Details
| Original language | English |
|---|---|
| Title of host publication | CIKM 2020 |
| Subtitle of host publication | Proceedings of the 29th ACM International Conference on Information and Knowledge Management |
| Publisher | Association for Computing Machinery (ACM) |
| Pages | 3141-3148 |
| Number of pages | 8 |
| ISBN (electronic) | 9781450368599 |
| Publication status | Published - 19 Oct 2020 |
| Event | 29th ACM International Conference on Information and Knowledge Management, CIKM 2020 - online, Virtual, Online, Ireland Duration: 19 Oct 2020 → 23 Oct 2020 |
Publication series
| Name | International Conference on Information and Knowledge Management, Proceedings |
|---|
Abstract
The Natural Language Processing (NLP) community has significantly contributed to the solutions for entity and relation recognition from a natural language text, and possibly linking them to proper matches in Knowledge Graphs (KGs). Considering Wikidata as the background KG, there are still limited tools to link knowledge within the text to Wikidata. In this paper, we present Falcon 2.0, the first joint entity and relation linking tool over Wikidata. It receives a short natural language text in the English language and outputs a ranked list of entities and relations annotated with the proper candidates in Wikidata. The candidates are represented by their Internationalized Resource Identifier (IRI) in Wikidata. Falcon 2.0 resorts to the English language model for the recognition task (e.g., N-Gram tiling and N-Gram splitting), and then an optimization approach for the linking task. We have empirically studied the performance of Falcon 2.0 on Wikidata and concluded that it outperforms all the existing baselines. Falcon 2.0 is open source and can be reused by the community; all the required instructions of Falcon 2.0 are well-documented at our GitHub repository (https://github.com/SDM-TIB/falcon2.0). We also demonstrate an online API, which can be run without any technical expertise. Falcon 2.0 and its background knowledge bases are available as resources at https://labs.tib.eu/falcon/falcon2/.
Keywords
- background knowledge, dbpedia, english morphology, entity linking, nlp, relation linking, wikidata
ASJC Scopus subject areas
- Business, Management and Accounting(all)
- General Business,Management and Accounting
- Decision Sciences(all)
- General Decision Sciences
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CIKM 2020: Proceedings of the 29th ACM International Conference on Information and Knowledge Management. Association for Computing Machinery (ACM), 2020. p. 3141-3148 (International Conference on Information and Knowledge Management, Proceedings).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Falcon 2.0
T2 - 29th ACM International Conference on Information and Knowledge Management, CIKM 2020
AU - Sakor, Ahmad
AU - Singh, Kuldeep
AU - Patel, Anery
AU - Vidal, Maria Esther
N1 - Funding Information: This work has received funding from the EU H2020 Project No. 727658 (IASIS).
PY - 2020/10/19
Y1 - 2020/10/19
N2 - The Natural Language Processing (NLP) community has significantly contributed to the solutions for entity and relation recognition from a natural language text, and possibly linking them to proper matches in Knowledge Graphs (KGs). Considering Wikidata as the background KG, there are still limited tools to link knowledge within the text to Wikidata. In this paper, we present Falcon 2.0, the first joint entity and relation linking tool over Wikidata. It receives a short natural language text in the English language and outputs a ranked list of entities and relations annotated with the proper candidates in Wikidata. The candidates are represented by their Internationalized Resource Identifier (IRI) in Wikidata. Falcon 2.0 resorts to the English language model for the recognition task (e.g., N-Gram tiling and N-Gram splitting), and then an optimization approach for the linking task. We have empirically studied the performance of Falcon 2.0 on Wikidata and concluded that it outperforms all the existing baselines. Falcon 2.0 is open source and can be reused by the community; all the required instructions of Falcon 2.0 are well-documented at our GitHub repository (https://github.com/SDM-TIB/falcon2.0). We also demonstrate an online API, which can be run without any technical expertise. Falcon 2.0 and its background knowledge bases are available as resources at https://labs.tib.eu/falcon/falcon2/.
AB - The Natural Language Processing (NLP) community has significantly contributed to the solutions for entity and relation recognition from a natural language text, and possibly linking them to proper matches in Knowledge Graphs (KGs). Considering Wikidata as the background KG, there are still limited tools to link knowledge within the text to Wikidata. In this paper, we present Falcon 2.0, the first joint entity and relation linking tool over Wikidata. It receives a short natural language text in the English language and outputs a ranked list of entities and relations annotated with the proper candidates in Wikidata. The candidates are represented by their Internationalized Resource Identifier (IRI) in Wikidata. Falcon 2.0 resorts to the English language model for the recognition task (e.g., N-Gram tiling and N-Gram splitting), and then an optimization approach for the linking task. We have empirically studied the performance of Falcon 2.0 on Wikidata and concluded that it outperforms all the existing baselines. Falcon 2.0 is open source and can be reused by the community; all the required instructions of Falcon 2.0 are well-documented at our GitHub repository (https://github.com/SDM-TIB/falcon2.0). We also demonstrate an online API, which can be run without any technical expertise. Falcon 2.0 and its background knowledge bases are available as resources at https://labs.tib.eu/falcon/falcon2/.
KW - background knowledge
KW - dbpedia
KW - english morphology
KW - entity linking
KW - nlp
KW - relation linking
KW - wikidata
UR - http://www.scopus.com/inward/record.url?scp=85095866274&partnerID=8YFLogxK
U2 - 10.1145/3340531.3412777
DO - 10.1145/3340531.3412777
M3 - Conference contribution
AN - SCOPUS:85095866274
T3 - International Conference on Information and Knowledge Management, Proceedings
SP - 3141
EP - 3148
BT - CIKM 2020
PB - Association for Computing Machinery (ACM)
Y2 - 19 October 2020 through 23 October 2020
ER -