 Natural Language Processing (NLP) tools and frameworks have significantly contributed with solutions to the problems of extracting entities and relations and linking them to the related knowledge graphs. Albeit effective, the majority of existing tools are available for only one knowledge graph . In this paper, we present Falcon 2.0,  a rule-based tool capable of accurately mapping entities and relations in short texts to resources in both DBpedia and Wikidatafollowing the same approach in both cases. The input of Falcon 2.0 is a short natural language text in the English language . Falcon 2.0 resorts to fundamental principles of the English morphology (e.g., N-Gram tiling and N-Gram splitting) and background knowledge of labels alignments obtained from studied knowledge graph to return as an output; the resulting entity and relation resources are either in the DBpedia or Wikidata knowledge graphs . We have empirically studied the impact using only Wikidata on Falcon 2.0 , and observed it is knowledge graph agnostic, i.e., Falcon 2.0 performance and behavior are not affected by the knowledge graph used as background knowledge . Falcon 2.0 is public and can be reused by the community . Additionally, Falcon 2.0 and its background knowledge bases are available as resources at URL
The Natural Language Processing (NLP) community has significantly contributed to the solutions for entity and relation recognition from the text, and possibly linking them to proper matches in Knowledge Graphs (KGs). Considering Wikidata as the background KG, still, there are limited tools to link knowledge within the text to Wikidata . In this paper, we present Falcon 2.0, 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 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 public and can be reused by the community ; all the required instructions of Falcon 2.0 are well-documented at our GitHub repository. 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 URL
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The
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tools and frameworks have significantly contributed with solutions to the problems of extracting entities and relations and
community has significantly contributed to the solutions for entity and relation recognition from the text, and possibly
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the related knowledge graphs. Albeit effective, the majority of existing tools are available for only one knowledge graph
proper matches in Knowledge Graphs (KGs). Considering Wikidata as the background KG, still, there are limited tools to link knowledge within the text to Wikidata
A
first joint entity, and relation linking tool over Wikidata. It receives
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rule-based tool capable of accurately mapping entities and relations in short texts to resources in both DBpedia and Wikidatafollowing the same approach in both cases. The input of Falcon 2.0 is a
R
.
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.
R
fundamental principles of the English morphology
the English language model for the recognition task
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and background knowledge of labels alignments obtained from studied knowledge graph to return as an output; the resulting entity and relation resources are either in the DBpedia or Wikidata knowledge graphs
, and then an optimization approach for linking task
R
impact using only Wikidata on
performance of
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, and observed it is knowledge graph agnostic, i.e., Falcon 2.0 performance and behavior are not affected by the knowledge graph used as background knowledge
on Wikidata and concluded that it outperforms all the existing baselines
R
. Additionally,
; all the required instructions of Falcon 2.0 are well-documented at our GitHub repository. We also demonstrate an online API, which can be run without any technical expertise.