Difference between revisions of "A Two-Stage Framework for Computing Entity Relatedness in Wikipedia"
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== Overview == | == Overview == | ||
Introducing a new dataset with human judgments of entity [[relatedness]], authors present a thorough study of all entity relatedness [[measures]] in recent literature based on [[Wikipedia]] as the knowledge graph. No clear dominance is seen between measures based on textual similarity and graph proximity. Some of the better measures involve expensive global graph computations. Authors then propose a new, space-efficient, computationally lightweight, two-stage framework for relatedness computation. In the first stage, a small weighted subgraph is dynamically grown around the two query entities; in the second stage, relatedness is derived based on computations on this subgraph. Authors system shows better agreement with human judgment than existing proposals both on the new dataset and on an established one. Authors also plug relatedness algorithm into a state-of-the-art entity linker and observe an increase in its accuracy and robustness. | Introducing a new dataset with human judgments of entity [[relatedness]], authors present a thorough study of all entity relatedness [[measures]] in recent literature based on [[Wikipedia]] as the knowledge graph. No clear dominance is seen between measures based on textual similarity and graph proximity. Some of the better measures involve expensive global graph computations. Authors then propose a new, space-efficient, computationally lightweight, two-stage framework for relatedness computation. In the first stage, a small weighted subgraph is dynamically grown around the two query entities; in the second stage, relatedness is derived based on computations on this subgraph. Authors system shows better agreement with human judgment than existing proposals both on the new dataset and on an established one. Authors also plug relatedness algorithm into a state-of-the-art entity linker and observe an increase in its accuracy and robustness. | ||
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| + | == Embed == | ||
| + | === Wikipedia Quality === | ||
| + | <code> | ||
| + | <nowiki> | ||
| + | Ponza, Marco; Ferragina, Paolo; Chakrabarti, Soumen. (2017). "[[A Two-Stage Framework for Computing Entity Relatedness in Wikipedia]]".DOI: 10.1145/3132847.3132890. | ||
| + | </nowiki> | ||
| + | </code> | ||
| + | |||
| + | === English Wikipedia === | ||
| + | <code> | ||
| + | <nowiki> | ||
| + | {{cite journal |last1=Ponza |first1=Marco |last2=Ferragina |first2=Paolo |last3=Chakrabarti |first3=Soumen |title=A Two-Stage Framework for Computing Entity Relatedness in Wikipedia |date=2017 |doi=10.1145/3132847.3132890 |url=https://wikipediaquality.com/wiki/A_Two-Stage_Framework_for_Computing_Entity_Relatedness_in_Wikipedia}} | ||
| + | </nowiki> | ||
| + | </code> | ||
| + | |||
| + | === HTML === | ||
| + | <code> | ||
| + | <nowiki> | ||
| + | Ponza, Marco; Ferragina, Paolo; Chakrabarti, Soumen. (2017). &quot;<a href="https://wikipediaquality.com/wiki/A_Two-Stage_Framework_for_Computing_Entity_Relatedness_in_Wikipedia">A Two-Stage Framework for Computing Entity Relatedness in Wikipedia</a>&quot;.DOI: 10.1145/3132847.3132890. | ||
| + | </nowiki> | ||
| + | </code> | ||
Revision as of 13:03, 3 February 2020
Authors | Marco Ponza Paolo Ferragina Soumen Chakrabarti |
|---|---|
Publication date | 2017 |
DOI | 10.1145/3132847.3132890 |
Links | Original |
A Two-Stage Framework for Computing Entity Relatedness in Wikipedia - scientific work related to Wikipedia quality published in 2017, written by Marco Ponza, Paolo Ferragina and Soumen Chakrabarti.
Overview
Introducing a new dataset with human judgments of entity relatedness, authors present a thorough study of all entity relatedness measures in recent literature based on Wikipedia as the knowledge graph. No clear dominance is seen between measures based on textual similarity and graph proximity. Some of the better measures involve expensive global graph computations. Authors then propose a new, space-efficient, computationally lightweight, two-stage framework for relatedness computation. In the first stage, a small weighted subgraph is dynamically grown around the two query entities; in the second stage, relatedness is derived based on computations on this subgraph. Authors system shows better agreement with human judgment than existing proposals both on the new dataset and on an established one. Authors also plug relatedness algorithm into a state-of-the-art entity linker and observe an increase in its accuracy and robustness.
Embed
Wikipedia Quality
Ponza, Marco; Ferragina, Paolo; Chakrabarti, Soumen. (2017). "[[A Two-Stage Framework for Computing Entity Relatedness in Wikipedia]]".DOI: 10.1145/3132847.3132890.
English Wikipedia
{{cite journal |last1=Ponza |first1=Marco |last2=Ferragina |first2=Paolo |last3=Chakrabarti |first3=Soumen |title=A Two-Stage Framework for Computing Entity Relatedness in Wikipedia |date=2017 |doi=10.1145/3132847.3132890 |url=https://wikipediaquality.com/wiki/A_Two-Stage_Framework_for_Computing_Entity_Relatedness_in_Wikipedia}}
HTML
Ponza, Marco; Ferragina, Paolo; Chakrabarti, Soumen. (2017). "<a href="https://wikipediaquality.com/wiki/A_Two-Stage_Framework_for_Computing_Entity_Relatedness_in_Wikipedia">A Two-Stage Framework for Computing Entity Relatedness in Wikipedia</a>".DOI: 10.1145/3132847.3132890.
