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Human-Assisted Graph Search: It’s Okay to Ask Questions Reported by Qi Liu

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Scenes Human Computation Crowding-sourcing service(Amazon’s Mechanical Turk)

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An example Image classification

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Terminology of the Problem HumanGS: abbreviation for human-assisted graph search Taxonomy: DAG(directed acyclic graph) Category: Node Question: Reachability Tricks: Not leaves, Not root, Just middle! Challenge: High Latency

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More Applications Manual Curation(insert a web into web-graph) Question: Is the item a kind of x?

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Apps(cont.) Debugging of Workflows Question: Is the output fragment at point x wrong?

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Apps(cont.) Filter Synthesis Question: Do you want all data items satisfying condition x to be part of the result?

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Apps(cont.) Interactive Search Question: Do you want more results like concept x?

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Dimensions of the problem Single/Multi (target set) Bounded/Unlimited (question set) DAG/Downward-Forest/Upward-Forest

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Define the Problem

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DAG property

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Candidate Set

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An Example Q(nissan,maxima)=yes => Cand(nissan,maxima)= {nissan,maxima,sentra} Q(mercedes,maxima)=no => Cand(mercedes,maxima)= V/{mercedes} Q(car,maxima)=yes => Cand(car,maxima)= V/{vehicle}

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Extending to|N|> 1

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Goal: Picking N set

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Single Target Node

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Single-Bounded

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Single-Bounded: DAG Conclusion: A NP-hard max-cover problem

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Single-Bounded: Downward-Forest

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Equivalence to the Partition Problem

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Show the equivalence

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An example

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Candidate Set : Partition

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Induction and Conclusion Minimum wcase(N) => the size of the largest partition that can be induced by N. Solved in PTIME!

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Single-Bounded: Upward-Forest

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Single-Unlimited For DAG, the question numbers vary from O(log n) to O(n)

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Single-Unlimited: Downward-Forest

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Single-Unlimited: Upward-Forest

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Multiple Target Nodes Multi-Bounded: DAG Lower-bound: NP-hard in n and k Upper-bound: Multi-Bounded: Downward/Upward-Forest – DP algorithm: O(k^2*n*6)

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Experiments

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The End

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