Graphical Models and Probabilistic Reasoning for Generating Linked Data from Tables

Graphical Models and Probabilistic Reasoning for Generating Linked Data from Tables

Varish Mulwad (@varish)University of Maryland, Baltimore County

Doctoral Consortium at ISWC 2011October 24, 2011

Guru: Dr. Tim Finin

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What ?

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Contribution

Name Team Position Height

Michael Jordan Chicago Shooting guard 1.98

Allen Iverson Philadelphia Point guard 1.83

Yao Ming Houston Center 2.29

Tim Duncan San Antonio Power forward 2.11

http://dbpedia.org/class/yago/NationalBasketballAssociationTeams

http://dbpedia.org/resource/Allen_Iverson Map literals as values of properties

dbprop:team

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Contribution

Name Team Position Height

Michael Jordan Chicago Shooting guard 1.98

Allen Iverson Philadelphia Point guard 1.83

Yao Ming Houston Center 2.29

Tim Duncan San Antonio Power forward 2.11

@prefix dbpedia: <http://dbpedia.org/resource/> .@prefix dbpedia-owl: <http://dbpedia.org/ontology/> .@prefix yago: <http://dbpedia.org/class/yago/> .

"Name"@en is rdfs:label of dbpedia-owl:BasketballPlayer ."Team"@en is rdfs:label of yago:NationalBasketballAssociationTeams .

"Michael Jordan"@en is rdfs:label of dbpedia:Michael Jordan .dbpedia:Michael Jordan a dbpedia-owl:BasketballPlayer .

"Chicago Bulls"@en is rdfs:label of dbpedia:Chicago Bulls .dbpedia:Chicago Bulls a yago:NationalBasketballAssociationTeams .

All this in a completely automated way !!

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Why ?

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Tables are everywhere !! … yet …

389, 697 raw and geospatial datasets0.071 % in RDF

The web – 154 million high quality relational tables [1]

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Current Systems

Problems with systems on the Semantic Web

– Require users to have knowledge of the Semantic Web

– Do not automatically link to existing classes and entities on the Semantic Web / Linked Data cloud

– RDF data in some cases is as useless as raw data– Majority of the work focused on relational data

where schema is available

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How ?

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A Table Interpretation Framework

Probabilistic Graphical Model / Joint Inference

Linked Data

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Joint Inference over evidence in a table

Probabilistic Graphical Models

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A graphical model for tables

C1 C2 C3

R11

R12

R13

R21

R22

R23

R31

R32

R33

Team

Chicago

Philadelphia

Houston

San Antonio

Class

Instance

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Parameterized graphical model

C1 C2C3

𝝍𝟓

R11 R12 R13 R21 R22 R23 R31 R32 R33

𝝍𝟑 𝝍𝟑 𝝍𝟑

𝝍𝟒 𝝍𝟒 𝝍𝟒

Function that captures the affinity between the column headers and row values

Row value

Variable Node: Column header

Captures interaction between column headers

Captures interaction between row values

Factor Node

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Nice, but … Will it work ?

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Evaluation

• Dataset of > 6000 tables [2]

• Compare our accuracy against our baseline system and the results in [2]

• Use Mean Average Precision [3] to compare a ‘ranked list of possible classes/entities’ against a ranked list obtained from human evaluators

• Experiment with datasets from www.data.gov

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References

1. Cafarella, M. J., Halevy, A., Wang, D. Z., Wu, E., Zhang, Y., 2008. Webtables: exploring the power of tables on the web. Proc. VLDB Endow.1 (1), 538-549.

2. Limaye, G., Sarawagi, S., Chakrabarti, S.: Annotating and searching web tables using entities, types and relationships. In: Proc. 36th Int. Conf. on Very Large Databases (2010)

3. Salton, G., Mcgill, M.J.: Introduction to Modern Information Retrieval. McGraw-Hill, Inc., New York (1986)

Thank You ! Questions ?

[email protected]

@varish Web:http://ebiq.org/h/Varish/Mulwad

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