Leveraging Lucene/Solr as a Knowledge Graph and Intent Engine

Trey Grainger Director of Engineering, Search & Recommendations

2015.10.15

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

Agenda• Introduction• Defining the problem – the need for Semantic Search• Building an Intent Engine

- Type-ahead prediction- Spelling Correction- Entity / Entity-type Resolution- Semantic Query Parsing- Query Augmentation- The Knowledge Graph

• Conclusion

Knowledge Graph

At CareerBuilder, Solr Powers...At CareerBuilder, Solr Powers...

Search by the Numbers

5

Powering 50+ Search Experiences Including:

100 million +Searches per day

30+Software Developers, Data

Scientists + Analysts

500+Search Servers

1,5 billion +Documents indexed and

searchable

1Global Search

Technology platform

...and many more

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)

But we also really want “things”, not “strings”…

Job Level Job title Company

Job Title Company School + Degree

Type-aheadPrediction

Knowledge Graph and 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

Type-ahead Predictions

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

Spelling Correction*

*Google “Solr Spell Check Component”

Entity / Entity-type Resolution

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

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.

Entity-type Recognition

Build classifiers trained onExternal data sources(Wikipedia, DBPedia, WordNet, etc.), as well asfrom our own domain.

The subject for a future talk / research paper…

java developer

registered nurse

emergency room

director

job title

skill

job level

locationwork typePortland, OR

part-time

Semantic Query Parsing

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!

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", hadoopsenior, java, "developer hadoop"

Input: senior hadoop developer java ruby on rails perl

Semantic Search Architecture – Query Parsing1) 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

Query Augmentation

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 Occupationsmachine learning: {15-1031.00 .58Computer Software Engineers, Applications

15-1011.00 .55Computer 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 phrasesjava developermachine learningregistered nurse

FST

Knowledge

Graph in

+

Query Enrichment

Document Enrichment

Document Enrichment

Knowledge Graph

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 APIAny product can leverage our core relationship scoring engine to score any list of entities against any other list

Full domain supportKeywords, job titles, skills, companies, job levels, locations, and all other taxonomies.

Intersections, overlaps, & relationship scoring, many levels deepUsers can either provide a list of items to score, or else have the system dynamically discover the most related items (or both).

Knowledge Graph

So how does it work?

Foreground vs. Background AnalysisEvery 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

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

2014 - 2015 Publications & PresentationsBooks: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

So What’s Next?

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 Occupationsmachine learning: {15-1031.00 .58Computer Software Engineers, Applications

15-1011.00 .55Computer 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 phrasesjava developermachine learningregistered 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…

Type-aheadPrediction

Knowledge Graph and 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

Additional References:

Contact Info

Yes, WE ARE HIRING @ . Come talk with me if you are interested…

Trey Grainger trey.grainger@careerbuilder.com @treygrainger

http://solrinaction.comConference discount (43% off): lusorevcftw

Other presentations: http://www.treygrainger.com