{"ID":2888865,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.22811","arxiv_id":"2507.22811","title":"DBLPLink 2.0 -- An Entity Linker for the DBLP Scholarly Knowledge Graph","abstract":"In this work we present an entity linker for DBLP's 2025 version of RDF-based Knowledge Graph. Compared to the 2022 version, DBLP now considers publication venues as a new entity type called dblp:Stream. In the earlier version of DBLPLink, we trained KG-embeddings and re-rankers on a dataset to produce entity linkings. In contrast, in this work, we develop a zero-shot entity linker using LLMs using a novel method, where we re-rank candidate entities based on the log-probabilities of the \"yes\" token output at the penultimate layer of the LLM.","short_abstract":"In this work we present an entity linker for DBLP's 2025 version of RDF-based Knowledge Graph. Compared to the 2022 version, DBLP now considers publication venues as a new entity type called dblp:Stream. In the earlier version of DBLPLink, we trained KG-embeddings and re-rankers on a dataset to produce entity linkings....","url_abs":"https://arxiv.org/abs/2507.22811","url_pdf":"https://arxiv.org/pdf/2507.22811v2","authors":"[\"Debayan Banerjee\",\"Tilahun Abedissa Taffa\",\"Ricardo Usbeck\"]","published":"2025-07-30T16:29:47Z","proceeding":"cs.CL","tasks":"[\"cs.CL\"]","methods":"[\"Large Language Model\"]","has_code":false}