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2007
ACM

Dynamic personalized pagerank in entity-relation graphs

9 years 10 months ago
Dynamic personalized pagerank in entity-relation graphs
Extractors and taggers turn unstructured text into entityrelation (ER) graphs where nodes are entities (email, paper, person, conference, company) and edges are relations (wrote, cited, works-for). Typed proximity search of the form type=person NEAR company"IBM", paper"XML" is an increasingly useful search paradigm in ER graphs. Proximity search implementations either perform a Pagerank-like computation at query time, which is slow, or precompute, store and combine per-word Pageranks, which can be very expensive in terms of preprocessing time and space. We present HubRank, a new system for fast, dynamic, spaceefficient proximity searches in ER graphs. During preprocessing, HubRank computes and indexes certain "sketchy" random walk fingerprints for a small fraction of nodes, carefully chosen using query log statistics. At query time, a small "active" subgraph is identified, bordered by nodes with indexed fingerprints. These fingerprints are adapt...
Soumen Chakrabarti
Added 21 Nov 2009
Updated 21 Nov 2009
Type Conference
Year 2007
Where WWW
Authors Soumen Chakrabarti
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