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Graphical Models and Probabilistic Reasoning for Generating Linked Data from Tables. Varish Mulwad ( @ varish ) University of Maryland, Baltimore County Doctoral Consortium at ISWC 2011 October 24, 2011. Guru: Dr. Tim Finin. What ?. Contribution.

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Graphical Models and Probabilistic Reasoning for Generating Linked Data from Tables


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graphical models and probabilistic reasoning for generating linked data from tables

Graphical Models and Probabilistic Reasoning for Generating Linked Data from Tables

VarishMulwad (@varish)University of Maryland, Baltimore CountyDoctoral Consortium at ISWC 2011October 24, 2011

Guru: Dr. Tim Finin

contribution
Contribution

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

dbprop:team

http://dbpedia.org/resource/Allen_Iverson

Map literals as values of properties

contribution1
Contribution

@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 !! 

tables are everywhere yet
Tables are everywhere !! … yet …

The web – 154 millionhigh quality relational tables [1]

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

current systems
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
a table interpretation framework
A Table Interpretation Framework

Linked Data

Probabilistic Graphical Model / Joint Inference

joint inference over evidence in a table
Joint Inference over evidence in a table
  • Probabilistic Graphical Models
a graphical model for tables
A graphical model for tables

Class

C2

C3

C1

R21

R31

R11

R12

R22

R32

R13

R23

R33

Instance

parameterized graphical model
Parameterized graphical model

Captures interaction between row values

R33

R11

R12

R13

R21

R22

R23

R31

R32

Row value

Factor Node

C2

C1

C3

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

Variable Node: Column header

Captures interaction between column headers

evaluation
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
references
References
  • 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.
  • 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)
  • Salton, G., Mcgill, M.J.: Introduction to Modern Information Retrieval. McGraw-Hill, Inc., New York (1986)
slide16

Thank You !

Questions ?

varish1@cs.umbc.edu@varish

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