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A Probabilistic Model for Linking Named Entities in Web Text with Heterogeneous Information Networks

Summary: SHINE is the first probabilistic model to link web-text named entities to heterogeneous information networks. Entity popularity and an entity object model built from meta-path constrained random walks; meta-path weights learned by EM without training data. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
4807
Venue
SIGMOD
Year
2014
Pagerank
4.4215831e-05
Overall Rank
8,948 | 37.82%
DOI
10.1145/2588555.2593676

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 4 of 4 citing papers.

Rank Citing Paper Year Venue Pagerank
8,008 Online Topic-Aware Entity Resolution Over Incomplete Data Streams 2021 SIGMOD 4.6037276e-05
9,136 TextCube: Automated Construction and Multidimensional Exploration 2019 VLDB 4.3843441e-05
9,258 Joint Open Knowledge Base Canonicalization and Linking 2021 SIGMOD 4.3648789e-05
11,730 ZigZag: Supporting Similarity Queries on Vector Space Models 2018 SIGMOD 4.1905499e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 4 of 4 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
765 PathSim: Meta Path-Based Top-K Similarity Search in Heterogeneous Information Networks 2011 VLDB 0.0001695147
2,405 Linking Temporal Records 2011 VLDB 8.8729897e-05
2,853 Building, Maintaining, and Using Knowledge Bases: A Report from the Trenches 2013 SIGMOD 8.0147968e-05
3,546 DBLP — Some Lessons Learned 2009 VLDB 6.9826428e-05
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