Original source: 10 Best WordPress Speed Testing Tools via DailySEOblog.
If you are still wondering what the hype about loading speed of websites is all about, you need to check out this article on Entrepreneur, which calls speed the new SEO for web developers! Given that the whole world and then some is online, just having a well-designed WordPress site isn’t enough anymore. You need...
Original source: How to Increase Traffic to Your Site: 5 Tips from Experts via DailySEOblog.
Everyone wants to drive traffic to their website. But why is this traffic so important? The simple answer is that each visitor to your site represents a business opportunity. Therefore, the larger the numbers, the more opportunities for you to capitalize on. It an important indicator and driver of business growth, especially for a small...
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Original source: 10 Best WordPress Caching Plugins via DailySEOblog.
With same day delivery services, TV shows streaming online within seconds, instant cabs, tables at restaurants or even booking a date with a smartphone app, we are a generation that has gotten used to instant gratification. The “I want it now” generation, who is addicted to the faster ways of doing things, isn’t ready to wait for...
Original source: 10 Tips to Speed Up WordPress Performance via DailySEOblog.
Is your website taking more than 3 seconds to load? If it is, stay prepared to lose a majority of your visitors. According to a report, The Need for Mobile Speed, by Google, 53% of mobile site visits are abandoned if pages take longer than 3 seconds to load. I don’t mean to scare you. What...
Original source: 10 WordPress Site Speed Optimization Plugins That Might Speed Up Your Blog via DailySEOblog.
Almost ten years ago, every 100ms of latency cost e-commerce giant Amazon 1% in sales. Today, it almost feels like the popular car racing game, Need for Speed, has taken over our lives, where all of us are constantly racing to do things as quickly as possible otherwise we’ll lose something. And, where else is this urgency most...
Original source: 10 Best WordPress Caching Plugins via DailySEOblog.
With same day delivery services, TV shows streaming online within seconds, instant cabs, tables at restaurants or even booking a date with a smartphone app, we are a generation that has gotten used to instant gratification. The “I want it now” generation, who is addicted to the faster ways of doing things, isn’t ready to wait for...
Original source: 10 Tips to Speed Up WordPress Performance via DailySEOblog.
Is your website taking more than 3 seconds to load? If it is, stay prepared to lose a majority of your visitors. According to a report, The Need for Mobile Speed, by Google, 53% of mobile site visits are abandoned if pages take longer than 3 seconds to load. I don’t mean to scare you. What...
Original source: 10 WordPress Site Speed Optimization Plugins That Might Speed Up Your Blog via DailySEOblog.
Almost ten years ago, every 100ms of latency cost e-commerce giant Amazon 1% in sales. Today, it almost feels like the popular car racing game, Need for Speed, has taken over our lives, where all of us are constantly racing to do things as quickly as possible otherwise we’ll lose something. And, where else is this urgency most...
What are query stream ontologies, and how might they change search?
Search engines trained us to use keywords when we searched – to try to guess what words or phrases might be the best ones to use to try to find something we are interested in. That we might have a situational or informational need to find out more about. Keywords were an important and essential part of SEO – trying to get pages to rank highly in search results for certain keywords found in queries that people would search for. SEOs still optimize pages for keywords, hoping to use a combination of information retrieval relevance scores and link-based PageRank scores, to get pages to rank highly in search results.
With Google moving towards a knowledge-based attempt to find “things” rather than “strings”, we are seeing patents that focus upon returning results that provide answers to questions in search results. One of those from January describes how query stream ontologies might be created from searcher’s queries, that can be used to respond to fact-based questions using information about attributes of entities.
There is a white paper from Google co-authored by the same people who are the inventors of this patent published around the time this patent was filed in 2014, and it is worth spending time reading through. The paper is titled, Biperpedia: An Ontology for Search Applications
The patent (and paper) both focus upon the importance of structured data. The summary for the patent tells us this:
Search engines often are designed to recognize queries that can be answered by structured data. As such, they may invest heavily in creating and maintaining high-precision databases. While conventional databases in this context typically have a relatively wide coverage of entities, the number of attributes they model (e.g., GDP, CAPITAL, ANTHEM) is relatively small.
The patent is:
Identifying entity attributes
Inventors: Alon Yitzchak Halevy, Fei Wu, Steven Euijong Whang and Rahul Gupta
Assignee: Google Inc. (Mountain View, CA)
US Patent: 9,864,795
Granted: January 9, 2018
Filed: October 28, 2014
Abstract
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating an ontology of entity attributes. One of the methods includes extracting a plurality of attributes based upon a plurality of queries; and constructing an ontology based upon the plurality of attributes and a plurality of entity classes.
The paper echoes sentiments in the patent, with statements such as this one:
For the first time in the history of the Web, structured data is a first-class citizen among search results. The main search engines make significant efforts to recognize when a user’s query can be answered using structured data.
To cut right to the heart of what this patent covers, it’s worth pulling out the first claim from the patent that expresses how much of an impact this patent may have from a knowledge-based approach to collecting data and indexing information on the Web. Like most patent language, it’s a long passage that tends to run on, but it is very detailed about the process that this patent covers:
1. A method comprising: generating an ontology of class-attribute pairs, wherein each class that occurs in the class-attribute pairs of the ontology is a class of entities and each attribute occurring in the class-attribute pairs of the ontology is an attribute of the respective entities in the class of the class-attribute pair in which the attribute occurs, wherein each attribute in the class-attribute pairs has one or more domains of instances to which the attribute applies and a range that is either a class of entities or a type of data, and wherein generating the ontology comprises: obtaining class-entity data representing a set of classes and, for each class, entities belonging to the class as instances of the class; obtaining a plurality of entity-attribute pairs, wherein each entity-attribute pair identifies an entity that is represented in the class-entity data and a candidate attribute for the entity; determining a plurality of attribute extraction patterns from occurrences of the entities identified by the entity-attribute pairs with the candidate attributes identified by the entity-attribute pairs in text of documents in a collection of documents, wherein determining the plurality of attribute extraction patterns comprises: identifying an occurrence of the entity and the candidate attribute identified by a first entity-attribute pair in a first sentence from a first document in the collection of documents; generating a candidate lexical attribute extraction pattern from the first sentence; generating a candidate parse attribute extraction pattern from the first sentence; and selecting the candidate lexical attribute extraction pattern and the candidate parse attribute extraction pattern as attribute extraction patterns if the candidate lexical attribute pattern and the candidate parse attribute extraction patterns were generated using at least a predetermined number of unique entity-attribute pairs; and applying the plurality of attribute extraction patterns to the documents in the collection of documents to determine entity-attribute pairs, and from the entity-attribute pairs and the class-entity data, for each of one or more entity classes represented in the class-entity data, attributes possessed by entities belonging to the entity class.
Rather than making this post just the claims of this patent (which are worth going through if you can tolerate the legalese), I’m going to pull out some information from the description which describes some of the implications of the process behind the patent. This first one tells us of the benefit of crowdsourcing an ontology, by building it from the queries of many searchers, and how that may mean that focusing upon matching keywords in queries with keywords in documents becomes less important than responding to queries with answers to questions:
Extending the number of attributes known to a search engine may enable the search engine to answer more precisely queries that lie outside a “long tail,” of statistical query arrangements, extract a broader range of facts from the Web, and/or retrieve information related to semantic information of tables present on the Web.
This patent provides a lot of information about how such an ontology might be used to assist search:
The present disclosure provides systems and techniques for creating an ontology of, for example, millions of (class, attribute) pairs, including 100,000 or more distinct attribute names, which is up to several orders of magnitude larger than available conventional ontologies. Extending the number of attributes “known” to a search engine may provide several benefits. First, additional attributes may enable the search engine to more precisely answer “long-tail” queries, e.g., brazil coffee production. Second, additional attributes may allow for extraction of facts from Web text using open information extraction techniques. As another example, a broad repository of attributes may enable recovery of the semantics of tables on the Web, because it may be easier to recognize attribute names in column headers and in the surrounding text.
I wrote about the topic of How Knowledge Base Entities can be Used in Searches to describe how Google might search a data store of attributes about entities such as movies to return search results by asking about facts related to a movie, such as “What is the movie where Robert Duvall loves the smell of Napalm in the morning?” By building up a detailed ontology that includes may facts can mean a search engine can answer many questions quickly. This may be how featured snippets may be responded to in the futured, but the patent that describes this approach is returning SERPs filled with links to web documents, rather than answers to questions.
That mention of open information extraction methods from the patent reminded me of an acquistion that Google made a few years ago when Google acquired Wavii in April of 2013. Wavii did research about open extraction as described in these papers:
A video that might be helpful to learn about how Open Information Extraction works is this one:
Open Information Extraction at Web Scale
An Ontology created from a query stream can lead to this kind of open information extraction
Google has been running a Webtables project for a few years, and has released a followup that describes how the project has been going. Semantics from Tables is mentioned in this patent, so it’s worth including some papers about the Webtables project to give you more information about them, if you hadn’t come across them before:
The process in the patent involves using a query stream to create an ontology. I enjoyed the statements in this patent about what an ontology was and how one works to help search. I recommend clicking through and reading the description in the patent along with the Biperpedia paper. This really is a transformation of search that brings it beyond keywords and understanding entities better, and how search works. This appears to be a very real future of Search:
Systems and techniques disclosed herein may extract attributes from a query stream, and then use extractions to seed attribute extraction from other text. For every attribute a set of synonyms and text patterns in which it appears is saved, thereby enabling the ontology to recognize the attribute in more contexts. An attribute in an ontology as disclosed herein includes a relationship between a pair of entities (e.g., CAPITAL of countries), between an entity and a value (e.g., COFFEE PRODUCTION), or between an entity and a narrative (e.g., CULTURE). An ontology as disclosed herein may be described as a “best-effort” ontology, in the sense that not all the attributes it contains are equally meaningful. Such an ontology may capture attributes that people consider relevant to classes of entities. For example, people may primarily express interest in attributes by querying a search engine for the attribute of a particular entity or by using the attribute in written text on the Web. In contrast to a conventional ontology or database schema, a best-effort ontology may not attach a precise definition to each attribute. However, it has been found that such an ontology still may have a relatively high precision (e.g., 0.91 for the top 100 attributes and 0.52 for the top 5000 attributes).
The ontologies that are created from query streams expressly to assist search applications are different from more conventional manually generated ontologies in a number of ways:
Ontologies as disclosed herein may be particularly well-suited for use in search applications. In particular, tasks such as parsing a user query, recovering the semantics of columns of Web tables, and recognizing when sentences in text refer to attributes of entities, may be performed efficiently. In contrast, conventional ontologies tend to be relatively inflexible or brittle because they rely on a single way of modeling the world, including a single name for any class, entity or attribute. Hence, supporting search applications with a conventional ontology may be difficult because mapping a query or a text snippet to the ontology can be arbitrarily hard. An ontology as disclosed herein may include one or more constructs that facilitate query and text understanding, such as attaching to every attribute a set of common misspellings of the attribute, exact and/or approximate synonyms, other related attributes (even if the specific relationship is not known), and common text phrases that mention the attribute.
The patent does include more about ontologies and schema and data sources and query patterns.
This is a direction that search is traveling towards, and if you want to know or do SEO, it’s worth learning about. SEO is changing, just as it has many times in the past.
I’ve also written a followup to this post on the Go Fish Digital blog at: SEO Moves From Keywords to Ontologies and Query Patterns
The post 3 Ways Query Stream Ontologies Change Search appeared first on SEO by the Sea ⚓.
Original source: 10 Best WordPress Caching Plugins via DailySEOblog.
With same day delivery services, TV shows streaming online within seconds, instant cabs, tables at restaurants or even booking a date with a smartphone app, we are a generation that has gotten used to instant gratification. The “I want it now” generation, who is addicted to the faster ways of doing things, isn’t ready to wait for...
Original source: 10 Tips to Speed Up WordPress Performance via DailySEOblog.
Is your website taking more than 3 seconds to load? If it is, stay prepared to lose a majority of your visitors. According to a report, The Need for Mobile Speed, by Google, 53% of mobile site visits are abandoned if pages take longer than 3 seconds to load. I don’t mean to scare you. What...
Original source: 10 WordPress Site Speed Optimization Plugins That Might Speed Up Your Blog via DailySEOblog.
Almost ten years ago, every 100ms of latency cost e-commerce giant Amazon 1% in sales. Today, it almost feels like the popular car racing game, Need for Speed, has taken over our lives, where all of us are constantly racing to do things as quickly as possible otherwise we’ll lose something. And, where else is this urgency most...
Original source: 10 Tips to Speed Up WordPress Performance via DailySEOblog.
Is your website taking more than 3 seconds to load? If it is, stay prepared to lose a majority of your visitors. According to a report, The Need for Mobile Speed, by Google, 53% of mobile site visits are abandoned if pages take longer than 3 seconds to load. I don’t mean to scare you. What...
Search engines trained us to use keywords when we searched – to try to guess what words or phrases might be the best ones to use to try to find something we are interested in. That we might have a situational or informational need to find out more about. Keywords were an important and essential part of SEO – trying to get pages to rank highly in search results for certain keywords found in queries that people would search for. SEOs still optimize pages for keywords, hoping to use a combination of information retrieval relevance scores and link-based PageRank scores, to get pages to rank highly in search results.
With Google moving towards a knowledge-based attempt to find “things” rather than “strings”, we are seeing patents that focus upon returning results that provide answers to questions in search results. One of those from January describes how query stream ontologies might be created from searcher’s queries, that can be used to respond to fact-based questions using information about attributes of entities.
There is a White paper from Google co-authored by the same people who are the inventors of this patent published around the time this patent was filed in 2014, and it is worth spending time reading through. The paper is titled, Biperpedia: An Ontology for Search Applications
The patent (and paper) both focus upon the inportance of structured data. The summary for the patent tells us this:
Search engines often are designed to recognize queries that can be answered by structured data. As such, they may invest heavily in creating and maintaining high-precision databases. While conventional databases in this context typically have a relatively wide coverage of entities, the number of attributes they model (e.g., GDP, CAPITAL, ANTHEM) is relatively small.
The patent is:
Identifying entity attributes
Inventors: Alon Yitzchak Halevy, Fei Wu, Steven Euijong Whang and Rahul Gupta
Assignee: Google Inc. (Mountain View, CA)
US Patent: 9,864,795
Granted: January 9, 2018
Filed: October 28, 2014
Abstract
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating an ontology of entity attributes. One of the methods includes extracting a plurality of attributes based upon a plurality of queries; and constructing an ontology based upon the plurality of attributes and a plurality of entity classes.
The paper echoes sentiments in the patent, with statements such as this one:
For the first time in the history of the Web, structured data is a first-class citizen among search results. The main search engines make significant efforts to recognize when a user’s query can be answered using structured data.
To cut right to the heart of what this patent covers, it’s worth pulling out the first claim from the patent that expresses how much of an impact this patent may have from a knowledge-based approach to collecting data and indexing information on the Web. Like most patent language, it’s a long passage that tends to run on, but it is very detailed about the process that this patent covers:
1. A method comprising: generating an ontology of class-attribute pairs, wherein each class that occurs in the class-attribute pairs of the ontology is a class of entities and each attribute occurring in the class-attribute pairs of the ontology is an attribute of the respective entities in the class of the class-attribute pair in which the attribute occurs, wherein each attribute in the class-attribute pairs has one or more domains of instances to which the attribute applies and a range that is either a class of entities or a type of data, and wherein generating the ontology comprises: obtaining class-entity data representing a set of classes and, for each class, entities belonging to the class as instances of the class; obtaining a plurality of entity-attribute pairs, wherein each entity-attribute pair identifies an entity that is represented in the class-entity data and a candidate attribute for the entity; determining a plurality of attribute extraction patterns from occurrences of the entities identified by the entity-attribute pairs with the candidate attributes identified by the entity-attribute pairs in text of documents in a collection of documents, wherein determining the plurality of attribute extraction patterns comprises: identifying an occurrence of the entity and the candidate attribute identified by a first entity-attribute pair in a first sentence from a first document in the collection of documents; generating a candidate lexical attribute extraction pattern from the first sentence; generating a candidate parse attribute extraction pattern from the first sentence; and selecting the candidate lexical attribute extraction pattern and the candidate parse attribute extraction pattern as attribute extraction patterns if the candidate lexical attribute pattern and the candidate parse attribute extraction patterns were generated using at least a predetermined number of unique entity-attribute pairs; and applying the plurality of attribute extraction patterns to the documents in the collection of documents to determine entity-attribute pairs, and from the entity-attribute pairs and the class-entity data, for each of one or more entity classes represented in the class-entity data, attributes possessed by entities belonging to the entity class.
Rather than making this post just the claims of this patent (which are worth going through if you can tolerate the legalese), I’m going to pull out some information from the description which describes some of the implications of the process behind the patent. This first one tells us of the benefit of crowdsourcing an ontology, by building it from the queries of many searchers, and how that may mean that focusing upon matching keywords in queries with keywords in documents becomes less important than responding to queries with answers to questions:
Extending the number of attributes known to a search engine may enable the search engine to answer more precisely queries that lie outside a “long tail,” of statistical query arrangements, extract a broader range of facts from the Web, and/or retrieve information related to semantic information of tables present on the Web.
This patent provides a lot of information about how such an ontology might be used to assist search:
The present disclosure provides systems and techniques for creating an ontology of, for example, millions of (class, attribute) pairs, including 100,000 or more distinct attribute names, which is up to several orders of magnitude larger than available conventional ontologies. Extending the number of attributes “known” to a search engine may provide several benefits. First, additional attributes may enable the search engine to more precisely answer “long-tail” queries, e.g., brazil coffee production. Second, additional attributes may allow for extraction of facts from Web text using open information extraction techniques. As another example, a broad repository of attributes may enable recovery of the semantics of tables on the Web, because it may be easier to recognize attribute names in column headers and in the surrounding text.
I wrote about the topic of How Knowledge Base Entities can be Used in Searches to describe how Google might search a data store of attributes about entities such as movies to return search results by asking about facts related to a movie, such as “What is the movie where Robert Duvall loves the smell of Napalm in the morning?” By building up a detailed ontology that includes may facts can mean a search engine can answer many questions quickly. This may be how featured snippets may be responded to in the futured, but the patent that describes this approach is returning SERPs filled with links to web documents, rather than answers to questions.
That mention of open information extraction methods from the patent reminded me of an acquistion that Google made a few years ago when Google acquired Wavii in April of 2013. Wavii did research about open extraction as described in these papers:
A video that might be helpful to learn about how Open Information Extraction works is this one:
Open Information Extraction at Web Scale
An Ontology created from a query stream can lead to this kind of open information extraction
Google has been running a Webtables project for a few years, and has released a followup that describes how the project has been going. Semantics from Tables is mentioned in this patent, so it’s worth including some papers about the Webtables project to give you more information about them, if you hadn’t come across them before:
The process in the patent involves using a query stream to create an ontology. I enjoyed the statements in this patent about what an ontology was and how one works to help search. I recommend clicking through and reading the description in the patent along with the Biperpedia paper. This really is a transformation of search that brings it beyond keywords and understanding entities better, and how search works. This appears to be a very real future of Search:
Systems and techniques disclosed herein may extract attributes from a query stream, and then use extractions to seed attribute extraction from other text. For every attribute a set of synonyms and text patterns in which it appears is saved, thereby enabling the ontology to recognize the attribute in more contexts. An attribute in an ontology as disclosed herein includes a relationship between a pair of entities (e.g., CAPITAL of countries), between an entity and a value (e.g., COFFEE PRODUCTION), or between an entity and a narrative (e.g., CULTURE). An ontology as disclosed herein may be described as a “best-effort” ontology, in the sense that not all the attributes it contains are equally meaningful. Such an ontology may capture attributes that people consider relevant to classes of entities. For example, people may primarily express interest in attributes by querying a search engine for the attribute of a particular entity or by using the attribute in written text on the Web. In contrast to a conventional ontology or database schema, a best-effort ontology may not attach a precise definition to each attribute. However, it has been found that such an ontology still may have a relatively high precision (e.g., 0.91 for the top 100 attributes and 0.52 for the top 5000 attributes).
The ontologies that are created from query streams expressly to assist search applications are different from more conventional manually generated ontologies in a number of ways:
Ontologies as disclosed herein may be particularly well-suited for use in search applications. In particular, tasks such as parsing a user query, recovering the semantics of columns of Web tables, and recognizing when sentences in text refer to attributes of entities, may be performed efficiently. In contrast, conventional ontologies tend to be relatively inflexible or brittle because they rely on a single way of modeling the world, including a single name for any class, entity or attribute. Hence, supporting search applications with a conventional ontology may be difficult because mapping a query or a text snippet to the ontology can be arbitrarily hard. An ontology as disclosed herein may include one or more constructs that facilitate query and text understanding, such as attaching to every attribute a set of common misspellings of the attribute, exact and/or approximate synonyms, other related attributes (even if the specific relationship is not known), and common text phrases that mention the attribute.
The patent does include more about ontologies and schema and data sources and query patterns.
This is a direction that search is traveling towards, and if you want to know or do SEO, it’s worth learning about. SEO is changing, just as it has many times in the past.
The post 3 Ways Query Stream Ontologies Change Search appeared first on SEO by the Sea ⚓.
They named the project Phil, because it sounded friendly. (For those who required an acronym, they had one handy: Probabilistic Hierarchical Inferential Learner.) That was bad news for a Google Engineer named Phil who kept getting emails about the system. He begged Harik to change the name, but Phil it was.
– Steven Levy, In The Plex: How Google Thinks, Works, and Shapes Our Lives.
How does Google decide which Adsense advertisements to show on which Web pages? How do they avoid showing inappropriate advertisements on those content pages? How does the document classification system they use to power those decisions work, and has its use been expanded beyond Google’s advertising system?
Steven Levy’s In the Plex describes the early days of Google’s Adsense program, where web publishers could sign up to display contextually relevant blocks of advertisements on their pages, and would receive a percentage of the advertising fee if someone clicked through an ad being shown. The name of the program, Adsense, originated with a company called Applied Semantics which Google acquired around the time that they were putting the finishing touches on this content-based advertising system.
According to Levy, the timing of the Applied Semantics acquisition and the use of the Adsense name caused many to believe that the advertising technology developed by Applied Semantics was at the heart of the Adsense program, when in reality the technology that powered the system was developed in-house, and was known as “PHIL.”
Note, I’m having a hard time finishing In the Plex. I keep on getting sidetracked by mentions of people and algorithms and events that I want to learn more about.
The mentions of PHIL in the book led me to dig in deeper, and see if I could uncover more information about PHIL. I came across the original provisional patent filing “Methods and apparatus for probabilistic hierarchical inferential learner,” application number 60/416,144, which was filed on October 3, 2002 and expired on July 19th, 2004 without having been published. I had never seen it before, and I suspect that very few outside of Google or the patent office have either, though I recognized a good amount of what I read in it.
PHIL was invented by Georges Harik and Noam Shazeer, and many of the ideas about how words and concepts associated with them might be clustered and classified can be found in the following patents where they are listed as co-inventors:
The following patent application describes how the Phil clustering system might be used to categorize many different types of documents:
Invented by David Gehrking, Ching Law, and Andrew Maxwell
US Patent Application 20060242147
Published October 26, 2006
Filed: April 22, 2005
Abstract
A Website may be automatically categorized by:
- Accepting Website information,
- Determining a set of scored clusters (e.g., semantic, term co-occurrence, etc.) for the Website using the Website information, and
- Determining at least one category (e.g., a vertical category) of a predefined taxonomy using at least some of the set of clusters.
A semantic cluster (e.g., a term co-occurrence cluster) may be automatically associated with one or more categories (e.g., vertical categories) of a predefined taxonomy by:
- Accepting a semantic cluster,
- Identifying a set of a one or more scored concepts using the accepted cluster,
- Identifying a set of one or more categories using at least some of the one or more scored concepts, and
- Associating at least some of the one or more categories with the semantic cluster.
A property (e.g., a Website) may be associated with one or more categories (e.g., vertical categories) of a predefined taxonomy by:
- Accepting information about the property,
- Identifying a set of a one or more scored semantic clusters (e.g., term co-occurrence clusters) using the accepted property information,
- Identifying a set of one or more categories (e.g., vertical categories) using at least some of the one or more scored semantic clusters, and
- Associating at least some of the one or more categories with the property.
The present invention concerns organizing information. In particular, the present invention concerns categorizing terms, phrases, documents and/or term co-occurrence clusters with respect to a taxonomy and using such categorized documents and/or clusters.
Some of the capabilities behind PHIL were pinpointed in one of the slides from a presentation by Google’s Ruchira S. Datta, during the Tenth Annual Bay Area Discrete Mathematics Day, PHIL: The Probabilistic Hierarchical Inferential Learner, April 9th, 2005
What Can We Do With Phil?
- We can compare the concepts occurring in queries, documents, and ads.
- We can compare the concepts between two documents, and form a distance metric for clustering similar documents.
- For a query with ambiguous meaning, we can present results corresponding to the alternate meanings, in proportion to their likelihood.
- We can use the concepts as features in order to classify texts.
- We can guess whether words are misspellings of each other based on the concepts they induce.
The provisional patent was refiled with slightly modified language in September of 2003, and was granted under the name Method and apparatus for characterizing documents based on clusters of related words. One of the major changes was the removal of references to the system as “PHIL”.
I wrote about that patent back in January, before I began reading In the Plex and before I knew of the Google Code name PHIL, in the post Why a Search Engine Might Cluster Concepts to Improve Search Results. At the time, I hadn’t realized that not only is Google using the type of classification described in the patent to classify web pages for purposes of its Adsense program, but that they had been for years and years.
Conclusion
We know that PHIL played an important and integral role in how Google classifies web pages to decide upon which ads to show on pages through Adsense. Some of the classifications provided as examples in the Categorizing objects patent include “sensitive” topics like the ones that I wrote about in my last post on How A Search Engine Might Classify Web Pages as Sensitive.
PHIL (Probabilistic Hierarchical Inferential Learner) classifies pages into different categories by learning about terms and concepts that co-occur upon pages, and clustering similar pages together. Google’s Phrase-Based Indexing also focuses upon identifying good phrases and co-occurrence of terms on pages, but using a different system, and focusing more upon ranking web pages. There have been two generations of Phrase Based Indexing patents, with the second group focusing upon implementing the system in a large scale indexing system, so there’s a possibility that it plays a similar role to PHIL on the web search side.
There is the possibility that the kind of taxonomy that PHIL places pages and sites within could be used in combination with other types of document classification systems, perhaps even one such as Panda, so that sites that cover similar types of topics might be compared against one another.
The post Google’s Second Most Important Algorithm? Before Google’s Panda, there was Phil appeared first on SEO by the Sea ⚓.
