Tables to Linked Data

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Tables to Linked Data

• Zareen Syed, Tim Finin, VarishMulwad and Anupam
  Joshi
• University of Maryland, Baltimore County

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http//ebiquity.umbc.edu/paper/html/id/474/
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Age of Big Data

• Availability of massive amounts of data is
  driving many technical advances on the Web and
  off
• Extracting linked data from text and tables will
  help
• Databases spreadsheets are obvious table
  sources, but many are in documents and Web pages,
  too
• A recent Google study found over 14B HTML tables
• M. Cafarella, A. Halevy, D. Wang, E. Wu, Y.
  Zhang, Webtables exploring the power of tables
  on the Web, VLDB, 2008.
• Only one in a 1000 had high-quality relational
  data, but these could be reliably identified by a
  ML trained classifier, resulting in 150M tables

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Problem given a table of data
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Goal Generate linked data

• _at_prefix dbp lthttp//dbpedia.org/resource/gt .
• _at_prefix dbpo lthttp//dbpedia.org/ontology/gt .
• _at_prefix xsd lthttp//www.w3.org/2001/XMLSchemagt
  .
• _at_prefix cyc lthttp//www.cyc.com/2004/06/04/cycgt
•  \
• dbpBoston dbpoPopulatedPlace/leaderName
  dbpThomas_Menino
• cycpartOf dbpMassachusetts
• dbpopopulationTotal "610000"xsdinteger .
• dbpNew_York_City
• ...

• Use classes, properties and instances from a
  linked data collection, e.g. DBpedia Cyc
  Geonames ...
• Confirm existing facts and discover new ones
• Create new entities as needed
• Create new relations when possible (harder)

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What data do we want
find relationships between columns
dbpolargestCity
dbpoMassachusettes
link cell values to entities
link cell values to entities
dbpoBoston
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What evidence can we find?

• Column ones type is populated place, or is it US
  city, or a reference to a NBA team?

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What do we want to extract?

• Column ones type is populated place, or is it US
  city, or a reference to a NBA team?
• Column twos type is person (or politician?) but
  is mayor a type or a relation and if the later,
  to what?

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What do we want to extract?

• Column ones type is populated place, or is it US
  city, or a reference to a NBA team?
• Column twos type is person (or politician?) but
  is mayor a type or a relation and if the later,
  to what?
• Rows give important evidence too Menino has a
  stronger connection to Boston than Massachusetts

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What do we want to extract?

• Column ones type is populated place, or is it US
  city, or a reference to a NBA team?
• Column twos type is person (or politician?) but
  is mayor a type or a relation and if the later,
  to what?
• Rows give important evidence too Menino has a
  stronger connection to Boston than Massachusetts
• Both cities and states have populations,

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A Web of Evidence

• Table Column headers, cell values, column
  position, column adjacency
• Language headers have meaning, synonyms,
• Ontologies capitalOf is a 11 relation between a
  GPE region and a city
• Significance pageRank-like metrics bias linking
• Facts the LD KB asserts Boston is in MA and that
  Bostons population is close to 610K
• Graph analysis PMI between Boston Menino is
  much higher than for Massachusetts

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Approach
Predict Class for Columns
Query Knowledge base
Input Table Headers and Rows
Re query Knowledge base using the new evidence
Link cell value to an entity using the new
results obtained
Identify Relationships between columns
Output Linked Data
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Wikitology

• A hybrid KB of structured unstructured
  information extracted from Wikipedia
• Augmented with knowledge from DBpedia, Freebase,
  Yago and Wordnet
• The interface via a specialized IR index
• Good for systems that need to do a combination of
  reasoning over text, graphs and RDF data

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Querying the KnowledgeBase
Wikitology
For every cell from the table Cell Value
Column Header Row Content
Baltimore City MD S.Dixon 640,000
Top N entities, Their Types, Page Rank (We use N
5)
1.Baltimore_Maryland2.Baltimore_County 3.John_Balt
imore
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Predicting Classes for Columns

• Set of Classes per column
• Score the classes
• Choose the top class from each of the four
  vocabularies Dbpedia, Freebase, Wordnet and Yago

dbpedia-owlPlace, dbpedia-owlArea,
yagoAmericanConductors, yagoLivingPeople,
dbpedia-owlPopulatedPlace, dbpedia-owlBand,
dbpedia-owlOrganisation, . . . . . .
Score w x ( 1 / R ) (1 w) Page Rank R
Entitys Rank E.g. Baltimore,dbpediaArea
0.89 Select the class that maximizes its sum of
score over the entire column Baltimore,
dbpediaArea Boston, dbpediaArea New
York, dbpediaArea 2.85
ColumnCity DbpediaPopulatedPlace WordnetCity F
reebaseLocation YagoCitiesinUnitedStates
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Linking table cell to entities

• Once the classes are predicted, we re-query the
  knowledgebase with this new evidence
• Along with the original query, we also include
  the predicted types
• We pick the highest ranking entity which matches
  the predicted type from the new results

For every cell from the table Cell Value
Column Header Row Content Predicted Column
Type
Top N entities, Their Types (We use N 5)
Wikitology
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Preliminary results entity linking

• In a preliminary evaluation, we used 5 Google
  Squared tables comprising 23 columns and 39 rows,
  comparing our results with human judgments
• The next will be on selected tables from the
  Google col-lection of gt2500 involving 6 domains
  bibliography, car, course, country, movie, people

Classes used Accuracy
Class Prediction for Columns Dbpedia 85.7
Class Prediction for Columns Freebase 90.5
Class Prediction for Columns Wordnet 71.4
Class Prediction of Columns Yago 71.4
Entity Linking 76.6
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Ongoing and Future work

• Identifying relationships between columns
• Modules for common special cases, e.g. numbers,
  acronyms, phone numbers, stock symbols, email
  addresses, URLs, etc.
• Replace heuristics by machine learning techniques
  for combining evidence and clustering
• Strategy for dealing with errors

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Conclusion

• Theres lots of data stored in tables in
  spread-sheets, databases, Web pages and documents
• In some cases we can interpret them and generate
  a linked data representation
• In others we can at least link some cell values
  to LOD entities
• This can help contribute data to the Web in a
  form that is easy for machines to understand and
  use

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