(Keynote) Peter Mika - “Making the Web Searchable”
Transcript of (Keynote) Peter Mika - “Making the Web Searchable”
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Making the Web Searchable
P R E S E N T E D B Y P e t e r M i k a , Y a h o o L a b s J u n e 2 5 , 2 0 1 5 ⎪
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Agenda
Web Search› How it works… and where it fails
Semantic Web› The promise and the reality
Semantic Search› Research and Applications at Yahoo
What’s next?› More intelligence!
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Search is really fast, but not particularly intelligent
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What it’s like to be a machine?
Roi Blanco
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What it’s like to be a machine?
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⏎↵⏏☐ģğğğ
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Real problem
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Search needs (more) intelligence
Current paradigm: ad-hoc document retrieval › Set of documents that each may fulfill the information need› Relevance of those documents is clearly established by
• Textual similarity
• Authority
Queries outside this paradigm are hard› Semantic gap between the query and the content
• Queries that require a deeper understanding of the world at large
› Complex needs where no document contains the answer
Analysis of aggregate behavior is a challenge› We may answer two queries perfectly, without knowing how they are related
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Semantic gap› Ambiguity
• jaguar
• paris hilton
› Secondary meaning
• george bush (and I mean the beer brewer in Arizona)
› Subjectivity
• reliable digital camera
• paris hilton sexy
› Imprecise or overly precise searches
• jim hendler
Complex needs› Missing information
• brad pitt zombie
• florida man with 115 guns
• 35 year old computer scientist living in barcelona
› Category queries
• countries in africa
• barcelona nightlife
› Transactional or computational queries
• 120 dollars in euros
• digital camera under 300 dollars
• world temperature in 2020
Examples of hard queries
Are there even any true keyword queries?
Users may have stopped asking them
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The Semant ic Web
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Enter the Semantic Web
A social-technical ecosystem where the meaning of content is explicit and sharedamong agents (humans and machines)› Shared identifiers for real-world entities› Standards for exchanging structured data
• Data modeled as a graph
› Shared formal, schema languages
• Names of entity and relationship types
• Constraints on the entities, relationships and attributes
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04/15/202311
“At the doctor's office, Lucy instructed her Semantic Web agent through her handheld Web browser. The agent promptly retrieved information about Mom's prescribed treatment from the doctor's agent, looked up several lists of providers, and checked for the ones in-plan for Mom's insurance within a 20-mile radius of her home and with a rating of excellent or very good on trusted rating services. It then began trying to find a match between available appointment times (supplied by the agents of individual providers through their Web sites) and Pete's and Lucy's busy schedules.” › The Semantic Web. Tim Berners-Lee, James Hendler,
Ora Lassila. Appeared in: Scientific American 284(5):34-43 (May 2001)
Huge expectations
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In a perfect world, the Semantic Web is the end-game for IR
#ROI_BLANCO
#ROI_BLANCO
#ROI_BLANCO
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The view from IR: skepticism
Mismatch to the IR problem› End-users
• Do not know the identifiers of things
• Not aware of the schema of the data
• Can’t be expected to learn complex query languages
› Focus on document retrieval
• Limited recognition of the value of direct answers (initially)
“Who is going to do the annotation?”› Focus still largely on text› Automated annotation/extraction tools produce poor results
Early experiments using external knowledge are unsuccessful› Query/document expansion using thesauri etc.
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The Semantic Web off to a slow start
Complex technology › Set of standards sold as a stack
• URI, RDF, RDF/XML, RDFa, JSON-LD, OWL, RIF, SPARQL, OWL-S, POWDER …
› Not very developer friendly
Ideals of knowledge representation hard to enforce on the Web
No clear value proposition› Chicken and egg problem
• No users/use cases, hence no data
• No data, because no users/use cases
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… and became practical
Simplified technology› Fewer, more developer friendly representations› Focusing on the lower layers of the stack› Data first, schemas/logic second
Giving up ideals about knowledge representation› Shared identifiers› Logical consistency› Distinction between real-world entities and web resources
Motivation for adoption› Search engines, Facebook, Pinterest, Twitter etc. investing in information extraction
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Two important achievements
Linked Open Data› Social movement to (re)publish existing datasets
• 100B+ triples of data
• Encyclopedic, governmental, geo, scientific datasets
• Impact: background knowledge– Basis for knowledge graphs in search engines
Metadata inside HTML pages› Facebook’s OGP and schema.org
• Over 15% of all pages have schema.org markup (2013)
• Personal information, images, videos, reviews, recipes etc.
• Impact: remove the need for automated extraction
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Example
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View source
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Caveat: not your perfect Semantic Web
Outdated, incorrect or incomplete data› Lack of write access or feedback mechanisms
Mistakes made by tools› Noisy information extraction› Entity linking (reconciliation)
Limited or no reuse of identifiers Metadata not always representative of content
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Semantic Search at Yahoo
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Semantic Search research (2007-)
Emergence of the Semantic Search field › Intersection of IR, NLP, DB and SemWeb
• ESAIR at SIGIR
• SemSearch at ESWC/WWW
• EOS and JIWES at SIGIR
• Semantic Search at VLDB
Exploiting semantic understanding in the retrieval process› User intent and resources are represented using semantic models
• Semantic models typically differ across NLP, DB and Semantic Web
› Semantic models are exploited in the matching and ranking of resources
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Semantic Search – a process view
Query
Construction
•Keywords•Forms•NL•Formal language
Query
Processing
• IR-style matching & ranking•DB-style precise matching•KB-style matching & inferences
Result
Presentati
on
•Query visualization•Document and data presentation•Summarization
Query
Refinemen
t
• Implicit feedback•Explicit feedback• Incentives
Document Representation
Knowledge Representation
Semantic ModelsResources
Documents
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Result presentation using metadata
Personal and private homepageof the same person(clear from the snippet but it could be also automaticallyde-duplicated)
Conferences he plans to attend and his vacations from homepageplus bio events from LinkedIn
Geolocation
“Microsearch”internal prototype(2007)
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Yahoo SearchMonkey (2008)
1. Extract structured data› Semantic Web markup
• Example:
<span property=“vcard:city”>Santa Clara</span>
<span property=“vcard:region”>CA</span>
› Information Extraction
2. Presentation› Fixed presentation templates
• One template per object type
› Applications
• Third-party modules to display data (SearchMonkey)
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Effectiveness of enhanced results
Explicit user feedback› Side-by-side editorial evaluation (A/B testing)
• Editors are shown a traditional search result and enhanced result for the same page
• Users prefer enhanced results in 84% of the cases and traditional results in 3% (N=384)
Implicit user feedback› Click-through rate analysis
• Long dwell time limit of 100s (Ciemiewicz et al. 2010)
• 15% increase in ‘good’ clicks
› User interaction model
• Enhanced results lead users to relevant documents (IV) even though less likely to clicked than textual (III)
• Enhanced results effectively reduce bad clicks!
See› Kevin Haas, Peter Mika, Paul Tarjan, Roi Blanco: Enhanced results for web search. SIGIR 2011: 725-
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Adoption among consumers of web content Google announces Rich Snippets - June, 2009
› Faceted search for recipes - Feb, 2011
Bing tiles – Feb, 2011 Facebook’s Like button and the Open Graph Protocol (2010)
› Shows up in profiles and news feed› Site owners can later reach users who have liked an object
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schema.org
Collaborative effort sponsored by large consumers of Web data› Bing, Google, and Yahoo! as initial founders (June, 2011)› Yandex joins schema.org in Nov, 2011
Agreement on a shared set of schemas for the Web› Available at schema.org in HTML and machine readable formats› Free to use under W3C Royalty Free terms
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Yahoo’s Knowledge Graph
Chicago Cubs
Chicago
Barack Obama
Carlos Zambrano
10% off ticketsfor
plays for
plays in
lives in
Brad Pitt
Angelina Jolie
Steven Soderbergh
George Clooney
Ocean’s Twelve
partner
directs
casts in
E/R
casts in
takes place in
Fight Club
casts in
Dust Brotherscasts
in
music by
Nicolas Torzec: Making knowledge reusable at Yahoo!: a Look at the Yahoo! Knowledge Base (SemTech 2013)
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Building the Knowledge Graph
Information extraction› Automated information extraction
• e.g. wrapper induction
› Metadata from HTML pages
• Focused crawler
› Public datasets (e.g. Dbpedia)› Proprietary data
Data fusion› Manual mapping from the source
schemas to the ontology› Supervised entity reconciliation
Ontology management › Editorially maintained OWL ontology
with 300+ classes › Covering the domains of interest of
Yahoo
Curation and quality assessment› Editors and user feedback still play a
large role
Bellare et al: WOO: A Scalable and Multi-tenant Platform for Continuous Knowledge Base Synthesis. PVLDB 2013Welch et al.: Fast and accurate incremental entity resolution relative to an entity knowledge base. CIKM 2012
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Entity linking/entity retrieval› Identifying the most relevant entity to
the query
Entity recommendation› Given that the user is interested in one
entity, which entity to recommend next?
Roi Blanco, Berkant Barla Cambazoglu, Peter Mika, Nicolas Torzec: Entity Recommendations in Web Search. ISWC 2013
Entity displays in web search
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The importance of entities
Entity mention query = <entity> {+ <intent>}› ~70% of queries contain a named entity (entity mention queries)
• brad pitt height
› ~50% of queries have an entity focus (entity seeking queries)
• brad pitt attacked by fans
› ~10% of queries are looking for a class of entities
• brad pitt movies
› Jeffrey Pound, Peter Mika, Hugo Zaragoza: Ad-hoc object retrieval in the web of data. WWW 2010: 771-780
Intent is typically an additional word or phrase • Disambiguate, most often by type e.g. brad pitt actor
• Specify action or aspect e.g. brad pitt net worth, toy story trailer
brad pitt height
how tall istall …
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Inverted index› Inspired by text retrieval
• Match individual keywords
• Score and aggregate
Parsing› Inspired by text parsing
• Find potential mentions of entities (spots) in query
• Score candidates for each spot
Two broad approaches to entity retrieval
brad
(actor) (boxer) (city)
(actor) (boxer) (lake)
pitt
brad pitt
(actor) (boxer)
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Retrieval-based approach
Experimented with different index structures› Horizontal: one field for text and one for property name
› Vertical: One field per property
› Combination: one field per property weight (best performance in both AND/OR mode)
Horizontal
Vertical
R-Vertical
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Retrieval-based approach
Ranking based on BM25F› R. Blanco, P. Mika, S. Vigna: Effective and Efficient Entity Search in RDF Data. ISWC 2011
› 42% improvement in MAP over best method in SemSearch 2010
› <100ms time for simple conjunctive queries
Open source implementation and demo using WebDataCommons data› glimmer.research.yahoo.com
› https://github.com/yahoo/Glimmer/
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Entity linking approach
Large-scale entity/alias dictionaries› Alias mining from usage data, Wikipedia etc.
Dynamic segmentation Novel method for scoring alias matches
› Completely unsupervised› Combination of
• Keyphraseness: how likely is a segment to be an entity mention?
• Commonness: How likely that a linked segment refers to a particular entity?
• Context-model based on word2vec representation
› Roi Blanco, Giuseppe Ottaviano and Edgar Meij. Fast and space-efficient entity linking in queries. WSDM 2015
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Results: effectiveness
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Significant improvement over external baselines and internal system› Measured on public Webscope dataset Yahoo Search Query Log to Entities
Search over Bing, top Wikipedia result
State-of-the-art in literature
A trivial search engine over Wikipedia
Our method: Fast Entity Linker (FEL)
FEL + context
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Two orders of magnitude faster than state-of-the-art› Simplifying assumptions at scoring time› Adding context independently› Dynamic pruning
Small memory footprint› Compression techniques, e.g. 10x
reduction in word2vec storage
Results: efficiency
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Related entity recommendations
Some users are short on time › Need for direct answers› Query expansion, question-answering, information boxes, rich results…
Other users have time at their hand› Long term interests such as sports, celebrities, movies and music› Long running tasks such as travel planning
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Example user sessions
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Spark system for related entity recommendations
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Machine learned ranking
Features from the Knowledge Graph and large-scale text sources› Unary
• Popularity features from text: probability, entropy, wiki id popularity …
• Graph features: PageRank on the entity graph, wikipedia, web graph
• Type features: entity type
› Binary
• Co-occurrence features from text: conditional probability, joint probability …
• Graph features: common neighbors …
• Type features: relation type
Regression model using Gradient Boosted Decision Trees (GBDT)› Trained on editorial data (cf. clicks)
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Evaluation
1. 10-fold cross-validation
2. Side-by-side testing› More appropriate for judging sets of results
• “Blondie and Mickey Gilley are 70’s performers and do not belong on a list of 60’s musicians.”
3. Online evaluation (bucket testing)› Small % of search traffic redirected to test system, another small % to the baseline system› Data collection over at least a week, looking for stat. significant differences that are also stable
over time› Metrics
• Coverage and Click-through Rate (CTR)
• Searches per browser-cookie (SPBC)
• Other key metrics should not impacted negatively, e.g. Abandonment and retry rate, Daily Active Users (DAU), Revenue Per Search (RPS), etc.
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Click-through rate (CTR) before and after the new system
Before release:
Gradually degrading performance due to lack of fresh data
After release:
Learning effect:users are starting to use the tool again
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What’s next?
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Summary
Information Retrieval› Reached the limits of the ad-hoc text retrieval paradigm› Needs to go beyond syntactic representations
Semantic Web› Provides means for knowledge representation and reasoning across the Web› Adoption has been slow, but picking up steadily
Applications in Web Search› Entity-based experiences
• Rich results, information boxes and related entities
› Question-answering
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Search needs even more intelligence
Representation› Modeling the World, not just what is on the Web› Modeling personal information and preferences› Modeling of intents (actions that can be taken on the World)
Understanding› Need better understanding of context› User profile, history and current state
Retrieval› (Guided) interaction› Predictive search
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Q&A
Many thanks to members of the Semantic Search team at Yahoo Labs London and to Yahoos around the world
Contact me› [email protected]› @pmika› http://www.slideshare.net/pmika/