1. Tables to Linked Data Zareen Syed, Tim Finin, Varish Mulwad and Anupam Joshi University of Maryland, Baltimore County http://ebiquity.umbc.edu/resource/html/id/???/ 0

2. Age of Big Data Availability of massive amounts of data is driving many technical advances Extracting linked data from text and tables will help Databases & spreadsheets are obvious sources for tables 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 about 0.1% had high-quality relational data But that’s about 150M tables! 1

3. Problem: given a table 2

4. Generate linked data @prefix dbp:. @prefix dbpo:. @prefix xsd:. @prefix cyc: \ dbp:Boston dbpo:PopulatedPlace/leaderName dbp:Thomas_Menino; cyc:partOf dbp:Massachusetts; dbpo:populationTotal "610000"^^xsd:integer. dbp:New_York_City …... @prefix dbp:. @prefix dbpo:. @prefix xsd:. @prefix cyc: \ dbp:Boston dbpo:PopulatedPlace/leaderName dbp:Thomas_Menino; cyc:partOf dbp:Massachusetts; dbpo:populationTotal "610000"^^xsd:integer. dbp:New_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) 3

5. What data do we want dbpo:Baltimore link cell values to entities find relationships between columns dbpo:Maryland dbpo:largestCity 4

6. What evidence can we find? Column one’s type is populated place, or is it US city, or a reference to a NBA team? 5

7. What do we want to extract? Column one’s type is populated place, or is it US city, or a reference to a NBA team? Column two’s type is person (or politician?) but is ‘mayor’ a type or a relation and if the later, to what? 5

8. What do we want to extract? Column one’s type is populated place, or is it US city, or a reference to a NBA team? Column two’s 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 5

9. What do we want to extract? Column one’s type is populated place, or is it US city, or a reference to a NBA team? Column two’s 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, … 5

10. A Web of Evidence Table: Column headers, cell values, column position, column adjacency Language: headers have meaning, synonyms, … Ontologies: capitalOf is a 1:1 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 Boston’s population is close to 610KBoston is in MA Graph analysis: PMI between Boston & Menino is much higher than for Massachusetts 6

11. Approach Query Knowledge base Predict Class for Columns Re query Knowledge base using the new evidence Link cell value to an entity using the new results obtained Input: Table Headers and Rows Identify Relationships between columns Output: Linked Data 7

12. 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 semi- structured data 8

13. Querying the Knowledge–Base For every cell from the table – Cell Value + Column Header + Row Content Top N entities, Their Types, Page Rank (We use N = 5) Wikitology Baltimore + City + MD + S.Dixon + 640,000 1.Baltimore_Maryland 2.Baltimore_County 3.John_Baltimore 9

14. 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-owl:Place, dbpedia-owl:Area, yago:AmericanConductors, yago:LivingPeople, dbpedia-owl:PopulatedPlace, dbpedia-owl:Band, dbpedia-owl:Organisation,... dbpedia-owl:Place, dbpedia-owl:Area, yago:AmericanConductors, yago:LivingPeople, dbpedia-owl:PopulatedPlace, dbpedia-owl:Band, dbpedia-owl:Organisation,... Score = w x ( 1 / R ) + (1 – w) Page Rank R: Entity’s Rank; E.g. [Baltimore,dbpedia:Area] = 0.89 Select the class that maximizes its sum of score over the entire column [Baltimore, dbpedia:Area] + [Boston, dbpedia:Area] + [New York, dbpedia:Area] = 2.85 Score = w x ( 1 / R ) + (1 – w) Page Rank R: Entity’s Rank; E.g. [Baltimore,dbpedia:Area] = 0.89 Select the class that maximizes its sum of score over the entire column [Baltimore, dbpedia:Area] + [Boston, dbpedia:Area] + [New York, dbpedia:Area] = 2.85 Column:City Dbpedia:PopulatedPlace Wordnet:City Freebase:Location Yago:CitiesinUnitedStates Column:City Dbpedia:PopulatedPlace Wordnet:City Freebase:Location Yago:CitiesinUnitedStates 10

15. Linking table cell to entities Once the classes are predicted, we re-query the knowledge– base 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) KB

16. 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 >2500 involving 6 domains: bibliography, car, course, country, movie, people Ckasses 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 Linking76.6% 11

17. 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 12

18. Conclusion There’s 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 13