knowledge and search: two swords sharpening one another by bill slawski
DESCRIPTION
From the SMX East 2014 Conference in New York City, NY. SESSION: What Is Hummingbird & The Entity Search Revolution?. PRESENTATION: Knowledge and Search: Two Swords Sharpening One Another - Given by Bill Slawski, @Bill_Slawski, Director of Search Marketing - Go Fish Digital . #SMX #21ATRANSCRIPT
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Bill Slawski SMX East 2014 (#smx #21A)
October 1, 2014 (9:00am-‐10:15am)
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When Sergey Gave Larry a Tour “We both found each other obnoxious,” Brin counters when I tell him of Page's response. "But we say it a little bit jokingly. Obviously we spent a lot of time talking to each other, so there was something there. We had a kind of bantering thing going." Page and Brin may have clashed, but they were clearly drawn together -‐ two swords sharpening one another.
The Birth of Google by John Battelle #smx #21A @bill_slawski
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#smx #21A @bill_slawski
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Larry Invents PageRank
Improved Text Searching in Hypertext Systems (pdf) #smx #21A @bill_slawski
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#smx #21A @bill_slawski
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Sergey Invents DIPRE
Extracting Patterns and Relations from the World Wide Web (pdf) #smx #21A @bill_slawski
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#smx #21A @bill_slawski
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Patterns!
#smx #21A @bill_slawski
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Andrew Hogue’s Team
Andrew Hogue’s Resume #smx #21A @bill_slawski
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Patterns!
#smx #21A @bill_slawski
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Gathering & AnnotaAng Knowledge
Browseable fact repository #smx #21A @bill_slawski
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Search becomes Knowledge
#smx #21A @bill_slawski
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Google’s Knowledge Graph
The Knowledge Graph #smx #21A @bill_slawski
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#smx #21A @bill_slawski
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Alexandria torpedo factory CC BY-‐SA 3.0 #smx #21A @bill_slawski
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Google Starts a ConversaAon
FAQ: All About The New Google “Hummingbird” Algorithm
#smx #21A @bill_slawski
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EnAAes become “Search EnAAes”
Search entity transition matrix and applications of the transition matrix Relationships between Search Entities #smx #21A @bill_slawski
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Which [lincoln]?
#smx #21A @bill_slawski
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Tracking Knowledge InformaAon Each record (herein referred to as a tuple: <document , query, data> ) comprises a query submitted by users, a document reference indicating the document selected by users in response to the query, and an aggregation of click data for all users or a subset of all users that selected the document reference in response to the query.
Propagating query classifications Using Query User Data to Classify Queries #smx #21A @bill_slawski
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Google is � Viewing Entities as Search Entities (“lincoln as a person” is a query)
� Shows that Google is possibly using an RDF (Resource Description Framework) schema for tracking information to calculate probabilities (based on user behavior) of what classification is meant by a query.
� Searching for Patterns in Queries and on Pages to answer questions
� Looking for Schema markup and schema-‐related facts and attributes information to create and understand context.
#smx #21A @bill_slawski
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Patterns!
#smx #21A @bill_slawski
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Query Revision Based on Context and SubsAtute Rules
Synonym identification based on co-‐occurring terms #smx #21A @bill_slawski
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For example, the user may enter the search query "What is the best place to find and eat Chicago deep dish style pizza?" In determining whether the term "restaurant" is a synonym for the query term "place", a synonym engine may evaluate the query term in the context of adjacent terms, such as "best" or "to," as well as non-‐adjacent terms, such as "Chicago" and "pizza." Such an evaluation may result in the decision that, in the context of the non-‐adjacent term "pizza," the term "restaurant" is a synonym of the query term "place."
#smx #21A @bill_slawski
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Knowledge Base Searches
Identifying entities using search results #smx #21A @bill_slawski
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The Future of the Knowledge Graph?
Knowledge Vault: A Web-‐Scale Approach to Probabilistic Knowledge Fusion #smx #21A @bill_slawski
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Incompleteness of Knowledge Graph
#smx #21A @bill_slawski
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Introducing the Knowledge Vault?
Constructing and Mining Web Scale Knowledge #smx #21A @bill_slawski
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Recovering Semantics of Tables on the Web (pdf) #smx #21A @bill_slawski
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Open Language InformaAon ExtracAon
Open Language Learning for Information Extraction (PDF) #smx #21A @bill_slawski
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<div itemscope itemtype ="http://schema.org/Movie"> <h1 itemprop="name"&g;Avatar</h1> <div itemprop="director" itemscope itemtype="http://schema.org/Person"> Director: <span itemprop="name">James Cameron</span> (born <span itemprop="birthDate">August 16, 1954)</span> </div> <span itemprop="genre">Science fiction</span> <a href="../movies/avatar-‐theatrical-‐trailer.html" itemprop="trailer">Trailer</a> </div>
Getting started with schema.org #smx #21A @bill_slawski
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Quizz: Targeted Crowdsourcing with a Billion (Potential) Users (pdf)
#smx #21A @bill_slawski
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Search + Knowledge, Sharpening One Another
#smx #21A @bill_slawski
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Thank you -‐ Bill Slawski � Director of Search Marketing at GoFishDigital � Author at SEO by the Sea � Tweet me at https://twitter.com/bill_slawski � About this session (#smx #21A) � Connect Author Entities: https://plus.google.com/u/1/+BillSlawski/posts
� Email me: [email protected]
#smx #21A @bill_slawski