng Fang. Use large language models for named entity disambiguation in academic knowledge graphs. In 2023 3rd International Conference on Education, Information Management and Service Science (EIMSS 2023), pages 681–691. Atlantis Press, 2023; https://doi.org/10.2991/978-94-6463-264-4_79
Contributors:
- Desiree Heim (DFKI)
- Markus Schröder (DFKI)
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- Definition/Description:
- Text2QueryLanguage (e.g. Text2SPARQL)
- Direct translation of user queries into equivalent knowledge graph query languages by prompting an LLM
- The knowledge graph structure can be given either by including a (sub)schema or a subgraph in the query translation prompt
- KG-RAG
- Can be done by extracting knowledge graph entities and potentially relevant relationships from the user query and retrieving relevant triples from the knowledge graph which are given as a context to the prompt containing the user query
- References: Jacopo D’Abramo, Andrea Zugarini, and Paolo Torroni.Dynamic few-shot learning for knowledge graph question answering.arXiv preprint arXiv:2407.01409, 2024; ] Dmitrii Pliukhin, Daniil Radyush, Liubov Kovriguina, and Dmitry Mouromtsev. Improving subgraph extraction algorihtms for one-shot sparql query generation with large language models. In QALD/SemREC@ ISWC, 2023.:https://doi.org/10.48550/arXiv.2407.01409, https://ceur-ws.org/Vol-3592/paper6.pdf
Contributors:
- Diego Collarana (FIT)
- Sven Hertling (FIZ), Harld Sack (FIZ), Heike Fliegl (FIZ)
- Desiree Heim (DFKI)
- Markus Schröder (DFKI)
- Sabine Mahr (word b sign)
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- Typically triple-based verbalization
- Different granularity of verbalizations (very exact subject/predicate/object sentences vs summary of subgraphs)
- ABOX statement verbalization: Often employed in RAG-based KGQA processes
- TBOX statement verbalization: Relevant for Text2QueryLanguage, Ontology-based KG Completion tasks
- References: Phillip Schneider, Manuel Klettner, Elena Simperl, and Florian Matthes. A comparative analysis of conversational large language models in knowledge-based text generation.arXiv preprint arXiv:2402.01495, 2024; Sven Hertling and Heiko Paulheim. Olala: Ontology matching with large languagemodels. In Proceedings of the 12th Knowledge Capture Conference 2023, pages 131–139, 2023: https://doi.org/10.48550/arXiv.2402.01495; https://doi.org/10.1145/3587259.3627571
Contributors:
- Daniel Baldassare (doctima)
- Sven Hertling (FIZ), Harald Sack (FIZ), Heike Fliegl (FIZ)
- Desiree Heim (DFKI)
- Markus Schröder (DFKI)
- Sabine Mahr (word b sign)
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