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Learning to Query: Focused Web Page Harvesting for Entity Aspects Yuan Fang 1 Vincent Zheng 2 Kevin Chang 23 Problem: Learning to Query (L2Q) 1 Institute for Infocomm Research, Singapore 2 Advanced Digital Sciences Center, Singapore 3 University of Illinois at Urbana-Champaign, USA Subproblem #1: Domain-aware L2Q Subproblem #2: Context-aware L2Q Result A: Effect of Domain+Context Entity type Common aspects celebrity spouse, age, net worth car safety, cost, interior business address, hours, phone abundant but scattered Business Analytics Vertical portal or search Overall Workflow: Iterative Querying Keywords (uniquely) identifying the entity Seed query: Target aspect: A pre-trained classifier for the target aspect Utility: (precision/recall) In each iteration, ݍ∗ ൌ arg max ሺ ݍሻ a) Domain Pages b) Template Abstraction <topic> <journal> Target Entity Marc Snir Peer Entities Philips Yu Andrew Ng ∗ ሺሻ ா + entity pages ா domain pages Domain Phase Entity Phase Utility regularization (i.e. supervision on target aspect) c) Bridging Domain & Entity Collective precision ஐ ⋃ஐ ⋂ஐ ஐ ⋃ஐሺሻ Collective recall ஐ ⋃ஐ ⋂ஐ ஐ pages for target aspect pages of candidate query RND: select query randomly P/R: optimize precision/recall without domain and context P/R+q: with domain pages but no templates, and without context P/R+t: with domain pages and templates, without context L2QP/L2QR: full approaches optimizing precision/recall Result B: Compare with Indep. Baselines L2QBAL: optimize for F-score, balancing L2QP & L2QR LM: language feedback model AQ: adaptive querying for text databases HR: harvest rate for hidden structured databases MQ: manually designed queries

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Page 1: Learning to Query: Focused Web Page Harvesting for Entity ... · Learning to Query: Focused Web Page Harvesting for Entity Aspects Yuan Fang1 Vincent Zheng2 Kevin Chang23 Problem:

Learning to Query:Focused Web Page Harvesting for Entity Aspects

Yuan Fang1 Vincent Zheng2 Kevin Chang23

Problem: Learning to Query (L2Q)

1 Institute for Infocomm Research, Singapore2 Advanced Digital Sciences Center, Singapore3 University of Illinois at Urbana-Champaign, USA

Subproblem #1: Domain-aware L2Q

Subproblem #2: Context-aware L2Q

Result A: Effect of Domain+Context

Entity type Common aspects

celebrity spouse, age, net worth

car safety, cost, interior

business address, hours, phone

abundant but scattered

Business Analytics

Vertical portal or search

Overall Workflow: Iterative Querying

Keywords (uniquely) identifying the entitySeed query:

Target aspect:A pre-trained classifierfor the target aspect

Utility: (precision/recall)

In each iteration, ∗ argmaxa) Domain Pages b) Template Abstraction

<topic> <journal>

Target Entity

Marc Snir

Peer Entities

Philips YuAndrew Ng

∗ +entity pages domain pages

Domain Phase Entity Phase

Utility regularization (i.e. supervision on target aspect)

c) Bridging Domain & Entity

Collective precision⋃ ⋂⋃Collective recall⋃ ⋂

pages for target aspect

pages of candidate query

RND: select query randomly

P/R: optimize precision/recall without domain and context

P/R+q: with domain pages but no templates, and without context

P/R+t: with domain pages and templates, without context

L2QP/L2QR: full approaches optimizing precision/recall

Result B: Compare with Indep. Baselines

L2QBAL: optimize for F-score, balancing L2QP & L2QR

LM: language feedback model

AQ: adaptive querying for text databases

HR: harvest rate for hidden structured databases

MQ: manually designed queries

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