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Information extraction for building knowledge basis

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Presentation given at PUC Rio on March 8, 2012

Presentation given at PUC Rio on March 8, 2012

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  • The spatial layout of content elements of a Web page helps human readers to understand the semantics of contents. In this page a user is able to identify details of a music band because descriptive information are close to the image and each music band is shown by using the same visual pattern._____________________________________________________________________ Introduction of human oriented,Browser makesrectangles *** usa la pagina last-fm***The web designer represent the pages for representing unitaria information L’Unitarietà is given by the spatial consistency of an information that give the semantics to a user and we want exploit such spatial consistency for query the pages. This is possible by exploiting as funzionano the layout engineThis is a page presentation oriented *** mettere last-fm col browser**Its internal representation in thisThe layout that make the browser is this.*** illuminare**The spatial layout of nodes ** mettere un rettangolo*** allow user to identify some homogeneous part of information that are the descriptions of these music band.This means presentation oriented human orientedi.e. the human understand that this information are referred to the same music band because they have a certain spatial continuity
  • Layout engines of Web browsers assign a rectangle to each DOM element. ___________________________________________________The internal code of a page is this How can we query the page using the spatial information?The browser when visualize the pages represent the information in their rectangles that we can call minimum bounding rectangle. In fact the layout engine assign to each node*** parallelotraildom e quellochevedi--- vedicoldplayèscritto qua dentro e siillumina, img e siillumina***For each node based on the stylesheet, what the web designer.Presentation oriented, all also the style is used for give emphasis so that the human understand the important information, so the name in bold. (sviluppifuturiusarli)
  • As shown in the the figure the complex, involved and nested structure of the DOM has a clear presentation that enable user to read and understand the meaning of information presented in the Web page.
  • The rectangular algebra is an extension of the Allen’s interval algebra to the two dimensional case. For example in this case the relatio x (b,e) y is intuitively obtained by applying interval algebra to both sides of the rectangle.__________________________________________________________So we could use the spatial model of geospatial database for representing the mutual relationships between objects***Mostra RA***The rectngular algebra define 169 relations, all the possible relations between rectangles *** mostrare la figurona***Between this and this in the relation algebra this relation is called so*** illumina****** Ritaglia un singolo rettangolo***-----------------Modelli del mondo geospaziale per rappresentare le mutue relazioniRAIlluminare 2 - albero non basato del nesting ma su contenimento e relazioni
  • In order to allow the representation of spatial relations existing between pairs of content items/rectangles laid out by the layout engine of a Web broser in the presentation of a Web page, we use the rectangular algebra relations model. This model is well known and widely adopted in geo-Spatial databases and has very interesting properties like invertibility that enable optimized evaluations of SXPath language.____________________________________________So we could use the spatial model of geospatial database for representing the mutual relationships between objects***Mostra RA***The rectngular algebra define 169 relations, all the possible relations between rectangles *** mostrare la figurona***Between this and this in the relation algebra this relation is called so*** illumina****** Ritaglia un singolo rettangolo***-----------------Modelli del mondo geospaziale per rappresentare le mutue relazioniRAIlluminare 2 - albero non basato del nesting ma su contenimento e relazioni
  • No comment. Già tutto nella slide.and has very interesting properties like invertibility that enable optimized evaluations of SXPath language._______________________________________So we could use the spatial model of geospatial database for representing the mutual relationships between objects***Mostra RA***The rectngular algebra define 169 relations, all the possible relations between rectangles *** mostrare la figurona***Between this and this in the relation algebra this relation is called so*** illumina****** Ritaglia un singolo rettangolo***-----------------Modelli del mondo geospaziale per rappresentare le mutue relazioniRAIlluminare 2 - albero non basato del nesting ma su contenimento e relazioni
  • By representing RA relations/spatial relation we obtain the SDOM where continuous arrows represent spatial containment and dotted arrows represent RA relations. This way we have a model of a Web page that represent all spatial relations existing between each pair of DOM nodes.Spatial relations enable also the definition of a spatial ordering along the 4 main direction North, South, East, and West as shown in the figure._____________________________Intuizione di DOMSo I can make a tree of the page not based on nesting of tags, but by using the spatial containment and spatial relations*** tirare fuori l’sdom****** sempre animando, mostrando sempre I due elementi scelti, ***Between image and radiohead there is the spatial relation (s, bi)I can represent this data model that do not capture the simple nesting of tags but catcht the spatial arrangment of the objects on the page*** con le animazioni***This is the new data model that I use called Spatial DOM. That is the Document Object Model with the objects of the DOM where the relations (queste scure) are containment relations, (quelle tratteggiate) are the Rarelations.It allows to introduce an ordering in the page using this model ----------------Nuovo modello che uso SDOMIntrodurre che permette di definire ordinamento spaziale nella pagina
  • The RA relation is too fine grained and verbose, difficult to use by a human. So we introduce also the Rectangular Cardinal Relations and topological relations (Two of the most intuitive and diffused spatial models) in order to map RA relations and allow user to query spatial relations in a more intuitive way.________________________________________________________Such relations are very complicated We need more intuitive relations to use So we use another geospatial model called RCR and Topological relations mapped with the RA modelDivide in regional tiles and it is simple
  • The RA relation is too fine grained and verbose, difficult to use by a human. So we introduce also the Rectangular Cardinal Relations and topological relations (Two of the most intuitive and diffused spatial models) in order to map RA relations and allow user to query spatial relations in a more intuitive way.________________________________________________________Such relations are very complicated We need more intuitive relations to use So we use another geospatial model called RCR and Topological relations mapped with the RA modelDivide in regional tiles and it is simple
  • In this slide is show a comparison between Xpath and SXPath. Suppose a user that need to extract details of a music band. By using Xptah the user need to know the intricate DOM structure. By using SXPth the user can exploit the visual pattern adopted by the Web designers for organizing details of the music bands._______________________
  • In this slide is show a comparison between Xpath and SXPath. Suppose a user that need to extract details of a music band. By using Xptah the user need to know the intricate DOM structure. By using SXPth the user can exploit the visual pattern adopted by the Web designers for organizing details of the music bands._______________________
  • SXPath expressions are also resilient. In fact, a gicen visual pattern can be queried in the same way on different web pages having different internal encodings.____________________________________Another advantage is that it is more general For instance, with only a query I can catch different DOMs because their spatial representation is the same.So it generalize the patterns Our language catch visual patterns, catch in general way visual patterns on Web pages Example 2A single data record can be split in different sub-treesWrapper induction techniques like DEPTA [Zhai et al.] recognize datarecords when they are encoded in the DOM as consecutive similarsubtrees-------------------Esempio 2Altrovantaggioacchiappo DOM diversiIl linguaggiocattura in manieragenerale pattern visuali
  • The SXPath language has been thought for supporting information extraction from presentation-oriented documents. It derives from Xpath so it do not requires the user to learn it from scratch.It is simple to learn and more human oriented than Xpath.SXPath maintain polynomial combined complexity and constitutes a stepping stone for different kinds of Web applications aimed at acquiring infromaiton from the Web.___________________
  • The SDOM essentially is the traditional DOM enriched by the set of rectangular algebra relations between each pair of nodes.________________________________________
  • The SXPath language is an extension of the XPath language. So beside traditional axes the SXPath language provides users with a new set of axes called spatial axed. Spatial axes are expressed by rectangular cardinal relations and topological relations that are more intuitive to use for human and that can be easily mapped into rectangular algebra relations.____________________________________
  • Spatial axes are defined as interpreted binary relations expressed by RCR and mapped into RA by the means of the function mu.________________________________________
  • As said before the SXPath language extends the Xpath language by spatial axes and spatial position functions.We have studied interesting fragments of SXPath corresponding to XPath fragments already studied in literature in order to have a clear picture of expressivity and complexity of the language. In particular, we studied the Core Xpath/SXPath (navigational core of Xpath/SXPath) and the WF/Spatial WF fragments (that allow position/spatial position functions).____________________________
  • The semantics of SXPath is given by using the concept of context introduced by Wadler and aopted also by Gottlob in its studies on XPath expressivity and complexity.In SXPath the context must be extended to spatial positions of nodes and context sizes for each direction. So we have a 12-tuple instead of a 3-tuple_____________________________________________________________________________
  • We have given the formal semantics of SXPath by using the denotation semantics approach. So in the main difference over the XPath formal semantics is given by the function that computes the spatial axes defined as shown here.____________________________________________________
  • Obviously, each expression is evaluated over the context as shown here.Of couse________________________________
  • The study of combined computational complexity of different SXPath fragments shows that SXPath maintain Polinomial time computational complexity. Obviously SXPath as a greater exponent in the polynomial because of the quadratic number of relation stored in the SDOM that need to be explored during the evaluation of spatial axes.We compute spatial axes by using the same dynamic programming approach suggested by Gottolob but we have to explore a quadratic number of further relation in the SDOM.________________________________________ Core SXPath queries can be evaluated in time O(SDS2 á SQS) where SDSis the size of the XML document, and SQS is the size of the query QProof Sketch There are O(SVv S2) many spatial relations to beconsidered in addition to the O(SVS) many relations of the DOMincurring a higher polynomial worst case complexityIn order to obtain a polynomial-time combined complexity bound for SXPathquery evaluation we use dynamic programming adopting the Context-ValueTable (CV-Table) principle introduced by Gottlob et al.Position and size are computed on demand, we compute all spatial positionfunctions in a loop for all pairs previousÉcurrent nodesFull SXPath computational costs are dominated by String Operations belongingto XPath 1.0In SWF the computation of spatial ordering generates a higher polynomial worstcase than XPath 1.0
  • The architecture of the system consists in a parser of SXPath expressions (Query parser), a builder of the SDOM an engine that efficiently evaluates SXPath queries.______________________
  • The GUI shows the DOM, allows to write queries, enables to check query results that are show on the screen._________________________________________
  • In these two log-log plots are shown data efficiency and query efficiency. For evaluating data efficiency we used a growing document size, while for evaluating query efficiency we used a query with increasing number of location steps.Plots show that the system behavior is polynomial with respect to both data and query sizes._________________________________________________________
  • For evaluating the usability we asked some students that already know the Xpath language to learn SXPath and use it for extracting product names and prices from a web pages.The experiment has shown that user found the language usable and effective._________________________________________________
  • In the second experiment we evaluated the effectiveness of Sxpath with respect to Xpath. We discovered that the possibility to exploit the visual appearance of Web pages allow to write queries by less attempts than in Xpath, that Sxpath location path are more syntetic and that Sxpath is resilient (the same query can be used on different Web site having very different internal encodings in terms of DOM trees).________________________________
  • In the second experiment we evaluated the effectiveness of Sxpath with respect to Xpath. We discovered that the possibility to exploit the visual appearance of Web pages allow to write queries by less attempts than in Xpath, that Sxpath location path are more syntetic and that Sxpath is resilient (the same query can be used on different Web site having very different internal encodings in terms of DOM trees).________________________________
  • In the second experiment we evaluated the effectiveness of Sxpath with respect to Xpath. We discovered that the possibility to exploit the visual appearance of Web pages allow to write queries by less attempts than in Xpath, that Sxpath location path are more syntetic and that Sxpath is resilient (the same query can be used on different Web site having very different internal encodings in terms of DOM trees).________________________________
  • In presentation-oriented documents the layout of elements in the internal representation provides visual cues that help human user to understand the meaning of contents.Both contents of Web pages and PDF documents are presented to users on a two dimensional Cartesian plane. The meaning of contents is clear only after rendering. For example, the PDF encoding consists in a (completely flat) stream of strings equipped with position in which they must appear on the page. The table in the figure can be understood only after rendering_________
  • By using document layout analysis and document understanding techniques, combined with table recognition methods, different parts of a PDF document can be recognized. This way element of a PDF document can be represented in a more abstract format like DOM or SDOM._________________
  • Transcript

    • 1. WeST – Web Science & Technologies University of Koblenz Landau, Germany Information Extraction for Building Knowledge Bases Steffen Staab Saqib Mir – European Bioinformatics InstituteErmelinda d‘Oro, Massimo Ruffolo – Univ. Calabria, Italy
    • 2. A FEW SLIDES WHERE WEST COMES FROMWeST – Web Science & Steffen Staab Slide 2Technologies staab@uni-koblenz.de
    • 3. WeST – Web Science & Steffen Staab Slide 3Technologies staab@uni-koblenz.de
    • 4. Institut WeST – Web Science & TechnologiesSemantic Web Web Retrieval Social Web Multimedia Web Software Web GESIS WeST – Web Science & Steffen Staab Slide 4 Technologies staab@uni-koblenz.de
    • 5. We (co-)organize conferences and schoolsWeST – Web Science & Steffen Staab Slide 5Technologies staab@uni-koblenz.de
    • 6. We build applications and develop methods… BTC 1. Prize 2011 1. Prize German Linked Open Gov Data Competition 2012 BTC 1. Prize 2008 German KM 1. Prize 2011WeST – Web Science & Steffen Staab Slide 6Technologies staab@uni-koblenz.de
    • 7. We teach Web ScienceMaster in Master in eGov@KoblenzWeb Science@Koblenz  Free tuition Free tuition  Start Fall 2012 Start Fall 2012  English English 2012 Web Science Summer School Lorentz Center, Leiden, The Netherlands, 9-13 July 2012WeST – Web Science & Steffen Staab Slide 7Technologies staab@uni-koblenz.de
    • 8. We are active in joint projects EU Integrated Project ROBUST (10 Partners): Risk and Opportunity management of huge-scale BUSiness communiTy cooperation EU Live+Gov - Reality Sensing, Mining and Augmentation for Mobile Citizen–Government Dialogue EU WeGov – where eGovernment meets the eSociety EU IP SocialSensor - Sensing User Generated Input for Improved Media Discovery and Experience EU Net2 – a networked for networked knowledge EU MOST – Marrying ontologies and Software TechnologiesWeST – Web Science & Steffen Staab Slide 8Technologies staab@uni-koblenz.de
    • 9. Steffen Staab, Saqib Mir, European Bioinformatics Institute Ermelinda d‘Oro, Massimo Ruffolo, Univ Calabria, Italy INFORMATION EXTRACTION FOR BUILDING KNOWLEDGE BASESWeST – Web Science & Steffen Staab Slide 9Technologies staab@uni-koblenz.de
    • 10. GENERAL MOTIVATIONWeST – Web Science & Steffen Staab Slide 10Technologies staab@uni-koblenz.de
    • 11. General objective: Extracting to LOD useAsExample hasLivedInWeST – Web Science & Steffen Staab Slide 11Technologies staab@uni-koblenz.de
    • 12. General objective: Analysing LOD useAsExample hasLivedInWeST – Web Science & Steffen Staab Slide 12Technologies staab@uni-koblenz.de
    • 13. http://lisa.west.uni-koblenz.de/lisa-demo/Family‘s analysis of Munich LOD + Open Street Map data WeST – Web Science & Steffen Staab Slide 13 Technologies staab@uni-koblenz.de
    • 14. http://lisa.west.uni-koblenz.de/lisa-demo/Entrepreneur‘s analysis of Munich LOD + Open Street Map data WeST – Web Science & Steffen Staab Slide 14 Technologies staab@uni-koblenz.de
    • 15. OBSERVATIONS ON INFORMATION EXTRACTIONWeST – Web Science & Steffen Staab Slide 15Technologies staab@uni-koblenz.de
    • 16. Challenges & Opportunities for IENot all web pages are created equalWeST – Web Science & Steffen Staab Slide 16Technologies staab@uni-koblenz.de
    • 17. Challenges & Opportunities for IESome challenges are the same, e.g. finding type instancesWeST – Web Science & Steffen Staab Slide 17Technologies staab@uni-koblenz.de
    • 18. Challenges & Opportunities for IESome challenges are the same, e.g. finding relation instancesWeST – Web Science & Steffen Staab Slide 18Technologies staab@uni-koblenz.de
    • 19. Challenges & Opportunities for IESome contain concepts and their descriptions, some don‘t No types here, few relation typesWeST – Web Science & Steffen Staab Slide 19Technologies staab@uni-koblenz.de
    • 20. Challenges & Opportunities for IEKnowing that they are instances and of which type Textual Positional indication indicationWeST – Web Science & Steffen Staab Slide 20Technologies staab@uni-koblenz.de
    • 21. Challenges & Opportunities for IETo some extentpositional and layoutindications work acrosslanguages and sitesWeST – Web Science & Steffen Staab Slide 21Technologies staab@uni-koblenz.de
    • 22. Challenges & Opportunities for IE owl:sameAs We should not only think about Web pages, but about Web sitesWeST – Web Science & Steffen Staab Slide 22Technologies staab@uni-koblenz.de
    • 23. Challenges & Opportunities for IE We should not only think about Web pages, but about Web sites owl:sameAsWeST – Web Science & Steffen Staab Slide 23Technologies staab@uni-koblenz.de
    • 24. Comparing related work to our objectivesRelated work objectives Our objectives IE on Web pages  IE on Web sites Acquiring instances and  Acquiring items relationship instances  Classifying items in  Instances  Concepts  Relation instances  Relationships  IE also based IE based on linear text on spatial position There is overlap and there are few exceptions in related workWeST – Web Science & Steffen Staab Slide 24Technologies staab@uni-koblenz.de
    • 25. OutlineThe Social Media-Case The Bio-Case Motivation State-of-the-Art Core idea of SXPath SXPath Language  Spatial Data Model  Syntax & Semantics  Complexity Implementation EvaluationWeST – Web Science & Steffen Staab Slide 25Technologies staab@uni-koblenz.de
    • 26. Presentation-oriented documentsAcquiring a music bandprofile:A music band photo thathas at east itsdescriptive informationMusic band profile band photo band name WeST – Web Science & Steffen Staab Slide 26 Technologies staab@uni-koblenz.de
    • 27. Presentation-oriented documentsWeST – Web Science & Steffen Staab Slide 27Technologies staab@uni-koblenz.de
    • 28. Presentation-oriented documents• HTML DOM structure is site specific• Spatial arrangements are rarely explicit• Spatial layout is hidden in complex nesting of layout elements• Intricate DOM treee structures are conceptually difficult to query for the user (or a tool!) WeST – Web Science & Steffen Staab Slide 28 Technologies staab@uni-koblenz.de
    • 29. Related WorkWeb Query languages Xpath 1.0 and XQuery1.0  Established  Too difficult to use for scraping from intricate DOM structuresVisual languages Spatial Graph Grammars [Kong et al.] are quite complex in term of both usability and efficiency Algebras for creating and querying multimedia interactive presentations (e.g. ppt) [Subrahmanian et al.]Web wrapper induction exploiting visual interface[Gottlob et al.] [Sahuguet et al.]  generate XPath location paths of DOM nodes  can benefit from using Spatial XPathWeST – Web Science & Steffen Staab Slide 29Technologies staab@uni-koblenz.de
    • 30. OutlineThe Social Media-Case The Bio-Case Motivation State-of-the-Art Core idea of SXPath SXPath Language  Spatial Data Model  Syntax & Semantics  Complexity Implementation EvaluationWeST – Web Science & Steffen Staab Slide 30Technologies staab@uni-koblenz.de
    • 31. Idea: Use Spatial Relations among DOM Nodes b eWeST – Web Science & Steffen Staab Slide 31Technologies staab@uni-koblenz.de
    • 32. Idea: Use Spatial Relations among DOM NodesWeST – Web Science & Steffen Staab Slide 32Technologies staab@uni-koblenz.de
    • 33. Idea: Use Spatial Relations among DOM NodesWeST – Web Science & Steffen Staab Slide 33Technologies staab@uni-koblenz.de
    • 34. Spatial DOM (SDOM)WeST – Web Science & Steffen Staab Slide 34Technologies staab@uni-koblenz.de
    • 35. Spatial Relations Among Nodes Rectangular Cardinal Relations (RCR) r1 E:NE r2 Spatial models allow for expressing disjunctive relations among regions Topological Relations WeST – Web Science & Steffen Staab Slide 35 Technologies staab@uni-koblenz.de
    • 36. XPath ExampleWeST – Web Science & Steffen Staab Slide 37Technologies staab@uni-koblenz.de
    • 37. SXPath ExampleWeST – Web Science & Steffen Staab Slide 38Technologies staab@uni-koblenz.de
    • 38. WeST – Web Science & Steffen Staab Slide 39Technologies staab@uni-koblenz.de
    • 39. From XPath 1.0 towards Spatial Querying with SXPathSXPath features adopts intuitive path notation:  axis::nodetest [pred]* adds to XPath  spatial axes  spatial position functions natural semantics for spatial querying maintains polynomial time combined complexityWeST – Web Science & Steffen Staab Slide 40Technologies staab@uni-koblenz.de
    • 40. Why SXPath? resilient wrappers an XPath for familiarity Information extraction Simplicity human oriented efficiency web applicationsWeST – Web Science & Steffen Staab Slide 41Technologies staab@uni-koblenz.de
    • 41. OutlineThe Social Media-Case The Bio-Case Motivation State-of-the-Art Core idea of SXPath SXPath Language  Spatial Data Model  Syntax & Semantics  Complexity Implementation EvaluationWeST – Web Science & Steffen Staab Slide 42Technologies staab@uni-koblenz.de
    • 42. Spatial DOM (SDOM)WeST – Web Science & Steffen Staab Slide 43Technologies staab@uni-koblenz.de
    • 43. Spatial Navigation AxesWeST – Web Science & Steffen Staab Slide 44Technologies staab@uni-koblenz.de
    • 44. Spatial Navigation AxesWeST – Web Science & Steffen Staab Slide 45Technologies staab@uni-koblenz.de
    • 45. Syntax of SXPathWeST – Web Science & Steffen Staab Slide 46Technologies staab@uni-koblenz.de
    • 46. Complexity ResultsWeST – Web Science & Steffen Staab Slide 50Technologies staab@uni-koblenz.de
    • 47. OutlineThe Social Media-Case The Bio-Case Motivation State-of-the-Art Core idea of SXPath SXPath Language  Spatial Data Model  Syntax & Semantics  Complexity Implementation EvaluationWeST – Web Science & Steffen Staab Slide 51Technologies staab@uni-koblenz.de
    • 48. SXPath System ArchitectureWeST – Web Science & Steffen Staab Slide 52Technologies staab@uni-koblenz.de
    • 49. SXPath SystemWeST – Web Science & Steffen Staab Slide 53Technologies staab@uni-koblenz.de
    • 50. Results of ExperimentsWeST – Web Science & Steffen Staab Slide 54Technologies staab@uni-koblenz.de
    • 51. Formative User StudyWeST – Web Science & Steffen Staab Slide 55Technologies staab@uni-koblenz.de
    • 52. Summative User StudyWeST – Web Science & Steffen Staab Slide 56Technologies staab@uni-koblenz.de
    • 53. Summative User StudyWeST – Web Science & Steffen Staab Slide 57Technologies staab@uni-koblenz.de
    • 54. Summative User StudyWeST – Web Science & Steffen Staab Slide 58Technologies staab@uni-koblenz.de
    • 55. Existing Extensions to PDFWeST – Web Science & Steffen Staab Slide 59Technologies staab@uni-koblenz.de
    • 56. Page Header Text Area and Paragraphs Table Item List Page Number Page FooterWeST – Web Science & Steffen Staab Slide 60Technologies staab@uni-koblenz.de
    • 57. OutlineThe Social Media Case The Bio-Case Motivation  Motivation State-of-the-Art  The (Biochemical) Deep Core idea of SXPath Web SXPath Language  Contributions  Spatial Data Model  Page-level wrapper induction  Syntax & Semantics  Site-wide wrapper  Complexity generation Implementation  Error Correction by Evaluation Mutual Reinforcement  Conclusions and Future DirectionsWeST – Web Science & Steffen Staab Slide 61Technologies staab@uni-koblenz.de
    • 58. >1000 Life Science DBs, number growing quicklyWeST – Web Science & Steffen Staab Slide 62Technologies staab@uni-koblenz.de
    • 59. Biochemical Web Sites: Observations - 1 Labeled Data Full survey: http://sabio.villa- bosch.de/labelsurvey.html (404) Total Labeled Unlabeled Unlabeled (Redundant) 754 719 19 16 Table 1: Data fields across 20 Biochemical Web sites WeST – Web Science & Steffen Staab Slide 63 Technologies staab@uni-koblenz.de
    • 60. Biochemical Web Sites: Observations - 2 Dynamic Web Pages WeST – Web Science & Steffen Staab Slide 64 Technologies staab@uni-koblenz.de
    • 61. Biochemical Web Sites: Observations - 3 Rich Site StructureWeST – Web Science & Steffen Staab Slide 65Technologies staab@uni-koblenz.de
    • 62. Biochemical Web Sites: Observations - 4 Web Services  Survey: 11 of 100 Databases1 provide APIs  Incomplete coverage  Varying granularity  No semantics in the service description 1 Databases indexed by the Nucleic Acids Research Journal (http://www3.oup.co.uk/nar/database/). Complete survey available at http://sabiork.villa-bosch.de/index.html/survey.htmlWeST – Web Science & Steffen Staab Slide 66Technologies staab@uni-koblenz.de
    • 63. Biochemical Web Sites: Implications Induce Wrapper Induce Wrapper Induce WrapperWeST – Web Science & Steffen Staab Slide 67Technologies staab@uni-koblenz.de
    • 64. Contributions Unsupervised Page-Level Wrapper Induction Unsupervised Site-Wide Wrapper Induction (Site Structure Discovery) Automatic Error Detection and Correction by Mutual ReinforcementWeST – Web Science & Steffen Staab Slide 68Technologies staab@uni-koblenz.de
    • 65. Page-Level Wrapper Induction – 1 D1 = {C00221, beta-D-Glucose, …, R01520, 1.1.1.47,…} O1 = {Entry, Name,…, Reaction, R00026, Enzyme,…, 3.2.1.21} //*[text()] D2 = {C00185, Cellobiose,…, R00306, 1.1.99.18,… } O2 = {Entry, Name,…, Reaction, R00026, Enzyme,…, 3.2.1.21} WeST – Web Science & Steffen Staab Slide 69 Technologies staab@uni-koblenz.de
    • 66. Page-Level Wrapper Induction - 2 Reclassify – Growing Data RegionsWeST – Web Science & Steffen Staab Slide 70Technologies staab@uni-koblenz.de
    • 67. Page-Level Wrapper Induction - 3 D1 = {C00221, beta-D-Glucose, …, R01520, 1.1.1.47, 3.2.1.21 …} O1 = {Entry, Name,…, Reaction, R00026, Enzyme,…,} D2 = {C00185, Cellobiose,…, R00306, 1.1.99.18, 3.2.1.21 … } O2 = {Entry, Name,…, Reaction, R00026, Enzyme,…,}WeST – Web Science & Steffen Staab Slide 71Technologies staab@uni-koblenz.de
    • 68. Page-Level Wrapper Induction - 4 Selecting Labels for Data html/…./table[1]/tr[8]/td[1]/…/code[1]/a[1] (“1.1.1.47” ) html/…./table[1]/tr[6]/th[1]/…/code[1]/ (“Reaction”) html/…./table[1]/tr[8]/th[1]/…/code[1]/ (“Enzyme”)WeST – Web Science & Steffen Staab Slide 72Technologies staab@uni-koblenz.de
    • 69. Page-Level Wrapper Induction - 5 Anchor the Path Enzyme - html/table[1]/tr[8]/th[1]/code[1]/ html/table[1]/tr[8]/td[1]/code[1]/a[1] html/table[1]/tr[8]/td[1]/code[1]/a[2] //*[text()=‘Enzyme’] ../…./../td[1]/code[1]/a[position()≥2]/text() Pivot Relative GeneralizeWeST – Web Science & Steffen Staab Slide 73Technologies staab@uni-koblenz.de
    • 70. Selected Sources KEGG, ChEBI, MSDChem  Basic qualitative data  Popular  Overlapping/complementary dataWeST – Web Science & Steffen Staab Slide 74Technologies staab@uni-koblenz.de
    • 71. Wrapper Induction - Evaluation SOURCE #L #D #S TP FN FP P R KEGG Compound 10 762 3 411 351 46 89.9 53.9 http://www.genome.jp/kegg/ compound/ 15 759 3 0 100 99.6 KEGG Reaction 10 205 3 173 32 0 100 84.4 http://www.genome.jp/kegg/ reaction/ 15 205 0 0 100 100 ChEBI 22 831 3 595 236 41 93.5 71.6 http://www.ebi.ac.uk/chebi 15 829 2 0 100 99.7 MSDChem 30 600 3 600 0 20 96.7 100 http://www.ebi.ac.uk/msd-srv/msdchem/ 15 600 0 20 96.7 100 Average (based on final wrappers for each source) 99.1 99.8 Table 2: Page-level wrapper induction results, 20 test pages (L=Labels, D=Data entries, S=Training pages) ~9 samples – ~99% P, ~98% RWeST – Web Science & Steffen Staab Slide 75Technologies staab@uni-koblenz.de
    • 72. Site-Wide Wrapper Induction: Observations Not all pages contain data (e.g. Legal disclaimers, contact pages, navigational menus)  An efficient approach should ignore these pages  We dont need to learn the entire site-structure WeST – Web Science & Steffen Staab Slide 76 Technologies staab@uni-koblenz.de
    • 73. Site-Wide Wrapper Induction: Observations - 2 Classified Link-Collections point to data-intensive pages of the same class.WeST – Web Science & Steffen Staab Slide 77Technologies staab@uni-koblenz.de
    • 74. Site-Wide Wrapper Induction: Observations - 3 Pages belong to the same class describe the same concepts  Some concepts are sometimes omitted  Ordering is always the sameWeST – Web Science & Steffen Staab Slide 78Technologies staab@uni-koblenz.de
    • 75. Site-Wide Wrapper Induction 1. Start with C0 L1 S={C0} 2. Follow all classified link-collections C0 C1 3. Generate wrappers L3 for each set of target L2 pages C2 4. Determine if new C3 class is formed 5. Add navigation step If C0 != Ci (i>0) S=S+Ci; 6. Repeat 2 – 5 for each Navigation Steps new class formed in 4 W= {(C0 → L1→ C0), (C0 → L2→ C2), (C0 → L3→ C3)}WeST – Web Science & Steffen Staab Slide 79Technologies staab@uni-koblenz.de
    • 76. Site-Wide Wrapper Induction – Evaluation SOURCE #C #C’ #D TP FN FP P R MSDChem 1 1 N/A N/A N/A N/A N/A N/A ChEBI 3 1 1711 1195 516 0 100 69.8 KEGG 10 7 6223 5044 1179 188 97 81.1 Average 98.5 75.5 Table 3: Site-wide wrapper induction results, 20 test pages for each class (C=Classes, C =Classes discovered, D=Data entries) WeST – Web Science & Steffen Staab Slide 80 Technologies staab@uni-koblenz.de
    • 77. Error Detection and Correction:Mutual Reinforcement Observation: Certain data reappear on more than one class of pagesWeST – Web Science & Steffen Staab Slide 81Technologies staab@uni-koblenz.de
    • 78. Error Detection and Correction:Mutual Reinforcement Reinforcement if reappearing data correctly classified as Data Otherwise it points to misclassification  Label-Data Mismatch • Correction: Introduce more samples  Label-Label Mismatch • Cannot be detectedWeST – Web Science & Steffen Staab Slide 82Technologies staab@uni-koblenz.de
    • 79. Where to go next? Reverse engineering production 1. LOD emitting RDF & RDFS 2. Navigation model what belongs to what 3. Interaction model (- not treated at all by us so far -) 4. Layout model spatial positioning Capture this generative model using machine learning  Relational learning • Markov logic programmes? • …?WeST – Web Science & Steffen Staab Slide 83Technologies staab@uni-koblenz.de
    • 80. Bibliography Linda d’Oro, Massimo Ruffolo, Steffen Staab. SXPath – Extending XPath towards Spatial Querying on Web Documents. In: PVLDB – Proceedings of the VLDB Endowment, 4(2): 129-140, 2010. S. Mir, S. Staab, I. Rojas. Site-Wide Wrapper Induction for Life Science Deep Web Databases. In: DILS-2009 – Proc. of the Data Integration in the Life Sciences Workshop, Manchester, UK, July 20-22, LNCS, Springer, 2009. Saqib Mir, Steffen Staab, Isabel Rojas. An Unsupervised Approach for Acquiring Ontologies and RDF Data from Online Life Science Databases. In: 7th Extended Semantic Web Conference (ESWC2010), Heraklion, Greece, May 30-June 3, 2010, pp. 319-333.WeST – Web Science & Steffen Staab Slide 84Technologies staab@uni-koblenz.de
    • 81. WeST – Web Science & Technologies University of Koblenz Landau, GermanyThank you for your attention!

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