Patterns of Semantic Integration


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This is a general presentation that is appropriate for anyone that is just learning concepts of semantic integration. This presentation covers some of the background concepts underlying semantics (Ogden\'s Semantic Triangle), lexical and conceptual mapping, metadata registries, metadata discovery and semantic thinking. Excellent for an introductory class in business semantics.

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  • Patterns of Semantic Integration

    1. 1. Patterns of Semantic Integration Riding the Next Wave April 2006 Dan McCreary President Dan McCreary & Associates [email_address] (952) 931-9198 Managed Metadata Solutions
    2. 2. Creative Commons 2.5 <ul><li>Attribution . You must attribute the work in the manner specified by the author or licensor. </li></ul><ul><li>Noncommercial . You may not use this work for commercial purposes. </li></ul><ul><li>Share Alike . If you alter, transform, or build upon this work, you may distribute the resulting work only under a license identical to this one. </li></ul>$ BY:
    3. 3. Patterns of Semantic Integration <ul><li>Our ever increasing understanding of solid-state physics has allowed Moore’s Law to proceed unabated for the last 40 years.  Exciting developments in quantum physics, nanotechnology and molecular self-assembly will continue this trend for the foreseeable future.  But why is it that an instructor can’t quickly import a database of 10,000 subject-appropriate lesson plans and quiz items into their learning-management system and dynamically adjust classroom content and assessments to individual student learning styles and interests?  The key to this and other computer-to-computer interoperability challenges lie in the difficulty computer systems have in finding and precisely exchanging data.  Enter the Semantic Web .  The designers of the current world-wide-web realized that the gateway to this does not require faster computers and networks but instead lies in the careful publishing and exchange of data semantics (or meaning) and the precise publishing data-that-describes-data (metadata) in a machine-readable structure.  This presentation will review patterns that researches around the world are using to make the job of computer integration easier allowing even ultimate frisbee™ coaches access to vast amounts of structured information. </li></ul>
    4. 4. Background for Dan McCreary <ul><li>Computer Consultant in Minneapolis </li></ul><ul><li>Became obsessed at a young age on computer-to-computer communications </li></ul><ul><li>Interested in OO, XML, semantics and business strategy </li></ul>
    5. 5. Pattern Themes <ul><li>We learn how to create and use models of the world to discover underlying patterns of nature </li></ul><ul><li>Computer-to-computer communication also uses models and allows us to find of underlying patterns to solve these problems </li></ul>
    6. 6. Agenda <ul><li>The steps required for precise exchange of information between computer systems </li></ul><ul><li>Define “semantics” and key concepts in the semantic web </li></ul><ul><ul><li>HTML, XML, RDF </li></ul></ul><ul><li>Discuss limitations of current HTML web and XML </li></ul><ul><li>Show how Semantic Web technologies solve many of these problems </li></ul><ul><li>Semantic patterns </li></ul><ul><li>Predictions </li></ul><ul><li>References </li></ul>
    7. 7. 1970 Sci-Fi Classic: “The Forbin Project” A New Intersystem Language! Lesson: Before you take over the world you must exchange semantically precise metadata!
    8. 8. Moore’s Law Creative Commons 1.0 Courtesy of Ray Kurzweil and Kurzweil Technologies, Inc
    9. 9. Thesis: We Need Semantics <ul><li>For the next revolution in computing </li></ul><ul><ul><li>We don’t need faster CPUs </li></ul></ul><ul><ul><li>We don’t need larger hard drives </li></ul></ul><ul><ul><li>We don’t need faster networks </li></ul></ul><ul><ul><li>We don’t need more HTML linking </li></ul></ul><ul><li>We need to link our concepts using semantic technologies </li></ul>
    10. 10. The Agent Vision <ul><li>The Semantic Web will bring structure to the meaningful content of Web pages, creating an environment where software agents roaming from page to page can readily carry out sophisticated tasks for users. </li></ul>The Semantic Web A new form of Web content that is meaningful to computers will unleash a revolution of new possibilities By Tim Berners-Lee, James Hendler and Ora Lassila
    11. 11. Overlapping Terminology Data Warehouse Data Mining Enterprise Application Integration (EAI) Metadata Discovery Statistical Analysis Pattern Discovery Relational Database Metadata Semantic Web Business Semantics Data Dictionary HTML Web
    12. 12. Computer Science Is About Abstraction Time Level of Abstraction 10100101 Machine Language MOV R0, A1 BNE F32C Assembly Language DO I=1, 100 I=I+1 FORTRAN Proc(i1, i2, o1) Structured Programming Object-oriented Programming XML GUI
    13. 13. Person to Person Dialog Sound Words Concepts Sentences Conversation Problem Solving higher abstraction
    14. 14. Computer to Computer Dialog Internet XML Tags Documents/XML Schema Graphs/Ontologies/RDF/OWL Semantic Integration Agents You Are Here
    15. 15. Semantic Triangle Concept Referent Refers To Symbolizes Stands For “ cat” Physical Objects A pattern of neural activity in our brain Symbol Ogden, C. K., & Richards, I. A. (1923) The Meaning of Meaning “ katze” (German) “ gato” (Spanish)
    16. 16. Symbols Can Only Directly Link to Concepts <ul><li>The link between a symbol is an INDIRECT link </li></ul><ul><li>The referent MUST pass through the Concept </li></ul><ul><li>Only symbols can be transmitted between computers </li></ul>Ogden, C. K., & Richards, I. A. (1923) The Meaning of Meaning Concept Referent “ cat” Symbol
    17. 17. The Problem of Semantic Ambiguity Did you say you were looking for mixed nuts ? context=food context=hardware People use context to derive the correct meaning.
    18. 18. 59 meanings of &quot;run&quot; &quot;run&quot; 18 noun &quot;senses&quot; 41 verb &quot;senses&quot; tally test footrace streak play … move fast scat go operate has form … &quot;the kids ran to the store&quot; &quot;the Yankees scored a run in the bottom of the 9th&quot; &quot;The experiment ran for over an hour&quot; &quot;her run of luck was just starting&quot; &quot;she broke mile run record&quot; &quot;the football 3 rd down play was a run &quot; &quot;13 other noun meanings…&quot; &quot;I would run from a ticking bomb.&quot; &quot;The path runs up the hill.&quot; &quot;you need training to run this machine.&quot; &quot;the movie plot runs like this.&quot; &quot;36 other verb meanings…&quot; Source: WordNet at Context
    19. 19. Analogy: English Dictionary source: Note: people use context to find the correct meaning. Term Metadata (data about data) Definitions
    20. 20. Word Senses “ run” tally test footrace streak play move fast scat go operate has form duration A single word maps To many concepts
    21. 21. Synonym Ring <ul><li><Person> Joe Smith <Person> </li></ul><ul><li><Individual> Joe Smith <Individual> </li></ul><ul><li><Human> Joe Smith <Human> </li></ul>Joe Smith Many symbols for the same object Refers To Symbolizes Stands For
    22. 22. I’m Thinking of an Animal… <ul><li>It has four legs </li></ul><ul><li>It has fur </li></ul><ul><li>It chases mice </li></ul><ul><li>It goes “meow” </li></ul>If you describe enough of the properties of a concept, you can have reasonable assurances that they are the same Note: since “concepts” are neural patterns in the brain the concept of “exact” is difficult to measure
    23. 23. Concept Linking Question: How can you tell if two concepts are the same if two systems don’t share the same symbol? Answer: If they have the same properties (and relationships) you can assume with reasonable probability they are the same concepts. symbol
    24. 24. Semantics is About Concept Linking <ul><li>Wouldn’t it be nice… </li></ul><ul><ul><li>If computers could name things internally or on a web site however they liked (keep using the current web) </li></ul></ul><ul><ul><li>But we could always link those names back to a centralized database of concepts </li></ul></ul><ul><ul><li>Computers could do this automatically just like they translate domain names ( into IP addresses ( </li></ul></ul><ul><ul><li>Then we could communicate precisely without dictating the names that are used inside a computer system or on a web page </li></ul></ul>
    25. 25. HTML Sample <ul><li><title> The Problem of Semantics </title> </li></ul><ul><li><p> This is a standard document that is sent between two computers using the <a href=&quot;;> HTTP <a> protocol. Note that other then the markup tags like <b> bold </b> there is very little that a computer can do to understand the meaning of the text. </p> </li></ul>Unless computers &quot;understand&quot; the words in the English language it will be very difficult for them to understand the meaning or semantics of the web.
    26. 26. What Computers &quot;See&quot; Today <ul><li><title>  </title> </li></ul><ul><li><p>  <a href=&quot;;>  <a>  <b>  </b>  </p> </li></ul>Unless computers &quot;understand&quot; the words in the English language it will be very difficult for them to understand the meaning or semantics of the web.
    27. 27. XML allows you to create new “tags” <ul><li><PersonGivenName> Joe </PersonGivenName> </li></ul><ul><li><PersonFamilyName> Smith </PersonFamilyName> </li></ul><ul><li><Address> 123 Main Street </Address> </li></ul><ul><li><City> Anytown </City> </li></ul><ul><li><State> Minnesota </State> </li></ul><ul><li><Phone> (651) 555-1234 </Phone> </li></ul>Without a data dictionary, it is difficult to know what the meaning of the data elements is. The tags appear in patterns but what they mean is still a mystery to a computer. <tag> </tag> data
    28. 28. Which external computers may not understand <ul><li><  >  </  > </li></ul><ul><li><  >  </  > </li></ul><ul><li><  >  </  > </li></ul><ul><li><  >  </  > </li></ul><ul><li><  >  </  > </li></ul>Without a “data dictionary”, it is difficult to know what the meaning of the data elements is. The tags appear in patterns but what they mean is still a mystery to a computer.
    29. 29. Metadata <ul><li>Metadata is any data that describes other data </li></ul><ul><li>Metadata is itself data and is stored in specialized structures (directed graphs) to aid comparison with other metadata </li></ul><ul><li>A controlled store of metadata is called a “registry” </li></ul>Data describes RDBMS document keywords tables web navigation columns source-code org-chart product-specs Metadata
    30. 30. Hypertext Links and Data Element Links The Semantic Web Metadata Registry A Metadata Registry B The semantic web is about linking conceptual data elements in published metadata registries The current HTML web is focused on linking published documents with HTML The Hypertext Web
    31. 31. Enter the URI… <ul><li>Today's web allows documents to be accessed by people if people put links in between documents – the hypertext web </li></ul><ul><li>But it is very difficult for machines to &quot;understand&quot; what we are saying and what we mean and what to do with the data </li></ul><ul><li>But machines CAN determine if two URIs match: </li></ul><SurName>Smith<SurName> <LastName>Smith</LastName> MDR Hey, you both “mean” the same thing!
    32. 32. Subject-Verb-Object Triple Person “ Joe ” Has-a-Given-Name The person is named “Joe”. <PersonGivenName> Joe </PersonGivenName>
    33. 33. Triples are Almost all URIs http://MyDictionay/DataElement/Person “ Dan” http://MyDictionay/DataElement/PersonGivenName URIs can point to a standard location in a metadata registry. The “type” of link.
    34. 34. Sample RDF Document <ul><li><?xml version=&quot;1.0&quot;?> </li></ul><ul><li>< RDF > </li></ul><ul><li>< Description about =&quot; &quot;> </li></ul><ul><li>< author > Dan McCreary </ author > </li></ul><ul><li>< created > 2006-01-01 </ created > </li></ul><ul><li>< modified > 2006-03-15 </ modified > </li></ul><ul><li></ Description > </li></ul><ul><li></ RDF > </li></ul>
    35. 35. Massive Databases of &quot;Triple Stores&quot; <ul><li>Triple store is: </li></ul><ul><li>- A database with just 3 Columns </li></ul><ul><li>- but millions/billions of rows </li></ul><ul><li>May require specialized hardware </li></ul><ul><li>Key Metrics: </li></ul><ul><li>- Time to load triples into application </li></ul><ul><li>- Time to save triples into database </li></ul><ul><li>- Time to browse to an element </li></ul><ul><li>- Time to configure system </li></ul><ul><li>Sample Projects: </li></ul><ul><li>Kowari </li></ul><ul><li>3Store </li></ul><ul><li>Sesame </li></ul>RDF &quot;Triple Store&quot; See: Object Predicate Subject
    36. 36. Semantic Web Standards Stack Source: Tim Berners-Lee URI/IRI Unicode XML Namespaces XML Query XML Schema RDF Model & Syntax Ontology (OWL) Rules/Query Logic Proof Trusted Semantic Web Signature Encryption
    37. 37. Example of Metadata Registry
    38. 38. Metaphor: The Translator Agent May I have a beer? Me gusteria una cerveza Customer (Spanish Only) Translation Service (Speaks Spanish and English) Internal Server (English Only) Coming right up!
    39. 39. Cost of Mapping <ul><li>Goal: create semantic maps to a few metadata standard, not many standards </li></ul>R 5 R 2 R 3 R 4 R 6 R 7 R N Mapping from one to many metadata registry to N other metadata registries: The O(N 2 ) problem R 2 R 3 R 4 R 5 R 6 R 7 R N ESB Mapping to one metadata registry The O(N) problem (ESB-Enterprise Service Bus) R 1 R 1
    40. 40. Semantic Mappers and Semantic Brokers Report Request In Model A Gartner: Vocabulary-based transformation XMLA: XML for Analysis Metadata Translation Service XML Response In Model A TDS In Model B Metadata Registry Model A Model B M etadata Mappings RDF Queries XML Results Data Warehouse (RDBMS) SQL or XMLA Queries In Model B
    41. 41. Wikipedia Rocks! <ul><li>It is currently burdensome to add new metadata to the registry </li></ul><ul><li>Would like to add “Edit this data element” (ala Wikis) </li></ul><ul><li>Ideally a “Semantic Wiki” </li></ul>See: Wikipedia: “Semantic Wiki”
    42. 42. Retrieving Data: An Evolution <ul><li>Shorten the time-to-report interval </li></ul><ul><li>Allow users to &quot;browse&quot; data sets interactively </li></ul><ul><li>Remove programmers with &quot;backlogs&quot; of reports </li></ul><ul><li>Users frequently waited days, weeks for months to get a custom report created </li></ul>Monthly “Green Bar” Reports Browseable Graphical Interface (Cognos) Increasing Responsiveness
    43. 43. Classification and Categorization <ul><li>Whenever we decide to break the continuous observable world into a predefined list of categories when each category has a label we call this a categorical value. These will then become the &quot;dimensions&quot; of our cube. </li></ul>&quot;red&quot; &quot;green&quot; &quot;blue&quot; George Lakoff: Women, Fire and Other Dangerous Things: What Categories Revel about the Mind Note: NO OVERLAP!
    44. 44. Metadata Discovery <ul><li>Tools that “scan” data sources and create new ontologies or mappings to existing ontologies </li></ul>Metadata Registry Data Source Mappings Relational Database
    45. 45. Federated Ontologies <ul><li>What do you do when you have more than one Ontology? </li></ul><ul><li>1) Combine </li></ul><ul><li>2) Map </li></ul><ul><li>3) Federate </li></ul><ul><li>Tools for combination and federation </li></ul>Multiple Overlapping Ontologies
    46. 46. Cost of Poor Semantics <ul><li>IT Departments spend 40-60% of their costs on Integration </li></ul><ul><li>90% of integration costs are due to poor semantics </li></ul><ul><li>If every application used and &quot;published&quot; a machine readable ontology with mappings to published ontologies integration could be almost &quot;automatic&quot; </li></ul>
    47. 47. Gartner <ul><li>Metadata cast into formal logics will drive interoperability, automation, cost cutting, better search capabilities and new business opportunities. </li></ul><ul><ul><li>Semantic Web Drives Data Management, Automation and Knowledge and Discovery </li></ul></ul><ul><ul><li>Alexander Linder </li></ul></ul><ul><ul><li>March 2005 </li></ul></ul><ul><ul><li>G00125145 </li></ul></ul>
    48. 48. Semantic Spectrum Time/Money High Semantic Clarity Strong Semantics Weak Semantics UML, XMI Taxonomies Ontologies Thesaurus RDF XML, XSLT See also: Wikipedia/semantic spectrum Glossaries OWL Controlled Vocabularies Word/HTML Concept Maps Enterprise Data Models
    49. 49. Structures for Increased Semantics HTML PDF Word PowerPoint Excel Access Server XML RDBMS RDF Taxonomies Ontologies SOA WSDL Increased Semantic Precision Source: Network Inference
    50. 50. Friend of a Friend <ul><li>A &quot;Proof of Concept for RDF&quot; </li></ul><ul><li>Requires each person to put an RDF file on their web pages </li></ul><ul><li>System in place to prevent spammers from getting e-mail accounts </li></ul><ul><li>Sample RDF vocabulary </li></ul><ul><li>Sample FoaF file: </li></ul><ul><li><foaf:Person> <foaf:name>Dan McCreary</foaf:name> < foaf:knows > <foaf:Person> <foaf:name>Bill Titus</foaf:name> </foaf:Person> </foaf:knows> </foaf:Person> </li></ul>©
    51. 51. Ontology Architectures <ul><li>One &quot;big&quot; ontology (see CycCorp </li></ul><ul><ul><li>Using a single &quot;Uber-Ontology&quot; </li></ul></ul><ul><ul><li>Akin to &quot;Boiling the Ocean&quot; </li></ul></ul><ul><li>Compared to: </li></ul><ul><ul><li>Many smaller ontologies </li></ul></ul><ul><ul><li>Micro-formats (RDF/A) </li></ul></ul><ul><ul><li>How to combine? </li></ul></ul>CYC contains over 3 Million &quot;assertions&quot; Source:
    52. 52. If You Give A Kid A Hammer… <ul><li>… the whole world becomes a nail </li></ul><ul><li>People solve problems with the tools they know </li></ul><ul><li>Semantics are new tools for solving computer-to-computer communication problems </li></ul><ul><li>Intelligent agents will be prevalent when we teach organization to publish their metadata </li></ul>
    53. 53. Cognitive Styles <ul><li>The way we solve problems is dependant on the tools we know how to use. </li></ul><ul><ul><li>Shoshana Zuboff (1988) </li></ul></ul><ul><ul><li>In the Age of the Smart Machine </li></ul></ul>Technology creates: - new ways of thinking - new ways of approaching and solving problems - new sets of &quot;Cognitive Styles&quot; It is only if we share these cognitive styles that we will be able to create a coherent technology strategy that everyone understands
    54. 54. Open The Door To The Semantic Web! <ul><li>Metadata publishing is hard </li></ul><ul><li>It is a foundation upon which the Semantic Web will be built </li></ul><ul><li>The benefits are indirect and need strong executive sponsorship </li></ul><ul><li>Metadata publishing is no “silver bullet” </li></ul><ul><li>I believe it is the most direct way to get to the Semantic Web </li></ul><ul><li>This will be the most practical way to build intelligent agents </li></ul>Agents Metadata Publishing
    55. 55. Questions & Answers <ul><li>If software is ever going to be able to effectively inter-operate (in ways that were not explicitly preconceived and engineered), it will be because applications share enough of the semantics of their data elements. </li></ul><ul><li>Doug Lenat, Cycorp </li></ul><ul><li>Semantic Technology Conference </li></ul><ul><li>2005 </li></ul>