1.
Bay Area Search
Searching on Intent: Knowledge Graphs,
Personalization, and Contextual Disambiguation
2015.11.10
Bay Area Search
Trey Grainger
Director of Engineering, Search & Recommendations

2.
Bay Area Search
Trey Grainger
Director of Engineering, Search & Recommendations
• Joined CareerBuilder in 2007 as a Software Engineer
• MBA, Management of Technology – Georgia Tech
• BA, Computer Science, Business, & Philosophy – Furman University
• Mining Massive Datasets (in progress) - 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

3.
Bay Area Search
Agenda
• Introduction
• Traditional Keyword Search vs. Personalization vs. Semantic Search
• Searching on Intent
- Type-ahead prediction
- Spelling Correction
- Entity / Entity-type Resolution
- Contextual Disambiguation
- Semantic Query Parsing
- Query Augmentation
- The Knowledge Graph
• Conclusion
Knowledge
Graph

4.
Bay Area Search
At CareerBuilder, Solr Powers...At CareerBuilder, Solr Powers...

5.
Search by the Numbers
5
Powering 50+ Search Experiences Including:
100million +
Searches per day
30+
Software Developers, Data
Scientists + Analysts
500+
Search Servers
1,5billion +
Documents indexed and
searchable
1
Global Search
Technology platform
...and many more

6.
Bay Area Search
Conceptual Framework for Information Retrieval:
Traditional
Keyword
Search
Recommendations
Semantic
Search
User Intent
Personalized
Search
Augmented
Search
Domain-aware
Matching

7.
Bay Area Search
Traditional Search

8.
Bay Area Search
Classic Lucene Relevancy Algorithm (though BM25 to be default soon):
*Source: Solr in Action, chapter 3
Score(q, d) =
∑ ( tf(t in d) · idf(t)2 · t.getBoost() · norm(t, d) ) · coord(q, d) · queryNorm(q)
t in q
Where:
t = term; d = document; q = query; f = field
tf(t in d) = numTermOccurrencesInDocument ½
idf(t) = 1 + log (numDocs / (docFreq + 1))
coord(q, d) = numTermsInDocumentFromQuery / numTermsInQuery
queryNorm(q) = 1 / (sumOfSquaredWeights ½ )
sumOfSquaredWeights = q.getBoost()2 · ∑ (idf(t) · t.getBoost() )2
t in q
norm(t, d) = d.getBoost() · lengthNorm(f) · f.getBoost()

9.
Bay Area Search
News Search : popularity and freshness drive relevance
Restaurant Search: geographical proximity and price range are critical
Ecommerce: likelihood of a purchase is key
Movie search: More popular titles are generally more relevant
Job search: category of job, salary range, and geographical proximity matter
TF * IDF of keywords can’t hold it’s own against good
domain-specific relevance factors!
That’s great, but what about domain-specific knowledge?

10.
Bay Area Search
Example of domain-specific relevancy calculation
News website:
/select?
fq=$myQuery&
q=_query_:"{!func}scale(query($myQuery),0,100)"
AND _query_:"{!func}div(100,map(geodist(),0,1,1))"
AND _query_:"{!func}recip(rord(publicationDate),0,100,100)"
AND _query_:"{!func}scale(popularity,0,100)"&
myQuery="street festival"&
sfield=location&
pt=33.748,-84.391
25%
25%
25%
25%
*Example from chapter 16 of Solr in Action

11.
Bay Area Search
Fancy boosting functions
Separating “relevancy” and “filtering” from the query:
q=_val_:"$keywords"&fq={!cache=false v=$keywords}&keywords=solr
Keywords (50%) + distance (25%) + category (25%)
q=_val_:"scale(mul(query($keywords),1),0,50)" AND
_val_:"scale(sum($radiusInKm,mul(query($distance),-1)),0,25)” AND
_val_:"scale(mul(query($category),1),0,25)"
&keywords=solr
&radiusInKm=48.28
&distance=_val_:"geodist(latitudelongitude.latlon_is,33.77402,-84.29659)”
&category=jobtitle:"java developer"
&fq={!cache=false v=$keywords}

12.
Bay Area Search
Personalization /
Recommendations

13.
Bay Area Search
John lives in Boston but wants to move to New York or possibly another big city. He is
currently a sales manager but wants to move towards business development.
Irene is a bartender in Dublin and is only interested in jobs within 10KM of her location
in the food service industry.
Irfan is a software engineer in Atlanta and is interested in software engineering jobs at a
Big Data company. He is happy to move across the U.S. for the right job.
Jane is a nurse educator in Boston seeking between $40K and $60K
Beyond domain knowledge… consider per-user knowledge

14.
Bay Area Search
http://localhost:8983/solr/jobs/select/?
fl=jobtitle,city,state,salary&
q=(
jobtitle:"nurse educator"^25 OR jobtitle:(nurse educator)^10
)
AND (
(city:"Boston" AND state:"MA")^15
OR state:"MA")
AND _val_:"map(salary, 40000, 60000,10, 0)”
*Example from chapter 16 of Solr in Action
Query for Jane
Jane is a nurse educator in Boston seeking between $40K and $60K

15.
Bay Area Search
{ ...
"response":{"numFound":22,"start":0,"docs":[
{"jobtitle":" Clinical Educator
(New England/ Boston)",
"city":"Boston",
"state":"MA",
"salary":41503},
…]}}
*Example documents available @ http://github.com/treygrainger/solr-in-action/
Search Results for Jane
{"jobtitle":"Nurse Educator",
"city":"Braintree",
"state":"MA",
"salary":56183},
{"jobtitle":"Nurse Educator",
"city":"Brighton",
"state":"MA",
"salary":71359}

16.
Bay Area Search
We built a recommendation engine!
What is a recommendation engine?
“A system that uses known information (or derived information from that known
information) to automatically suggest relevant content”
Our example was just an attribute based recommendation… but we can also use
any behavioral-based features, as well (i.e. collaborative filtering).
What did we just do?

17.
Bay Area Search
For full coverage of building a recommendation engine in Solr…
See my talk from Lucene Revolution 2012 (Boston):

18.
Bay Area Search
Personalized Search
Why limit yourself to JUST explicit search or JUST automated recommendations?
By augmenting your user’s explicit queries with information you know about them, you can
personalize their search results.
Examples:
A known software engineer runs a blank job search in New York…
Why not show software engineering higher in the results?
A new user runs a keyword-only search for nurse
Why not use the user’s IP address to boost documents geographically closer?

19.
Bay Area Search
Semantic Search

20.
Bay Area Search
What’s the problem we’re trying to solve today?
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)

21.
Bay Area Search
...we also really want to
search on “things”, not “strings”…
Job Level Job title Company
Job Title Company School + Degree

22.
Bay Area Search
Type-ahead
Prediction
Building an Intent Engine
Search Box
Semantic Query
Parsing
Intent Engine
Spelling Correction
Entity / Entity
Type Resolution
Machine-learned
Ranking
Relevancy Engine (“re-expressing intent”)
User Feedback
(Clarifying Intent)
Query Re-writing Search Results
Query
Augmentation
Knowledge
Graph
Contextual
Disambiguation

23.
Bay Area Search
Type-ahead Predictions

24.
Semantic Autocomplete
• Shows top terms for any search
• Breaks out job titles, skills, companies,
related keywords, and other
categories
• Understands abbreviations, alternate
forms, misspellings
• Supports full Boolean syntax and
multi-term autocomplete
• Enables fielded search on entities, not
just keywords

25.
Spelling Correction

26.
Entity / Entity-type
Resolution

27.
Bay Area Search
Differentiating related terms
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

28.
Bay Area Search
Building a Taxonomy of Entities
Many ways to generate this:
• Topic Modelling
• Clustering of documents
• Statistical Analysis of interesting phrases
• Buy a dictionary (often doesn’t work for
domain-specific search problems)
• …
Our strategy:
Generate a model of domain-specific phrases by
mining query logs for commonly searched phrases within the domain [1]
[1] K. Aljadda, M. Korayem, T. Grainger, C. Russell. "Crowdsourced Query Augmentation through Semantic Discovery of Domain-specific Jargon," in IEEE Big Data 2014.

29.
Bay Area Search
Entity-type Recognition
Build classifiers trained on
External data sources
(Wikipedia, DBPedia,
WordNet, etc.), as well as
from our own domain.
The subject for a future
talk / research paper…
java developer
registered nurse
emergency room
director
job title
skill
job level
location
work type
Portland, OR
part-time

30.
Bay Area Search
Contextual Disambiguation

31.
Bay Area Search
How do we handle phrases with ambiguous meanings?
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
… …

32.
Bay Area Search
Discovering ambiguous phrases
1) Classify user’s who ran each
search in the search logs
(i.e. by the job title
classifications of the jobs to
which they applied)
3) Segment the search term => related search terms list by classification,
to return a separate related terms list per classification
2) Create a probabilistic graphical model of those classifications mapped
to each keyword phrase.

33.
Bay Area Search
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
… …

34.
Bay Area Search
Using the disambiguated meanings
In a situation where a user searches for an ambiguous phrase, what information can we
use to pick the correct underlying meaning?
1. Any pre-existing knowledge about the user:
• User is a software engineer
• User has previously run searches for “c++” and “linux”
2. Context within the query:
• User searched for windows AND driver vs. courier OR driver
3. If all else fails (and there is no context), use the most commonly occurring meaning.
driver 1: linux, windows, embedded
2: truck driver, cdl driver, delivery driver, class b driver, cdl, courier

35.
Bay Area Search
Semantic Query Parsing

36.
Bay Area Search
Query Parsing: The whole is greater than the sum of the parts
project manager vs. "project" AND "manager"
building architect vs. "building" AND "architect"
software architect vs. "software" AND "architect"
Consider: a "software architect" designs and builds software
a "building architect" uses software to design architecture
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)
≠
Identifying the correct phrase (not just the parts) is crucial here!

37.
Bay Area Search

38.
Bay Area Search
Probabilistic Query Parser
Goal: given a query, predict which
combinations of keywords should be
combined together as phrases
Example:
senior java developer hadoop
Possible Parsings:
senior, java, developer, hadoop
"senior java", developer, hadoop
"senior java developer", hadoop
"senior java developer hadoop”
"senior java", "developer hadoop”
senior, "java developer", hadoop
senior, java, "developer hadoop"

39.
Bay Area Search
Input: senior hadoop developer java ruby on rails perl

40.
Bay Area Search
Semantic Search Architecture – Query Parsing
1) Generate the previously discussed taxonomy of
Domain-specific phrases
• You can mine query logs or actual text of documents for
significant phrases within your domain [1]
2) Feed these phrases to SolrTextTagger (uses Lucene FST
for high-throughput term lookups)
3) Use SolrTextTagger to perform entity extraction
on incoming queries (tagging documents is also possible)
4) Also invoke probabilistic parser to dynamically identify
unknown phrases from a corpus of data (language model)
5) Shown on next slides:
Pass extracted entities to a Query Augmentation phase to
rewrite the query with enhanced semantic understanding
[1] K. Aljadda, M. Korayem, T. Grainger, C. Russell. "Crowdsourced Query Augmentation through Semantic Discovery of
Domain-specific Jargon," in IEEE Big Data 2014.
[2] https://github.com/OpenSextant/SolrTextTagger

41.
Bay Area Search
Query Augmentation

42.
Bay Area Search
machine learning
Keywords:
Search Behavior,
Application Behavior, etc.
Job Title Classifier, Skills Extractor, Job Level Classifier, etc.
Semantic Query
Augmentation
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
Semantic Search Architecture – Query Augmentation
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
+

43.
Bay Area Search
Query Enrichment

44.
Bay Area Search
Document Enrichment

45.
Bay Area Search
Document Enrichment

46.
Bay Area Search
Knowledge Graph

47.
Bay Area Search
Serves as a “data science toolkit” API that allows dynamically navigating and pivoting through
multiple levels of relationships between items in our domain. Compare the relationships of skills to
keywords, job titles to skills to keywords, skills to government occupation codes, skills to experience
level, etc.
Knowledge Graph API
Core similarity engine, exposed via API
Any product can leverage our core relationship scoring
engine to score any list of entities against any other list
Full domain support
Keywords, job titles, skills, companies, job levels,
locations, and all other taxonomies.
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

48.
Bay Area Search
So how does it work?
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

49.
Bay Area Search
Knowledge Graph – Potential Use Cases
Cross-walk between Types
• Have an ID field, but want to enable free text search
on the most associated entity with that ID?
• Have a “state” (geo) search box, but want to accept
any free-text location and map it to the right state?
• Have an old classification taxonomy and want to
know how the values from the old system now map
into the new values?
Build User Profiles from Search Logs
• If someone searches for “Java”, and then “JQuery”,
and then “CSS”, and then “JSP”, what do those have
in common?
• What if they search for “Java”, and then “C++”, and
then “Assembly”?
Discover Relationships Between Anything
• If I want to become a data scientist and know
Python, what libraries should I learn?
• If my last job was mid-level software engineer and
my current job is Engineering Lead, what are my
most likely next roles?
Traverse arbitrarily deep, Sort on anything
• Build an instant co-occurrence matrix, sort the top
values by their relatedness, and then add in any
number of additional dimensions (RAM permitting).
Data Cleansing
• Have dirty taxonomies and need to figure out which
items don’t belong?
• Need to understand the conceptual cohesion of a
document (vs spammy or off-topic content)?
Knowledge
Graph

50.
Bay Area Search
2014-2015 Publications & Presentations
Books:
Solr in Action - A comprehensive guide to implementing scalable search using Apache Solr
Research papers:
● Crowdsourced Query Augmentation through Semantic Discovery of Domain-specific jargon - 2014
● Towards a Job title Classification System - 2014
● Augmenting Recommendation Systems Using a Model of Semantically-related Terms
Extracted from User Behavior - 2014
● sCooL: A system for academic institution name normalization - 2014
● PGMHD: A Scalable Probabilistic Graphical Model for Massive Hierarchical Data Problems - 2014
● SKILL: A System for Skill Identification and Normalization – 2015
● Carotene: A Job Title Classification System for the Online Recruitment Domain - 2015
● WebScalding: A Framework for Big Data Web Services - 2015
● A Pipeline for Extracting and Deduplicating Domain-Specific Knowledge Bases - 2015
● Macau: Large-Scale Skill Sense Disambiguation in the Online Recruitment Domain - 2015
● Improving the Quality of Semantic Relationships Extracted from Massive User Behavioral Data – 2015
● Query Sense Disambiguation Leveraging Large Scale User Behavioral Data - 2015
Speaking Engagements:
● Over a dozen in the last year: Lucene/Solr Revolution 2014, WSDM 2014, Atlanta Solr Meetup, Atlanta Big Data Meetup, Second
International Syposium on Big Data and Data Analytics, RecSys 2014, IEEE Big Data Conference 2014 (x2), AAAI/IAAI 2015, IEEE Big Data
2015 (x6), Lucene/Solr Revolution 2015, and Bay Area Search Meetup

51.
Bay Area Search
So What’s Next?

52.
Bay Area Search
machine learning
Keywords:
Search Behavior,
Application Behavior, etc.
Job Title Classifier, Skills Extractor, Job Level Classifier, etc.
Semantic Query
Augmentation
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
Semantic Search Architecture – Query Augmentation
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
+
This Piece:
How do you construct the
best possible queries?
The answer… Learning to Rank
(Machine-learned Ranking)
That can be a topic for next time…

53.
Bay Area Search
Type-ahead
Prediction
Building an Intent Engine
Search Box
Semantic Query
Parsing
Intent Engine
Spelling Correction
Entity / Entity
Type Resolution
Machine-learned
Ranking
Relevancy Engine (“re-expressing intent”)
User Feedback
(Clarifying Intent)
Query Re-writing Search Results
Query
Augmentation
Knowledge
Graph
Contextual
Disambiguation

54.
Bay Area Search
Conceptual Framework for Information Retrieval:
Traditional
Keyword
Search
Recommendations
Semantic
Search
User Intent
Personalized
Search
Augmented
Search
Domain-aware
Matching

55.
Bay Area Search
Additional References:

56.
Bay Area Search
Bonus Slides
Audience question: how can you
discover terms / related terms
without having query logs to mine?

57.
Bay Area Search
One Option: Clustering on documents to find semantic links

58.
Bay Area Search
Setting up Clustering in solrconfig.xml
<searchComponent name="clustering" enable=“true“ class="solr.clustering.ClusteringComponent">
<lst name="engine">
<str name="name">default</str>
<str name="carrot.algorithm">
org.carrot2.clustering.lingo.LingoClusteringAlgorithm</str>
<str name="MultilingualClustering.defaultLanguage">ENGLISH</str>
</lst>
</searchComponent>
<requestHandler name="/clustering" enable=“true" class="solr.SearchHandler">
<lst name="defaults">
<str name="clustering.engine">default</str>
<bool name="clustering.results">true</bool>
<str name="fl">*,score</str>
</lst>
<arr name="last-components">
<str>clustering</str>
</arr>
</requestHandler>

59.
Bay Area Search
Clustering Query
/solr/clustering/?q=solr
&rows=100
&carrot.title=titlefield
&carrot.snippet=titlefield
&LingoClusteringAlgorithm.desiredClusterCountBase=25
//clustering & grouping don’t currently play nicely
Allows you to dynamically identify “concepts” and their prevalence
within a user’s top search results

60.
Bay Area Search
Original Query: q=solr
Clustering Results
Clusters Identified:
Developer (22)
Java Developer (13)
Software (10)
Senior Java Developer (9)
Architect (6)
Software Engineer (6)
Web Developer (5)
Search (3)
Software Developer (3)
Systems (3)
Administrator (2)
Hadoop Engineer (2)
Java J2EE (2)
Search Development (2)
Software Architect (2)
Solutions Architect (2)
Identify Relationships:

61.
Bay Area Search
q="solr" OR ("Developer”^0.22 or "Java Developer"^0.13 or
"Software "^0.10 or "Senior Java Developer"^0.9 or "Architect"^0.6
or "Software Engineer"^0.6 or "Web Developer"^0.5 or
"Search"^0.3 or "Software Developer"^0.3 or "Systems"^0.3 or
"Administrator"^0.2 or "Hadoop Engineer"^0.2 or "Java J2EE"^0.2
or "Search Development"^0.2 or "Software Architect"^0.2 or
"Solutions Architect"^0.2)
Just plug in those semantic relationships as before…

62.
Bay Area Search
Contact Info
Yes, WE ARE HIRING @ . Come talk with me if you are interested…
Trey Grainger
trey.grainger@careerbuilder.com
@treygrainger
http://solrinaction.com
Conference discount (39% off): 39solrmu
Other presentations:
http://www.treygrainger.com