DIADEM WWW 2012

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Search engines are the sinews of the web. These sinews have become strained, however: Where the web's function once was a mix of library and yellow pages, it has become the central marketplace for information of almost any kind. We search more and more for objects with specific characteristics, a car with a certain mileage, an affordable apartment close to a good school, or the latest accessory for our phones. Search engines all too often fail to provide reasonable answers, making us sift through dozens of websites with thousands of offers--never to be sure a better offer isn't just around the corner. What search engines are missing is understanding of the objects and their attributes published on websites.

Automatically identifying and extracting these objects is akin to alchemy: transforming unstructured web information into highly structured data with near perfect accuracy. With DIADEM we present a formula for this transformation, but at a price: DIADEM identifies and extracts data from a website with high accuracy. The price is that for this task we need to provide DIADEM with extensive knowledge about the ontology and phenomenology of the domain, i.e., about entities (and relations) and about the representation of these entities in the textual, structural, and visual language of a website of this domain. In this demonstration, we demonstrate with a first prototype of DIADEM that, in contrast to alchemists, DIADEM has developed a viable formula.

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  • the examples are the red thread that might get us out of the labyrinth\n
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  • more precisely in “Uncovering the Relational Web”\n
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  • cf. “Google’s deep web crawl”\ncf. WebTables\ncf. “Recovering Semantics of Tables on the Web”\n\n
  • cf. “Google’s deep web crawl”\ncf. WebTables\ncf. “Recovering Semantics of Tables on the Web”\n\n
  • cf. “Google’s deep web crawl”\ncf. WebTables\ncf. “Recovering Semantics of Tables on the Web”\n\n
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  • BERyL, abbreviating Block classification with Extraction Rules and machine Learning\n\n
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  • A M B E R (Adaptable Model-based Extraction of Result Pages),\n\n
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  • OPAL (ontology based web pattern analysis with logic)\n\n
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  • DIADEM WWW 2012

    1. 1. DIADEM domain-centric intelligent automated data extraction methodology DIADEM Domain-centric, Intelligent, Automated Data Extraction Tim Furche April 18th, 2012 @ WWW 2012 • Department of Computer Science, Oxford University
    2. 2. 1 What? 2
    3. 3. DIADEM ›❯ What?1 Data Extraction with DIADEM fully automated, but domain-centric based on extensive domain knowledge no per site training at all no user input other than the domain model we aim for complete extraction of the domain works on the vast majority web sites of a domain extracts the vast majority of records of each site main target: websites with structured records 3
    4. 4. DIADEM ›❯ What?1 Domain-Centric Data Extraction Blackbox that turns any of the thousands of websites of a domain into structured data 1 <?xml version ="1.0" encoding="UTF-8"? 2 <results> 3 <tyre> 4 <brand>Star Performer</brand> 5 <profile>HP</profile> 6 <price>42.60</price> 7 </tyre> 8 <tyre> 9 <brand>High Performer</brand> 10 <profile>HS-3</profile> 11 <price>39.40</price> 12 </tyre> 13 ... 14 </results> 4
    5. 5. DIADEM ›❯ What?1 Domain-Centric Data Extraction Blackbox that turns any of the thousands of websites of a domain into structured data 1 <?xml version ="1.0" encoding="UTF-8"? 2 <results> 3 <tyre> 4 <brand>Star Performer</brand> 5 <profile>HP</profile> 6 <price>42.60</price> 7 </tyre> 8 <tyre> 9 <brand>High Performer</brand> DIADEM 10 <profile>HS-3</profile> 11 <price>39.40</price> 12 </tyre> 13 ... 14 </results> 4
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    19. 19. About 7,070 results (0.18 seconds) Advanced search DIADEM ›❯ The StateChangethe Game Your location: Oxford - of1 Everything Sort by: Relevance Images Buy Sony Vaio Laptops Now | johnlewis.com Videos “Product” Search for Properties View our range of Sony Vaio laptops at John Lewis online now. News johnlewis.com is rated 296 reviews www.johnlewis.com/sony-vaio Shopping More Sony Vaio Laptops - Clearance Sale Now On | europc.co.uk Buy Securely Online. www.europc.co.uk/sony-laptop-saleShow only Google Checkout Oxford Street, Woodstock - OX20 £895pcm Free shipping Sony VAIO Y Series VPC-YA1V9E/B - Core i3 1.33 GHz - 11.6″ - 4 GB ... £601 cheaper than market Black, Microsoft Windows 7 Professional 64-bit Edition, 1.46 kg, Lithium Ion batteryFloor plan 29 cm x Basics Highlights Map 6 hour(s), from 4 stores New items 20.3 cm x 2.5 cm Property Type: Apartment Available Date: 26/09/2011 Compare prices DetailsAny category The deceptively quick Y series packs pleasing performance in an ultra-thin frame. Whether youre On Market: very long (3+ weeks) Bedrooms: 3House type: running multiple programs while writing a paperthan average (65/100) Energy rating: better ...Laptop Power Adaptorsflat Add to Shopping List Nearby: train station; M40; Thame town centrehouse BatteriesLaptopbungalowAny price • Reception room Sony VAIO Y Series VPC-Y11M1E/S - Pentium 1.3 GHz - 13.3″ - 4 GB ... £390… • tripple-glazed windowsUp to £500 Silver, Microsoft Windows 7 Home Premium 64-bit Edition, 1.8 kg, Lithium Ion battery 10 hour(s), 32.6 from 5 storesPrice: £600£500 – cm x 22.7 cm x 3.2 cmOver £600 The deceptively quick Y Series packs pleasing performance-in an ultra-thin frame. An Intel Pentium Wolvercote, North Oxford OX2 Compare prices £825pcmLow High ultra-low voltage processor helps ensure that ... average Basics Highlights Map Floor planRating: to£ 3 reviews - Add to Shopping List Property Type: Apartment Available Date: 26/09/2011 Details£ Go On Market: very long (3+ weeks) Bedrooms: 3 Sony VAIO Y Series VPC-Y21S1E/L -than average (65/100) Energy rating: better Pentium 1.2 GHz - 13.3″ - 4 GB ... £498Any brandBedrooms: Blue, Microsoft Windows 7 Home Premium 64-bit Edition, 1.8 kg, Lithium Ion battery 9 hour(s), 32.6 cm Nearby: train station; M40; Thame town centre from 5 storesSony x 22.7 cm x 3.2 cm16 Compare prices Your easy-to-use multimedia companion - travels anywhere in blue with long battery life and easy VAIO • Reception roomAny store solutions. • tripple-glazed windowsOthers:Overstock.com Add to Shopping ListPlay.com Bennett Crescent, Oxford - OX4 £995pcmTesco.com Sony VAIO Y Series VPC-Y21S1E/PHighlights 1.2 GHz - 13.3″ - 4 Floor... Basics - Pentium Map GB plan £520 averageAria Technology Pink, Microsoft Windows 7 Home Premium 64-bit Edition, 1.8 kg, Lithium Ion battery 9 hour(s), 32.6 cm from 4 stores x 22.7 cm x 3.2 cm Property Type: Apartment Available Date: 26/09/2011 DetailsOyyy.co.uk Compare prices The deceptively quick Y Series packs pleasing performance in an ultra-thin frame. Whether youre On Market: very long (3+ weeks) Bedrooms: 3 More running multiple programs while writing a paperthan average (65/100) Energy rating: better ... Add to Shopping List Nearby: train station; M40; Thame town centre • Reception room Sony VAIO Y Series VPC-Y11V9E/S - Core 2 Duo 1.3 GHz - 13.3″ - 4 ... • tripple-glazed windows £450 Silver, Microsoft Windows 7 Professional 64-bit Edition, 1.8 kg, Lithium Ion battery 9 hour(s), 32.6 cm x from 3 stores 22.7 cm x 3.2 cm The deceptively quick Y series packs pleasing performance in an ultra-thin frame. An Intel Core 2 Duo Compare prices 6 ultra-low voltage processor helps ensure ...
    20. 20. Web Data Extraction2 Scenario ➀: Electronics retailer electronics retailer: online market intelligence comprehensive overview of the market daily information on price, shipping costs, trends, product mix by product, geographical region, or competitor thousands of products hundreds of competitors nowadays: specialised companies mostly manual, interpolation large cost 7
    21. 21. Web Data Extraction › Scenarios2 Scenario ➁: Supermarket chain supermarket chain competitors’ product prices special offer or promotion (time sensitive) new products, product formats & packaging 8
    22. 22. Web Data Extraction › Scenarios2 Scenario ➂: Hotel Agency online travel agency best price guarantee prices of competing agencies average market price taken and report history 9
    23. 23. Web Data Extraction › Scenarios2 Scenario ➃: Hedge Fund house price index published in regular intervals by national statistics agency affects share values of various industries hedge fund: online market intelligence to predict the house price index 10
    24. 24. Web Data Extraction › Scenarios2 Scenario ➄: Construction tenders from all over the world existing aggregators expensive, often incomplete yet need to be published (online) by law in most countries 11
    25. 25. Web Data Extraction › Scenarios2 Scenario ➅: Supporting Scientists automatic document analysis and annotation data extraction from scientific databases improving search for scientific literature 12
    26. 26. 1 About us … 13
    27. 27. 1 About us … DIADEM lab at Oxford University 13
    28. 28. 1 About us … DIADEM lab at Oxford University 2010 2011 2012 2013 2014 2015 13
    29. 29. 1 About us … DIADEM lab at Oxford University 2010 2011 2012 2013 2014 2015 13
    30. 30. 1 About us … DIADEM lab at Oxford University 2010 2011 2012 2013 2014 2015 13
    31. 31. 1 About us … DIADEM lab at Oxford University 2010 2011 2012 2013 2014 2015 13
    32. 32. 2 How: Knowledge 14
    33. 33. DIADEM ›❯ Knowledge2 Data Extraction Three steps in data extraction: finding the relevant pages interaction (forms) identifying the relevant objects segmentation extracting the relevant attributes alignment In all cases: derive patterns from examples 15
    34. 34. DIADEM ›❯ Automation in Data Extraction2 Bad News: Nobody Can do it Yet Wrapper Induction high accuracy (ML) high accuracy Template low supervision Discovery low supervision 16
    35. 35. DIADEM ›❯ Automation in Data Extraction2 Bad News: Nobody Can do it Yet Wrapper Induction high accuracy (ML) high accuracy Template low supervision Discovery low supervision 16
    36. 36. DIADEM ›❯ Knowledge2 Knowledge in Data Extraction 17
    37. 37. DIADEM ›❯ Knowledge2 Knowledge in Data Extraction what’s “knowledge” here observational: what to observe, annotations that a certain text is highlighted, that a certain keyword appears in it phenomenological: how observations become concepts that a text “...:” to the close north-west of a field is that field’s label ontological: schema, concepts & constraints e.g., “bathroom”, “every property must have a location” orthogonal: script knowledge for web pages both domain-independent and domain-dependent 17
    38. 38. DIADEM ›❯ Knowledge2 Knowledge in Data Extraction what’s “knowledge” here phenomenon observational: what to observe, annotations that a certain text is highlighted, that a certain keyword appears in it phenomenological: how observations become concepts that a text “...:” to the close north-west of a field is that field’s label ontological: schema, concepts & constraints e.g., “bathroom”, “every property must have a location” orthogonal: script knowledge for web pages both domain-independent and domain-dependent 17
    39. 39. DIADEM ›❯ Knowledge2 Knowledge in Data Extraction what’s “knowledge” here phenomenon observational: what to observe, annotations that a certain text is highlighted, that a certain keyword appears in it phenomenological: how observations become concepts that a text “...:” to the close north-west of a field is that field’s label idea/noumenon ontological: schema, concepts & constraints e.g., “bathroom”, “every property must have a location” orthogonal: script knowledge for web pages both domain-independent and domain-dependent 17
    40. 40. DIADEM ›❯ Knowledge2 Knowledge in Data Extraction what’s “knowledge” here phenomenon observational: what to observe, annotations that a certain text is highlighted, that a certain keyword mapping appears in it phenomenological: how observations become concepts that a text “...:” to the close north-west of a field is that field’s label idea/noumenon ontological: schema, concepts & constraints e.g., “bathroom”, “every property must have a location” orthogonal: script knowledge for web pages both domain-independent and domain-dependent 17
    41. 41. DIADEM ›❯ Knowledge2 Trend: Towards Domain- Observational only: Su, Wang, Lochovsky. ODE, TODS 2009 Ontological only: Fazzinga, Flesca, Tagarelli. Schema-based Web wrapping. K&IS 2011 Observational & ontological: Dalvi, Kumar, Soliman. Automatic Wrappers for Large Scale Web Extraction, VLDB 2011. (AutoWrapper in the following) Venetis, Halevy, Madhavan, et al. Recovering Semantics of 18
    42. 42. DIADEM ›❯ Knowledge2 Trend: Towards Domain- Observational only: Su, Wang, Lochovsky. ODE, TODS 2009 Ontological only: Fazzinga, Flesca, Tagarelli. Schema-based Web wrapping. K&IS 2011 shallow ontology, better for single attribute extraction Observational & ontological: Dalvi, Kumar, Soliman. Automatic Wrappers for Large Scale Web Extraction, VLDB 2011. (AutoWrapper in the following) Venetis, Halevy, Madhavan, et al. Recovering Semantics of 18
    43. 43. DIADEM ›❯ Knowledge2 Trend: Towards Domain- Observational only: Su, Wang, Lochovsky. ODE, TODS 2009 Ontological only: Fazzinga, Flesca, Tagarelli. Schema-based Web wrapping. K&IS 2011 shallow ontology, better for single attribute extraction Observational & ontological: Dalvi, Kumar, Soliman. Automatic Wrappers for Large Scale Web Extraction, VLDB 2011. (AutoWrapper in the following) Venetis, Halevy, Madhavan, et al. Recovering Semantics of 18
    44. 44. DIADEM ›❯ Knowledge2 DIADEM: Suffused by Knowledge Key insight ➊: all three types of knowledge every piece of DIADEM is driven by knowledge exploration: script/interaction knowledge block/form/result page/description analysis all combine all three types algorithms: search for “consistent” interpretation informed by domain knowledge rather than uninformed as, e.g., in AutoWrappers 19
    45. 45. ➏ Model Explorer script/interaction ontological ➎ Interpretation ➊ phenomenological ➍ Observed FactsBrowser observational ➌ DOM ➋ 20
    46. 46. ➏ Model Explorer script/interaction ontological ➎ Interpretation ➊ phenomenological ➍ imperfect Observed Facts observer (incomplete, ambigue)Browser observational ➌ DOM ➋ 20
    47. 47. ➏ Model Explorer script/interaction ontological ➎ per-se Interpretation consistent interpretation ➊ phenomenological ➍ imperfect Observed Facts observer (incomplete, ambigue)Browser observational ➌ DOM ➋ 20
    48. 48. ➏ Model consistent Explorer interpretation script/interaction ontological ➎ per-se Interpretation consistent interpretation ➊ phenomenological ➍ imperfect Observed Facts observer (incomplete, ambigue)Browser observational ➌ DOM ➋ 20
    49. 49. DIADEM ›❯ Knowledge2 All in one … Finding the pages := crawling, web forms, etc. form understanding (OPAL) and navigation (BERYL) Segmentation := divide into records, cells, etc. page segmentation (BERYL) and record segmentation (AMBER) Alignment := class of a record, attribute, column, etc. attribute alignment (AMBER) and attribute extraction (Oxtractor) 21
    50. 50. DIADEM ›❯ Knowledge2 All in one … DEMO Finding the pages := crawling, web forms, etc. form understanding (OPAL) and navigation (BERYL) PAPER Segmentation := divide into records, cells, etc. page segmentation (BERYL) and record segmentation (AMBER) Alignment := class of a record, attribute, column, etc. attribute alignment (AMBER) and attribute extraction (Oxtractor) 21
    51. 51. DIADEM ›❯ Knowledge2 All in one … DEMO Finding the pages := crawling, web forms, etc. form understanding (OPAL) and navigation (BERYL) PROFOUND PAPER Segmentation := divide into records, cells, etc. page segmentation (BERYL) and record segmentation (AMBER) Alignment := class of a record, attribute, column, etc. attribute alignment (AMBER) and attribute extraction (Oxtractor) 21
    52. 52. DIADEM ›❯ Knowledge2 All in one … DEMO Finding the pages := crawling, web forms, etc. form understanding (OPAL) and navigation (BERYL) PROFOUND PAPER Segmentation := divide into records, cells, etc. page segmentation (BERYL) and record segmentation (AMBER) DEMO Alignment := class of a record, attribute, column, etc. attribute alignment (AMBER) and attribute extraction (Oxtractor) 21
    53. 53. DIADEM ›❯ Knowledge2 All in … two … All the analysis is integrated but separated from the actual extraction only samples pages sufficient to generate an exhaustive wrapper script knowledge guides the exploration and “stop” strategy Large-scale extraction: OXPath in the Cloud → OXLatin separate, cloud-based extraction efficient, highly-scalable extraction language & analysis SCOUT: Provisioning and scheduling in cloud computing under external global constraints 22
    54. 54. DIADEM ›❯ Knowledge2 All in … two … All the analysis is integrated but separated from the actual extraction only samples pages sufficient to generate an exhaustive wrapper script knowledge guides the exploration and “stop” strategy Large-scale extraction: OXPath in the Cloud → OXLatin separate, cloud-based extraction DEMO efficient, highly-scalable extraction language & analysis SCOUT: Provisioning and scheduling in cloud computing under external global constraints 22
    55. 55. DEMO 23
    56. 56. 3 DIADEM 24
    57. 57. 3 DIADEM DIADEM 24
    58. 58. DIADEM ›❯ Inside3 A Journey into DIADEM Examples of knowledge (and its representation) in DIADEM observational: clues for price (“looks like a price”) and location representation: Gazetteers, JAPE rules, WEKA classifiers & Datalog¬,Agg rules phenomenological: a real estate record and its attributes representation: Datalog¬,Agg,± rules ontological: constraints for real estate form representation: template language on top of Datalog¬,Agg, ± rules 25
    59. 59. DIADEM ›❯ Inside _by<Model,AType>3 TEMPLATE annotated { 2 <Model>::annotated_by<AType>(X) ( node_of_interest(X), gate::annotation(X, <AType>, _). } BERyL: Navigation Blocks 4 TEMPLATE in_proximity<Model,Property(Close)> { <Model>::in_proximity<Property>(X) ( node_of_interest(X), 6 std::proximity(Y,X), <Property(Close)>. } TEMPLATE num_in_proximity<Model,Property(Close)> { <Model>::in_proximity<Property>(X,Num) ( node_of_interest(X), feature model: derived #count(N: observed facts 8 std::proximity(Close,X), Num = from <Property(Close)>). } 10 TEMPLATE relative_position<Model,Within(Height,Width)> { through Datalog program with templates <Model>::relative_position<Within>(X, (PosH, PosV)) ( node_of_interest(X), 12 css::box(X, LeftX, TopX, _, _), <Within(Height,Width)>, less than two dozen lines of code 100·TopX PosH = 100·LeftX , PosV = Height . } Width 14 TEMPLATE contained_in<Model,Container(Left,Top,Bottom,Right)> { <Model>::contained_in<Container>(X) ( node_of_interest(X), 16 css::box(X,LeftX,TopX,RightX,BottomX), <Container(Left,Top,Right,Bottom)>, Left < LeftX < RightX < Right, Top < TopX < BottomX < Bottom. } 18 TEMPLATE closest<Model,Relation(Closest,X),Property(Closest),Test(Closest)> { Precision Recall F1 <Model>::closest<Relation>_with<Property>_is<Test>(X) ( node_of_interest(X), <Relation(Closest,X)>, <Property(Closest)>, <Test(Closest)>, 1.00 20 ¬(<Relation(Y,X)>, <Property(Y)>, <Relation(Y,Closest)>). } 0.98 Fig. 4: BERy L feature templates In a similar way, the second template defines a boolean feature that holds for nodes 0.97 of interest, if there is another node in their proximity for which Property(Close) is true. To instantiate it to nodes that are annotated with PAGINATION, we write 0.95 26 Real Estate Carsproximity<Model,Property(Close)> INSTANTIATE in_ Retail Forums Total
    60. 60. DIADEM ›❯ Inside3 Phenomenological: Record How to find the boundaries of records in a page? Record := representation of single entity of the domain values, structure, layout: similar to other records on the page clearly separated from other records in a regular structure (data area) content-rich (text, attributes) Attribute := value of a certain attribute type of an entity similar (content, structure, layout) to same attributes in other records often labeled or with specific value type Data area := area of repeated, regular records 27
    61. 61. DIADEM ›❯ Inside3 Phenomenological: Record How to find the boundaries of records in a page? Record := representation of single entity of the domain values, structure, layout: similar to other records on the page clearly separated from other records in a regular structure (data area) content-rich (text, attributes) Attribute := value of a certain attribute type of an entity similar (content, structure, layout) to same attributes in other records often labeled or with specific value type Data area := area of repeated, regular records 27
    62. 62. DIADEM ›❯ Inside3 Phenomenological: Record Exhaustive search is inefficient and only addresses low precision low recall is at least as much of an issue + contradicting annotations may be a clue per se therefore: AMBER search informed by domain knowledge use domain knowledge to guess data area & record segmentation support alignment with domain knowledge 28
    63. 63. D1 M1,1 M1,3 E D2 D3M1,2 M1,4 … …consistent_cluster_members(C, N1, N2,identification Figure 3: Data area N3) :- pivot(N1), pivot(N2), ... similar_depth(N1, N2), similar_depth(N2, N3), similar_depth(N1,N3), similar_tree_distance(N1, N2, N3).cluster(C,N)dominance: The pivot nodes in E of allorganized ratherits of order :- continuous, lca, contains at least one are mandatoriesregularly, whereas the pivot nodes in D1 vary quite notably. How- 29ever, there variation is small enough that M1,1 to M1,4 are depth and
    64. 64. precision recall10099.5 9998.5 98 data areas records attributes Real Estate (100 pages) 30
    65. 65. precision recall10099.5 9998.5 98 data areas records attributes Real Estate (100 pages) precision recall 100 97.5 95 92.5 90 price postcode location bathroom bedroom reception legal type 30
    66. 66. precision recall precision recall100 10099.5 99.5 99 9998.5 98.5 98 98 data areas records attributes data areas records attributes Real Estate Used Car (100 pages) (100 pages) precision recall 100 97.5 95 92.5 90 price postcode location bathroom bedroom reception legal type 30
    67. 67. DIADEM ›❯ Inside3 Ontological: Constraints for real Annotation schema: Λ=(A,<,≺,(isLabela, isValuea: a ∈ A)) set A of annotation types a transitive, reflexive subclass relation < a transitive, irreflexive, antisymmetric precedence relation ≺ and two characteristic functions isLabela and isValuea on text nodes for each a ∈ A. Domain schema: Σ = (Λ,T,CT ,CΛ) annotation schema Λ set of domain types T CT, CΛ: map domain types to classification & structural constraints 31
    68. 68. Real-Estate Form Buy/Rent Form Geographic Features Location Buy/Rent Location Type of Use PriceBuy/Rent Buy/Rent Location Location Location Area/Branch Type of Use Type of Use Bedroom Min-Price Max-Price Button Location/… Office Min. Bedrooms Price Range (£) to Buying Renting Local National Residential Commercial Submit All Any 0 700 32
    69. 69. TEMPLATE segment<C>{2 segment<C>(G)( child(N1 ,G),not child(N2 ,G) not(concept<C>(N2 ) _ segment<C>(N2 )) }4 TEMPLATE segment_range<C,CM > {6 segment<C>(G)( concept<CM >(N1 ),concept<CM >(N2 ), N1 6= N2 , child(N1 ,G),child(N2 ,G) }8 TEMPLATE segment_with_unique<C,U> {10 segment<C>(G)( child(N1 ,G), concept<U>(N1 ,G),not child(N2 ,G), N1 6= N2 ,not(concept<C>(N2 ) _ segment<C>(N2 )) . }12 TEMPLATE unique<C> {14 unique<C>(N1,G)( concept<C>(N1 ),child(N1 ,G), ¬(child(N2 ,G),N1 6=N2 ,concept<C>(N2 )) } 33 Figure 9: OPAL - TL structural constraints
    70. 70. Precision Recall F-score 10.985 0.970.955 0.94 UK Real Estate (100) UK Used Car (100) ICQ (98) Tel-8 (436) 34
    71. 71. Precision Recall F-score 10.985 0.970.955 0.94 UK Real Estate (100) UK Used Car (100) ICQ (98) Tel-8 (436) 1 0.98 0.96 0.94 0.92 0.9 Airfare Auto Book Job US R.E. 34
    72. 72. Precision Recall F-score 10.985 0.970.955 0.94 UK Real Estate (100) UK Used Car (100) ICQ (98) Tel-8 (436) 1 0.98 0.96 0.94 0.92 Dragut et al., VLDB, 0.9 2009 Airfare Auto Book Job US R.E. 34
    73. 73. DIADEM ›❯ Inside3 Contribution of Scopes field segment layout domain Real-estate Used-car 0.6 0.7 0.8 0.9 1 35
    74. 74. DIADEM ›❯ Future4 Summary Examples of knowledge (and its representation) in DIADEM observational: clues for price (“looks like a price”) and location representation: Gazetteers, JAPE rules, WEKA classifiers & Datalog¬,Agg rules phenomenological: a real estate record and its attributes representation: Datalog¬,Agg,± rules ontological: constraints for real estate form representation: template language on top of Datalog¬,Agg, ± rules 36
    75. 75. DIADEM ›❯ Future4 Where are we? Known knowns: we know what and how site-specific or supervised data extraction Known unknowns: we know what templates need to be discovered but: what we are interested in is known DIADEM 0.2 will mostly cover this Unknown unknowns: where we don’t even know what we are looking for never-ending learning of domain concepts semi-supervised 37
    76. 76. DIADEM ›❯ Future4 Where are we? Known knowns: we know what and how site-specific or supervised data extraction Known unknowns: we know what templates need to be discovered but: what we are interested in is known DIADEM 0.2 will mostly cover this Unknown unknowns: where we don’t even know what we are looking for never-ending learning of domain concepts semi-supervised 37

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