Speakers: Mayank Gupta, SVP of Data, LPL Financial and Wren Chan, Vice President of Foundational Architecture and Innovation, LPL Financial

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Building a Knowledge Graph &
applying it.

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Example “Meta-graph” – Enterprise Architecture Data
Model as a Graph

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MAY THE FORCE (KNOWLEDGE) BE WITH YOU!
LPL is in the early stages of
applying Graph
Technology, but we can
already see the power as
well as potential.
Infrastructure
Scalable Storage, Fault Tolerance, Networking,
Data Centers, Monitoring, Alerts, etc.
Data
High-quality Primary Datasets,
Well-defined Domain Schemas
Graph
ETL and Queries,
Graph Data Model, Mappings
Knowledge
Ontologies,
Modality, Provenance
Logic
Inference
Proofs
From “Building an Enterprise Knowledge
Graph at Uber: Lessons from Reality”

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What is a Knowledge Graph?
Reference Reading
on Graphs

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How can we apply it…
Reference Reading
on Graphs

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Benefits of using a Knowledge Graph?
They allow us to do the following:
1. Capture complex relationships easily
2. Change our understanding of complex relationships easily
3. Enable AI and ML algorithms to apply inferencing and
create additional insights
4. Share information and content more holistically

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Pilot “Meta-graph"

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Using Hume – building the knowledge graph

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Vendor Slides
Hume – Slide 24 to 26

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Vendor Slides

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Experiment Info

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Use Case 1: Optimize Search with Automated Tagging
Based on initial feedback from the business, search relevancy of the top 10 search results on
random search terms is on average 60%. This shows that we were able to leverage the
knowledge graph to build out a functional search application.

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Use Case 2: Clustering Related Documents by Tags
We were able to successfully cluster documents based tags extracted from the knowledge
graph. These clusters show potential duplicates as well as identifying what could be the most
relevant.

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Use Case 3: Building a Chat Bot
Chatbot Flow Diagram
The flows
captured in
the Red and
Blue boxes
have been
reproduced in
Lex based on
a more
intuitive graph
data model

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Use Case 3: Building a Chat Bot
The sub-graph shown above represents the underlying graph that drives the
chatbot as implemented in Lex. We leverage the nodes and relationships to
determine what are additional clarifying questions (e.g., Slot) to ask to fulfill a
query (e.g., Intent).

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Q u e s t i n s

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Additional Resources
q Udemy > Neo4j Foundations
q YouTube > GraphAware > Using Knowledge Graphs to predict customer needs, improve
product quality and save costs
q YouTube > Neo4j > Knowledge Graph Search with Elasticsearch
q Neo4j > Graph Algorithms
q GraphAware Blog > Bringing Mining and Searching Text with Graph Databases
q GraphAware Blog > Efficient unsupervised keywords extraction using graphs
q GraphAware Blog > Bring Order to Chaos: A Graph-Based Journey from Textual Data to
Wisdom
q GraphAware Resources > Taming text with Neo4j: The Graphaware NLP Framework

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Appendix – Knowledge Graph Schema
q Document is Resource Center Document
q Keyword is top 25 keywords extracted via NLP plugin from Document
q Concept is loaded from the knowledge graph on ConceptNet
q GlossaryIndexItem is from LPL Ontology
q InvestopediaTerm is term from Investopedia Financial Term Dictionary
q InvestopediaKeyword is top 25 keywords extracted via NLP plugin from the definition of the
InvestopediaTerm
q Community represents clusters created from running Louvain algorithm for community
detection against Keywords in an attempt to find related Documents
q CallCenter* represents analytics data that was provided for how the call center route
questions to the relevant Document. The analytics data also includes the search terms used
by the advisors with the top Document selected and the number of clicks.