The document discusses natural language search using knowledge graphs. It provides an overview of knowledge graphs and how they can help with natural language search. Specifically, it discusses how knowledge graphs can represent relationships and semantics in unstructured text. It also describes how semantic knowledge graphs are generated in Solr and how they can be used for tasks like query understanding, expansion and disambiguation.

1. Natural Language Search
with Knowledge Graphs
Trey Grainger

2. Trey Grainger
Chief Algorithms Officer
• Previously: SVP of Engineering @ Lucidworks; Director of Engineering @ CareerBuilder
• Georgia Tech – MBA, Management of Technology
• Furman University – BA, Computer Science, Business, & Philosophy
• Stanford University – Information Retrieval & Web Search
Other fun projects:
• Co-author of Solr in Action, plus numerous research publications
• Advisor to Presearch, the decentralized search engine
• Lucene / Solr contributor
About Me

3. Trey Grainger
Meet the Authors
Wednesday
3PM Lincoln West
http://aiPoweredSearch.com
Launched this week! (Early Access)

4. Agenda
• About Lucidworks
• What is Natural Language Search?
• What is a Knowledge Graph (and related terminology)?
• Philosophy of Language (enough to get the approach…)
• Semantic Knowledge Graphs
• Knowledge Graph Goals for Natural Language Search
• Relevant Tools
• Solr Text Tagger
• Solr’s Semantic Knowledge Graph
• Automated Graph Generation
• Demos!

5. Basic Keyword Search
(inverted index, tf-idf, bm25,
multilingual text analysis, query
formulation, etc.)
Query Intent
(query classification, semantic
query parsing, knowledge
graphs, concept expansion,
rules, clustering, classification)
Relevancy Tuning
(signals, AB testing/genetic
algorithms, Learning to Rank,
Neural Networks)
Self-learning
Relevance Engineering Sophistication
Context for
this Talk
Taxonomies / Entity
Extraction
(entity recognition, basic
ontologies, synonyms, etc.)

6. The Search & AI Conference
COMPANY BEHIND
Who are we?
300+ CUSTOMERS ACROSS THE
FORTUNE 1000
400+EMPLOYEES
OFFICES IN
San Francisco, CA (HQ)
Raleigh-Durham, NC
Cambridge, UK
Bangalore, India
Hong Kong
Employ about
40% of the active
committers on
the Solr project
40%
Contribute over
70% of Solr's open
source codebase
70%
DEVELOP & SUPPORT
Apache

7. Industry’s most powerful
Intelligent Search & Discovery Platform.

8. Let the most respected
analysts in the world
speak on our behalf
Dassault Systèmes
Mindbreeze
Coveo
Microsoft
Attivio
Expert System
Smartlogic
Sinequa
IBM
IHS Markit
Funnelback
Micro Focus
COMPLETENESS OF VISION
ABILITYTOEXECUTE
CHALLENGERS LEADERS
NICHE PLAYERS VISIONARIES
Source: June 2018 Gartner Magic Quadrant report on Insight Engines.
© Gartner, Inc.

9. What is
Natural Language Search?

12. What is a Knowledge Graph?
(vs. Ontology vs. Taxonomy vs. Synonyms, etc.)

14. Overly Simplistic Definitions
Alternative Labels: Substitute words with identical meanings
[ CTO => Chief Technology Officer; specialise => specialize ]
Synonyms List: Provides substitute words that can be used to represent
the same or very similar things
[ human => homo sapien, mankind; food => sustenance, meal ]
Taxonomy: Classifies things into Categories
[ john is Human; Human is Mammal; Mammal is Animal ]
Ontology: Defines relationships between types of things
[ animal eats food; human is animal ]
Knowledge Graph: Instantiation of an
Ontology (contains the things that are related)
[ john is human; john eats food ]
In practice, there is significant overlap…

16. What sort of Knowledge Graph can
help us with the kinds of problems we
encounter in Search use cases?

18. most often used in
reference to
“free text”

19. But… unstructured data is really
more like “hyper-structured”
data. It is a graph that contains
much more structure than typical
“structured data.”

20. Structured Data
Employees Table
id name company start_date
lw100 Trey
Grainger
1234 2016-02-01
dis2 Mickey
Mouse
9123 1928-11-28
tsla1 Elon Musk 5678 2003-07-01
Companies Table
id name start_date
1234 Lucidworks 2016-02-01
5678 Tesla 1928-11-28
9123 Disney 2003-07-01
Discrete
Values
Continuous
Values
Foreign
Key

21. Unstructured Data
Trey Grainger works at Lucidworks.
He is speaking at the Activate 2019 conference.
#Activate19 (Activate) is being held in
Washington, DC September 9-12, 2019. Trey got
his masters from Georgia Tech.

22. Trey Grainger works for Lucidworks.
He is speaking at the Activate 2019
conference.
#Activate19
(Activate) is being held in Washington, DC
September 9-12, 2019.
Trey got his masters degree from
Georgia Tech.
Trey’s Voicemail
Unstructured Data

23. Trey Grainger works for Lucidworks.
He is speaking at the Activate 2019
conference.
#Activate19
(Activate) is being held in Washington, DC
September 9-12, 2019.
Trey got his masters degree from
Georgia Tech.
Trey’s Voicemail
Foreign Key?

24. Trey Grainger works for Lucidworks.
He is speaking at the Activate 2019
conference.
#Activate19
(Activate) is being held in Washington, DC
September 9-12, 2019.
Trey got his masters degree from
Georgia Tech.
Trey’s Voicemail
Fuzzy Foreign Key? (Entity Resolution)

25. Trey Grainger works for Lucidworks.
He is speaking at the Activate 2019
conference.
#Activate19
(Activate) is being held in Washington, DC
September 9-12, 2019.
Trey got his masters degree from
Georgia Tech.
Trey’s Voicemail
Fuzzier Foreign Key? (metadata, latent features)

26. Trey Grainger works for Lucidworks.
He is speaking at the Activate 2019
conference.
#Activate19
(Activate) is being held in Washington, DC
September 9-12, 2019.
Trey got his masters degree from
Georgia Tech.
Trey’s Voicemail
Fuzzier Foreign Key? (metadata, latent features)
Not so fast!

29. Giant Graph of Relationships...
Trey Grainger works for Lucidworks.
He is speaking at the Activate 2019
conference.
#Activate19
(Activate) is being held in Washington, DC
September 9-12, 2019.
Trey got his masters degree from
Georgia Tech.
Trey’s Voicemail

30. How do we easily harness this
“semantic graph” of relationships
within unstructured information?

31. Semantic Knowledge Graph

32. id: 1
job_title: Software Engineer
desc: software engineer at a
great company
skills: .Net, C#, java
id: 2
job_title: Registered Nurse
desc: a registered nurse at
hospital doing hard work
skills: oncology, phlebotemy
id: 3
job_title: Java Developer
desc: a software engineer or a
java engineer doing work
skills: java, scala, hibernate
field doc term
desc
1
a
at
company
engineer
great
software
2
a
at
doing
hard
hospital
nurse
registered
work
3
a
doing
engineer
java
or
software
work
job_title 1
Software
Engineer
… … …
Terms-Docs Inverted IndexDocs-Terms Forward IndexDocuments
Source: Trey Grainger,
Khalifeh AlJadda,
Mohammed Korayem,
Andries Smith.“The Semantic
Knowledge Graph: A
compact, auto-generated
model for real-time traversal
and ranking of any
relationship within a domain”.
DSAA 2016.
Knowledge
Graph
field term postings
list
doc pos
desc
a
1 4
2 1
3 1, 5
at
1 3
2 4
company 1 6
doing
2 6
3 8
engineer
1 2
3 3, 7
great 1 5
hard 2 7
hospital 2 5
java 3 6
nurse 2 3
or 3 4
registered 2 2
software
1 1
3 2
work
2 10
3 9
job_title java developer 3 1
… … … …

33. Serves as a “data science toolkit” API that allows dynamically navigating and pivoting through multiple levels of
relationships between items in a domain.
Semantic Knowledge Graph API
Core similarity engine, exposed via API
Any product can leverage the core relationship scoring
engine to score any list of entities against any other
list
Full domain support
Keywords, categories, tags, based upon any field on your
documents. Graph is build automatically from the
content representing your domain.
Intersections, overlaps, & relationship
scoring, many levels deep
Users can either provide a list of items to score, or else
have the system dynamically discover the most related
items (or both).
Knowledge
Graph

34. Source: Trey Grainger,
Khalifeh AlJadda,
Mohammed Korayem,
Andries Smith.“The
Semantic Knowledge
Graph: A compact, auto-
generated model for real-
time traversal and ranking of
any relationship within a
domain”. DSAA 2016.
Knowledge
Graph
Set-theory View
Graph View
How the Graph Traversal Works
skill:
Java
skill:
Scala
skill:
Hibernate
skill:
Oncology
has_related_skill
has_related_skill
has_related_skill
doc 1
doc 2
doc 3
doc 4
doc 5
doc 6
skill:
Java
skill: Java
skill:
Scala
skill:
Hibernate
skill:
Oncology
Data Structure View
Java
Scala Hibernate
docs
1, 2, 6
docs
3, 4
Oncology
doc 5

35. DOI: 10.1109/DSAA.2016.51
Conference: 2016 IEEE International Conference on
Data Science and Advanced Analytics (DSAA)
Source: Trey Grainger,
Khalifeh AlJadda, Mohammed
Korayem, Andries Smith.“The
Semantic Knowledge Graph: A
compact, auto-generated
model for real-time traversal
and ranking of any relationship
within a domain”. DSAA 2016.
Knowledge
Graph
Graph Traversal
Data Structure View
Graph View
doc 1
doc 2
doc 3
doc 4
doc 5
doc 6
skill:
Java
skill: Java
skill: Scala
skill:
Hibernate
skill:
Oncology
doc 1
doc 2
doc 3
doc 4
doc 5
doc 6
job_title:
Software
Engineer
job_title:
Data
Scientist
job_title:
Java
Developer
……
Inverted Index
Lookup
Forward Index
Lookup
Forward Index
Lookup
Inverted Index
Lookup
Java
Java
Developer
Hibernate
Scala
Software
Engineer
Data
Scientist
has_related_skill has_related_skill
has_related_skill
has_related_job_title
has_related_job_title
has_related_job_title
has_related_job_title
has_related_job_title
has_related_job_title

36. Scoring of Node Relationships (Edge Weights)
Foreground vs. Background Analysis
Every term scored against it’s context. The more
commonly the term appears within it’s foreground
context versus its background context, the more
relevant it is to the specified foreground context.
countFG(x) - totalDocsFG * probBG(x)
z = --------------------------------------------------------
sqrt(totalDocsFG * probBG(x) * (1 - probBG(x)))
{ "type":"keywords”, "values":[
{ "value":"hive", "relatedness":0.9773, "popularity":369 },
{ "value":"java", "relatedness":0.9236, "popularity":15653 },
{ "value":".net", "relatedness":0.5294, "popularity":17683 },
{ "value":"bee", "relatedness":0.0, "popularity":0 },
{ "value":"teacher", "relatedness":-0.2380, "popularity":9923 },
{ "value":"registered nurse", "relatedness": -0.3802 "popularity":27089 } ] }
We are essentially boosting terms which are more related to some known feature
(and ignoring terms which are equally likely to appear in the background corpus)
+
-
Foreground Query:
"Hadoop"
Knowledge
Graph

37. Related term vector (for query concept expansion)
http://localhost:8983/solr/stack-exchange-health/skg

38. Content-based Recommendations (More Like This on Steroids)
http://localhost:8983/solr/job-postings/skg

39. Differentiating related terms
Misspellings: managr => manager
Synonyms: cpa => certified public accountant
rn => registered nurse
r.n. => registered nurse
Ambiguous Terms*: driver => driver (trucking) ~80% likelihood
driver => driver (software) ~20% likelihood
Related Terms: r.n. => nursing, bsn
hadoop => mapreduce, hive, pig
*differentiated based upon user and query context

40. Thought Exercise
What do you think of when I say the
word “driver”?
What about “architect”?

41. Use Case: Query Disambiguation
Source: M. Korayem, C. Ortiz, K. AlJadda, T. Grainger. "Query Sense Disambiguation Leveraging Large Scale User Behavioral Data". IEEE Big Data 2015.
Example Related Keywords (representing multiple meanings)
driver truck driver, linux, windows, courier, embedded, cdl,
delivery
architect autocad drafter, designer, enterprise architect, java
architect, designer, architectural designer, data architect,
oracle, java, architectural drafter, autocad, drafter, cad,
engineer
… …

42. Use Case: Query Disambiguation
Example Related Keywords (representing multiple meanings)
driver truck driver, linux, windows, courier, embedded, cdl,
delivery
architect autocad drafter, designer, enterprise architect, java
architect, designer, architectural designer, data architect,
oracle, java, architectural drafter, autocad, drafter, cad,
engineer
… …
Source: M. Korayem, C. Ortiz, K. AlJadda, T. Grainger. "Query Sense Disambiguation Leveraging Large Scale User Behavioral Data". IEEE Big Data 2015.

43. A few methodologies:
1) Query Log Mining
2) Semantic Knowledge Graph
Knowledge Graph

44. Disambiguation by Category Example
Meaning 1: Restaurant => bbq, brisket, ribs, pork, …
Meaning 2: Outdoor Equipment => bbq, grill, charcoal, propane, …

45. Disambiguated meanings (represented as term vectors)
Example Related Keywords (Disambiguated Meanings)
architect 1: enterprise architect, java architect, data architect, oracle, java, .net
2: architectural designer, architectural drafter, autocad, autocad drafter, designer,
drafter, cad, engineer
driver 1: linux, windows, embedded
2: truck driver, cdl driver, delivery driver, class b driver, cdl, courier
designer 1: design, print, animation, artist, illustrator, creative, graphic artist, graphic,
photoshop, video
2: graphic, web designer, design, web design, graphic design, graphic designer
3: design, drafter, cad designer, draftsman, autocad, mechanical designer, proe,
structural designer, revit
… …
Source: M. Korayem, C. Ortiz, K. AlJadda, T. Grainger. "Query Sense Disambiguation Leveraging Large Scale User Behavioral Data". IEEE Big Data 2015.

46. Every term or phrase is a
Context-dependent cluster of
meaning with an ambiguous label

47. What does “love” mean?
http://localhost:8983/solr/thesaurus/skg

48. What does “love” mean in the context of “hug”?
http://localhost:8983/solr/thesaurus/skg
"embrace"

49. What does “love” mean in the context of “child”?
http://localhost:8983/solr/thesaurus/skg

50. So what’s the end goal here?
User’s Query:
machine learning research and development Portland, OR software
engineer AND hadoop, java
Traditional Query Parsing:
(machine AND learning AND research AND development AND portland)
OR (software AND engineer AND hadoop AND java)
Semantic Query Parsing:
"machine learning" AND "research and development" AND "Portland, OR"
AND "software engineer" AND hadoop AND java
Semantically Expanded Query:
"machine learning"^10 OR "data scientist" OR "data mining" OR "artificial intelligence")
AND ("research and development"^10 OR "r&d") AND
AND ("Portland, OR"^10 OR "Portland, Oregon" OR {!geofilt pt=45.512,-122.676 d=50 sfield=geo})
AND ("software engineer"^10 OR "software developer")
AND (hadoop^10 OR "big data" OR hbase OR hive) AND (java^10 OR j2ee)

51. Semantic Search Components:
• Apache Solr
• Solr Text Tagger
• Semantic Knowledge Graph
• Statistical Phrase Identifier
• Fusion Semantic Query Pipelines
• Fusion AI Synonyms Job
• Fusion AI Token & Phrase Spell Correction Job
• Fusion AI Head/Tail Analysis Job
• Fusion AI Phrase Identification Job
• Fusion Query Rules Engine

52. I talked about integrating these last year:
See my Activate 2018 talk on
“How to Build a Semantic Search System”
For details on extended Lucidworks Fusion capabilities.

53. For Today’s talk, I’ll only focus on these two
• Solr Text Tagger
• Semantic Knowledge Graph

56. Graph Query Parser
• Query-time, cyclic aware graph traversal is able to rank documents based on relationships
• Provides controls for depth, filtering of results and inclusion
of root and/or leaves
• Limitations: distributed queries only traverse intra-shard docs
Examples:
• http://localhost:8983/solr/graph/query?fl=id,score&
q={!graph from=in_edge to=out_edge}id:A
• http://localhost:8983/solr/my_graph/query?fl=id&
q={!graph from=in_edge to=out_edge
traversalFilter='foo:[* TO 15]'}id:A
• http://localhost:8983/solr/my_graph/query?fl=id&
q={!graph from=in_edge to=out_edge maxDepth=1}foo:[* TO 10]

57. Example Query:

58. Demo Data
Places (also includes geonames database)
Entities (includes search commands)
Text Content
[ Web crawl of restaurant and product reviews sites ]

59. Find Location (Graph Query)
http://localhost:8983/solr/POI/select

60. Graph Traversal converted to Facet
http://localhost:8983/solr/POI/select

61. For Remaining keywords, find doc type + related terms
http://localhost:8983/solr/POI/select

62. Full Knowledge Graph Traversal in Single Request!

63. Tricks for
Automated Graph Generation

64. Named Entity Recognition (NER)
NER translates…
Barack Obama was the president of the United States of America. Before that, Obama was a
senator.
into…
<person id="barack_obama">Barack Obama</person> was the <role>president</role> of the
<country id="usa">United States of America</country>. Before that, <person
id="barack_obama">Obama</person> was a <role>senator</role>.
In Solr, this would become:
text: Barack Obama was the president of the United States of America. Before that, Obama was a
senator.
person: Barack Obama
country: United States of America
role: [ president, senator ]

65. Open Information Extraction
(automatic RDF triple extraction / explicit knowledge graph learning)

66. Demo!

67. Demo!

68. popular barbeque near Activate
(popular same as "good", "top", "best")
Hotels near Activate
hotels near popular BBQ in Washington DC
BBQ near airports near Activate
hotels near movie theaters in Washington DC …
And that’s really just the beginning!

69. But it’s unfortunately also the end
of our time today : (

70. We operationalize AI for the
largest businesses on the planet.

71. Trey Grainger
trey@lucidworks.com
@treygrainger
Other presentations:
http://www.treygrainger.com
50% Discount code: mlgrainger
http://aiPoweredSearch.com
Books:

72. Thank You