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Lecture semantic lifting_presentation


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  • 1. SemanticSemantic CMS Community Lifting for Traditional Content Lecturer Resources Organization Date of presentation Co-funded by the 1 Copyright IKS Consortium European Union
  • 2. Page: Part I: Foundations(1) Introduction of Content Foundations of Semantic (2) Management Web Technologies Part II: Semantic Content Part III: Methodologies Management Knowledge Interaction Requirements Engineering(3) (7) and Presentation for Semantic CMS(4) Knowledge Representation and Reasoning (8) Designing Semantic CMS Semantifying(5) Semantic Lifting (9) your CMS Storing and Accessing Designing Interactive(6) Semantic Data (10) Ubiquitous Copyright IKS Consortium
  • 3. Page: 3 What is this Lecture about? We have learned ... Part II: Semantic Content  ... how to build ontologies Management representing complex Knowledge Interaction (3) knowledge domains. and Presentation  ... a way to reason about knowledge. (4) Knowledge Representation and Reasoning We need a way ...  ... to extract knowledge from (5) Semantic Lifting content in a automatic way  Semantic Lifting Storing and Accessing (6) Semantic Data Copyright IKS Consortium
  • 4. Page: 4 Overview What is semantic lifting? Core concepts Scenarios Requirements Technologies  Semantic Reengineering  Semantic Enhancements of textual content Copyright IKS Consortium
  • 5. Page: 5 What is “Semantic Lifting”? Semantic Lifting refers to the process of associating content items with suitable semantic objects as metadata to turn “unstructured” content items into semantic knowledge resources Semantic Lifting makes explicit “hidden” metadata in content items Copyright IKS Consortium
  • 6. Page: 6 Semantic Lifting Targets Semantic Reengineering of structured data  Semantic Lifting harmonizes metadata representations  Semantic Lifting reengineers data from an existing resource so that the data from the resource can be reused within in a semantic repository Semantic Content Enhancement  Semantic Lifting generates additional metadata and annotations by semantic analysis of content items  Semantic Lifting classifies content objects by means of semantic annotations Copyright IKS Consortium
  • 7. Page: 7 Structured Content Structured content provides implicit semantics through the structure definition  Table definitions in relational databases, XML schemata, field definitions for adressbooks, calendars, etc. Application programs are designed to „know“ how to interpret the structures and the data within. Semantic Lifting is used for Reengineering to support data exchange and seamless interoperability between different systems Copyright IKS Consortium
  • 8. Page: 8 Unstructured Content Unstructured content  Images, texts, videos, music, web pages composed of various types of media items  Meaningful only to humans not to machines Content must be described semantically by metadata to become meaningful to machines, e.g. what the text or image is about. Semantic Lifting is used as content enhancement Copyright IKS Consortium
  • 9. Page: 9 Mixed Content  No dichotomy of structured and unstructured content  Structured databases are used to store unstructured content types, such as texts, images etc.  Documents can be composed of unstructured content items such as free text and images as well as more structured information, e.g. tables and chartsFree text Structured content Copyright IKS Consortium
  • 10. Page: 10 Metadata: Variants Metadata exist in many forms:  Free text descriptions  Descriptive content related keywords or tags from fixed vocabularies or in free form  Taxonomic and classificatory labels  Media specific metadata, such a mime-types, encoding, language, bit rate  Media-type specific structured metadata schemes such as EXIF for photos, IPTC tags for images, ID3-tags for MP3, MPEG-7 for videos, etc.  Content related structured knowledge markup, e.g. to specify what objects are shown in an image or mentioned in a text, what the actors are doing, etc. Copyright IKS Consortium
  • 11. Page: 11 Metadata: Variants Inline metadata are part of content  ID3 tags embedded in MP3 files Offline metadata are kept separate from content Copyright IKS Consortium
  • 12. Page: 12 Formal semantic metadata Data representation in a formalism with a formal semantic interpretation that defines the concept of (logical) entailment for reasoning:  Soundness: conclusions are valid entailments  Completeness: every valid entailment can be deduced  Decidability: a procedure exists to determine whether a conclusion can be deduced Embodiments:  Logics  Knowledge Representation Systems, Description Logics  Semantic Web: RDF, OWL Copyright IKS Consortium
  • 13. Page: 13 „Semantics“ in CMS CMSsystems provide various methods to include metadata  Organize content in hierarchies  Hierarchical taxonomies  Attachment of properties to content items for metadata  Content type definitions with inheritance These methods are used in CMS systems in ad-hoc fashion without clear semantics. Therefore no well- defined reasoning is possible. Copyright IKS Consortium
  • 14. Page: 14 Semantic Lifting Usage Content Creation and Acquisition  Authoring content  Support content editors in providing metadata of specified types  Uploading external content/documents  automatic extraction and analysis, e.g. for indexing  Importing content from external sources/documents  Integration of external content into content repository  Content needs to be transformed to match internal CMS structures and metadata schemes  Crossreferencing/linking among CMS content items and external content  Detect related or additional content  Add pointers/links to related or additional content Copyright IKS Consortium
  • 15. Page: 15 Semantic Lifting Usage Access to external documents and content repositories  Semantic harmonization with CMS semantic structures  Semantic interoperability in data exchange with other content repositories TheCMS needs to understand the data structures used by external services and programs  E.g synchronization of a local calendar from Outlook with an external calendar based on iCalendar format  E.g. Importing RDF from a Linked Data endpoint such as dbpedia TheCMS must present its data in a form understood by external target services or programs Copyright IKS Consortium
  • 16. Page: 16 Semantic Lifting Usage Publishing content with metadata  Metadata need to be transformed into a form compatible with the publication format  E.g. converting FreeDB metadata into ID3 tags for inclusion in an MP3 file Copyright IKS Consortium
  • 17. Page: 17 Publishing Web Content with semantic metadata Augmenting web content with structured information becomes increasingly important Several methods have emerged in recent years to include structured metadata in Web pages  Microformats  RDFa  Microdata (HTML5) Supported by the major search engines to improve search and result presentation, e.g. Google („Rich Snippets), Bing, Yahoo Copyright IKS Consortium
  • 18. Page: 18 Augmenting Web Content The HTML code contains a review of a restaurant in plain text using only line breaks for structuring Without specialized information extraction analysis tools it cannot be interpreted, e.g. that it is a review (of what and when?), who the reviewer was, etc.<div>L’Amourita PizzaReviewed by Ulysses Grant on Jan 6.Delicious, tasty pizza on Eastlake!LAmourita serves up traditional wood-fired Neapolitan-style pizza,brought to your table promptly and without fuss. An ideal neighborhoodpizza joint.Rating: 4.5</div> Copyright IKS Consortium
  • 19. Page: 19 Microformats Same text but additional span elements with class attributes to encode the type of contained information (hReview) and the properties of that type<div class="hreview"> <span class="item"> <span class="fn">L’Amourita Pizza</span> </span> Reviewed by <span class="reviewer">Ulysses Grant</span> on <span class="dtreviewed"> Jan 6<span class="value-title" title="2009-01-06"></span> </span>. <span class="summary">Delicious, tasty pizza on Eastlake!</span> <span class="description">LAmourita serves up traditional wood-fired Neapolitan-style pizza, brought to your table promptly and without fuss. An ideal neighborhood pizza joint.</span> Rating: <span class="rating">4.5</span></div> Copyright IKS Consortium
  • 20. Page: 20 RDFa Same text but additional attributes and span elements encoding a RDF structure:  namespace declaration of the used ontology  RDF class encoded by typeof attribute and its properties by a property attribute<div xmlns:v="" typeof="v:Review"> <span property="v:itemreviewed">L’Amourita Pizza</span> Reviewed by <span property="v:reviewer">Ulysses Grant</span> on <span property="v:dtreviewed" content="2009-01-06">Jan 6</span>. <span property="v:summary">Delicious, tasty pizza on Eastlake!</span> <span property="v:description">LAmourita serves up traditional wood-fired Neapolitan-style pizza, brought to your table promptly and without fuss. An ideal neighborhood pizza joint.</span> Rating: <span property="v:rating">4.5</span></div> Copyright IKS Consortium
  • 21. Page: 21 Microdata (HTML5) Same text but additional attributes and span elements:  A class declaration as value of an itemtype attribute and its properties as values of an itemprop attribute<div> <div itemscope itemtype=""> <span itemprop="itemreviewed">L’Amourita Pizza</span> Reviewed by <span itemprop="reviewer">Ulysses Grant</span> on <time itemprop="dtreviewed" datetime="2009-01-06">Jan 6</time>. <span itemprop="summary">Delicious, tasty pizza in Eastlake!</span> <span itemprop="description">LAmourita serves up traditional wood-firedNeapolitan-style pizza, brought to your table promptly and without fuss. An ideal neighborhood pizzajoint.</span> Rating: <span itemprop="rating">4.5</span> </div></div> Copyright IKS Consortium
  • 22. Page: 22 Lifting Requirements: OverviewTop-level requirements  Semantic Associations with Content  Semantic Harmonization  Semantic Linking  Interactive Lifting  Customizability  Semantically Transparent Structured Content Sources Copyright IKS Consortium
  • 23. Page: 23 Semantic Associations with Content Unstructured content and information must be supplied with structured semantic annotations and metadata.  Support for various content/media types  Information extraction from text, topic classification, image tagging, …  Support for creation of semantic annotations in content authoring Copyright IKS Consortium
  • 24. Page: 24 Semantic Harmonization Metadataand annotations must be harmonized with requirements for semantic processing in the CMS  Reengineering methods, interpreters and wrappers for all types and formats of metadata and annotations, e.g. tags, microformats, XML Metadata ( MPEG-7, …), ID3 tags, EXIF data, …  Ensure semantic interoperability of data and annotation schemes within the CMS and across external resources  Ontology mapping and harmonization of annotations  Externalmetadata  Metadata generated by semantic analysis Copyright IKS Consortium
  • 25. Page: Slide 25 Semantic Linking Liftingmust enable the interlinking of content objects by semantic relationships.  Internal linking of content items within the CMS  links to external resources, e.g. Linked Open Data  Establish semantic relatedness of content for different views as well as different search, navigation and browsing strategies, …  Directsemantic links among content items and metadata  Similarity relations over sets of content items  Clustering of content items Copyright IKS Consortium
  • 26. Page: Slide 26 Interactive Lifting Lifting must interact with CMS users.  Suggest semantic annotations during content creation  Support for various publishing formats such as microformats, RDFa, etc.  Automatic annotations (autotagging) with optional correction option  Learning capabilities and adaptability of automatic annotation components from user feedback Copyright IKS Consortium
  • 27. Page: 27 Customizability Liftingcomponents must be customizable by CMS users/customers.  Users must not be restricted to predefined vocabularies, ontologies, …  Domain ontologies, terminologies, tag sets are defined by CMS users/customers.  Browsers and editors for component resources are necessary. Copyright IKS Consortium
  • 28. Page: 28 Transparent Structured Content Sources Structured content sources need to be reengineered to semantic resources  Support uniform data access to structured content repositories, e.g. SPARQL end points based on D2RQ technologies for transparent access to RDF and non-RDF databases  Extraction of ontologies from database structures, schemata, XML, resources, …  Alignment and mapping of the descriptions Copyright IKS Consortium
  • 29. Page: 29 Semantic Reengineering of structured data sources Focus on tools for reengineering structured data sources to RDF representations Many tools and platforms for  D2R Servers: Exhibit relational DBs as RDF  Talis platform: Linked Open Data  Triplify: like D2R but in PHP  Virtuoso middleware  Krextor/OntoCape: generating RDF from XML  Various Transformers for inducing RDF ontologies and instance data from XSD and XML More details in presentation on Knowledge Representation (KReS) Copyright IKS Consortium
  • 30. Page: 30 Semantic Content Enhancements: Overview Focus here is on textual content Metadata Extraction from existing content in various formats to make embedded metadata explicit Information Extraction from textual content:  Named Entities  Coreference  Relationships Classification and Clustering of content items  Statistical methods and tools  Semantic classification based on ontological definitions Copyright IKS Consortium
  • 31. Page: 31 Information Extraction Rule based approaches for shallow text analysis  Usually based on Finite State technology: fast, robust  Cascaded processing  Based on templates as target structures to be filled  Example platforms:  GATE  SProUT Can be used for nearly any kind of extraction/annotation task, including Named-Entity-Recognition (NER) Easy customization Copyright IKS Consortium
  • 32. Page: 32 Information Extraction Semi-supervised learning approaches  Rule induction from corpora  Use example annotations as seeds for bootstrapping  Pattern Rules learned from contextual features with generalization over contexts Copyright IKS Consortium
  • 33. Page: 33 Named Entities Statistical Approaches: examples  Lingpipe: Hidden Markov Models  OpenNLP: Maximum Entropy Models  Stanford NER: Conditional Random Fields Statistical models crated by supervised learning techniques  Large annotated corpora required Customization diffcult except by re-annotation/re-training Not suitable for any type of named entity Copyright IKS Consortium
  • 34. Page: 34NER Document Copyright IKS Consortium
  • 35. Page: 35NER Markup for a Web Page Copyright IKS Consortium
  • 36. Page: 36 IE TemplateA Person Template (asTyped Featured Structure)instantiated from text.The template supports theextraction of variousproperties of a person. Copyright IKS Consortium
  • 37. Page: 37 Classification Assign a data item to some predefined class Statistical classification Numerous methods, e.g.:  Bayes classifiers  K-Nearest Neighbor (KNN)  Support Vector Machines (SVM) Copyright IKS Consortium
  • 38. Page: 38 Semantic Classification Semanticclassification in Knowledge Representation Formalisms  Infer the item„s class from the item„s properties by matching them with the class definitions: Which classes allow for these properties?Assume that our ontology contains 2 classes with some properties SpatialThing: latitude, longitude PopulatedPlace: populationPaderborn is an object with latidude „51°43′0″N“, longitude „8°46′0″E“ and apopulation of 146283.Then we can infer that Paderborn is a SpatialThing as that are the things thathave latitudes and longitudes in our ontology. Also, we can infer that it is aPopulatedPlace as that are the things that have a population. Copyright IKS Consortium
  • 39. Page: 39 Clustering Detection of classes in a data set Partitioning data into classes in an unsupervised way with high intra-class similarity low inter-class similarity Main variants:  Hierarchical clustering  Agglomerative  Partitioning clustering  K-Means Copyright IKS Consortium
  • 40. Page: 40 Tools for Classification and Clustering Generic:  WEKA: Java library implementing several dozen methods for data mining. Application to textual data requires special preprocessing. Text:  MALLET: Java library with implementations of major methods for text and document classification and clustering Copyright IKS Consortium
  • 41. Page: 41 Evaluation Measures Standard evaluation measures for IE/IR etc. systems: tp tn  Accuracy: acc tp fp tn fn tp tp = true positive  Precision: prec tp fp tn = true negative  Recall: recall tp fp = false positive tp fn fn = false negative  F-Measure : F 2 prec recall prec recall Copyright IKS Consortium
  • 42. Page: 42 Evaluation Measures: Classification A confusion matrix which reports on the classification of 27 wines by grape variety. The reference in this case is the true variety and the response arises from the blind evaluation of a human judge. =9/(9+3+1) Many-way Confusion Matrix Response Cabernet Syrah Pinot Precision Recall F-Measure Refer- Cabernet 9 3 0 0,69 0,75 0,72 ence Syrah 3 5 1 0,56 0,56 0,56 Pinot 1 1 4 0,80 0,67 0,73 Macro average 0,68 0,66 0,67 Overall accuracy 0,67 =4/(1+1+4) Copyright IKS Consortium
  • 43. Page: 43 Evaluation Measures: NER Reference annotations:  [Microsoft Corp.] CEO [Steve Ballmer] announced the release of [Windows 7] today Recognized annotations:  [Microsoft Corp.] [CEO] [Steve] Ballmer announced the release of Windows 7 [today]-> Microsoft Corp. CEO Steve Ballmer announced the release of Windows 7 today Counts EntitiesPrecision: 1/(1+3) = 0,25 TP 1 [Microsoft Corp.]Recall: 1/(1+2) = 0,33 TNF-Measure: FP 3 [CEO] [Steve] 2*0,25*0,33/(0,25+0,33) = 0,28 [today] FN 2 [Windows 7] [Steve Ballmer] Copyright IKS Consortium
  • 44. Page: 44 NER Evaluation Nobel Prize Corpus from NYT, BBC, CNN 538 documents (Ø 735 words/document)  28948 person, 16948 organization occurrences Sprout Calais Stanford OpenNLP NER Precision 77,26 94,22 73,21 57,69 Recall 65,85 86,66 73,62 42,86 F1 71,10 90,28 73,41 49,18 Copyright IKS Consortium
  • 45. Page: 45 References Microformats: RDFa: Google Rich Snippets: Linked Data: Linked Data: Heath and Bizer, Linked Data: Evolving the Web into a Global Data Space. Morgan & Claypool, 2011. (Online: Information Extraction: Moens, Information Extraction: Algorithms and Prospects in a Retrieval Context. Springer 2006 Text Mining: Feldman and Sanger, The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data, CUP, 2007 Copyright IKS Consortium