Veda Semantic Technology

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Convert Data to Actionable Intelligence

Convert Data to Actionable Intelligence

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  • 1. Convert Data to Actionable Intelligence
  • 2. Veda Overview – Origin and Growth 1.Semantic Technology IP 2.Originated at Fraunhoffer Institute, Germany 3.Product enhanced over the last decade 4.End to end Semantic Framework2
  • 3. Veda Overview - COP Model 1.Covers all aspects of a Business Solution 2.Has the ability to work on Structured and Unstructured Data 3.The framework enables rapid deployment of Semantic Applications3
  • 4. Veda Overview - Product Portfolio Area Technology / Tools Description Web Crawler Supports HTTP with authentication Data Email Crawler Supports IMAP, POP3 protocols Collect Aggregation Database Crawler Supports relational databases File Crawler Word Reader, PDF Reader, Text Reader, HTML Reader Information Visual Entity Extractor Domain specific entity extraction platform Extraction Term Extraction NLP based concept, phrase identification Semantic Net API & S-net editor OrganIze Editor Information Ontology Editor OWL editor, Rules Organization AutoClassification Lazy (term-based) and machine learning Ontology Data Maps data to the ontology Mapping Semantic Matching Context based retrieval using semantic net Present Information Inference Engine Supports OWL DL Lite Retrieval and Latent Semantic Latent semantic analysis and information retrieval Analysis Indexing Semantic Rule Engine Rule engine with inferencing capabilities Visual entity extraction technique Patents Filed Method of Context-based information retrieval4
  • 5. Product Strategy  Semantic Search  Standards  Text Analysis  Semantic  Automated Search Reasoning Evolution of Semantic Technology  Standards  Semantic  Text Analysis Content Mgmt.  Semantic  Automated  Knowledge Search Reasoning based Apps  Semantic  Standards  Semantic  Semantic Social Search  Text Analysis Content Mgmt. Computing  Standards  Automated  Knowledge  Linked Data  Text Analysis Reasoning based Apps  Predictive  Automated  Semantic  Semantic Social Analytics Reasoning Content Mgmt. Computing  Semantic  Knowledge  Linked Data Content Mgmt. based Apps  Predictive  Knowledge  Semantic Social Analytics Legend based Computing applications  Linked Data Matured  Semantic Social  Predictive Advanced Computing Analytics Progressive  Linked Data Inception 2011 2012 2013 20145
  • 6. Product Map vis-à-vis Opportunity Areas Functional Areas Technology Components Availability in Content Social Media Semantic Advertising Text Consumer Semantic Veda Management Platform Search Tools Analysis Insights Mobile and Platform Web Apps Content Web crawler Yes Y Y Y Y Aggregators Email crawler Yes Y Y Y Y File crawler Yes Y Y Y Y Database crawler Yes Y Y Y Adapter for Online Feeds (RSS, Atom) WIP Y Y Y Y Y Adapter for Twitter Yes Y Y Y Y Y Adapater for Facebook Yes Y Y Y Y Y Adapter for LinkedIn Yes Y Y Y Y Semantic Semantic Net Editor Yes Y Y Y Y Y Y Network / Ontology Editor Yes Y Y Y Y Y Y Ontology Semantic Rule Editor Yes Y Y Y Y Y Y Semantic Storage Ontology storage (RDBMS) Yes Y Y Y Y Y of Content Semantic Net Storage (RDBMS, XML) Yes Y Y Y Y Y Semantic Semantic Net based classification Yes Y Y Y Y Y Organization of Bayes Naïve Classifier Yes Y Y Y Content Ontology Inference Engine (support for Yes Y Y Y Y OWL 1.0) Semantic Semantic Matching (Semantic Net) Yes Y Y Y Retrieval Semantic Browsing Yes Y Y Y Y Latent Semantic Indexing Yes Y Y Semantic Net API Yes Y Y Y OWL API Yes Y Y Y Y Semantic Preprocessing - Identification of Yes Y Y Y Y Y Y Y understanding of paragraphs and sentences content and Preprocessing - Identification of POS Yes Y Y Y Y Y Y Y natural language Preprocessing - Identification of term Yes Y Y Y Y Y Y Y query Preprocessing - Word Stemming Yes Y Y Y Y Y Y Y Preprocessing - Removal of stopwords Yes Y Y Y Y Y Y Y Weighting schemes for terms Yes Y Y Y Y Y Y Y Named Entity Recognition and WIP Y Y Y Y Y Y Y Relationships (NLP based) Named Entity Recognition (Visual Yes Y Y Y Y Y Y Y Segregation) Phrase Identification Yes Y Y Y Y Y Y Y6 Sentiment Analysis WIP Y Y Y
  • 7. Veda Solutions Currently Deployed Veda for Business Process Workflow • Configurable to any Business requirement across Industries • Sources of content can be structured AND Unstructured • Can be integrated to various Business Applications - ERP, Content Management, Portals, etc. • Configurable User Interface with features such as: – Saving of Search for later reference – Tabbed Views – No. of results to be displayed with sort order7
  • 8. Veda Solutions Currently Deployed Veda Social Media Analytics  Registration & log in  Inputs from Social Media  Inputs from Blogs, Websites  Heirarchy & Relevance Analysis  Sentiment Analysis  Rich Reporting8
  • 9. Veda Solutions Currently Deployed Veda Recruiter9
  • 10. Veda Solutions Currently Deployed Veda Patent Search  Registration & log in  Subscription  Payment Gateway  Keyword Search  Semantic Search  Rich Internet Application  Saved Search  Filters10
  • 11. Industry Examples of Applicability of Veda • Healthcare: Patient Treatment Process using • Patient’s past records • Recent treatment of similar ailments across the Hospital / State / Country, etc. • Medical Research information available in the Internet • Professionals: Tax / Legal / Patent Filing using • Case Laws, Guidelines, Notifications • Statutory Records, Past Filings11
  • 12. Industry Examples of Applicability of Veda • Manufacturing: Purchase Process using • Past Purchase documents • Material documents / brochures • Live Pricing information from websites of Suppliers, Exchanges, e-Commerce sites • Human Relations: Recruitment / Appraisal Process using • Past Employee Records • Profiles from Social Networking sites12 • Recent performance data
  • 13. Key Differentiators 1. One of the few Semantic Product Companies worldwide that has an end-to-end technology coverage 2. One of the few Semantic companies worldwide adopting a Business Application centric view 3. One of the few Semantic Organizations with a strong in-house Implementation Team 4. Ability to provide different Commercial Models based on Customer requirements13
  • 14. Thank YouAnand RamakrishnanPresident – Sales and MarketingeMudhra Consumer Services Limitedanand.r@emudhra.com+91 9900541568