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G001 Speakers New Frontiers in Enterprise Search Monday, August 20, 2018 11:00am-12:00pm #G001 SPEAKERS TODD FRIEDLICH RICK KRZYMINSKI SIMON PECOVNIK Senior Manager KM Technology & Chief Knowledge VP of Product Management Innovation Management Officer iManage LLC Ropes & Gray LLP Baker Donelson @tfriedlich CONVERSATIONAL SEARCH • Conversational Search • Voice Search PERSONAL ASSISTANTS CHATBOTS @ HOME • “Alexa, Turn off the lights.” • “OK Google, Set alarm for 6:30am” • “Siri, Where is ‘Eight Grade’ playing” I thought this was a session on search! Leslie Knope in the Alexa, Pawnee office is an Who is an expert on expert in Blockchain Blockchain? “I ALWAYS FEEL LIKE SOMEBODY'S WATCHING ME.” “Siri is listening to you, but she’s not spying” (Naked Security by Sophos) “Google tracks your movements, like it or not” (AP) “Here's how the Alexa spying scandal could become Amazon's worst nightmare” (Business Insider) NATURAL LANGUAGE PROCESSING (NLP) Intent Entity Who is an expert in Bitcoin? ContentSource:”Expertise” AND Skill:”Bitcoin” SIMPLE ANSWERS? Leslie Knope has experience with bitcoin. CANONICAL TERMS AND SYNONYMS Intent Entity Who is an expert in Bitcoin?Bitcoin? CANONICAL TERMS AND SYNONYMS Smart Contracts Who is an expert in Bitcoin? Ethereum Blockchain CLARIFYING QUESTIONS Leslie Knope has experience with bitcoin. Bitcoin is often a synonym for Blockchain, Ethereum, and Smart Contracts. Would you like me to expand my search to include those terms? MORE CLARIFICATION Contextual Conversational • What do we know about the user? • Expand search to include beyond LOB • Practice Group systems? • Office location • Position • Take action on results? • What do we know about the result? • Useful results? • Out of office? • Calendar availability? • Permissions THE MEDIUM IS THE MESSAGE NEW APPROACHES TO ENTERPRISE SEARCH ENTERPRISE SEARCH TOOLS • Recommind Decisiv Search – SharePoint 2010 • Microsoft Search with Handshake Guided Search – SharePoint 2016 on Prem NEXT FRONTIER • KM Tagging • Tailored to the User • Legal Research Tab • Tie in with Intranet Pages KM TAGGING TAILORED TO THE USER TAILORED TO THE USER TAILORED TO THE USER Legal Research LEGAL • External data RESEARCH • Better adoption TAB • What’s next? TIE IN WITH INTRANET PAGES SEARCH IN THE ENTERPRISE USING NATURAL LANGUAGE AGENDA • Typical Challenge – Enterprise Search • Juta Use Case And Why it is Important • Our NLP solution using iManage Insight • How Does it Work? • The End Result and the Future 1. Information and Knowledge is Everywhere – Documents – Scanned pages – Emails – Websites – Application data 2. Intuitive user interface Typical – KM capabilities – Customizable Challenges – Mobile ready 3. Client use cases – Find experts – Clause library – Know How search 1. Broad Connectivity with Pipeline Connector framework Enabling easy set up and manage connections to disparate data sources NER Clustering Auto-classification How Insight Addresses 2. Intuitive Insight web-interface with These result templates Personalisation Contextual type-ahead Challenges? 3. Support client use cases by utilizing the Knowledge Graph Expose tacit knowledge Learn to rank Expertise location Juta Use Case Varied data sources Granular Security • Case law publications • Restriction on user/group level o Multiple countries • Time-restricted access o Legacy to modern (scans + • Source IP access restriction various systems) • Knowledge base/definitions Improve site search capabilities and navigation Connected data Platform for a wide-variety of users: Citation, cross-referenced data is • matches a typical interaction for expert connected using Knowledge Graph, • cater for non-expert users enabling getting to answer quicker NLP – It Makes All The Difference Users used natural language in current tools already But the underlying system wasn’t. Bridge the gap with current tools Search capability oriented to improve search for expert and non-expert users. Use the natural language processing capability of the platform By integrating RAVN Engine with Insight this single index serves multiple purposes. Example query: Find all the cases by Innes CJ that deal with pensions. Typical keyword query Gets all the documents with all keywords mentioned Example query: Find all the cases by Innes CJ that deal with pensions. Natural language search Using: • Stop-words • Stemming • Field biasing • Relevancy algorithm Ultimately keywords are used. Biasing is heavily used for better results. Example query: Find all the cases by Innes CJ that deal with pensions. Natural language search with NLP enabled 1. Algorithm identifies that Innes CJ is a judge – and applies a filter. 2. Search identifies cases as a type of content – and boosts “Case law” results. 3. Pension is an important noun in the sentence, and system biases results and highlights the term in the summaries. Getting Started with NLP Analysis of past queries (AI needs training data) – Sample queries from SME – Past actual query logs – Query trends Metadata connections and connection strength – Courts, citations, mentions Pre-process and organize content – Slow start (we focused on two models) and improved gradually – Date range improvements – Definition model … Data Ingestion Text analysis and extraction of useful meaning from text. Including: • Metadata • Facts (citations) • Mentions and other concepts from unstructured text Describe the domain and generate Behind the Knowledge Graph (KG) Scenes Capture User’s Query NLP analysis to gain deeper language understanding The Science User’s Query Workflow Process Query Remainder Further models for processing the query and 04 deriving structure and information from the words Extraction Models Deeper analysis of the query. Created models are used 03 to extract information from the query Definition Match Looking for fuzzy matches in order to decide weather or not to 02 parse query as a definition Parse Query Platform performs common NLP tasks, such as tokenization, sentence 01 segmentation, entity extraction, chunking, parsing, etc. • Widely Applicable – Models will need to be build based on the use case • Added Value – Entirely new way to interact with the complex enterprise data and work more productively – Adds value by helping to automate or The End Result improve search • Recognize limitations – We show the evidence, we don’t quite answer Guilty/Not guilty questions yet What’s Next? Further user experience work • Real-time interactive visualizations (inverse facets, real-time entity information) • Make answers even more transparent (graphical representation of key triggers) Answering free text questions with answers • Trend analysis of natural language searches • Building of “fact” database from the given content Get even more relevant information • Utilize more of already captured knowledge in the rest of the platform • Combine NLP with context , user preferences and behavior. .
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