Introducing a new dataset with human judgments of entity relatedness, we 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. We 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. Our system shows better agreement with human judgment than existing proposals both on the new dataset and on an established one. We also plug our relatedness algorithm into a state-of-the-art entity linker and observe an increase in its accuracy and robustness.
|Titolo:||Two-stage framework for computing entity relatedness in Wikipedia|
|Anno del prodotto:||2017|
|Appare nelle tipologie:||4.1 Contributo in Atti di convegno|