Last year, Forrester said “Knowledge graphs provide contextual windows into master data domains and the links between domains” (The Forrester Wave, Master Data Management). Knowledge graphs are the key to providing the semantic and link analysis capabilities required by modern applications. Providing relevant information to the user performing search queries or navigating a site is a complex task. It requires a huge set of data, a process of progressive improvements, and self tuning parameters together with infrastructure that can support them. To add to the complexity, this search infrastructure must be introduced seamlessly into the existing platform, with access to relevant data flows to provide always up-to-date data. Moreover, it should allow for easy addition of new data sources to cater to new requirements, without affecting the entire system or the current relevance. In all e-commerce sites, text search and catalog navigation are not only the entry points for users but they are also the main “salespeople.” Compared with web search engines, this use case has the advantage that the set of “items” to be searched is more controlled and regulated.

1. GraphAware®
ENHANCING KNOWLEDGE
GRAPH SEARCH
WITH ELASTICSEARCH
Luanne Misquitta
Principal Consultant @ GraphAware
Alessandro Negro
Chief Scientist @ GraphAware
graphaware.com
@graph_aware, @luannem, @AlessandroNegro

2. ‣ The rise of Knowledge Graphs
‣ Relevant Search
‣ Knowledge Graphs for e-Commerce
‣ Infrastructure
‣ Combined Search Approaches
OUTLINE
GraphAware®

3. “Knowledge graphs provide contextual windows
into master data domains and the links between
domains”
KNOWLEDGE GRAPH
CONNECTING THE DOTS
GraphAware®
The Forrester Wave, Master Data Management

4. THE RISE OF
KNOWLEDGE GRAPHS
GraphAware®
E-Commerce
‣ Many data sources
‣ Category hierarchies
‣ Marketing strategies
Enterprise Networks
‣ Uncover new opportunities, hidden leads
Finance
‣ Textual corpora such as financial
documents contain a wealth of
knowledge
‣ Structured knowledge of entities and
relationships

5. Medicine & Health
‣ Dynamic ontologies where data is
categorized and organised around
people, places, things and events
‣ Patterns in disease progression, causal
relations involving disease and
symptoms, new relationships previously
unrecognised
Criminal Investigation & Intelligence
‣ Obfuscated information
‣ Traceability to sources of information
GraphAware®
THE RISE OF
KNOWLEDGE GRAPHS

6. DATA SPARSITY
PROBLEM
GraphAware®
Collaborative Filtering
‣ Cold Start
Content Based Recommendation
‣ Missing Data
‣ Wrong Data
Text Search
‣ User agnostic
‣ Relevant Search

7. ‣ Entity centric view of linked data
‣ Self descriptive
‣ Allows for deriving of information
‣ Ontology can be extended or revised,
supporting continuously running data pipelines
‣ Provides traceability into provenance of data
KNOWLEDGE GRAPHS
A KNOWLEDGE BASE
ON STEROIDS
GraphAware®

8. KNOWLEDGE GRAPH:
DATA CONVERGENCE
GraphAware®

9. RELEVANT SEARCH
GraphAware®
“Relevance is the practice of improving search
results for users by satisfying their information
needs in the context of a particular user
experience, while balancing how ranking
impacts business’s needs.”

10. RELEVANT SEARCH
DIMENSIONS
GraphAware®

11. KNOWLEDGE GRAPHS
THE MODEL
Search architecture must be able to handle highly heterogenous data
Knowledge Graphs represent the information structure for relevant search
Graphs are the right representation for:
‣ Information Extraction
‣ Recommendation Engines
‣ Context Representation
‣ Rule Engine

12. Critical aspects and peculiarities:
‣ Defined and controlled set of searchable Items
‣ Multiple category hierarchies
‣ Marketing strategy
‣ User feedback and interactions
‣ Supplier information
‣ Business constraints
THE USE CASE
E-COMMERCE
GraphAware®
→ Text search and catalog navigation as Sales People

13. KNOWLEDGE GRAPH
FOR E-COMMERCE
GraphAware®

14. INFRASTRUCTURE
A 10K-FOOT VIEW
GraphAware®

15. A graph centric approach
THE DATA FLOW
GraphAware®
‣ Async data ingestion
‣ Data Pipeline
‣ Single Neo4j Writer
‣ Microservice approach for
isolation and scalability
‣ Event notification
‣ Multiple views exported into
Elasticsearch

16. THE NEO4J ROLES
GraphAware®
‣ Single source of truth
‣ Cleansing
‣ Fast access to connected data
‣ Query
‣ Knowledge Graph store
‣ Merging External Data
‣ Existing Data Augmentation

17. Natural Language Processing
‣ Unsupervised Topic Identification
‣ Word2Vec
‣ Clustering (Label Propagation)
EXTERNALISE INTENSE
PROCESSES
GraphAware®
Recommendation model building
‣ Content-Based
‣ Collaborative Filtering (internal and
external)

18. Fast, Reliable and Easy-to-tune textual searches
‣ Multiple views for multiple scopes:
‣ Catalog Navigation and Search
‣ Faceting
‣ Product details page
‣ Product variants aggregation
‣ Autocomplete
‣ Suggestion
THE ELASTICSEARCH
ROLES
GraphAware®
→ It is not used as a database

19. Any components of relevance-scoring calculation
corresponding to a meaningful and measurable
information
Two techniques to control relevancy:
‣ Signal Modeling
‣ Ranking Function
Note: balance precision and recall
Multiple sources
CRAFTING
SIGNALS
GraphAware®

20. → Users as a new source of information
GraphAware®
Profile-based personalisation:
‣ Explicit: Users provide profile
information
‣ Implicit: Profile created from user
interactions
Behavioural-Based personalisation
‣ Focus on User-Item Interaction
‣ Make explicit the relationships
among users and items
PERSONALISING
SEARCH
Tying personalisation back to search
‣ Query-time personalisation
‣ Index-time personalisation

21. → Search for things, not for strings
CONCEPT
SEARCH
GraphAware®
Basic Approaches:
‣ Concept field (Manual Tagging)
‣ Synonyms
Content Augmentation (ML based)
‣ Co-occurrence
‣ Latent Semantic Analysis
‣ Latent Dirichlet Allocation
‣ Word2Vec

22. COMBINED SEARCH
APPROACHES
GraphAware®

23. Knowledge Graphs can
‣ store easy-to-query model
‣ gather data from multiple sources
‣ be easily extended
Search Engines can
‣ provide fast, reliable and easy-to-
tune textual search
‣ provide features like faceting,
autocomplete
CONCLUSION
GraphAware®
→ By combining them, it is possible to offer an unlimited
set of services to the end users

24. www.graphaware.com
@graph_aware
GraphAware
GraphAware®
world’s #1 Neo4j consultancy