SlideShare a Scribd company logo
The Semantic Knowledge Graph:
A compact, auto-generated model for real-time traversal
and ranking of any relationship within a domain
Trey Grainger
SVP of Engineering
Lucidworks
Khalifeh AlJadda
Lead Data Scientist
CareerBuilder
Mohammed Korayem
Data Scientist
CareerBuilder
Andries Smith
Software Engineer
CareerBuilder
Trey Grainger
SVP of Engineering
• Previously Director of Engineering @ CareerBuilder
• MBA, Management of Technology – Georgia Tech
• BA, Computer Science, Business, & Philosophy – Furman University
• Information Retrieval & Web Search - Stanford University
Fun outside of CB:
• Co-author of Solr in Action, plus a handful of research papers
• Frequent conference speaker
• Founder of Celiaccess.com, the gluten-free search engine
• Lucene/Solr contributor
About Me
Terminology / Background
A Graph
DSAA 2016
Montreal
Quebec Canada
Semantic
Knowledge
Graph Paper
Trey
Grainger
Mohammed
Korayem
Andries
Smith
Khalifeh
AlJadda
in_country
Node / Vertex
Edge
“Solr is the popular, blazing-fast,
open source enterprise search
platform built on Apache Lucene™.”
Key Solr Features:
● Multilingual Keyword search
● Relevancy Ranking of results
● Faceting & Analytics
● Highlighting
● Spelling Correction
● Autocomplete/Type-ahead Prediction
● Sorting, Grouping, Deduplication
● Distributed, Fault-tolerant, Scalable
● Geospatial search
● Complex Function queries
● Recommendations (More Like This)
● … many more
*source: Solr in Action, chapter 2
Term Documents
a doc1 [2x]
brown doc3 [1x] , doc5 [1x]
cat doc4 [1x]
cow doc2 [1x] , doc5 [1x]
… ...
once doc1 [1x], doc5 [1x]
over doc2 [1x], doc3 [1x]
the doc2 [2x], doc3 [2x],
doc4[2x], doc5 [1x]
… …
Document Content Field
doc1 once upon a time, in a land far,
far away
doc2 the cow jumped over the moon.
doc3 the quick brown fox jumped over
the lazy dog.
doc4 the cat in the hat
doc5 The brown cow said “moo”
once.
… …
What you SEND to Lucene/Solr:
How the content is INDEXED into
Lucene/Solr (conceptually):
The inverted index
/solr/select/?q=apache solr
Term Documents
… …
apache doc1, doc3, doc4,
doc5
…
hadoop doc2, doc4, doc6
… …
solr doc1, doc3, doc4,
doc7, doc8
… …
doc5
doc7 doc8
doc1 doc3
doc4
solr
apache
apache solr
Matching queries to documents
Related Work
Knowledge
Graph
Related Work
• Primarily related to ontology Learning.
• Recently, large-scale knowledge bases that utilize
ontologies (FreeBase [4], DBpedia [5], and YAGO
[6, 7]) have been constructed using structured
sources such as Wikipedia infoboxes.
• Other approaches (DeepDive [8], Nell2RDF [9],
and PROSPERA [10]) crawl the web and use
machine learning and natural language processing
to build web-scale knowledge graphs.
Problem Description
Knowledge
Graph
Challenges we are solving
Because current knowledge bases / ontology learning systems typically
requires explicitly modeling nodes and edges into a graph ahead of time, this
unfortunately presents several limitations to the use of such a knowledge graph:
• Entities not modeled explicitly as nodes have no known relationships to any other
entities.
• Edges exist between nodes, but not between arbitrary combinations of nodes, and therefore
such a graph is not ideal for representing nuanced meanings of an entity when appearing
within different contexts, as is common within natural language.
• Substantial meaning is encoded in the linguistic representation of the domain that is
lost when the underlying textual representation is not preserved: phrases, interaction of
concepts through actions (i.e. verbs), positional ordering of entities and the phrases containing
those entities, variations in spelling and other representations of entities, the use of adjectives
to modify entities to represent more complex concepts, and aggregate frequencies of
occurrence for different representations of entities relative to other representations.
• It can be an arduous process to create robust ontologies, map a domain into a graph
representing those ontologies, and ensure the generated graph is compact, accurate,
comprehensive, and kept up to date.
Knowledge
Graph
Semantic Data Encoded into Free Text Content
e en eng engi engineer engineers
engineer engineersNode Type: Term
software
engineer
software
engineers
electrical
engineering
engineer
engineering software
…
…
…
Node Type:
Character Sequence
Node Type:
Term Sequence
Node Type:
Document
id: 1
text: looking for a software
engineerwith degree in
computer science or
electrical engineering
id: 2
text: apply to be a software
engineer and work with
other great software
engineers
id: 3
text: start a great careerin
electrical engineering
…
…
Model
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 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
… … … …
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 Uninverted IndexDocuments Knowledge
Graph
Knowledge
Graph
Set-theory View
Graph View
How the Graph Traversal Works
skill: Java
skill: Scala
skill:
Hibernate
skill:
Oncology
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
Knowledge
Graph
Graph Model
Structure:
Single-level Traversal / Scoring:
Multi-level Traversal / Scoring:
Knowledge
Graph
Multi-level 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
Doc Values Index
Lookup
Doc Values Index
Lookup
Inverted Index
Lookup
Java
Java
Developer
Hibernate
Scala
Software
Engineer
Data
Scientist
has_related_job_title
has_related_job_title
Knowledge
Graph
Scoring nodes in the Graph
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": 0.9765, "popularity":369 },
{ "value":”spark", "relatedness": 0.9634, "popularity":15653 },
{ "value":".net", "relatedness": 0.5417, "popularity":17683 },
{ "value":"bogus_word", "relatedness": 0.0, "popularity":0 },
{ "value":"teaching", "relatedness": -0.1510, "popularity":9923 },
{ "value":"CPR", "relatedness": -0.4012, "popularity":27089 } ] }
+
-
Foreground Query:
"Hadoop"
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
Multi-level Graph Traversal with Scores
software engineer*
(materialized node)
Java
C#
.NET
.NET
Developer
Java
Developer
Hibernate
ScalaVB.NET
Software
Engineer
Data
Scientist
Skill
Nodes
has_related_skillStarting
Node
Skill
Nodes
has_related_skill Job Title
Nodes
has_related_job_title
0.90
0.88 0.93
0.93
0.34
0.74
0.91
0.89
0.74
0.89
0.780.72
0.48
0.93
0.76
0.83
0.80
0.64
0.61
0.780.55
Knowledge
Graph
Materialization of new nodes through shared documents
engineer
engineers
software engineer*
(materialized node)
engineer*
(materialized node)
Software
doc 1
doc 2
doc 3
doc 4
doc 5
doc 6
links_to
links_to
Implementation
Open Sourced!
Knowledge
Graph
Populating the Graph
Knowledge
Graph
Knowledge
Graph
Experiments
Knowledge
Graph
Data Cleansing
{ "type":"keywords”, "values":[
{ "value":"hive", "relatedness": 0.9765, "popularity":369 },
{ "value":”spark", "relatedness": 0.9634, "popularity":15653 },
{ "value":".net", "relatedness": 0.5417, "popularity":17683 },
{ "value":"bogus_word", "relatedness": 0.0, "popularity":0 },
{ "value":"teaching", "relatedness": -0.1510, "popularity":9923 },
{ "value":"CPR", "relatedness": -0.4012, "popularity":27089 } ] }
Foreground Query: "Hadoop"
Experiment: Data analyst
manually annotated 500
pairs of terms found together
in real query logs as
“relevant” or “not relevant”
Results: SKG removed 78%
of the terms while maintaining
a 95% accuracy at removing
the correct noisy pairs from
the input data.
Knowledge
Graph
Predictive Analytics
Knowledge
Graph
Search Expansion
Experiment: Take an initial query, and expand keyword
phrases to include the most related entities to that query
Example:
The Semantic Search Problem
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)
machine learning
Keywords:
Search Behavior,
Application Behavior, etc.
Job Title Classifier, Skills Extractor, Job Level Classifier, etc.
Semantic
Interpretation
keywords:((machine learning)^10 OR
{ AT_LEAST_2: ("data mining"^0.9, matlab^0.8,
"data scientist"^0.75, "artificial intelligence"^0.7,
"neural networks"^0.55)) }
{ BOOST_TO_TOP: ( job_title:(
"software engineer" OR "data manager" OR
"data scientist" OR "hadoop engineer")) }
Modified Query:
Related Occupations
machine learning:
{15-1031.00 .58
Computer Software Engineers, Applications
15-1011.00 .55
Computer and Information Scientists, Research
15-1032.00 .52
Computer Software Engineers, Systems Software }
machine learning:
{ software engineer .65,
data manager .3,
data scientist .25,
hadoop engineer .2, }
Common Job Titles
Query Expansion
Related Phrases
machine learning:
{ data mining .9,
matlab .8,
data scientist .75,
artificial intelligence .7,
neural networks .55 }
Known keyword
phrases
java developer
machine learning
registered nurse
FST
Knowledge
Graph in
+
The Semantic Knowledge Graph
Knowledge
Graph
Document Summarization
Experiment: Pass in raw text
(extracting phrases as needed), and
rank their similarity to the documents
using the SKG.
Additionally, can traverse the graph
to “related” entities/keyword phrases
NOT found in the original document
Applications: Content-based and
multi-modal recommendations
(no cold-start problem), data cleansing
prior to clustering or other ML methods,
semantic search / similarity scoring
Document Enrichment – Find / Score Relationships
Document Summarization – Rank / Clean Keywords
Knowledge
Graph
Future Work
• Semantic Search (more experiments)
• Search Engine Relevancy Algorithms
• Trending Topics
• Recommendation Systems
• Root Cause Analysis
• Abuse Detection
Knowledge
Graph
Conclusion
Applications:
The Semantic Knowledge Graph has numerous applications, including
automatically building ontologies, identification of trending topics over time,
predictive analytics on timeseries data, root-cause analysis surfacing concepts
related to failure scenarios from free text, data cleansing, document
summarization, semantic search interpretation and expansion of queries,
recommendation systems, and numerous other forms of anomaly detection.
Main contribution of this paper:
The introduction (and open sourcing) of the the Semantic Knowledge Graph, a
novel and compact new graph model
that can dynamically materialize and score the relationships between any arbitrary
combination of entities represented within a corpus of documents.
Knowledge
Graph
References
Contact Info
Trey Grainger
trey.grainger@lucidworks.com
@treygrainger
http://solrinaction.com
Other presentations:
http://www.treygrainger.com

More Related Content

What's hot

Building an Enterprise Knowledge Graph @Uber: Lessons from Reality
Building an Enterprise Knowledge Graph @Uber: Lessons from RealityBuilding an Enterprise Knowledge Graph @Uber: Lessons from Reality
Building an Enterprise Knowledge Graph @Uber: Lessons from Reality
Joshua Shinavier
 
Knowledge Graph for Machine Learning and Data Science
Knowledge Graph for Machine Learning and Data ScienceKnowledge Graph for Machine Learning and Data Science
Knowledge Graph for Machine Learning and Data Science
Cambridge Semantics
 
Intro to Graphs and Neo4j
Intro to Graphs and Neo4jIntro to Graphs and Neo4j
Intro to Graphs and Neo4j
jexp
 
Vector database
Vector databaseVector database
Vector database
Guy Korland
 
Optimizing Your Supply Chain with the Neo4j Graph
Optimizing Your Supply Chain with the Neo4j GraphOptimizing Your Supply Chain with the Neo4j Graph
Optimizing Your Supply Chain with the Neo4j Graph
Neo4j
 
Deep dive into LangChain integration with Neo4j.pptx
Deep dive into LangChain integration with Neo4j.pptxDeep dive into LangChain integration with Neo4j.pptx
Deep dive into LangChain integration with Neo4j.pptx
TomazBratanic1
 
Collaboratively Creating the Knowledge Graph of Life
Collaboratively Creating the Knowledge Graph of LifeCollaboratively Creating the Knowledge Graph of Life
Collaboratively Creating the Knowledge Graph of Life
Chris Mungall
 
Slides: Knowledge Graphs vs. Property Graphs
Slides: Knowledge Graphs vs. Property GraphsSlides: Knowledge Graphs vs. Property Graphs
Slides: Knowledge Graphs vs. Property Graphs
DATAVERSITY
 
Introduction to Knowledge Graphs
Introduction to Knowledge GraphsIntroduction to Knowledge Graphs
Introduction to Knowledge Graphs
mukuljoshi
 
Danish Business Authority: Explainability and causality in relation to ML Ops
Danish Business Authority: Explainability and causality in relation to ML OpsDanish Business Authority: Explainability and causality in relation to ML Ops
Danish Business Authority: Explainability and causality in relation to ML Ops
Neo4j
 
Graphs for Data Science and Machine Learning
Graphs for Data Science and Machine LearningGraphs for Data Science and Machine Learning
Graphs for Data Science and Machine Learning
Neo4j
 
Get Started with the Most Advanced Edition Yet of Neo4j Graph Data Science
Get Started with the Most Advanced Edition Yet of Neo4j Graph Data ScienceGet Started with the Most Advanced Edition Yet of Neo4j Graph Data Science
Get Started with the Most Advanced Edition Yet of Neo4j Graph Data Science
Neo4j
 
Neo4j 4 Overview
Neo4j 4 OverviewNeo4j 4 Overview
Neo4j 4 Overview
Neo4j
 
Getting Started with Knowledge Graphs
Getting Started with Knowledge GraphsGetting Started with Knowledge Graphs
Getting Started with Knowledge Graphs
Peter Haase
 
Leveraging Lucene/Solr as a Knowledge Graph and Intent Engine
Leveraging Lucene/Solr as a Knowledge Graph and Intent EngineLeveraging Lucene/Solr as a Knowledge Graph and Intent Engine
Leveraging Lucene/Solr as a Knowledge Graph and Intent Engine
Trey Grainger
 
Enterprise Knowledge Graph
Enterprise Knowledge GraphEnterprise Knowledge Graph
Enterprise Knowledge Graph
Lukas Masuch
 
GraphFrames: Graph Queries In Spark SQL
GraphFrames: Graph Queries In Spark SQLGraphFrames: Graph Queries In Spark SQL
GraphFrames: Graph Queries In Spark SQL
Spark Summit
 
The Next Generation of AI-powered Search
The Next Generation of AI-powered SearchThe Next Generation of AI-powered Search
The Next Generation of AI-powered Search
Trey Grainger
 
Intro to Neo4j and Graph Databases
Intro to Neo4j and Graph DatabasesIntro to Neo4j and Graph Databases
Intro to Neo4j and Graph Databases
Neo4j
 
Querying the Wikidata Knowledge Graph
Querying the Wikidata Knowledge GraphQuerying the Wikidata Knowledge Graph
Querying the Wikidata Knowledge Graph
Ioan Toma
 

What's hot (20)

Building an Enterprise Knowledge Graph @Uber: Lessons from Reality
Building an Enterprise Knowledge Graph @Uber: Lessons from RealityBuilding an Enterprise Knowledge Graph @Uber: Lessons from Reality
Building an Enterprise Knowledge Graph @Uber: Lessons from Reality
 
Knowledge Graph for Machine Learning and Data Science
Knowledge Graph for Machine Learning and Data ScienceKnowledge Graph for Machine Learning and Data Science
Knowledge Graph for Machine Learning and Data Science
 
Intro to Graphs and Neo4j
Intro to Graphs and Neo4jIntro to Graphs and Neo4j
Intro to Graphs and Neo4j
 
Vector database
Vector databaseVector database
Vector database
 
Optimizing Your Supply Chain with the Neo4j Graph
Optimizing Your Supply Chain with the Neo4j GraphOptimizing Your Supply Chain with the Neo4j Graph
Optimizing Your Supply Chain with the Neo4j Graph
 
Deep dive into LangChain integration with Neo4j.pptx
Deep dive into LangChain integration with Neo4j.pptxDeep dive into LangChain integration with Neo4j.pptx
Deep dive into LangChain integration with Neo4j.pptx
 
Collaboratively Creating the Knowledge Graph of Life
Collaboratively Creating the Knowledge Graph of LifeCollaboratively Creating the Knowledge Graph of Life
Collaboratively Creating the Knowledge Graph of Life
 
Slides: Knowledge Graphs vs. Property Graphs
Slides: Knowledge Graphs vs. Property GraphsSlides: Knowledge Graphs vs. Property Graphs
Slides: Knowledge Graphs vs. Property Graphs
 
Introduction to Knowledge Graphs
Introduction to Knowledge GraphsIntroduction to Knowledge Graphs
Introduction to Knowledge Graphs
 
Danish Business Authority: Explainability and causality in relation to ML Ops
Danish Business Authority: Explainability and causality in relation to ML OpsDanish Business Authority: Explainability and causality in relation to ML Ops
Danish Business Authority: Explainability and causality in relation to ML Ops
 
Graphs for Data Science and Machine Learning
Graphs for Data Science and Machine LearningGraphs for Data Science and Machine Learning
Graphs for Data Science and Machine Learning
 
Get Started with the Most Advanced Edition Yet of Neo4j Graph Data Science
Get Started with the Most Advanced Edition Yet of Neo4j Graph Data ScienceGet Started with the Most Advanced Edition Yet of Neo4j Graph Data Science
Get Started with the Most Advanced Edition Yet of Neo4j Graph Data Science
 
Neo4j 4 Overview
Neo4j 4 OverviewNeo4j 4 Overview
Neo4j 4 Overview
 
Getting Started with Knowledge Graphs
Getting Started with Knowledge GraphsGetting Started with Knowledge Graphs
Getting Started with Knowledge Graphs
 
Leveraging Lucene/Solr as a Knowledge Graph and Intent Engine
Leveraging Lucene/Solr as a Knowledge Graph and Intent EngineLeveraging Lucene/Solr as a Knowledge Graph and Intent Engine
Leveraging Lucene/Solr as a Knowledge Graph and Intent Engine
 
Enterprise Knowledge Graph
Enterprise Knowledge GraphEnterprise Knowledge Graph
Enterprise Knowledge Graph
 
GraphFrames: Graph Queries In Spark SQL
GraphFrames: Graph Queries In Spark SQLGraphFrames: Graph Queries In Spark SQL
GraphFrames: Graph Queries In Spark SQL
 
The Next Generation of AI-powered Search
The Next Generation of AI-powered SearchThe Next Generation of AI-powered Search
The Next Generation of AI-powered Search
 
Intro to Neo4j and Graph Databases
Intro to Neo4j and Graph DatabasesIntro to Neo4j and Graph Databases
Intro to Neo4j and Graph Databases
 
Querying the Wikidata Knowledge Graph
Querying the Wikidata Knowledge GraphQuerying the Wikidata Knowledge Graph
Querying the Wikidata Knowledge Graph
 

Similar to The Semantic Knowledge Graph

The Relevance of the Apache Solr Semantic Knowledge Graph
The Relevance of the Apache Solr Semantic Knowledge GraphThe Relevance of the Apache Solr Semantic Knowledge Graph
The Relevance of the Apache Solr Semantic Knowledge Graph
Trey Grainger
 
SDSC18 and DSATL Meetup March 2018
SDSC18 and DSATL Meetup March 2018 SDSC18 and DSATL Meetup March 2018
SDSC18 and DSATL Meetup March 2018
CareerBuilder.com
 
How to Build a Semantic Search System
How to Build a Semantic Search SystemHow to Build a Semantic Search System
How to Build a Semantic Search System
Trey Grainger
 
TechEvent 2019: Artificial Intelligence in Dev & Ops; Martin Luckow - Trivadis
TechEvent 2019: Artificial Intelligence in Dev & Ops; Martin Luckow - TrivadisTechEvent 2019: Artificial Intelligence in Dev & Ops; Martin Luckow - Trivadis
TechEvent 2019: Artificial Intelligence in Dev & Ops; Martin Luckow - Trivadis
Trivadis
 
Vital AI MetaQL: Queries Across NoSQL, SQL, Sparql, and Spark
Vital AI MetaQL: Queries Across NoSQL, SQL, Sparql, and SparkVital AI MetaQL: Queries Across NoSQL, SQL, Sparql, and Spark
Vital AI MetaQL: Queries Across NoSQL, SQL, Sparql, and Spark
Vital.AI
 
Odsc 2019 entity_reputation_knowledge_graph
Odsc 2019 entity_reputation_knowledge_graphOdsc 2019 entity_reputation_knowledge_graph
Odsc 2019 entity_reputation_knowledge_graph
venkatramanJ4
 
Babasaheb javca
Babasaheb javcaBabasaheb javca
Babasaheb javca
Babasaheb Pawar
 
From keyword-based search to language-agnostic semantic search
From keyword-based search to language-agnostic semantic searchFrom keyword-based search to language-agnostic semantic search
From keyword-based search to language-agnostic semantic search
CareerBuilder.com
 
Mark Tortoricci - Talent42 2015
Mark Tortoricci - Talent42 2015Mark Tortoricci - Talent42 2015
Mark Tortoricci - Talent42 2015
Talent42
 
CV
CVCV
Neo4j in Depth
Neo4j in DepthNeo4j in Depth
Neo4j in Depth
Max De Marzi
 
OSCON 2014: Data Workflows for Machine Learning
OSCON 2014: Data Workflows for Machine LearningOSCON 2014: Data Workflows for Machine Learning
OSCON 2014: Data Workflows for Machine Learning
Paco Nathan
 
RamaRaju_Profile
RamaRaju_ProfileRamaRaju_Profile
RamaRaju_Profile
Ramaraju Dantuluri
 
Reflected intelligence evolving self-learning data systems
Reflected intelligence  evolving self-learning data systemsReflected intelligence  evolving self-learning data systems
Reflected intelligence evolving self-learning data systems
Trey Grainger
 
Data science presentation
Data science presentationData science presentation
Data science presentation
MSDEVMTL
 
Gaurav agarwal
Gaurav agarwalGaurav agarwal
Gaurav agarwal
Gaurav Agarwal
 
urttyo_banerjee
urttyo_banerjeeurttyo_banerjee
urttyo_banerjee
Urttyo Banerjee
 
Data-Oriented Programming: making data a first-class citizen
Data-Oriented Programming: making data a first-class citizenData-Oriented Programming: making data a first-class citizen
Data-Oriented Programming: making data a first-class citizen
Manning Publications
 
Tejas bichave m tech python
Tejas bichave  m tech pythonTejas bichave  m tech python
Tejas bichave m tech python
tejas bichave
 
Recruiting for Drupal #Hiring
Recruiting for Drupal #HiringRecruiting for Drupal #Hiring
Recruiting for Drupal #Hiring
Gaurav Gaur
 

Similar to The Semantic Knowledge Graph (20)

The Relevance of the Apache Solr Semantic Knowledge Graph
The Relevance of the Apache Solr Semantic Knowledge GraphThe Relevance of the Apache Solr Semantic Knowledge Graph
The Relevance of the Apache Solr Semantic Knowledge Graph
 
SDSC18 and DSATL Meetup March 2018
SDSC18 and DSATL Meetup March 2018 SDSC18 and DSATL Meetup March 2018
SDSC18 and DSATL Meetup March 2018
 
How to Build a Semantic Search System
How to Build a Semantic Search SystemHow to Build a Semantic Search System
How to Build a Semantic Search System
 
TechEvent 2019: Artificial Intelligence in Dev & Ops; Martin Luckow - Trivadis
TechEvent 2019: Artificial Intelligence in Dev & Ops; Martin Luckow - TrivadisTechEvent 2019: Artificial Intelligence in Dev & Ops; Martin Luckow - Trivadis
TechEvent 2019: Artificial Intelligence in Dev & Ops; Martin Luckow - Trivadis
 
Vital AI MetaQL: Queries Across NoSQL, SQL, Sparql, and Spark
Vital AI MetaQL: Queries Across NoSQL, SQL, Sparql, and SparkVital AI MetaQL: Queries Across NoSQL, SQL, Sparql, and Spark
Vital AI MetaQL: Queries Across NoSQL, SQL, Sparql, and Spark
 
Odsc 2019 entity_reputation_knowledge_graph
Odsc 2019 entity_reputation_knowledge_graphOdsc 2019 entity_reputation_knowledge_graph
Odsc 2019 entity_reputation_knowledge_graph
 
Babasaheb javca
Babasaheb javcaBabasaheb javca
Babasaheb javca
 
From keyword-based search to language-agnostic semantic search
From keyword-based search to language-agnostic semantic searchFrom keyword-based search to language-agnostic semantic search
From keyword-based search to language-agnostic semantic search
 
Mark Tortoricci - Talent42 2015
Mark Tortoricci - Talent42 2015Mark Tortoricci - Talent42 2015
Mark Tortoricci - Talent42 2015
 
CV
CVCV
CV
 
Neo4j in Depth
Neo4j in DepthNeo4j in Depth
Neo4j in Depth
 
OSCON 2014: Data Workflows for Machine Learning
OSCON 2014: Data Workflows for Machine LearningOSCON 2014: Data Workflows for Machine Learning
OSCON 2014: Data Workflows for Machine Learning
 
RamaRaju_Profile
RamaRaju_ProfileRamaRaju_Profile
RamaRaju_Profile
 
Reflected intelligence evolving self-learning data systems
Reflected intelligence  evolving self-learning data systemsReflected intelligence  evolving self-learning data systems
Reflected intelligence evolving self-learning data systems
 
Data science presentation
Data science presentationData science presentation
Data science presentation
 
Gaurav agarwal
Gaurav agarwalGaurav agarwal
Gaurav agarwal
 
urttyo_banerjee
urttyo_banerjeeurttyo_banerjee
urttyo_banerjee
 
Data-Oriented Programming: making data a first-class citizen
Data-Oriented Programming: making data a first-class citizenData-Oriented Programming: making data a first-class citizen
Data-Oriented Programming: making data a first-class citizen
 
Tejas bichave m tech python
Tejas bichave  m tech pythonTejas bichave  m tech python
Tejas bichave m tech python
 
Recruiting for Drupal #Hiring
Recruiting for Drupal #HiringRecruiting for Drupal #Hiring
Recruiting for Drupal #Hiring
 

More from Trey Grainger

Balancing the Dimensions of User Intent
Balancing the Dimensions of User IntentBalancing the Dimensions of User Intent
Balancing the Dimensions of User Intent
Trey Grainger
 
Reflected Intelligence: Real world AI in Digital Transformation
Reflected Intelligence: Real world AI in Digital TransformationReflected Intelligence: Real world AI in Digital Transformation
Reflected Intelligence: Real world AI in Digital Transformation
Trey Grainger
 
Thought Vectors and Knowledge Graphs in AI-powered Search
Thought Vectors and Knowledge Graphs in AI-powered SearchThought Vectors and Knowledge Graphs in AI-powered Search
Thought Vectors and Knowledge Graphs in AI-powered Search
Trey Grainger
 
Natural Language Search with Knowledge Graphs (Chicago Meetup)
Natural Language Search with Knowledge Graphs (Chicago Meetup)Natural Language Search with Knowledge Graphs (Chicago Meetup)
Natural Language Search with Knowledge Graphs (Chicago Meetup)
Trey Grainger
 
Natural Language Search with Knowledge Graphs (Activate 2019)
Natural Language Search with Knowledge Graphs (Activate 2019)Natural Language Search with Knowledge Graphs (Activate 2019)
Natural Language Search with Knowledge Graphs (Activate 2019)
Trey Grainger
 
AI, Search, and the Disruption of Knowledge Management
AI, Search, and the Disruption of Knowledge ManagementAI, Search, and the Disruption of Knowledge Management
AI, Search, and the Disruption of Knowledge Management
Trey Grainger
 
Measuring Relevance in the Negative Space
Measuring Relevance in the Negative SpaceMeasuring Relevance in the Negative Space
Measuring Relevance in the Negative Space
Trey Grainger
 
Natural Language Search with Knowledge Graphs (Haystack 2019)
Natural Language Search with Knowledge Graphs (Haystack 2019)Natural Language Search with Knowledge Graphs (Haystack 2019)
Natural Language Search with Knowledge Graphs (Haystack 2019)
Trey Grainger
 
The Future of Search and AI
The Future of Search and AIThe Future of Search and AI
The Future of Search and AI
Trey Grainger
 
Searching for Meaning
Searching for MeaningSearching for Meaning
Searching for Meaning
Trey Grainger
 
The Intent Algorithms of Search & Recommendation Engines
The Intent Algorithms of Search & Recommendation EnginesThe Intent Algorithms of Search & Recommendation Engines
The Intent Algorithms of Search & Recommendation Engines
Trey Grainger
 
Building Search & Recommendation Engines
Building Search & Recommendation EnginesBuilding Search & Recommendation Engines
Building Search & Recommendation Engines
Trey Grainger
 
Intent Algorithms: The Data Science of Smart Information Retrieval Systems
Intent Algorithms: The Data Science of Smart Information Retrieval SystemsIntent Algorithms: The Data Science of Smart Information Retrieval Systems
Intent Algorithms: The Data Science of Smart Information Retrieval Systems
Trey Grainger
 
Self-learned Relevancy with Apache Solr
Self-learned Relevancy with Apache SolrSelf-learned Relevancy with Apache Solr
Self-learned Relevancy with Apache Solr
Trey Grainger
 
The Apache Solr Smart Data Ecosystem
The Apache Solr Smart Data EcosystemThe Apache Solr Smart Data Ecosystem
The Apache Solr Smart Data Ecosystem
Trey Grainger
 
South Big Data Hub: Text Data Analysis Panel
South Big Data Hub: Text Data Analysis PanelSouth Big Data Hub: Text Data Analysis Panel
South Big Data Hub: Text Data Analysis Panel
Trey Grainger
 
Reflected Intelligence: Lucene/Solr as a self-learning data system
Reflected Intelligence: Lucene/Solr as a self-learning data systemReflected Intelligence: Lucene/Solr as a self-learning data system
Reflected Intelligence: Lucene/Solr as a self-learning data system
Trey Grainger
 
Searching on Intent: Knowledge Graphs, Personalization, and Contextual Disamb...
Searching on Intent: Knowledge Graphs, Personalization, and Contextual Disamb...Searching on Intent: Knowledge Graphs, Personalization, and Contextual Disamb...
Searching on Intent: Knowledge Graphs, Personalization, and Contextual Disamb...
Trey Grainger
 
Semantic & Multilingual Strategies in Lucene/Solr
Semantic & Multilingual Strategies in Lucene/SolrSemantic & Multilingual Strategies in Lucene/Solr
Semantic & Multilingual Strategies in Lucene/Solr
Trey Grainger
 
Crowdsourced query augmentation through the semantic discovery of domain spec...
Crowdsourced query augmentation through the semantic discovery of domain spec...Crowdsourced query augmentation through the semantic discovery of domain spec...
Crowdsourced query augmentation through the semantic discovery of domain spec...
Trey Grainger
 

More from Trey Grainger (20)

Balancing the Dimensions of User Intent
Balancing the Dimensions of User IntentBalancing the Dimensions of User Intent
Balancing the Dimensions of User Intent
 
Reflected Intelligence: Real world AI in Digital Transformation
Reflected Intelligence: Real world AI in Digital TransformationReflected Intelligence: Real world AI in Digital Transformation
Reflected Intelligence: Real world AI in Digital Transformation
 
Thought Vectors and Knowledge Graphs in AI-powered Search
Thought Vectors and Knowledge Graphs in AI-powered SearchThought Vectors and Knowledge Graphs in AI-powered Search
Thought Vectors and Knowledge Graphs in AI-powered Search
 
Natural Language Search with Knowledge Graphs (Chicago Meetup)
Natural Language Search with Knowledge Graphs (Chicago Meetup)Natural Language Search with Knowledge Graphs (Chicago Meetup)
Natural Language Search with Knowledge Graphs (Chicago Meetup)
 
Natural Language Search with Knowledge Graphs (Activate 2019)
Natural Language Search with Knowledge Graphs (Activate 2019)Natural Language Search with Knowledge Graphs (Activate 2019)
Natural Language Search with Knowledge Graphs (Activate 2019)
 
AI, Search, and the Disruption of Knowledge Management
AI, Search, and the Disruption of Knowledge ManagementAI, Search, and the Disruption of Knowledge Management
AI, Search, and the Disruption of Knowledge Management
 
Measuring Relevance in the Negative Space
Measuring Relevance in the Negative SpaceMeasuring Relevance in the Negative Space
Measuring Relevance in the Negative Space
 
Natural Language Search with Knowledge Graphs (Haystack 2019)
Natural Language Search with Knowledge Graphs (Haystack 2019)Natural Language Search with Knowledge Graphs (Haystack 2019)
Natural Language Search with Knowledge Graphs (Haystack 2019)
 
The Future of Search and AI
The Future of Search and AIThe Future of Search and AI
The Future of Search and AI
 
Searching for Meaning
Searching for MeaningSearching for Meaning
Searching for Meaning
 
The Intent Algorithms of Search & Recommendation Engines
The Intent Algorithms of Search & Recommendation EnginesThe Intent Algorithms of Search & Recommendation Engines
The Intent Algorithms of Search & Recommendation Engines
 
Building Search & Recommendation Engines
Building Search & Recommendation EnginesBuilding Search & Recommendation Engines
Building Search & Recommendation Engines
 
Intent Algorithms: The Data Science of Smart Information Retrieval Systems
Intent Algorithms: The Data Science of Smart Information Retrieval SystemsIntent Algorithms: The Data Science of Smart Information Retrieval Systems
Intent Algorithms: The Data Science of Smart Information Retrieval Systems
 
Self-learned Relevancy with Apache Solr
Self-learned Relevancy with Apache SolrSelf-learned Relevancy with Apache Solr
Self-learned Relevancy with Apache Solr
 
The Apache Solr Smart Data Ecosystem
The Apache Solr Smart Data EcosystemThe Apache Solr Smart Data Ecosystem
The Apache Solr Smart Data Ecosystem
 
South Big Data Hub: Text Data Analysis Panel
South Big Data Hub: Text Data Analysis PanelSouth Big Data Hub: Text Data Analysis Panel
South Big Data Hub: Text Data Analysis Panel
 
Reflected Intelligence: Lucene/Solr as a self-learning data system
Reflected Intelligence: Lucene/Solr as a self-learning data systemReflected Intelligence: Lucene/Solr as a self-learning data system
Reflected Intelligence: Lucene/Solr as a self-learning data system
 
Searching on Intent: Knowledge Graphs, Personalization, and Contextual Disamb...
Searching on Intent: Knowledge Graphs, Personalization, and Contextual Disamb...Searching on Intent: Knowledge Graphs, Personalization, and Contextual Disamb...
Searching on Intent: Knowledge Graphs, Personalization, and Contextual Disamb...
 
Semantic & Multilingual Strategies in Lucene/Solr
Semantic & Multilingual Strategies in Lucene/SolrSemantic & Multilingual Strategies in Lucene/Solr
Semantic & Multilingual Strategies in Lucene/Solr
 
Crowdsourced query augmentation through the semantic discovery of domain spec...
Crowdsourced query augmentation through the semantic discovery of domain spec...Crowdsourced query augmentation through the semantic discovery of domain spec...
Crowdsourced query augmentation through the semantic discovery of domain spec...
 

Recently uploaded

How UiPath Discovery Suite supports identification of Agentic Process Automat...
How UiPath Discovery Suite supports identification of Agentic Process Automat...How UiPath Discovery Suite supports identification of Agentic Process Automat...
How UiPath Discovery Suite supports identification of Agentic Process Automat...
DianaGray10
 
leewayhertz.com-Generative AI tech stack Frameworks infrastructure models and...
leewayhertz.com-Generative AI tech stack Frameworks infrastructure models and...leewayhertz.com-Generative AI tech stack Frameworks infrastructure models and...
leewayhertz.com-Generative AI tech stack Frameworks infrastructure models and...
alexjohnson7307
 
Mastering OnlyFans Clone App Development: Key Strategies for Success
Mastering OnlyFans Clone App Development: Key Strategies for SuccessMastering OnlyFans Clone App Development: Key Strategies for Success
Mastering OnlyFans Clone App Development: Key Strategies for Success
David Wilson
 
Computer HARDWARE presenattion by CWD students class 10
Computer HARDWARE presenattion by CWD students class 10Computer HARDWARE presenattion by CWD students class 10
Computer HARDWARE presenattion by CWD students class 10
ankush9927
 
Feature sql server terbaru performance.pptx
Feature sql server terbaru performance.pptxFeature sql server terbaru performance.pptx
Feature sql server terbaru performance.pptx
ssuser1915fe1
 
Premium Girls Call Mumbai 9920725232 Unlimited Short Providing Girls Service ...
Premium Girls Call Mumbai 9920725232 Unlimited Short Providing Girls Service ...Premium Girls Call Mumbai 9920725232 Unlimited Short Providing Girls Service ...
Premium Girls Call Mumbai 9920725232 Unlimited Short Providing Girls Service ...
shanihomely
 
Dublin_mulesoft_meetup_Mulesoft_Salesforce_Integration (1).pptx
Dublin_mulesoft_meetup_Mulesoft_Salesforce_Integration (1).pptxDublin_mulesoft_meetup_Mulesoft_Salesforce_Integration (1).pptx
Dublin_mulesoft_meetup_Mulesoft_Salesforce_Integration (1).pptx
Kunal Gupta
 
Using LLM Agents with Llama 3, LangGraph and Milvus
Using LLM Agents with Llama 3, LangGraph and MilvusUsing LLM Agents with Llama 3, LangGraph and Milvus
Using LLM Agents with Llama 3, LangGraph and Milvus
Zilliz
 
(CISOPlatform Summit & SACON 2024) Regulation & Response In Banks.pdf
(CISOPlatform Summit & SACON 2024) Regulation & Response In Banks.pdf(CISOPlatform Summit & SACON 2024) Regulation & Response In Banks.pdf
(CISOPlatform Summit & SACON 2024) Regulation & Response In Banks.pdf
Priyanka Aash
 
Litestack talk at Brighton 2024 (Unleashing the power of SQLite for Ruby apps)
Litestack talk at Brighton 2024 (Unleashing the power of SQLite for Ruby apps)Litestack talk at Brighton 2024 (Unleashing the power of SQLite for Ruby apps)
Litestack talk at Brighton 2024 (Unleashing the power of SQLite for Ruby apps)
Muhammad Ali
 
Girls call Kolkata 👀 XXXXXXXXXXX 👀 Rs.9.5 K Cash Payment With Room Delivery
Girls call Kolkata 👀 XXXXXXXXXXX 👀 Rs.9.5 K Cash Payment With Room Delivery Girls call Kolkata 👀 XXXXXXXXXXX 👀 Rs.9.5 K Cash Payment With Room Delivery
Girls call Kolkata 👀 XXXXXXXXXXX 👀 Rs.9.5 K Cash Payment With Room Delivery
sunilverma7884
 
leewayhertz.com-AI agents for healthcare Applications benefits and implementa...
leewayhertz.com-AI agents for healthcare Applications benefits and implementa...leewayhertz.com-AI agents for healthcare Applications benefits and implementa...
leewayhertz.com-AI agents for healthcare Applications benefits and implementa...
alexjohnson7307
 
Acumatica vs. Sage Intacct vs. NetSuite _ NOW CFO.pdf
Acumatica vs. Sage Intacct vs. NetSuite _ NOW CFO.pdfAcumatica vs. Sage Intacct vs. NetSuite _ NOW CFO.pdf
Acumatica vs. Sage Intacct vs. NetSuite _ NOW CFO.pdf
BrainSell Technologies
 
Mule Experience Hub and Release Channel with Java 17
Mule Experience Hub and Release Channel with Java 17Mule Experience Hub and Release Channel with Java 17
Mule Experience Hub and Release Channel with Java 17
Bhajan Mehta
 
(CISOPlatform Summit & SACON 2024) Cyber Insurance & Risk Quantification.pdf
(CISOPlatform Summit & SACON 2024) Cyber Insurance & Risk Quantification.pdf(CISOPlatform Summit & SACON 2024) Cyber Insurance & Risk Quantification.pdf
(CISOPlatform Summit & SACON 2024) Cyber Insurance & Risk Quantification.pdf
Priyanka Aash
 
Girls Call Churchgate 9910780858 Provide Best And Top Girl Service And No1 in...
Girls Call Churchgate 9910780858 Provide Best And Top Girl Service And No1 in...Girls Call Churchgate 9910780858 Provide Best And Top Girl Service And No1 in...
Girls Call Churchgate 9910780858 Provide Best And Top Girl Service And No1 in...
maigasapphire
 
Step-By-Step Process to Develop a Mobile App From Scratch
Step-By-Step Process to Develop a Mobile App From ScratchStep-By-Step Process to Develop a Mobile App From Scratch
Step-By-Step Process to Develop a Mobile App From Scratch
softsuave
 
(CISOPlatform Summit & SACON 2024) Keynote _ Power Digital Identities With AI...
(CISOPlatform Summit & SACON 2024) Keynote _ Power Digital Identities With AI...(CISOPlatform Summit & SACON 2024) Keynote _ Power Digital Identities With AI...
(CISOPlatform Summit & SACON 2024) Keynote _ Power Digital Identities With AI...
Priyanka Aash
 
Types of Weaving loom machine & it's technology
Types of Weaving loom machine & it's technologyTypes of Weaving loom machine & it's technology
Types of Weaving loom machine & it's technology
ldtexsolbl
 
Connector Corner: Leveraging Snowflake Integration for Smarter Decision Making
Connector Corner: Leveraging Snowflake Integration for Smarter Decision MakingConnector Corner: Leveraging Snowflake Integration for Smarter Decision Making
Connector Corner: Leveraging Snowflake Integration for Smarter Decision Making
DianaGray10
 

Recently uploaded (20)

How UiPath Discovery Suite supports identification of Agentic Process Automat...
How UiPath Discovery Suite supports identification of Agentic Process Automat...How UiPath Discovery Suite supports identification of Agentic Process Automat...
How UiPath Discovery Suite supports identification of Agentic Process Automat...
 
leewayhertz.com-Generative AI tech stack Frameworks infrastructure models and...
leewayhertz.com-Generative AI tech stack Frameworks infrastructure models and...leewayhertz.com-Generative AI tech stack Frameworks infrastructure models and...
leewayhertz.com-Generative AI tech stack Frameworks infrastructure models and...
 
Mastering OnlyFans Clone App Development: Key Strategies for Success
Mastering OnlyFans Clone App Development: Key Strategies for SuccessMastering OnlyFans Clone App Development: Key Strategies for Success
Mastering OnlyFans Clone App Development: Key Strategies for Success
 
Computer HARDWARE presenattion by CWD students class 10
Computer HARDWARE presenattion by CWD students class 10Computer HARDWARE presenattion by CWD students class 10
Computer HARDWARE presenattion by CWD students class 10
 
Feature sql server terbaru performance.pptx
Feature sql server terbaru performance.pptxFeature sql server terbaru performance.pptx
Feature sql server terbaru performance.pptx
 
Premium Girls Call Mumbai 9920725232 Unlimited Short Providing Girls Service ...
Premium Girls Call Mumbai 9920725232 Unlimited Short Providing Girls Service ...Premium Girls Call Mumbai 9920725232 Unlimited Short Providing Girls Service ...
Premium Girls Call Mumbai 9920725232 Unlimited Short Providing Girls Service ...
 
Dublin_mulesoft_meetup_Mulesoft_Salesforce_Integration (1).pptx
Dublin_mulesoft_meetup_Mulesoft_Salesforce_Integration (1).pptxDublin_mulesoft_meetup_Mulesoft_Salesforce_Integration (1).pptx
Dublin_mulesoft_meetup_Mulesoft_Salesforce_Integration (1).pptx
 
Using LLM Agents with Llama 3, LangGraph and Milvus
Using LLM Agents with Llama 3, LangGraph and MilvusUsing LLM Agents with Llama 3, LangGraph and Milvus
Using LLM Agents with Llama 3, LangGraph and Milvus
 
(CISOPlatform Summit & SACON 2024) Regulation & Response In Banks.pdf
(CISOPlatform Summit & SACON 2024) Regulation & Response In Banks.pdf(CISOPlatform Summit & SACON 2024) Regulation & Response In Banks.pdf
(CISOPlatform Summit & SACON 2024) Regulation & Response In Banks.pdf
 
Litestack talk at Brighton 2024 (Unleashing the power of SQLite for Ruby apps)
Litestack talk at Brighton 2024 (Unleashing the power of SQLite for Ruby apps)Litestack talk at Brighton 2024 (Unleashing the power of SQLite for Ruby apps)
Litestack talk at Brighton 2024 (Unleashing the power of SQLite for Ruby apps)
 
Girls call Kolkata 👀 XXXXXXXXXXX 👀 Rs.9.5 K Cash Payment With Room Delivery
Girls call Kolkata 👀 XXXXXXXXXXX 👀 Rs.9.5 K Cash Payment With Room Delivery Girls call Kolkata 👀 XXXXXXXXXXX 👀 Rs.9.5 K Cash Payment With Room Delivery
Girls call Kolkata 👀 XXXXXXXXXXX 👀 Rs.9.5 K Cash Payment With Room Delivery
 
leewayhertz.com-AI agents for healthcare Applications benefits and implementa...
leewayhertz.com-AI agents for healthcare Applications benefits and implementa...leewayhertz.com-AI agents for healthcare Applications benefits and implementa...
leewayhertz.com-AI agents for healthcare Applications benefits and implementa...
 
Acumatica vs. Sage Intacct vs. NetSuite _ NOW CFO.pdf
Acumatica vs. Sage Intacct vs. NetSuite _ NOW CFO.pdfAcumatica vs. Sage Intacct vs. NetSuite _ NOW CFO.pdf
Acumatica vs. Sage Intacct vs. NetSuite _ NOW CFO.pdf
 
Mule Experience Hub and Release Channel with Java 17
Mule Experience Hub and Release Channel with Java 17Mule Experience Hub and Release Channel with Java 17
Mule Experience Hub and Release Channel with Java 17
 
(CISOPlatform Summit & SACON 2024) Cyber Insurance & Risk Quantification.pdf
(CISOPlatform Summit & SACON 2024) Cyber Insurance & Risk Quantification.pdf(CISOPlatform Summit & SACON 2024) Cyber Insurance & Risk Quantification.pdf
(CISOPlatform Summit & SACON 2024) Cyber Insurance & Risk Quantification.pdf
 
Girls Call Churchgate 9910780858 Provide Best And Top Girl Service And No1 in...
Girls Call Churchgate 9910780858 Provide Best And Top Girl Service And No1 in...Girls Call Churchgate 9910780858 Provide Best And Top Girl Service And No1 in...
Girls Call Churchgate 9910780858 Provide Best And Top Girl Service And No1 in...
 
Step-By-Step Process to Develop a Mobile App From Scratch
Step-By-Step Process to Develop a Mobile App From ScratchStep-By-Step Process to Develop a Mobile App From Scratch
Step-By-Step Process to Develop a Mobile App From Scratch
 
(CISOPlatform Summit & SACON 2024) Keynote _ Power Digital Identities With AI...
(CISOPlatform Summit & SACON 2024) Keynote _ Power Digital Identities With AI...(CISOPlatform Summit & SACON 2024) Keynote _ Power Digital Identities With AI...
(CISOPlatform Summit & SACON 2024) Keynote _ Power Digital Identities With AI...
 
Types of Weaving loom machine & it's technology
Types of Weaving loom machine & it's technologyTypes of Weaving loom machine & it's technology
Types of Weaving loom machine & it's technology
 
Connector Corner: Leveraging Snowflake Integration for Smarter Decision Making
Connector Corner: Leveraging Snowflake Integration for Smarter Decision MakingConnector Corner: Leveraging Snowflake Integration for Smarter Decision Making
Connector Corner: Leveraging Snowflake Integration for Smarter Decision Making
 

The Semantic Knowledge Graph

  • 1. The Semantic Knowledge Graph: A compact, auto-generated model for real-time traversal and ranking of any relationship within a domain Trey Grainger SVP of Engineering Lucidworks Khalifeh AlJadda Lead Data Scientist CareerBuilder Mohammed Korayem Data Scientist CareerBuilder Andries Smith Software Engineer CareerBuilder
  • 2. Trey Grainger SVP of Engineering • Previously Director of Engineering @ CareerBuilder • MBA, Management of Technology – Georgia Tech • BA, Computer Science, Business, & Philosophy – Furman University • Information Retrieval & Web Search - Stanford University Fun outside of CB: • Co-author of Solr in Action, plus a handful of research papers • Frequent conference speaker • Founder of Celiaccess.com, the gluten-free search engine • Lucene/Solr contributor About Me
  • 4. A Graph DSAA 2016 Montreal Quebec Canada Semantic Knowledge Graph Paper Trey Grainger Mohammed Korayem Andries Smith Khalifeh AlJadda in_country Node / Vertex Edge
  • 5. “Solr is the popular, blazing-fast, open source enterprise search platform built on Apache Lucene™.”
  • 6. Key Solr Features: ● Multilingual Keyword search ● Relevancy Ranking of results ● Faceting & Analytics ● Highlighting ● Spelling Correction ● Autocomplete/Type-ahead Prediction ● Sorting, Grouping, Deduplication ● Distributed, Fault-tolerant, Scalable ● Geospatial search ● Complex Function queries ● Recommendations (More Like This) ● … many more *source: Solr in Action, chapter 2
  • 7. Term Documents a doc1 [2x] brown doc3 [1x] , doc5 [1x] cat doc4 [1x] cow doc2 [1x] , doc5 [1x] … ... once doc1 [1x], doc5 [1x] over doc2 [1x], doc3 [1x] the doc2 [2x], doc3 [2x], doc4[2x], doc5 [1x] … … Document Content Field doc1 once upon a time, in a land far, far away doc2 the cow jumped over the moon. doc3 the quick brown fox jumped over the lazy dog. doc4 the cat in the hat doc5 The brown cow said “moo” once. … … What you SEND to Lucene/Solr: How the content is INDEXED into Lucene/Solr (conceptually): The inverted index
  • 8. /solr/select/?q=apache solr Term Documents … … apache doc1, doc3, doc4, doc5 … hadoop doc2, doc4, doc6 … … solr doc1, doc3, doc4, doc7, doc8 … … doc5 doc7 doc8 doc1 doc3 doc4 solr apache apache solr Matching queries to documents
  • 10. Knowledge Graph Related Work • Primarily related to ontology Learning. • Recently, large-scale knowledge bases that utilize ontologies (FreeBase [4], DBpedia [5], and YAGO [6, 7]) have been constructed using structured sources such as Wikipedia infoboxes. • Other approaches (DeepDive [8], Nell2RDF [9], and PROSPERA [10]) crawl the web and use machine learning and natural language processing to build web-scale knowledge graphs.
  • 12. Knowledge Graph Challenges we are solving Because current knowledge bases / ontology learning systems typically requires explicitly modeling nodes and edges into a graph ahead of time, this unfortunately presents several limitations to the use of such a knowledge graph: • Entities not modeled explicitly as nodes have no known relationships to any other entities. • Edges exist between nodes, but not between arbitrary combinations of nodes, and therefore such a graph is not ideal for representing nuanced meanings of an entity when appearing within different contexts, as is common within natural language. • Substantial meaning is encoded in the linguistic representation of the domain that is lost when the underlying textual representation is not preserved: phrases, interaction of concepts through actions (i.e. verbs), positional ordering of entities and the phrases containing those entities, variations in spelling and other representations of entities, the use of adjectives to modify entities to represent more complex concepts, and aggregate frequencies of occurrence for different representations of entities relative to other representations. • It can be an arduous process to create robust ontologies, map a domain into a graph representing those ontologies, and ensure the generated graph is compact, accurate, comprehensive, and kept up to date.
  • 13. Knowledge Graph Semantic Data Encoded into Free Text Content e en eng engi engineer engineers engineer engineersNode Type: Term software engineer software engineers electrical engineering engineer engineering software … … … Node Type: Character Sequence Node Type: Term Sequence Node Type: Document id: 1 text: looking for a software engineerwith degree in computer science or electrical engineering id: 2 text: apply to be a software engineer and work with other great software engineers id: 3 text: start a great careerin electrical engineering … …
  • 14. Model
  • 15. 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 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 … … … … 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 Uninverted IndexDocuments Knowledge Graph
  • 16. Knowledge Graph Set-theory View Graph View How the Graph Traversal Works skill: Java skill: Scala skill: Hibernate skill: Oncology 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
  • 17. Knowledge Graph Graph Model Structure: Single-level Traversal / Scoring: Multi-level Traversal / Scoring:
  • 18. Knowledge Graph Multi-level 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 Doc Values Index Lookup Doc Values Index Lookup Inverted Index Lookup Java Java Developer Hibernate Scala Software Engineer Data Scientist has_related_job_title has_related_job_title
  • 19. Knowledge Graph Scoring nodes in the Graph 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": 0.9765, "popularity":369 }, { "value":”spark", "relatedness": 0.9634, "popularity":15653 }, { "value":".net", "relatedness": 0.5417, "popularity":17683 }, { "value":"bogus_word", "relatedness": 0.0, "popularity":0 }, { "value":"teaching", "relatedness": -0.1510, "popularity":9923 }, { "value":"CPR", "relatedness": -0.4012, "popularity":27089 } ] } + - Foreground Query: "Hadoop"
  • 20. 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 Multi-level Graph Traversal with Scores software engineer* (materialized node) Java C# .NET .NET Developer Java Developer Hibernate ScalaVB.NET Software Engineer Data Scientist Skill Nodes has_related_skillStarting Node Skill Nodes has_related_skill Job Title Nodes has_related_job_title 0.90 0.88 0.93 0.93 0.34 0.74 0.91 0.89 0.74 0.89 0.780.72 0.48 0.93 0.76 0.83 0.80 0.64 0.61 0.780.55
  • 21. Knowledge Graph Materialization of new nodes through shared documents engineer engineers software engineer* (materialized node) engineer* (materialized node) Software doc 1 doc 2 doc 3 doc 4 doc 5 doc 6 links_to links_to
  • 28. Knowledge Graph Data Cleansing { "type":"keywords”, "values":[ { "value":"hive", "relatedness": 0.9765, "popularity":369 }, { "value":”spark", "relatedness": 0.9634, "popularity":15653 }, { "value":".net", "relatedness": 0.5417, "popularity":17683 }, { "value":"bogus_word", "relatedness": 0.0, "popularity":0 }, { "value":"teaching", "relatedness": -0.1510, "popularity":9923 }, { "value":"CPR", "relatedness": -0.4012, "popularity":27089 } ] } Foreground Query: "Hadoop" Experiment: Data analyst manually annotated 500 pairs of terms found together in real query logs as “relevant” or “not relevant” Results: SKG removed 78% of the terms while maintaining a 95% accuracy at removing the correct noisy pairs from the input data.
  • 30. Knowledge Graph Search Expansion Experiment: Take an initial query, and expand keyword phrases to include the most related entities to that query Example:
  • 31. The Semantic Search Problem 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)
  • 32. machine learning Keywords: Search Behavior, Application Behavior, etc. Job Title Classifier, Skills Extractor, Job Level Classifier, etc. Semantic Interpretation keywords:((machine learning)^10 OR { AT_LEAST_2: ("data mining"^0.9, matlab^0.8, "data scientist"^0.75, "artificial intelligence"^0.7, "neural networks"^0.55)) } { BOOST_TO_TOP: ( job_title:( "software engineer" OR "data manager" OR "data scientist" OR "hadoop engineer")) } Modified Query: Related Occupations machine learning: {15-1031.00 .58 Computer Software Engineers, Applications 15-1011.00 .55 Computer and Information Scientists, Research 15-1032.00 .52 Computer Software Engineers, Systems Software } machine learning: { software engineer .65, data manager .3, data scientist .25, hadoop engineer .2, } Common Job Titles Query Expansion Related Phrases machine learning: { data mining .9, matlab .8, data scientist .75, artificial intelligence .7, neural networks .55 } Known keyword phrases java developer machine learning registered nurse FST Knowledge Graph in +
  • 34. Knowledge Graph Document Summarization Experiment: Pass in raw text (extracting phrases as needed), and rank their similarity to the documents using the SKG. Additionally, can traverse the graph to “related” entities/keyword phrases NOT found in the original document Applications: Content-based and multi-modal recommendations (no cold-start problem), data cleansing prior to clustering or other ML methods, semantic search / similarity scoring
  • 35. Document Enrichment – Find / Score Relationships
  • 36. Document Summarization – Rank / Clean Keywords
  • 37. Knowledge Graph Future Work • Semantic Search (more experiments) • Search Engine Relevancy Algorithms • Trending Topics • Recommendation Systems • Root Cause Analysis • Abuse Detection
  • 38. Knowledge Graph Conclusion Applications: The Semantic Knowledge Graph has numerous applications, including automatically building ontologies, identification of trending topics over time, predictive analytics on timeseries data, root-cause analysis surfacing concepts related to failure scenarios from free text, data cleansing, document summarization, semantic search interpretation and expansion of queries, recommendation systems, and numerous other forms of anomaly detection. Main contribution of this paper: The introduction (and open sourcing) of the the Semantic Knowledge Graph, a novel and compact new graph model that can dynamically materialize and score the relationships between any arbitrary combination of entities represented within a corpus of documents.