An End-to-End Re-Evaluation of Table Entity-Linkers
Martin Pekár Christensen, Katja Hose, Matteo Lissandrini
Open MIND
Problems Identified (4)
Incomplete entity linker evaluation: Existing evaluations of table-to-KG entity linkers are incomplete because they focus on specific applications and aggregate output metrics rather than component effectiveness and scalability.
Entity linker reproducibility: Many entity linkers are difficult to reproduce due to unavailable source code or dependence on irreproducible public endpoints and datasets.
Incomplete entity linker evaluation: Existing evaluations of table-to-KG entity linkers are incomplete because they focus on specific applications and aggregate output metrics rather than component effectiveness and scalability.
Entity linker reproducibility: Many entity linkers are difficult to reproduce due to unavailable source code or dependence on irreproducible public endpoints and datasets.
Proposed Solutions (4)
End-to-end component evaluation: The paper proposes an in-depth evaluation of state-of-the-art entity linkers that assesses the quality and scalability of individual entity linking components.
Multi-benchmark KG linker evaluation: The paper evaluates entity linkers on four existing entity linking benchmarks using DBpedia and Wikidata.
End-to-end component evaluation: The paper proposes an in-depth evaluation of state-of-the-art entity linkers that assesses the quality and scalability of individual entity linking components.
Multi-benchmark KG linker evaluation: The paper evaluates entity linkers on four existing entity linking benchmarks using DBpedia and Wikidata.
Results (3)
Bottleneck identification:
Candidate generation is crucial:
Entity linkers often irreproducible:
Research Domain
Knowledge graph entity linking for tables