@inproceedings{radevski-etal-2023-linking,
title = "Linking Surface Facts to Large-Scale Knowledge Graphs",
author = "Radevski, Gorjan and
Gashteovski, Kiril and
Hung, Chia-Chien and
Lawrence, Carolin and
Glava{\v{s}}, Goran",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-main.445",
doi = "10.18653/v1/2023.emnlp-main.445",
pages = "7189--7207",
abstract = "Open Information Extraction (OIE) methods extract facts from natural language text in the form of ({``}subject{''}; {``}relation{''}; {``}object{''}) triples. These facts are, however, merely surface forms, the ambiguity of which impedes their downstream usage; e.g., the surface phrase {``}Michael Jordan{''} may refer to either the former basketball player or the university professor. Knowledge Graphs (KGs), on the other hand, contain facts in a canonical (i.e., unambiguous) form, but their coverage is limited by a static schema (i.e., a fixed set of entities and predicates). To bridge this gap, we need the best of both worlds: (i) high coverage of free-text OIEs, and (ii) semantic precision (i.e., monosemy) of KGs. In order to achieve this goal, we propose a new benchmark with novel evaluation protocols that can, for example, measure fact linking performance on a granular triple slot level, while also measuring if a system has the ability to recognize that a surface form has no match in the existing KG. Our extensive evaluation of several baselines show that detection of out-of-KG entities and predicates is more difficult than accurate linking to existing ones, thus calling for more research efforts on this difficult task. We publicly release all resources (data, benchmark and code) on https://github.com/nec-research/fact-linking.",
}
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<abstract>Open Information Extraction (OIE) methods extract facts from natural language text in the form of (“subject”; “relation”; “object”) triples. These facts are, however, merely surface forms, the ambiguity of which impedes their downstream usage; e.g., the surface phrase “Michael Jordan” may refer to either the former basketball player or the university professor. Knowledge Graphs (KGs), on the other hand, contain facts in a canonical (i.e., unambiguous) form, but their coverage is limited by a static schema (i.e., a fixed set of entities and predicates). To bridge this gap, we need the best of both worlds: (i) high coverage of free-text OIEs, and (ii) semantic precision (i.e., monosemy) of KGs. In order to achieve this goal, we propose a new benchmark with novel evaluation protocols that can, for example, measure fact linking performance on a granular triple slot level, while also measuring if a system has the ability to recognize that a surface form has no match in the existing KG. Our extensive evaluation of several baselines show that detection of out-of-KG entities and predicates is more difficult than accurate linking to existing ones, thus calling for more research efforts on this difficult task. We publicly release all resources (data, benchmark and code) on https://github.com/nec-research/fact-linking.</abstract>
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%0 Conference Proceedings
%T Linking Surface Facts to Large-Scale Knowledge Graphs
%A Radevski, Gorjan
%A Gashteovski, Kiril
%A Hung, Chia-Chien
%A Lawrence, Carolin
%A Glavaš, Goran
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F radevski-etal-2023-linking
%X Open Information Extraction (OIE) methods extract facts from natural language text in the form of (“subject”; “relation”; “object”) triples. These facts are, however, merely surface forms, the ambiguity of which impedes their downstream usage; e.g., the surface phrase “Michael Jordan” may refer to either the former basketball player or the university professor. Knowledge Graphs (KGs), on the other hand, contain facts in a canonical (i.e., unambiguous) form, but their coverage is limited by a static schema (i.e., a fixed set of entities and predicates). To bridge this gap, we need the best of both worlds: (i) high coverage of free-text OIEs, and (ii) semantic precision (i.e., monosemy) of KGs. In order to achieve this goal, we propose a new benchmark with novel evaluation protocols that can, for example, measure fact linking performance on a granular triple slot level, while also measuring if a system has the ability to recognize that a surface form has no match in the existing KG. Our extensive evaluation of several baselines show that detection of out-of-KG entities and predicates is more difficult than accurate linking to existing ones, thus calling for more research efforts on this difficult task. We publicly release all resources (data, benchmark and code) on https://github.com/nec-research/fact-linking.
%R 10.18653/v1/2023.emnlp-main.445
%U https://aclanthology.org/2023.emnlp-main.445
%U https://doi.org/10.18653/v1/2023.emnlp-main.445
%P 7189-7207
Markdown (Informal)
[Linking Surface Facts to Large-Scale Knowledge Graphs](https://aclanthology.org/2023.emnlp-main.445) (Radevski et al., EMNLP 2023)
ACL
- Gorjan Radevski, Kiril Gashteovski, Chia-Chien Hung, Carolin Lawrence, and Goran Glavaš. 2023. Linking Surface Facts to Large-Scale Knowledge Graphs. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 7189–7207, Singapore. Association for Computational Linguistics.