Knowledge and Search: Two Swords Sharpening One Another By Bill Slawski

Bill Slawski SMX East 2014 (#smx #21A)

October 1, 2014 (9:00am-‐10:15am)

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

#smx #21A @bill_slawski

Larry Invents PageRank

Improved Text Searching in Hypertext Systems (pdf) #smx #21A @bill_slawski


Sergey Invents DIPRE

Extracting Patterns and Relations from the World Wide Web (pdf) #smx #21A @bill_slawski


Patterns!


Andrew Hogue’s Team

Andrew Hogue’s Resume #smx #21A @bill_slawski

Patterns!


Gathering & AnnotaAng Knowledge

Browseable fact repository #smx #21A @bill_slawski

Search becomes Knowledge


Google’s Knowledge Graph

The Knowledge Graph #smx #21A @bill_slawski


Alexandria torpedo factory CC BY-‐SA 3.0 #smx #21A @bill_slawski

Google Starts a ConversaAon

FAQ: All About The New Google “Hummingbird” Algorithm


EnAAes become “Search EnAAes”

Search entity transition matrix and applications of the transition matrix Relationships between Search Entities #smx #21A @bill_slawski

Which [lincoln]?


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

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.


Patterns!


Query Revision Based on Context and SubsAtute Rules

Synonym identification based on co-‐occurring terms #smx #21A @bill_slawski

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."


Knowledge Base Searches

Identifying entities using search results #smx #21A @bill_slawski

The Future of the Knowledge Graph?

Knowledge Vault: A Web-‐Scale Approach to Probabilistic Knowledge Fusion #smx #21A @bill_slawski

Incompleteness of Knowledge Graph


Introducing the Knowledge Vault?

Constructing and Mining Web Scale Knowledge #smx #21A @bill_slawski

Recovering Semantics of Tables on the Web (pdf) #smx #21A @bill_slawski

Open Language InformaAon ExtracAon

Open Language Learning for Information Extraction (PDF) #smx #21A @bill_slawski

<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

Quizz: Targeted Crowdsourcing with a Billion (Potential) Users (pdf)


Search + Knowledge, Sharpening One Another


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]


Internet

Knowledge and Search: Two Swords Sharpening One Another By Bill Slawski