The Knowledge Graph That Listens
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Enterprises that are building Knowledge Graphs are rapidly getting a grip on unstructured data with current advances in Natural Language Processing (NLP) techniques. But there is still a large mass of unstructured data that is untapped and that is spoken conversations with customers. Speech to text for general purpose conversations (e.g. Google, Alexa, Siri) have proven themselves in the market to be highly accurate. However, speech recognition technology for domain specific industries with lots of product names, industry lingo, and acronyms often creates a challenge for accuracy and usefulness of the content. In this Webinar we will demonstrate how taxonomy driven speech recognition helps solve these industry specific terminology challenges for real-time voice capture and how this process augments an Enterprise Knowledge Graph for customer insights. video youtube.com/allegrograph
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The Knowledge Graph That Listens
- 1. Franz Inc The Knowledge Graph that listens to you Dr. Jans Aasman (allegrograph.com)
- 2. Are you building a knowledge graph already?
- 3. So Why is Speech Technology important for Knowledge Graphs? • Most Knowledge Graphs in the enterprise are 360 views on customers • To really understand your customer you need to listen to what they say! • Add that understanding to your knowledge graph and learn and become better at understanding your customer
- 4. What could you do in your enterprise if Speech Technology was perfect? • Really hear and understand your customer! • What do they think about you and your products and your processes • Automatic Transcription of the interaction with your customer • Legal compliance, • Analyze complaints, • Call and caller classification, • Find patterns in calls • Identify callers using biometrics • And mitigate risks • Are your agents saying the right things? • Are they really helping the caller, are they conveying the right information, are they hitting all the required talking points, do they use the right objection handing, are they polite, non-confrontational, etc, etc.
- 5. What could you do in your enterprise if Speech Technology was perfect? • Optimize your sales process • Analyze successful and failing conversations • Did your sales agent talk about Budget, Authority, Need and Timeline? (BANT) • Did your agent talk about the right products given the expressed needs of the caller • When the customer brings up a competitive product, can you instantly help the agent with the right object handling? • When you now the industry of the caller, can you provide the agent with use cases for that industry • Based on the nature of the call: should you transfer to technical help or technical consultant or try to close a deal. • Help the sales agent write the perfect ‘sales notes’
- 6. Is Speech Technology already perfect?
- 7. History: I got into it early in my career
- 8. A complaint in 2014: speech technology only 95%
- 9. Recent quote
- 10. The big players (Google, MS, IBM, Apple, and many more) now all claim that they turned the dream of speech recognition into a reality
- 11. But be that as it may In the context of understanding your customers YOUR enterprise will say Wow! 95 % is 95 % more (or infinitely more) than I can do now
- 12. But how can you tie NLP and Speech Technology together in one coherent context so you really can learn, understand and help your customers? • Our answer: a Semantic Knowledge Graph
- 13. So what do we do with NLP and Speech Technology and Knowledge Graphs.
- 14. We do NLP in various domains • Do you want to live forever? • Biography Knowledge Graphs for interesting (and/or public) figures • What do my agents talk about? What conversation style leads to more sales? • A knowledge graph for Intelligent Call Centers
- 15. No part or process to be used without permission.© • Sales Skillset + Mindset + Culture • Integrated End-To-End Sales Capabilities • Technology + Software Focused (Cloud) • Corporate + Partner Ecosystems Expertise • Strategy + Design + Execution • Proprietary Technology Platforms • Atlanta (USA) HQ + 12 Global Offices • SiriusDecisions Premier Partner • 12+ year Track Record of Growth 15
- 16. Challenge: understand conversations to make agents smarter u What are my customers and agents talking about (via chat and email and voice conversations) u Product categories, and sellable products u Competitive products (and objection handling) u What are the features and functions my customers care about (and how do I related that to my main products and the competitors products) u What is the momentum for product X u What is the sentiment as a function of the type of product u Why is agent X so good at selling product Y u Does agent ?X steer a conversation in the direction of Y? u Where is the agent in the sales cycle (did she hit every BANT category?)
- 17. Challenge: how do I make my agents smarter u What recommended products and (industry) use cases should I present to my agent based on u Persona u Industry u Demand scenario u Competitor u Sales Cycle u How can I help my agent with u Objection handling u Understanding the functionality of products
- 18. No part or process to be used without permission.© 18
- 19. What did we build? u Industry Knowledge Graph (KG) u Around companies, subsidiaries, contacts, products and services, industry and product taxonomies, competing products, current technology stack, propensity to buy, company news, hiring?, white papers – updated over time u Taxonomies u Taxonomy Based Entity Extraction &Sentiment Analysis u Examples of analytics u Text classification: find personas, demand scenarios, industry type u Product Recommendations u Speech recognition u Custom services and how to make life for application developers easier
- 20. Foundational shared taxonomy N3 universal sales-cycle and IT terms & concepts taxonomy Campaign Notes & Chats Taxonomies in N3 Knowledge graph The Taxonomy enables identification of - • Words • Terms • Titles • Concepts • Products • Buyer intent • BANT (the actual sales qualifying stages of BANT) • and Sentiment in the Chats
- 21. N3 - Sales Cycle Foundational Shared Taxonomy Hierarchy of SALES concepts and terms Synonyms, Broader, Narrower, Related concepts and terms This application is where the Taxonomies are created – these are built to the W3C SKOS industry standard
- 22. Hierarchy of Cisco Product concepts and terms Synonyms, Broader, Narrower, Related concepts and terms N3 – Cisco Specific Products Taxonomy Here we see the Cisco Products terminology organized in the taxonomy
- 23. Taxonomy based entity extraction u Regular taxonomy editors pretty good at entity extraction when prefLabels and altLabels are regular words. u AllegroGraph has entity extractor with specializations for product names. u Providing altLabels for every product too time consuming, can be automated u Also needed for post processing when doing speech technology u Built in from AG 6.6 u When you need place names, people names, organizations, currencies, etc. we use specialized entity extractors like Cogito or IBM Natural Language Understanding u Also come with automatic linking to dbpedia, geonames, etc… u Python Spacy if you need NLP capabilities -> POS, special language models, rules u All of the above offer some form of Sentiment Analysis
- 24. Taxonomy based entity extraction [3]u Shows example in Gruff!
- 26. 0 5 10 15 20 25 30 Tiona Hill Christopher Spade Devin Smith Sidney Carr Laura Pugh Billy Young JacobHolmes Yannick Souna George Hanna LeahWagner Mary Rowland-Doud Robert Edwards Paul Worley Brad Mcdougald Daulton Tyler AllisonSlocomb Ashley Etheridge Elise Nemeth Meghan Bush Wesley Sites Nicole Robinette Jeff Thompson Number of Negative, Neutral and Positive Chats 1200 1000 800 600 400 200 0 Ratio of Positive to Negative Chats Query Results- Chat Sentiment – AI deduced Some BDR agents are more positive than others
- 27. Quantity Category Query Results “What high-level technology categories do people chat about most ?”
- 28. Query Results “What sellable products (SKUs) are mentioned the most?” Quantity Product
- 29. 0 10 20 30 40 50 60 70 80 90 100 George Hanna Nicole Robinette AshleyEtheridge M eghan BushW esleySitesSidneyCarr Brad M cdougald Elise Nem eth JacobHolm es PaulW orley JeffThom pson LeahW agnerDevin Sm ithLauraPughBillyYoung Daulton Tyler YannickSounaTionaHill M aryRowland-Doud RobertEdw ards Percentage of mention of sellable products to overall Chat Query Results Some BDR agents talk more about sellable/SKUs than others %
- 30. Product % Product % Are BDRs spending their time on the right products? Query Results – for BDRs Laura vs Sidney % mention of specific SKUs for overall product mentions
- 31. Query Results Top Sellable Product (SKU) by Industry
- 32. Product Recommendations u If a customer talks about X a BDR should bring up Y u Based on Oddsratios u Temporal co-occurrence corrected for frequency of each element in pair u Used in Logistics, Health Care, Chomsky Graph
- 33. The Green boxes are AI created likelihoods that these other Products (connected blue boxes) will also be discussed.
- 34. Taxonomy BuildingEntity Extractor AGraph Sem-Ingest Analytic Tools Real-time Decision support Ad Hoc Queries AG Sentiment Analyzer Machine Learning Text Classifier CCOR Generator: CoOccurence, Correlation, Odds Ratio REST-Semantic Search Taxonomy Tools AI Labelers Guided Advisors Taxonomy Entities Sentiments Classification Statistical Relations Labels Event Knowledge Graph Text Text, email, chats Voice to TextDocuments (PDF, PPT, Word) AllegroGraph KG Platform Knowledge Graph Platform
- 35. Taxonomy Tools AI Labelers Guided Advisors Taxonomy Building Taxonomy BuildingEntity Extractor AG Sentiment Analyzer Machine Learning Text Classifier CCOR Generator: CoOccurence, Correlation, Odds Ratio REST-Semantic Search Taxonomy Entities Sentiments Classification Statistical Relations Labels Event Knowledge Graph Text AllegroGraph KG Platform
- 36. Taxonomy Tools AI Labelers Guided Advisors Entity Discovery and Extraction Taxonomy BuildingEntity Extractor AG Sentiment Analyzer Machine Learning Text Classifier CCOR Generator: CoOccurence, Correlation, Odds Ratio REST-Semantic Search Taxonomy Entities Sentiments Classification Statistical Relations Labels Event Knowledge Graph Text AllegroGraph KG Platform
- 37. Taxonomy Tools AI Labelers Guided Advisors Sentiment Analysis Taxonomy BuildingEntity Extractor AG Sentiment Analyzer Machine Learning Text Classifier CCOR Generator: CoOccurence, Correlation, Odds Ratio REST-Semantic Search Taxonomy Entities Sentiments Classification Statistical Relations Labels Event Knowledge Graph Text AllegroGraph KG Platform
- 38. Taxonomy Tools AI Labelers Guided Advisors Classification Taxonomy BuildingEntity Extractor AG Sentiment Analyzer Machine Learning Text Classifier CCOR Generator: CoOccurence, Correlation, Odds Ratio REST-Semantic Search Taxonomy Entities Sentiments Classification Statistical Relations Labels Event Knowledge Graph Text AllegroGraph KG Platform
- 39. Taxonomy Tools AI Labelers Guided Advisors Statistical Relationships Taxonomy BuildingEntity Extractor AG Sentiment Analyzer Machine Learning Text Classifier CCOR Generator: CoOccurence, Correlation, Odds Ratio REST-Semantic Search Taxonomy Entities Sentiments Classification Statistical Relations Labels Event Knowledge Graph Text AllegroGraph KG Platform
- 40. Taxonomy Tools AI Labelers Guided Advisors Semantic Exploration “Search” Taxonomy BuildingEntity Extractor AG Sentiment Analyzer Machine Learning Text Classifier CCOR Generator: CoOccurence, Correlation, Odds Ratio REST-Semantic Search Taxonomy Entities Sentiments Classification Statistical Relations Labels Event Knowledge Graph Text AllegroGraph KG Platform Semantic Exploration
- 41. Taxonomy Tools AI Labelers Guided Advisors Applications Taxonomy BuildingEntity Extractor AG Sentiment Analyzer Machine Learning Text Classifier CCOR Generator: CoOccurence, Correlation, Odds Ratio REST-Semantic Search Taxonomy Entities Sentiments Classification Statistical Relations Labels Event Knowledge Graph Text AllegroGraph KG Platform Analytic Tools Real-time Decision support Ad Hoc Queries
- 42. Taxonomy trained speech recognizer Taxonomy Entities Sentiments Classification Statistical Relations Labels Event Knowledge Graph Text Taxonomies Speech- Recognizer Text Output Entity Extractor
- 43. And here a quick demo of a note taking app
- 44. And here it is in the knowledge graph
- 45. Conclusion • If you want to understand your customer better • Then really really really listen to him/her • Put your understanding and learning in a knowledge graph • And we are here to help!
- 46. Documents: JSON, JSON-LD Graphs: RDF, Quads, Properties Storage: Triple Attributes, Security Filters, Compression, Indexing, Full-text Transactions: “Real” ACID, 2 Phase Commit Management: Security, Multi-Master Replication, Backup/Restore, Warm Failover Stored Procs: JavaScript Lisp Prolog SPARQL Magic Predicates Reasoning: RDFS++ OWL2-RL Prolog Probabilistic NLP: Taxonomies Entity Extract Text Classify Sentiment Machine Learning Speech Recognition ETL: RDBMS CSV TEXT NoSQL Events: Geospatial Temporal Social REST GUI: GRUFF/AGWebView Java Python Lisp Built-In Integrations Cloud: Amazon AWS Microsoft Azure Data Science: Anaconda R Studio Knowledge: Linked Open Data Editors: Ontology, Taxonomy NoSQL: Cloudera, MongoDB, Solr Containers: Docker, VMWare Massively Parallel - Federation and Sharding OSS Clients SPARQL Prolog AllegroGraph Architecture