Semantic Web in Action: Ontology-driven information search, integration and analysis
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Amit Sheth's Keynote talk given at: “Semantic Web in Action: Ontology-driven information search, integration and analysis,” Net Object Days 2003 and MATES03, Erfurt, Germany, September 23, 2003. http://knoesis.org Note: slides 51-55 have audio.
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Semantic Web in Action: Ontology-driven information search, integration and analysis
- 1. Talk Abstract Semantic Web in Action Ontology-driven information search, integration and analysis Net Object Days and MATES, Erfurt, September 23, 2003 Amit Sheth Semagix , Inc. and LSDIS Lab , University of Georgia
- 4. Broad Scope of Semantic (Web) Technology Other dimensions: how agreements are reached, … Lots of Useful Semantic Technology (interoperability, Integration) Cf: Guarino, Gruber Gen. Purpose, Broad Based Scope of Agreement Task/ App Domain Industry Common Sense Degree of Agreement Informal Semi-Formal Formal Agreement About Data/ Info. Function Execution Qos Current Semantic Web Focus Semantic Web Processes
- 9. Metadata and Ontology: Primary Semantic Web enablers
- 10. Semagix Freedom Architecture (a platform for building ontology-driven information system) Ontology © Semagix, Inc. Content Sources Semi- Structured CA Content Agents Structured Unstructured Documents Reports XML/Feeds Websites Email Databases CA CA Knowledge Sources KA KS KS KA KA KS Knowledge Agents KS Metabase Semantic Enhancement Server Entity Extraction, Enhanced Metadata, Automatic Classification Semantic Query Server Ontology and Metabase Main Memory Index Metadata adapter Metadata adapter Existing Applications ECM EIP CRM
- 16. Ontology Creation and Maintenance Process
- 17. 1. Ontology Model Creation (Description) 2. Knowledge Agent Creation 3. Automatic aggregation of Knowledge 4. Querying the Ontology Ontology Creation and Maintenance Steps © Semagix, Inc. Ontology Semantic Query Server
- 24. AML Ontology Schema (Descriptional Component) © Semagix, Inc.
- 29. Video with Editorialized Text on the Web Automatic Classification & Metadata Extraction (Web page) Auto Categorization Semantic Metadata
- 30. Extraction Agent Enhanced Metadata Asset Ontology-directed Metadata Extraction (Semi-structured data) Web Page © Semagix, Inc.
- 37. Automatic Semantic Annotation of Text: Entity and Relationship Extraction KB, statistical and linguistic techniques
- 39. AML Ontology Schema (Assertional Component) Subset of the entire ontology © Semagix, Inc.
- 42. Scalable Architecture SQS SQS SQS SES SES SES Metabase Ontology cluster scale-up Semantic Application LOAD BALANCER LOAD BALANCER
- 44. BLENDED BROWSING & QUERYING INTERFACE VideoAnywhere and Taalee Semantic Search Engine ATTRIBUTE & KEYWORD QUERYING uniform view of worldwide distributed assets of similar type SEMANTIC BROWSING Targeted e-shopping/e-commerce assets access
- 45. Semantic Enhancement used in Semantic Search Click on first result for Jamal Anderson View metadata. Note that Team name and League name are also included in the metadata Search for ‘Jamal Anderson’ in ‘Football’ View the original source HTML page. Verify that the source page contains no mention of Team name and League name . They are value-additions to the metadata to facilitate easier search.
- 47. Bill Gates relationships within text in the document relationships across documents in the same corpus Ontology Corpus of documents Databases relationships across documents outside of the same corpus Single document belonging to a corpus Semantic Information Integration spanning three layers of semantic relationships
- 49. Semantic Application Example: Equity Research Dashboard with Blended Semantic Querying and Browsing Focused relevant content organized by topic ( semantic categorization ) Automatic Content Aggregation from multiple content providers and feeds Related relevant content not explicitly asked for (semantic associations) Competitive research inferred automatically Automatic 3 rd party content integration
- 50. Semantic Information Integration in Portals Sample content item that is explicitly or implicitly associated semantically to facets in user profile User profile as a context for semantic integration of diverse yet relevant content Semantic integration and presentation of various types of personalized content items in one place
- 51. Anti Money Laundering – Know Your Customer Risk Profiles are developed for individuals or companies. If the risk profile changes based on new information the individuals Risk Profile and Branch Aggregate Risk Profile is automatically updated R
- 52. View Risk Scores for a specific company or customer
- 53. Additional tools allow the user to navigate around the content
- 54. Additional tools allow the user to navigate around the content
- 55. Additional tools allow the user to navigate around the content R
Editor's Notes
- Semantics (of information, communication) is a very old area, and extensive work on Semantic Technology has been going on for well over a decade (many projects on semantic interoperability, semantic information brokering) Semantic Web and related visions are being achieved in various depth and scope – mostly starting with targeted applications where requirements are much better understood and scope is manageable