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DIADEM A Short Overview  Georg Gottlob
 
Web data extraction WEB HTML pages layout   Corporate edp apps structured data, Databases, XML WRAPPER Goal:  Make web contents accessible to electronic data processing Wrappers:  HTML  select    extract    annotate   XML
 
MSO Monadic Datalog Elog Lixto Visual Wrapper   =  Suite Logic  Database theory DB programming Application design
Lixto Visual Developer (VD) Navigation Steps Mozilla Web Browser Extraction Configuration
 
. .
Need for Automatic Extraction Technology Example:  Real Estate UK 17,000 sites Many not covered by aggregators We do have a list of all homepages (Yellow Pgs. UK) Manual or semi-automatic wrapping too expensive - wrapper construction - testing - keeping track of changes  No tool or method can do it fully automatically. Other domains:   Hospitals,restaurants, schools, travel agents,  airlines, hospitals, pharmaceutical companies and retail companies   such as supermarket chains…..
Need for Automatic Extraction Technology (2)   All search engine providers need it!  Many work on it. Keywords:      Vertical search,     object search,    semantic search. Raghu Ramakrishnan , Yahoo!, March 2009:  “ no one really has done this successfully at scale yet ” Alon Halevy , Google, Feb. 2009: “ Current technologies are not good enough yet to provide what search engines really need. […] any successful approach would probably need a combination of knowledge and learning.”
The Blackbox we want to construct BLACKBOX Application domain with  thousands of websites URL Application relevant  Structured data (XML or RDF)  To achieve this, we plan to combine a host of  annotators with a new knowledge-based approach.
Real estate Restauran t s Relationship to SeCo & Webdam Q:  Find apartments in Milan whose prices are    average in  quarters  were restaurant  quality > average. Results Web service A Web service
How to achieve it? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
High level reasoning: -  Goal oriented - Conceptual domain objects. - Conceptual interaction elements - High-level object ontology - Domain knowledge
 
Bottom-up (low-level) annotation Monochromatic Rectangle  Georaphic search  facility   Postcode input field Active map  … . ISA ISA Occurs in Price search  facility   … . … . Occurs in … . 105 105 127 [(02873,227) (03900,417)] Geo-Price-Searchbox ISA [(02873,227) (03900,417)]
Top-down reasoning   Property Search Facility   Property List   Single Property Description   Specially highlighted property   part-of m 1
Bottom-up processing  Top-down reasoning   Monochromatic Rectangle  Georaphic search  facility   Postcode input field Active map  … . ISA ISA Occurs in Price search  facility   … . … . Occurs in … . 105 105 127 [(02873,227) (03900,417)] Property Search Facility   Property List   Single Property Description   Geo-Price-Searchbox ISA [(02873,227) (03900,417)] Specially highlighted property   Phenomenology part-of m 1
table(T) &  occurs_in(T,areaselection) &  occurs_in(T,priceselection)    goodtable(T). goodtable(T) & child(Parent,T)    containsgoodtable(Parent).  goodtable(T) &     containsgoodtable(T)     propertysearchmask(T). If a table contains an area selection input field and a price selection field, both of which are not simultaneously contained in a smaller table, then this table is the property search mask Datalog for  Web-Object Reasoning
Crucial steps ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
 
Crucial steps ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
The Data Model ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Object creation in Datalog + table(T1) &  table(T2) &  sameColor(T1,T2) & isNeighbourRight(T1,T2)        X (tablebox(X) &  contains(X,T1) &  contains(X,T2)). PRODUCT Toshiba Protégé cx Dell 25416  Dell 23233 Acer 78987 PRICE 480 360  470 390
Object creation in Datalog + table(T1) &  table(T2) &  sameColor(T1,T2) & isNeighbourRight(T1,T2)        X (tablebox(X) &  contains(X,T1) &  contains(X,T2)). PRODUCT Toshiba Protégé cx Dell 25416  Dell 23233 Acer 78987 PRICE 480 360  470 390 T1  T2
Object creation in Datalog + table(T1) &  table(T2) &  sameColor(T1,T2) & isNeighbourRight(T1,T2)        X (tablebox(X) &  contains(X,T1) &  contains(X,T2)). PRODUCT Toshiba Protégé cx Dell 25416  Dell 23233 Acer 78987 PRICE 480 360  470 390 PRICE 480 360  470 390 T1  T2
Object creation in Datalog + table(T1) &  table(T2) &  sameColor(T1,T2) & isNeighbourRight(T1,T2)        X (tablebox(X) &  contains(X,T1) &  contains(X,T2)). Deduction in Datalog +  undecidable  (TGDs)
Object creation in Datalog + table(T1) &  table(T2) &  sameColor(T1,T2) & isNeighbourRight(T1,T2)        X (tablebox(X) &  contains(X,T1) &  contains(X,T2)). Deduction in Datalog +  undecidable  (TGDs) Datalog    : require  guardedness  of rule bodies.  Decidable, linear-time data complexity.
Datalog  Family  of languages. Incorporates  ontological reasoning  (>DL-LITE) Further research needed for extending it  so to be an ideal language for web objects. Transitivity:  containedin(T1,T2), containedin(T2,T3)    containedin (T1,T3)
Datalog  Family  of languages. Incorporates  ontological reasoning  (>DL-LITE) Further research needed for extending it  so to be an ideal language for web objects. Transitivity:  containedin(T1,T2), containedin(T2,T3)    containedin (T1,T3)  unguarded!
DL-LITE DL-LITE  Datalog[    ,  ;Lin]   Professor        TeachesTo  Professsor(x)       y TeachesTo(x,y)     TeachesTo -      Student  TeachesTo(x,y)     Student(y)  HasTutor -      TeachesTo  HasTutor(x,y) ->TeachesTo(y,x)  funct(HasTutor)  HasTutor(x,y) & HasTutor(x,y’)  (always innocuous!)   & Neq(y,y’)       Professor       Student  Professor(x) & Student(x)        DL-Lite core DL-Lite R DL-Lite F
Crucial steps ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
We will use various existing tools and techniques (rather than re-invent the wheel)  Low & Intermediate Level Annotation ,[object Object],[object Object],[object Object],[object Object],[object Object]
 
 
 
Extraction from PDF Tamir Hassan
Crucial steps ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Navigation & Interaction
Crucial steps ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
 
 
OXPath ,[object Object],[object Object],[object Object],[object Object],[object Object]
Result Extraction ,[object Object],/<XQ>  Atomic results regardless of  presentation (list, table, etc.)
Result Extraction <XQ> :  For  each atomic  result  A Let price  = A/.../.../text() description  = A/.../.../../text() ........ Return <rental area=Oxford> <price>  1,200  </price> <bedrooms>  3  </bedrooms> <bathrooms>  1  </bathrooms> <type>  Flat  </type> <location>  George Street,OX1  </location> <description> ... </description> <otherInfo>  Furnished; Long let -  more than six months </otherInfo> ... <ental> price description Type OtherInfo Bathrooms location type  = A/.../.../../text()
Crucial steps ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

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Web Data Extraction Como2010

  • 1. DIADEM A Short Overview Georg Gottlob
  • 2.  
  • 3. Web data extraction WEB HTML pages layout Corporate edp apps structured data, Databases, XML WRAPPER Goal: Make web contents accessible to electronic data processing Wrappers: HTML  select  extract  annotate  XML
  • 4.  
  • 5. MSO Monadic Datalog Elog Lixto Visual Wrapper   =  Suite Logic Database theory DB programming Application design
  • 6. Lixto Visual Developer (VD) Navigation Steps Mozilla Web Browser Extraction Configuration
  • 7.  
  • 8. . .
  • 9. Need for Automatic Extraction Technology Example: Real Estate UK 17,000 sites Many not covered by aggregators We do have a list of all homepages (Yellow Pgs. UK) Manual or semi-automatic wrapping too expensive - wrapper construction - testing - keeping track of changes No tool or method can do it fully automatically. Other domains: Hospitals,restaurants, schools, travel agents, airlines, hospitals, pharmaceutical companies and retail companies such as supermarket chains…..
  • 10. Need for Automatic Extraction Technology (2) All search engine providers need it! Many work on it. Keywords:  Vertical search,  object search,  semantic search. Raghu Ramakrishnan , Yahoo!, March 2009: “ no one really has done this successfully at scale yet ” Alon Halevy , Google, Feb. 2009: “ Current technologies are not good enough yet to provide what search engines really need. […] any successful approach would probably need a combination of knowledge and learning.”
  • 11. The Blackbox we want to construct BLACKBOX Application domain with thousands of websites URL Application relevant Structured data (XML or RDF) To achieve this, we plan to combine a host of annotators with a new knowledge-based approach.
  • 12. Real estate Restauran t s Relationship to SeCo & Webdam Q: Find apartments in Milan whose prices are  average in quarters were restaurant quality > average. Results Web service A Web service
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  • 14. High level reasoning: - Goal oriented - Conceptual domain objects. - Conceptual interaction elements - High-level object ontology - Domain knowledge
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  • 16. Bottom-up (low-level) annotation Monochromatic Rectangle Georaphic search facility Postcode input field Active map … . ISA ISA Occurs in Price search facility … . … . Occurs in … . 105 105 127 [(02873,227) (03900,417)] Geo-Price-Searchbox ISA [(02873,227) (03900,417)]
  • 17. Top-down reasoning Property Search Facility Property List Single Property Description Specially highlighted property part-of m 1
  • 18. Bottom-up processing Top-down reasoning Monochromatic Rectangle Georaphic search facility Postcode input field Active map … . ISA ISA Occurs in Price search facility … . … . Occurs in … . 105 105 127 [(02873,227) (03900,417)] Property Search Facility Property List Single Property Description Geo-Price-Searchbox ISA [(02873,227) (03900,417)] Specially highlighted property Phenomenology part-of m 1
  • 19. table(T) & occurs_in(T,areaselection) & occurs_in(T,priceselection)  goodtable(T). goodtable(T) & child(Parent,T)  containsgoodtable(Parent). goodtable(T) &  containsgoodtable(T)  propertysearchmask(T). If a table contains an area selection input field and a price selection field, both of which are not simultaneously contained in a smaller table, then this table is the property search mask Datalog for Web-Object Reasoning
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  • 24. Object creation in Datalog + table(T1) & table(T2) & sameColor(T1,T2) & isNeighbourRight(T1,T2)     X (tablebox(X) & contains(X,T1) & contains(X,T2)). PRODUCT Toshiba Protégé cx Dell 25416 Dell 23233 Acer 78987 PRICE 480 360 470 390
  • 25. Object creation in Datalog + table(T1) & table(T2) & sameColor(T1,T2) & isNeighbourRight(T1,T2)     X (tablebox(X) & contains(X,T1) & contains(X,T2)). PRODUCT Toshiba Protégé cx Dell 25416 Dell 23233 Acer 78987 PRICE 480 360 470 390 T1 T2
  • 26. Object creation in Datalog + table(T1) & table(T2) & sameColor(T1,T2) & isNeighbourRight(T1,T2)     X (tablebox(X) & contains(X,T1) & contains(X,T2)). PRODUCT Toshiba Protégé cx Dell 25416 Dell 23233 Acer 78987 PRICE 480 360 470 390 PRICE 480 360 470 390 T1 T2
  • 27. Object creation in Datalog + table(T1) & table(T2) & sameColor(T1,T2) & isNeighbourRight(T1,T2)     X (tablebox(X) & contains(X,T1) & contains(X,T2)). Deduction in Datalog + undecidable (TGDs)
  • 28. Object creation in Datalog + table(T1) & table(T2) & sameColor(T1,T2) & isNeighbourRight(T1,T2)     X (tablebox(X) & contains(X,T1) & contains(X,T2)). Deduction in Datalog + undecidable (TGDs) Datalog  : require guardedness of rule bodies. Decidable, linear-time data complexity.
  • 29. Datalog  Family of languages. Incorporates ontological reasoning (>DL-LITE) Further research needed for extending it so to be an ideal language for web objects. Transitivity: containedin(T1,T2), containedin(T2,T3)  containedin (T1,T3)
  • 30. Datalog  Family of languages. Incorporates ontological reasoning (>DL-LITE) Further research needed for extending it so to be an ideal language for web objects. Transitivity: containedin(T1,T2), containedin(T2,T3)  containedin (T1,T3) unguarded!
  • 31. DL-LITE DL-LITE Datalog[  ,  ;Lin] Professor   TeachesTo Professsor(x)   y TeachesTo(x,y)  TeachesTo -  Student TeachesTo(x,y)  Student(y) HasTutor -  TeachesTo HasTutor(x,y) ->TeachesTo(y,x) funct(HasTutor) HasTutor(x,y) & HasTutor(x,y’) (always innocuous!) & Neq(y,y’)   Professor   Student Professor(x) & Student(x)   DL-Lite core DL-Lite R DL-Lite F
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  • 37. Extraction from PDF Tamir Hassan
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  • 45. Result Extraction <XQ> : For each atomic result A Let price = A/.../.../text() description = A/.../.../../text() ........ Return <rental area=Oxford> <price> 1,200 </price> <bedrooms> 3 </bedrooms> <bathrooms> 1 </bathrooms> <type> Flat </type> <location> George Street,OX1 </location> <description> ... </description> <otherInfo> Furnished; Long let - more than six months </otherInfo> ... <ental> price description Type OtherInfo Bathrooms location type = A/.../.../../text()
  • 46.

Editor's Notes

  1. The XQuery statement (FLWOR expression) outputs a single XML document per result page Produces a flat (i.e. no hierarchy) XML document per some prescribed schema Key intuition – notion of atomic result, regardless of presentation (list, table, etc.) Atomic results analogous to RDBMS query returns (attributes form tuples); field inputs are preserved as element attribute values