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...

• Type: Conference
• Year: 2007
• Where: WWW
• Authors: Soumen Chakrabarti
• Keywords: Average Query Time, ER Graphs, Internet Technology, Query-time Pagerank Computation, WWW 2007