1. Redefining Perspectives A thought leadership forum for technologists interested in defining a new future June - 2015 COPYRIGHT ©2015 SAPIENT CORPORATION | CONFIDENTIAL

2. Session 2 Semantic Search – the technology and its application in financial markets COPYRIGHT ©2015 SAPIENT CORPORATION | CONFIDENTIAL

3. Search 3 COPYRIGHT ©2015 SAPIENT CORPORATION | CONFIDENTIAL http://www.indiatechonline.com/images/special_feature/idc-emc-suudy-on-digital-universe-165.jpg

4. Search 4 COPYRIGHT ©2015 SAPIENT CORPORATION | CONFIDENTIAL Keyword based Search Engine User “Give me what I Said”

5. ENTERPRISE ECOSYSTEM Search – Enterprise Ecosystem 5 COPYRIGHT ©2015 SAPIENT CORPORATION | CONFIDENTIAL 60% of Enterprise Data are Unstructured Structured Data Trading Reference Security Search Silos Keyword Based Search Semi Structured Data Wiki Vendor Data Reports “Give me what I asked” Unstructured Data Research Company Filings Feeds Data Search Silos Custom Search Application

6. Semantic Search – Making Results Relevant 6 COPYRIGHT ©2015 SAPIENT CORPORATION | CONFIDENTIAL Context & Intent based, Meaning & Relationships among words

7. Semantic Search – Making Results Relevant 7 COPYRIGHT ©2015 SAPIENT CORPORATION | CONFIDENTIAL Disambiguate “Give me what I Want; Not just what I Say”

8. Search – Enterprise Semantic Ecosystem 8 COPYRIGHT ©2015 SAPIENT CORPORATION | CONFIDENTIAL Enterprise Semantic Search Knowledge Discovery Enterprise ContentEnterprise Semantic Search Linked Data DBPedia Freebase Internal Knowledge Base Enterprise Data Models Content Extraction Context Mapping Contextual Meaning Inferencing Structured Unstructured Semi-Structured

9. We’ll focus on… We will consider a Financial Domain Investment Bank Use Case How Semantic Search Platform is built technically in-line with the use case COPYRIGHT ©2015 SAPIENT CORPORATION | CONFIDENTIAL

10. The Use Case – Investment Research 10 COPYRIGHT ©2015 SAPIENT CORPORATION | CONFIDENTIAL A typical Research team in an Investment Bank performs the following: Manually gather research information Analyze gathered documents to find requested information Challenges: High volume of research corpus Manual Analysis results in Inaccuracy Longer Response Time Time to Market Lower ROI  Automate Routine Requests  Faster response. But limited benefit.  Problem still remains for Complex Information Requests  Outsourcing Research Team  Potential Cost Savings  Problem not solved but moved to a different place. QoS risks.  Ontology Based Semantic Search  Faster Response  More Relevant and Contextual Search Results  Knowledge Discovery through Inferencing  Domain and technology expertise required Current Scenario OptionsPros/Cons

11. The Use Case – Investment Research 11 COPYRIGHT ©2015 SAPIENT CORPORATION | CONFIDENTIAL Build a Semantic Search platform that leverages latest advancements in Search and Natural Language Processing to make Investment Research Experience significantly more efficient and effective Maximize ROI on Market Research Spending Get Insight to Timely Industry Information Find and Discover Actionable Knowledge Perform Informed Investment Decisions

12. The Use Cases – Potential Search Queries 12 COPYRIGHT ©2015 SAPIENT CORPORATION | CONFIDENTIAL ‘HSBC Holdings Plc’ ‘Asset Write down’ Asia Interest rate risk private banks Western Europe Documents about banks based out of Paris and talk about interest rates volatility in Western Europe Companies in Eastern Europe whose turnover is greater than $100 million and face challenge of nationalization Show me documents about Retail Banks in South Asia whose P/E ratio is greater than 20.0 Do a proximity search on ‘Regulatory Change’ with reference to ‘Retail Banking’ Looking for documents published by HSBC and authored by Ronit Ghose

13. Enabling Semantic Search - Approaches 1.Lexicon and Ontological Based Search 2.Statistical Analysis and/or Pattern Matching Search 13 COPYRIGHT ©2015 SAPIENT CORPORATION | CONFIDENTIAL

14. Enabling Semantic Search – 4 Pillars 14 COPYRIGHT ©2015 SAPIENT CORPORATION | CONFIDENTIAL Reasoning Engines Natural Language Processing Ontology Semantic Analysis

15. Enabling Semantic Search – Core Concepts 15 COPYRIGHT ©2015 SAPIENT CORPORATION | CONFIDENTIAL Model defined using constructs for: Concepts – classes Relationships – properties (object and data) Rules – axioms and constraints Instances of concepts – individuals (data) Uses W3C standards RDF/S and OWL Relationships Concepts/Classes Instances What is Ontology ? It’s an Knowledge Model, assembly of concepts in which all possible relationships that might exist among concepts are explicitly mapped. it captures knowledge so that, Questions can be answered New Insights can be generated

16. Enabling Semantic Search – Core Concepts 16 COPYRIGHT ©2015 SAPIENT CORPORATION | CONFIDENTIAL Data stored in Triples Expressed as Subject : Predicate : Object Internal Knowledge Base DISCOVER NEW INSIGHTS Pranab MukherjeeNew DelhiIndia Lives inIs in Lives in Get me documents about Retail Banks in Eastern Europe which have net profit great than $10 million and are facing challenges of nationalization

17. Putting It All Together - Application Process Flow 17 COPYRIGHT ©2015 SAPIENT CORPORATION | CONFIDENTIAL Content Providers Content Extraction & Standardization Standardized Document Step 1 Content Ingestion Classification Ontology Tagging Meta Data Document Store XML and Triple Storage Indexing & Querying Step 2 Content Delivery Search Engagement Step 3

18. Components – Content Extraction & Standardization 18 COPYRIGHT ©2015 SAPIENT CORPORATION | CONFIDENTIAL Unstructured Content Text Extraction & Standardization Metadata Extracted Textual Content Extract Meaning from Unstructured Data Transform into Structured Data for Auto Tagging

19. Components – Content Ingestion 19 COPYRIGHT ©2015 SAPIENT CORPORATION | CONFIDENTIAL Ontology Management A tool that supports lists, controlled vocabularies, taxonomies, thesauri or ontologies: Concepts/Terms Taxonomy Associative Relationships Synonyms http://wiki.opensemanticframework.org/index.php/Ontology_Tools

20. Components – Content Ingestion 20 COPYRIGHT ©2015 SAPIENT CORPORATION | CONFIDENTIAL Content Classification Analyze document Add metadata ‘tags’ that describe that documents which are sourced from Ontology Example : Classification Results

21. Components – Data Store & Search Engine 21 COPYRIGHT ©2015 SAPIENT CORPORATION | CONFIDENTIAL

22. Typical Architecture 22 COPYRIGHT ©2015 SAPIENT CORPORATION | CONFIDENTIAL CORE PLATFORM STORAGE LAYER PRESENTATION LAYER Free-Text Search Ontology Driven Search Graph SearchCollaborationEngagement CORE SERVICES Logging Caching Security Monitoring Indexes Content Store Triples Inferencing SPARQL XQUERY Classification Server Ontology Server RuleSets Inference Engine ONTOLOGY MGMT Ontology Creation RuleSets Entity Extraction Inferencing CONTENT DELIVERY Query pre- processor Query Builder Inference Engine Results post- processor CONTENT INGESTION Import Classification/ Indexing Standardization / Structuring Storage

23. Semantic Search – Opportunities & Beyond 23 COPYRIGHT ©2015 SAPIENT CORPORATION | CONFIDENTIAL Augmented Reality Other Possibilities? http://augmentedpixels.com/wp-content/uploads/2014/04/augmented-reality-iphone- football-concept.jpg http://www.ventures-africa.com/wp- content/uploads/2015/01/original_aefd15169aaebd3f037b5ed672db6de1.png

24. QuestionAnswer Thank you COPYRIGHT ©2015 SAPIENT CORPORATION | CONFIDENTIAL