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Truth Discovery with Multiple Conflicting Information Providers on the Web. KDD 07. Motivation. Example: Authors of books We tried to find out who wrote the book “Rapid Contextual Design”. Many different sets of authors from different online bookstores. Accurate information. Incomplete

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Truth Discovery with Multiple Conflicting InformationProviders on the Web

KDD 07


Motivation l.jpg
Motivation

  • Example: Authors of books

    • We tried to find out who wrote the book “Rapid Contextual Design”.

      • Many different sets of authors from different online bookstores

Accurate

information

Incomplete

information


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Motivation

  • Is the world-wide web always trustable?

  • Unfortunately, the answer is “NO”.

  • There is no guarantee for the correctness of information on the web.


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Motivation

  • Different web sites often provide conflicting information on a subject.

  • 54% of Internet users trust news web sites

  • 26% for web sites that sell products

  • 12% for blogs


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Veracity

  • Veracity i.e., conformity to truth

  • How to find true facts from a large amount of conflicting information on many subjects that is provided by various web sites.

  • This paper invent an algorithm called TRUTHFINDER,

    • A web site is trustworthy if it provides many pieces of true information, and a piece of information is likely to be true if it is provided by many trustworthy web sites.


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Authors

Emotions

Authors

Emotions

Bookstore

Readers

Books

News

Problem Definitions

Facts: properties of the objects


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Problem Definitions

  • Definition 1: (Confidence of facts.)

    • The confidence of a fact f (denoted by s(f)) is the probability of f being correct, according to the best of our knowledge.

  • Definition 2: (Trustworthiness of web sites.)

    • The trustworthiness of a web site w (denoted by t(w)) is the expected confidence of the facts provided by w.


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Problem Definitions

  • Implication between facts

    • Imp( f1 f2 ) :

      • how much f2’s confidence should be increased or decreased according to f1’s confidence.

    • Imp( f1 f2 ) is a value between -1 and 1.

      • A positive value indicates if f1 is correct, f2 is likely to be correct.

      • A negative value means if f1 is correct, f2 is likely to be wrong.

    • Imp( f1 f2 ) = sim(f1, f2) - base_sim,

      • where sim(f1, f2) is the similarity between f1 and f2, and base_sim is a threshold for similarity.

T

F


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Computational Model

  • Heuristic 1: Usually there is only one true fact for a property of an object.

  • Heuristic 2: This true fact appears to be the same or similar on different web sites.

  • Heuristic 3: The false facts on different web sites are less likely to be the same or similar.

  • Heuristic 4: In a certain domain, a web site that provides mostly true facts for many objectswill likely provide true facts for other objects.


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Computational Model

  • If a fact is provided by many trustworthy web sites, it is likely to be true

  • If a fact is conflicting with the facts provided by many trustworthy web sites, it is unlikely to be true.

  • A web site is trustworthy if it provides facts with high confidence

  • Web site trustworthiness and fact confidence can be determined by each other

  • True facts are more consistent than false facts (Heuristic 3)


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Computational Model- Basic Inference

  • Basic Inference

t(w1) = 0.9 and t(w2) = 0.99

t(w2) = 1.1 × t(w1)

(w2) = 2× (w1)


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Computational Model- Basic Inference

  • f1 is provided by w1 and w2, if f1 is wrong then both w1 and w2 are wrong

  • the probability that both of them are wrong is

    • (1 − t(w1)) · (1 − t(w2))

  • the probability that f1 is not wrong is

    • 1 − (1 − t(w1)) · (1 − t(w2))

  • s(f) can be computed as :


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Computational Model- Basic Inference


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Computational Model- Inferences between Facts

  • Adjusted confidence score

New score of the fact


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Computational Model- Handling Additional Subtlety

  • Different web sites are not independent with each other

    • dampening factor 

  • the confidence of a fact f can easily be negative


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Computational Model- Iterative Computation

New score of the fact


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Experiments

  • Baseline

    • Voting :

      • This method chooses the fact that is provided by most web sites.

  •  = 0.5

  •  = 0.3

  • Book Authors Dataset:

    • 1,265 computer science books

    • Using ISBN search on www.abebooks.com

      • 894 bookstores and 34,031 listings

      • 5.4 different authors / book


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Experiments

  • randomly select 100 books

  • manually find out their authors

  • accuracy :

  • Partially correct facts:

    • last name , first name and middle name : 3:2:1

    • for example : “Graeme Simsion” is 5/6 (omit middle name)

  • If f1 has x authors and f2 has y authors, and there are z shared ones, then imp(f1 f2) = z/x − base-sim

    • base-sim = 0.5


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Experiments- Book Authors Dataset


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Experiments- Book Authors Dataset

  • One book may make multiple errors

  • Miss authors:

    • only provide subset of all authors


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Experiments- Query with Google

  • Google is good at finding authoritative web sites.

  • But do these web sites provide accurate information ?

  • Compare the online bookstores

    • highest ranks by Google vs.

    • highest trustworthiness found by TruthFinder

  • Querying Google with “bookstore”

    • Find all bookstores that exist in their dataset from the top 300 Google results.


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Experiments- Query with Google


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Conclusion

  • Veracity problem

  • TRUTHFINDERAlgorithm


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