Context Clusters and Query Suggestions at GoogleA new patent application from Google tells us about how the search engine may use context to find query suggestions before a searcher has completed typing in a full query. After seeing this patent, Iâve been thinking about previous patents Iâve seen from Google that have similarities.
Itâs not the first time Iâve written about a Google Patent involving query suggestions. Iâve written about a couple of other patents that were very informative, in the past:
In both of those, the inclusion of entities in a query impacted the suggestions that were returned. This patent takes a slightly different approach, by also looking at context. Context Clusters in Query SuggestionsWeâve been seeing the word Context spring up in Google patents recently. Context terms from knowledge bases appearing on pages that focus on the same query term with different meanings, and we have also seen pages that are about specific people using a disambiguation approach. While these were recent, I did blog about a paper in 2007, which talks about query context with an author from Yahoo. The paper was Using Query Contexts in Information Retrieval. The abstract from the paper provides a good glimpse into what it covers:
The Google patent doesnât take a user-based approach ether, but does look at some user contexts and interests. It sounds like searchers might be offered a chance to select a context cluster before showing query suggestions:
I often look up the inventors of patents to get a sense of what else they may have written, and worked upon. I looked up Jakob D. Uszkoreit in LinkedIn, and his profile doesnât surprise me. He tells us there of his experience at Google:
This passage reminded me of the search results being shown to me by the Google Assistant, which are based upon interests that I have shared with Google over time, and that Google allows me to update from time to time. If the inventor of this patent worked on Google Assistant, that doesnât surprise me. I havenât been offered context clusters yet (and wouldnât know what those might look like if Google did offer them. I suspect if Google does start offering them, I will realize that I have found them at the time they are offered to me.) Like many patents do, this one tells us what is âinnovativeâ about it. It looks at:
It also tells us that it will calculate probabilities that certain context clusters might be requested by a searcher. So how does Google know what to suggest as context clusters?
The Patent in this patent application is: (US20190050450) Query Composition System
What are Context Clusters as Query Suggestions?The patent tells us that context clusters might be triggered when someone is starting a query on a web browser. I tried it out, starting a search for âmoviesâ and got a number of suggestions that were combinations of queries, or what seem to be context clusters: The patent says that context clusters would appear before someone began typing, based upon topics and user information such as location. So, if I were at a shopping mall that had a movie theatre, I might see Search suggestions for movies like the ones shown here: One of those clusters involved âMovies about Businessâ, which I selected, and it showed me a carousel, and buttons with subcategories to also choose from. This seems to be a context cluster: This seems to be a pretty new idea, and may be something that Google would announce as an availble option when it becomes available, if it does become available, much like they did with the Google Assistant. I usually check through the news from my Google Assistant at least once a day. If it starts offering search suggestions based upon things like my location, it could potentially be very interesting. User Query HistoriesThe patent tells us that context clusters selected to be shown to a searcher might be based upon previous queries from a searcher, and provides the following example:
Itâs not easy to tell whether the examples I provided about movies above are related to this patent or if it is tied more closely to the search results that appear in Google Assistant results. Itâs worth reading through and thinking about potential experimental searches to see if they might influence the results that you may see. It is interesting that Google may attempt to anticipate what is suggests to show to us as query suggestions, after showing us search results based upon what it believes are our interests based upon searches that we have performed or interests that we have identified for Google Assistant. The contex cluster may be related to the location and time that someone accesses the search engine. The patent provides an example of what might be seen by the searcher like this:
I could see running such a search at a shopping mall, to learn more about the location I was at, and what I could find there, from dining places to movies being shown. That sounds like it could be the start of an interesting adventure. Copyright © 2019 SEO by the Sea â. This Feed is for personal non-commercial use only. If you are not reading this material in your news aggregator, the site you are looking at may be guilty of copyright infringement. Please contact SEO by the Sea, so we can take appropriate action immediately. Plugin by Taragana The post Context Clusters in Search Query Suggestions appeared first on SEO by the Sea â. from http://www.seobythesea.com/2019/02/context-clusters-search-query-suggestions/
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~ Vernor Vinge, A Deepness in the Sky In a science fiction novel set far in the future, Vernor Vinge writes about how people might engage in software archaeology. I understand the desire to do that, looking at some patents that give us hints about how technology is changing, and processes behind search engines do as well. Google has just been granted a continuation patent for universal search. This post is looking at how the patents covering universal search at Google have changed. This post is not intended as a lesson on how patents work, but knowing something about how continuation patents work, can provide some insights into the processes that people at Google are trying to protect when they have updated the universal search patent. This post is also not intended as an analysis of patents, but rather a look at how search works, and has changed in the last dozen years or so A patent is pursued by a company to protect the process described within the patent. It isnât unusual that the process protected by a patent might change in some way as it is implemented, and put into use. What sometimes happens when that takes place is that the company that was originally assigned the initial patent might file another patent. One referred to as a continuation patent, which takes the original granted date of the first version of the patent as the start time for protection under the patent. The continuation patents are usually very similar to the earlier versions of the patents, with the description sections often being very close to identical. The parts of the patents that change are the claims sections, which are what prosecuting attorneys deciding whether to grant a patent look at and review to see if the patents are new, non-obvious and useful, and should be granted. So, in looking at updated patents covering a specific process, ideally it makes sense to look at how the claims have changed over time. The Original Universal Search Patent ApplicationBefore the patent was granted, I wrote about it in the post How Google Universal Search and Blended Results May Work which was about the Universal Search Patent application published in 2008. That patent was granted, and the claims from the original filing of the patent were updated from the original application, when it was granted in 2011 (Sometimes processes in original applications have to be amended for the patent to be granted, and the claims may change to match those). The First Universal Search PatentIn the 2011 granted version of Interleaving Search Results, the first six claims to the patent give us a flavor for what the patent covers:
The Second Universal Search PatentWe know that Google introduced Universal Search Results at a Searchology presentation in 2007 (a few months before the patent was filed originally), and the patent has been updated since then, with a continuation patent titled Interleaving Search Results granted in 2015, which has new claims, which insert the concept of historic click data into those. Here are the first five claims from that version of the patent:
The Updated Universal Search PatentThe newest version of Interleaving Search Results is still a pending patent application at this point, published on January 2, 2019 Publication Number: 3422216 Abstract: (EN) A method comprising receiving a plurality of first search results that satisfy a search query directed to a first search engine, each of the plurality of first search results having a respective first score, receiving a second search result from a second search engine, the second search result having a second score, wherein the search query is not directed to the second search engine, wherein at least one of the first and second scores is based on characteristics of queries or results of queries learned from user click data; and determining from the second score whether to present the second search result, and if so, presenting the first search results in an order according to their respective scores, and presenting the second search result at a position relative to the order, the position being determined using the first scores and the second score
Changes to Universal SearchIf you look at them, you will see David Baileyâs name on those patents. He wrote a guest post at Search Engine land about Universal Search that provides a lot of insight into how it works and the title of the post refers to that: An Insiderâs View Of Google Universal Search Itâs worth reading though his analysis of Universal search carefully before trying to compare the claims from one version of the patent to another The second version of the claims refer to historic click data, and the newest version changes that to âuser click dataâ, but doesnât provide any insights into why that change in the claims was made. Weâve heard spokespeople from Google tell us that they donât utilize user click data to rank content, so this gets a little confusing if they are taken at their word. Another difference in the latest claims is where it refers to multiple distinct scoring features, and how each type of search that is blended into results has some unique scoring feature that sets it apart from the results inserted on to the search results page from a search engine before it. We do know that different types of search are ranked based upon different signals, such as freshness being important for news results, and links often for Web results. So results shown in universal search may all be relevant for a query searched for, but have some element that considers some unique features that adds diversity to what we see in SERPs. Copyright © 2019 SEO by the Sea â. This Feed is for personal non-commercial use only. If you are not reading this material in your news aggregator, the site you are looking at may be guilty of copyright infringement. Please contact SEO by the Sea, so we can take appropriate action immediately. Plugin by Taragana The post Universal Search Updated at Google appeared first on SEO by the Sea â. from http://www.seobythesea.com/2019/01/universal-search-updated-at-google/ How A Knowledge Graph Updates ItselfTo those of us who are used to doing Search Engine Optimization, weâve been looking at URLs filled with content, and links between that content, and how algorithms such as PageRank (based upon links pointed between pages) and information retrieval scores based upon the relevance of that content have been determining how well pages rank in search results in response to queries entered into search boxes by searchers. Web pages connected by links have been seen as information points connected by nodes. This was the first generation of SEO. Search has been going through a transformation. Back in 2012, Google introduced something it refers to as the knowledge graph, in which they told us that they would begin focusing upon indexing things instead of strings. By âstrings,â they were referring to words that appear in queries, and in documents on the Web. By âthings,â they were referring to named entities, or real and specific people, places, and things. When people searched at Google, the search engines would show Search Engine Results Pages (SERPs) filled with URLs to pages that contained the strings of letters that we were searching for. Google still does that, and is slowly changing to showing search results that are about people, places, and things.
Google started showing us in patents how they were introducing entity recognition to search, as I described in this post: They now show us knowledge panels in search results that tell us about the people, places, and things they recognize in the queries we perform. In addition to crawling webpages and indexing the words on those pages, Google is collecting facts about the people, places, and things it finds on those pages. A Google Patent that was just granted in the past week tells us about how Googleâs knowledge graph updates itself when it collects information about entities, their properties and attributes and relationships involving them. This is part of the evolution of SEO that is taking place today â learning how Search is changing from being based upon search to being based upon knowledge. What does the patent tell us about knowledge? This is one of the sections that details what a knowledge graph is like that Google might collect information about when it indexes pages these days:
Note that SEO is no longer just about how often certain words appear on pages of the Web, what words appear in links to those pages, in page titles, and headings, alt text for images, and how often certain words may be repeated or related words may be used. Google is looking at the facts that are mentioned about entities, such as entity types like a âperson,â and properties, such as âDate of Birth,â or âGender.â Note that quote also mentions the word âSchemaâ as in âThese relationships define in part a schema associated with the entity type [Person].â As part of the transformation of SEO from Strings to Things, The major Search Engines joined forces to offer us information on how to use Schema for structured data on the Web to provide a machine readable way of sharing information with search engines about the entities that we write about, their properties, and relationships. Iâm writing about this patent because I am participating in a Webinar online about Knowledge Graphs and how those are being used, and updated. The Webinar is tomorrow at: Iâm writing about this Google Patent, because it starts out with the following line which it titles âBackground:â This disclosure generally relates to updating information in a database. Data has previously been updated by, for example, user input. This line points to the fact that this approach no longer needs to be updated by users, but instead involves how Google knowledge graphs update themselves. Updating Knowledge GraphsI attended a Semantic Technology and Business conference a couple of year ago, where the head of Yahooâs knowledge base presented, and he was asked a number of questions in a question and answer session after he spoke. Someone asked him what happens when information from a knowledge graph changes and it needs to be updated? His Answer was that a knowledge graph would have to be updated manually to have new information place within it. That wasnât a satisfactory answer because it would have been good to hear that the information from such a source could be easily updated. Iâve been waiting for Google to answer a question like this, which made seeing a line like this one from this patent a good experience:
This would be a knowledge graph update, so that patent provides details using language that reflects that exacly:
How does the search engine do this? The patent provides more information that fills in such details. The approaches to achieve this would be to:
Copyright © 2018 SEO by the Sea â. This Feed is for personal non-commercial use only. If you are not reading this material in your news aggregator, the site you are looking at may be guilty of copyright infringement. Please contact SEO by the Sea, so we can take appropriate action immediately. Plugin by Taragana The post How Googleâs Knowledge Graph Updates Itself by Answering Questions appeared first on SEO by the Sea â. from http://feedproxy.google.com/~r/seobythesea/Tesr/~3/6XWRhgceypo/ I came across this statement on the Web earlier this week, and wondered about it, and decided to investigate more: If there are multiple instances of the same document on the web, the highest authority URL becomes the canonical version. The rest are considered duplicates. ~ Link inversion, the least known major ranking factor. I read that article from Dejan SEO, and thought it was worth exploring more. As I was looking around at Google patents that included the word âAuthorityâ in them, I found this patent which doesnât quite say the same thing that Dejan does, but is interesting in that it finds ways to distinguish between duplicate pages on different domains based upon priority rules, which is interesting in determining which duplicate page might be the highest authority URL for a document. The patent is:
Identifying a primary version of a document Abstract
Since the claims of a patent are what patent examiners at the USPTO look at when they are prosecuting a patent, and deciding whether or not it should be granted. I thought it would be worth looking at the claims contained within the patent to see if they helped encapsulate what it covered. The first one captures some aspects of it that are worth thinking about while talking about different document versions of particular documents, and how the metadata associated with a document might be looked at to determine which is the primary version of a document:
This doesnât advance the claim that the primary version of a document is considered the canonical version of that document, and all links pointed to that document are redirected to the primary version. There is another patent that shares an inventor with this one that refers to one of the duplicate content URL being chosen as a representative page, though it doesnât use the phrase âcanonical.â From that patent: Duplicate documents, sharing the same content, are identified by a web crawler system. Upon receiving a newly crawled document, a set of previously crawled documents, if any, sharing the same content as the newly crawled document is identified. Information identifying the newly crawled document and the selected set of documents is merged into information identifying a new set of documents. Duplicate documents are included and excluded from the new set of documents based on a query-independent metric for each such document. A single representative document for the new set of documents is identified in accordance with a set of predefined conditions. In some embodiments, a method for selecting a representative document from a set of duplicate documents includes: selecting a first document in a plurality of documents on the basis that the first document is associated with a query independent score, where each respective document in the plurality of documents has a fingerprint that identifies the content of the respective document, the fingerprint of each respective document in the plurality of documents indicating that each respective document in the plurality of documents has substantially identical content to every other document in the plurality of documents, and a first document in the plurality of documents is associated with the query-independent score. The method further includes indexing, in accordance with the query independent score, the first document thereby producing an indexed first document; and with respect to the plurality of documents, including only the indexed first document in a document index. This other patent is: Representative document selection for a set of duplicate documents Abstract
Regardless of whether the primary version of a set of duplicate documents is treated as the representative document as suggested in this second patent (whatever that may mean exactly), I think itâs important to get a better understanding of what a primary version of a document might be. The primary version patent provides some reasons why one of them might be considered a primary version: (1) Including of different versions of the same document does not provide additional useful information, and it does not benefit users. Those are the three reasons this duplicate document patent says it is ideal to identify a primary version from different versions of a document that appears on the Web. The search engine also wants to furnish âthe most appropriate and reliable search result.â How does it work?The patent tells us that one method of identifying a primary version is as follows. The different versions of a document are identified from a number of different sources, such as online databases, websites, and library data systems. For each document version, a priority of authority is selected based on: (1) The metadata information associated with the document version, such as
(2) As a second step, the document versions are then determined for length qualification using a length measure. The version with a high priority of authority and a qualified length is deemed the primary version of the document. If none of the document versions has both a high priority and a qualified length, then the primary version is selected based on the totality of information associated with each document version. The patent tells us that scholarly works tend to work under the process in this patent:
Meta data that might be looked at during this process could include such things as:
The patent goes into more depth about the methodology behind determining the primary version of a document:
The patent includes a table illustrating the source-priority list. The patent includes some alternative approaches as well. It tells us that âthe priority measure for determining whether a document version has a qualified priority can be based on a qualified priority value.â
Take awaysI was in a Google Hangout on air within the last couple of years where I and a number of other SEOs (Ammon Johns, Eric Enge, Jennifer Slegg, and I) asked some questions to John Mueller and Andrey Lipattse, and we asked some questions about duplicate content. It seems to be something that still raises questions among SEOs. The patent goes into more detail regarding determining which duplicate document might be the primary document. We canât tell whether that primary document might be treated as if it is at the canonical URL for all of the duplicate documents as suggested in the Dejan SEO article that I started with a link to in this post, but it is interesting seeing that Google has a way of deciding which version of a document might be the primary version. I didnât go into much depth about quantified lengths being used to help identify the primary document, but the patent does spend some time going over that. Is this a little-known ranking factor? The Google patent on identifying a primary version of duplicate documents does seem to find some importance in identifying what it believes to be the most important version among many duplicate documents. Iâm not sure if there is anything here that most site owners can use to help them have their pages rank higher in search results, but itâs good seeing that Google may have explored this topic in more depth. Copyright © 2018 SEO by the Sea â. This Feed is for personal non-commercial use only. If you are not reading this material in your news aggregator, the site you are looking at may be guilty of copyright infringement. Please contact SEO by the Sea, so we can take appropriate action immediately. Plugin by Taragana The post How Google might Identify Primary Versions of Duplicate Pages appeared first on SEO by the Sea â. from http://feedproxy.google.com/~r/seobythesea/Tesr/~3/6vBWs5EtsmQ/
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This past March, Google was granted a patent that involves giving quality scores to queries (the quote above is from that patent). The patent refers to high scoring queries as augmentation queries. Interesting to see that searcher selection is one way that might be used to determine the quality of queries. So, when someone searches. Google may compare the SERPs they receive from the original query to augmentation query results based upon previous searches using the same query terms or synthetic queries. This evaluation against augmentation queries is based upon which search results have received more clicks in the past. Google may decide to add results from an augmentation query to the results for the query searched for to improve the overall search results. How does Google find augmentation queries? One place to look for those is in query logs and click logs. As the patent tells us:
This doesn’t mean that Google is using clicks to directly determine rankings But it is deciding which augmentation queries might be worth using to provide SERPs that people may be satisfied with. There are other things that Google may look at to decide which augmentation queries to use in a set of search results. The patent points out some other factors that may be helpful:
I’ve seen white papers from Google before mentioning synthetic queries, which are queries performed by the search engine instead of human searchers. It makes sense for Google to be exploring query spaces in a manner like this, to see what results are like, and using information such as structured data as a source of those synthetic queries. I’ve written about synthetic queries before at least a couple of times, and in the post Does Google Search Google? How Google May Create and Use Synthetic Queries. Implicit Signals of Query QualityIt is an interesting patent in that it talks about things such as long clicks and short clicks, and ranking web pages on the basis of such things. The patent refers to such things as “implicit Signals of query quality.” More about that in the patent here:
The reasons for the process behind the patent are explained in the description section of the patent where we are told:
A quality signal for a query term can be defined in this way:
The patent can be found at: Query augmentation Abstract
References Cited about Augmentation QueriesThese were a number of references cited by the applicants of the patent, which looked interesting, so I looked them up to see if I could find them to read them and share them here.
This is a Second Look at Augmentation QueriesThis is a continuation patent, which means that it was granted before, with the same description, and it now has new claims. When that happens, it can be worth looking at the old claims and the new claims to see how they have changed. I like that the new version seems to focus more strongly upon structured data. It tells us that it might use structured data in sites that appear for queries as synthetic queries, and if those meet the performance threshold, they may be added to the search results that appear for the original queries. The claims do seem to focus a little more on structured data as synthetic queries, but it doesn’t really change the claims that much. They haven’t changed enough to publish them side by side and compare them. What Google Has Said about Structured Data and RankingsGoogle spokespeople had been telling us that Structured Data doesn’t impact rankings directly, but what they have been saying does seem to have changed somewhat recently. In the Search Engine Roundtable post, Google: Structured Data Doesn’t Give You A Ranking Boost But Can Help Rankings we are told that just having structured data on a site doesn’t automatically boost the rankings of a page, but if the structured data for a page is used as a synthetic query, and it meets the performance threshold as an augmentation query, it might be shown in rankings, thus helping in rankings (as this patent tells us.) Note that this isn’t new, and the continuation patent’s claims don’t appear to have changed that much so that structured data is still being used as synthetic queries, and is checked to see if they work as augmented queries. This does seem to be a really good reason to make sure you are using the appropriate structured data for your pages. Copyright © 2018 SEO by the Sea ⚓. This Feed is for personal non-commercial use only. If you are not reading this material in your news aggregator, the site you are looking at may be guilty of copyright infringement. Please contact SEO by the Sea, so we can take appropriate action immediately. Plugin by Taragana The post Quality Scores for Queries: Structured Data, Synthetic Queries and Augmentation Queries appeared first on SEO by the Sea ⚓. from http://feedproxy.google.com/~r/seobythesea/Tesr/~3/OSrYzTDXupk/ My last Post was Five Years of Google Ranking Signals, and I start that post by saying that there are other posts about ranking signals that have some issues. But, I don’t want to turn people away from looking at one recent post that did contain a lot of useful information. Cyrus Shepard recently published a post about Google Sucess Factors on Zyppy.com which I would recommend that you also check out. Cyrus did a video with Ross Hudgins on Seige Media where he talked about those Ranking signals with Cyrus, called Google Ranking Factors with Cyrus Shepard. I’m keeping this post short on purpose, to make the discussion about ranking the focus of this post, and the star. There is some really good information in the Video and in the post from Cyrus. Cyrus takes a different approach on writing about ranking signals from what I wrote, but it’s worth the time visiting and listening and watching.
And have fun learning to rank. Copyright © 2018 SEO by the Sea ⚓. This Feed is for personal non-commercial use only. If you are not reading this material in your news aggregator, the site you are looking at may be guilty of copyright infringement. Please contact SEO by the Sea, so we can take appropriate action immediately. Plugin by Taragana The post Learning to Rank appeared first on SEO by the Sea ⚓. from http://feedproxy.google.com/~r/seobythesea/Tesr/~3/HV2IJZkJH8s/ Organic Search1. Domain Age and Rate of Linking
Semantic Search31. Searches using Structured Data Local Search36. Travel Time for Local Results Voice SearchNews SearchGoogle Ranking SignalsThere are some other pages about Google Ranking Signals that donât consider up-to-date information or sometimes use questionable critical thinking to argue that some of the signals that they include are actually something that Google considers. Iâve been blogging about patents from Google, Yahoo, Microsoft, and Apple since 2005, and have been exploring what those might say are ranking signals for over a decade. Representatives from Google have stated that âJust because we have a patent on something, doesnât mean we are using it.â The first time I heard them say that was after Go Daddy started advertising domain registrations of up to 10 years, because one Google patent (Information Retrieval Based on Historical Data) said that they might look at length of domain registration as a ranking signal, based on the thought that a âspammer would likely only register a domain for a period of one year.â (but actually, many people register domains for one year, and have their registrations on auto-renewal, so a one year registration is not evidence that a person registering a domain for just one year is a spammer.). Iâve included some ranking signals that are a little older, but most of the things Iâve listed are from the past five years, often with blog posts Iâve written about them, and patents that go with them. This list is a compilation of blog posts that I have been working on for years, taking many hours of regular searching through patent filings, and reading blog posts from within the Search and SEO industries, and reading through many patents that I didnât write about, and many that I have. If you have questions about any of the signals Iâve listed, please ask about them in the comments. Some of the patents I have blogged about have not been implemented by Google yet, but could be. A company such as Google files a patent to protect the intellectual property behind their ideas, the work that their search engineers and testing teams put into those ideas. It is worth looking at, reading, and understanding many of these patents because they provide some insights into ideas that Google may have explored when developing ranking signals, and they may give you ideas of things that you may want to explore, and questions to keep in mind when you are working upon optimizing a site. Patents are made public to inspire people to innovate and invent and understand new ideas and inventions. Organic Search1. Domain Age and Rate of Linking Google does have a patent called Document scoring based on document inception date, in which they tell us that they will often use the date that they first crawl a site, or the first time they see a document referenced in another site, as the age of that site. The patent also tells us that Google may look at the links pointed to a site, and calculate what the average rate of links pointed to a site may be and use that information to rank a site, based upon that linking. 2. Use of Keywords Matt Cutts wrote a newsletter for librarians in which he explained how Google crawled the web, making an inverted index of the Web with terms found on Documents from the Web that it would match up with query terms when people performed searches. It shows us the importance of Keywords in queries and how Google finds words that contain those keywords as an important part of performing searches. A copy of that newsletter can be found here: https://www.analistaseo.es/wp-content/uploads/2014/09/How-Google-Index-Rank.pdf 3. Related Phrases Google Recently updated its first phrase-based indexing patent, which tells us in its claims that pages with more related phrases on them rank higher than pages with less related phrases on them. That patent is: Phrase-based searching in an information retrieval system. Related phrases are phrases that are complete phrases that may predict the topic a page it appears upon is about. Google might look at the queries that a page is optimized for, and look at the highest ranking pages for those query terms, and see which meaningful complete phrases frequently occur (or co-occur) on those high ranking pages. I wrote about the updating of this patent in the post Google Phrase-Based Indexing Updated. Google tells us about how they are indexing related phrases in an inverted index (like the term-based inverted index from #2) in the patent Index server architecture using tiered and sharded phrase posting lists 4. Keywords in Main Headings, Lists, and Titles I wrote the post Google Defines Semantic Closeness as a Ranking Signal after reading the patent, Document ranking based on semantic distance between terms in a document. The Abstract of this patent tells us that:
If a list in page has a heading on it, the items in that list are all considered to be equal distance away from the list. The words contained under a main heading on a page are all considered to be equal distance away from that main heading. All of the words on a page are considered to be equal distance away from the title to that page. So, a page that is titled âFordâ which has the word âmotorsâ on that page is considered to be relevant for the phrase âFord Motors.â Here is an example of how that semantic closeness works with a heading and a list: 5. Page Speed Google has announced repeatedly that they consider Page Speed to be a ranking signal, including in the Google Blog post: Using site speed in web search ranking, and also in a patent that I wrote about in the post, Googleâs Patent on Site Speed as a Ranking Signal. The patent assigned to Google about Page Speed is Using resource load times in ranking search results. The patent tells us that this load time signal may be based upon measures of how long it takes a page to load on a range of devices:
6. Watch Times for a page While it may appear to be based upon videos, there is a Google Patent that tells us that it may rank pages higher if they are watched for longer periods of time than other pages. The post I wrote about this patent on is: Google Watch Times Algorithm For Rankings?, and the patent it is about is, Watch time based ranking. A page may contain video or images or audio, and a watch time for those may make a difference too. Hereâs a screenshot from the patent showing some examples: 7. Context Terms on a Page I wrote the post Google Patents Context Vectors to Improve Search, about the patent User-context-based search engine. The patent tells us that it may look at words that have more than one meaning in knowledge bases (such as bank, which could mean a building money is stored in, or the ground on one side of a river, or what a plane does when it turns in the air.) The search engine may take terms from that knowledge base that show what meaning was intended and collect them at âContext Termsâ and it might look for those context terms when indexing pages those words are on, so that it indexes the correct meaning 8. Language Models Using Ngrams Google may give pages quality scores based upon language models created from those pages when it looks at the ngrams on the pages of a site. This is similar to the Google Book Ngram Viewer. I wrote about this in the post Using Ngram Phrase Models to Generate Site Quality Scores based upon the patent Predicting site quality The closer the quality score for a page is to a high-quality page from a training set, the higher the page may rank. 9. Gibberish Content This may sound a little like #8 above. Google may use ngrams to tell if the words on a page are gibberish, and reduce the ranking of a page. I wrote about this in a post titled, Google Scoring Gibberish Content to Demote Pages in Rankings?, about the patent Identifying gibberish content in resources. Here is an ngram analysis using a well-known phrase, with 5 words in it: The quick brown fox jumps Ngrams from a complete page might be collected like that, and from a collection of good pages and bad pages, to build language models (and Google has done that with a lot of books, as we see from the Google Ngram Viewer covering a very large collection of books.) It would be possible to tell which pages are gibberish from such a set of language models. This Gibberish content patent also mentions a keyword stuffing score that it would try to identify. 10. Authoritative Results If they do, the authoritative results may be merged into the original results. The way it describes authoritative results:
11. How Well Databases Answers Match Queries This patent doesnât seem to have been implemented yet. But it might, and is worth thinking about. I wrote the post How Google May Rank Websites Based Upon Their Databases Answering Queries, based upon the patent Resource identification from organic and structured content. It tells us that Google might look at searches on a site, and how a site might answer them, to see if they are similar to the queries that Google receives from searchers. If they are, it might rank results from those sites higher. The patent also shows us that it might include the database results from such sites within Google Search results. If you start seeing that happening, you will know that Google decided to implement this patent. Here is the screenshot from the patent: 12. Suspicious Activity to Increase Rankings Another time that Google publicly stated that âjust because we have a patent doesnât mean we use it, came shortly after I wrote about a patent in a post I called The Google Rank-Modifying Spammers Patent based upon the patent Ranking documents. It tells us about a transition rank that Google may assign to a site where they see activity that might be suspicious, such as keyword stuffing. Instead of improving the ranks of pages, they might decrease them, or rerank them randomly. The motivation behind it appears to be to have those people making changes to do more drastic things. The patent tells us:
13. Popularity Scores for Events Some patents provide a list of the âAdvantagesâ of following a process in the patent, as does this one: The following advantages are described by the patent in following the approach it describes. 1) Events in a given location can be ranked so that popular or interesting events can be easily identified. 14.The Amount of Weight from a Link is Based upon the Probability that someone might click upon it I came across an update to the reasonable surfer patent, which focused more upon anchor text used in links than the earlier version of the patent, and told us that the amount of weight (PageRank) that might pass through a link was based upon the likelihood that someone might click upon that link. The post is Googleâs Reasonable Surfer Patent Updated based upon this patent Ranking documents based on user behavior and/or feature data. Since this is a continuation patent, it is worth looking at the claims in the patent to see what they say it is about. They do mention how ranking is affected, including the impact of anchor text and words before and after a link.
15. Biometric Parameters while Viewing Results This patent was one that I wondered about whether or not Google would implement, and suspect that many people would be upset if they did. I wrote about it in Satisfaction a Future Ranking Signal in Google Search Results?, based upon Ranking Query Results Using Biometric Parameters. Google may watch through a smart phoneâs reverse camera to see the reaction of someone looking at results in response to a query, and if they appear to be unsatisfied with the results, those results may be demoted in future search results. 16. Click-Throughs Weâve been told by Google Spokespeople that click-throughs are too noisy to use as a ranking signal, and yet a patent came out which describes how they might be used in such a way. With some thresholds, like clicks not counting until after the first 100, or a certain amount of time passes. The post I wrote about it in was Google Patents Click-Through Feedback on Search Results to Improve Rankings, based upon Modifying search result ranking based on a temporal element of user feedback Rand Fishkin sent me a message saying that his experience has been that clicks were counting as ranking signals, but he was also seeing thresholds of around 500 clicks before clicks would make a difference. Itâs difficult to tell with some signals, especially when Google makes statements about them not being signals in use. And Rand responded about what I said in the post about thresholds as well: 17. Site Quality Scores If you search for âseobythesea named entitiesâ it is a signal that you have an expectation that you can find information about named entities on the site seobythesea.com. If you do a site operator search such as âsite:http://www.seobythesea.com named entitiesâ you again are showing that you expect to be able to find information about a particular topic on this site. These are considered queries that refer to a particular site. They are counted against queries that are considered to be associated with a particular site. So, if there are more referring queries than associated queries, the quality score for a site is higher. If there are less referring queries than associated queries, then the quality score is lower. The post I wrote about this was How Google May Calculate Site Quality Scores (from Navneet Panda) based upon the patent Site quality score. A lower site quality score can mean a lower rank, as the patent tells us:
18. Disambiguating People Like the patent about covering terms with more than one meaning by including context terms on their pages, when you write about people who may share a name with someone else, if they are also on sites such as Wikipedia, and disambiguated entries, make sure you include context terms on your page that makes it easier to tell which person you are write about. The post I covered this in was Google Shows Us Context is King When Indexing People, based upon the patent Name disambiguation using context terms 19. Effectiveness and Affinity If you search for something on a phone such as a song, and you have a music app on that phone that has that song upon it, Google may tell you what the song you are searching for is, and that you can access it on the app that you have loaded on your phone. Social network affinities seem to be related to this. If you ask a question that might involve someone whom you might be connected to on a social network, they might be pointed out to you. See Effectiveness and Affinity as Search Ranking Signals (Better Search Experiences) about Ranking search results. 20. Quotes Google seems to know who said what and has a patent on it. See Google Searching Quotes of Entities on the patent Systems and methods for searching quotes of entities using a database. 21. Category Duration Visits Could visits to specific Categories of a site have a positive effect on the rankings of those visited sites? We know that people from Google have said that use behavior signals like this tend to be noisy; but what are you to think when the patent I was writing about describes ways to reduce noise from such signals? The post is A Panda Patent on Website and Category Visit Durations, and it is about a patent co-authored by Navneet Panda titled Website duration performance based on category durations. 22.Repeat Clicks and Visit Durations I want to believe when Google Spokespeople say that Google doesnât use click data to rank pages, but I keep on seeing patents from Navneet Panda that Googleâs Panda Update was named after which describes user behavior that may have an impact. The post is Click a Panda: High Quality Search Results based on Repeat Clicks and Visit Duration, and the patent it is about is one called Ranking search results 23 Environmental Information Google can listen to a television playing, and respond to a question such as âWho is starring in this movie I am watching? I wrote about it in Google to Use Environmental Information in Queries, and the post is based upon the patent 24. Traffic Producing Links Google might attempt to estimate how much traffic links to a site might bring to that site. If it believes that the links arenât bringing much traffic, it may discount the value of those links. I wrote about this in the post Did the Groundhog Update Just Take Place at Google? 25. Freshness 26. Media Consumption History Google Media Consumption History Patent Filed 27. Geographic Coordinates A patent called Determining geographic locations for place names in a fact repository was updated in a continuation patent, which I wrote about in Google Changes How they Understand Place Names in a Knowledge Graph. The claims from the patent were updated to include many mentions of âGeographic Coordinatesâ which indicated that including Latitude and Longitude information in Schema for a site might not be a bad idea. Itâs impossible to say, based upon the patent that they use those signals in ordinary websites that arenât knowledge base sites like a Wikipedia or an IMDB or Yahoo Finance. But it seemed very reasonable to believe that if they were hoping to see information in that form in those places that it wouldnât hurt on Websites that were concerned about their locations as well (especially since knowledge bases seem to be the source of facts for many sites in places such as knowledge panels.) 28. Low Quality How Google May Classify Sites as Low Quality Sites 29. Television Watching Google Granted Patent on Using What You Watch on TV as a Ranking Signal http://www.seobythesea.com/wp-content/tv-as-ranking-signal.jpg 30. Quality Rankings Semantic Search31. Searches using Structured Data Google recently published a patent which showed how Structured data in the form of JSON-LD might be used on a page, and might cause Google to search for values of attributes of entities described in that structured data, such as what book was published by a certain author during a specific time period. The patent explained how Google could search through the structured data to find answers to a query like that. My post is Google Patent on Structured Data Focuses upon JSON-LD, and the patent it covers is . 32. Related Entities A search for an entity with a property or attribute that may not be the most noteworthy, but may be known may be findable in search results. In a post about this, I used and example query about âWhere was George Washington a Surveyor?â since he is most well know for having been President. The post is Related Entity Scores in Knowledge Based Searches, based on the patent Providing search results based on sorted properties. 33.Nearby Locations How Google May Interpret Queries Based on Locations and Entities (Tested) 34 Attributes of Entities 35. Natural Language Search Results Local Search36. Travel Time for Local Results How far someone may be will to travel to a place may be a reason why Google might increase the ranking of a business in local search results. I wrote about this in the post Ranking Local Businesses Based Upon Quality Measures including Travel Time based upon the patent Determining the quality of locations based on travel time investment. Would you drive an hour away for a slice of pizza? If so, it must be pretty good pizza. The abstract from the patent tells us this:
37. Reverse Engineering of Spam Detection in Local Results In the post How Google May Respond to Reverse Engineering of Spam Detection, I wrote about the patent Reverse engineering circumvention of spam detection algorithms. I remembered how Google responded when people brought up the Google Rank-Modifying Spammers Patent, that I wrote about in #13, telling people that just because they had a patent doesnât mean they necessarily use it. This patent is slightly different from the Rank modifying spammerâs patent, in that it only applies to local search, and it may keep a spamming sight from appearing at all, or appearing if continued activity keeps on setting off flags. As the patent abstract tells us:
38. Surprisingness in Business Names in Local Search Another patent that is about spam in local search is one I wrote about in the post Google Fights Keyword Stuffed Business Names Using a Surprisingnesss Value written about the patent Systems and methods of detecting keyword-stuffed business titles. This patent targets keyword stuffed business names that include prominent business names to try to confuse the search engine. Examples include such names as âLocksmith restaurant,â and âCourtyard 422 Y st Marriott.â 39. Local Expert Reviews Iâve been hearing people suggest that reviews can help a local search rank higher, and I have seen reviews considered equivalent to a mention in the Google patent on Location Prominence. But, Iâve now also seen a Google patent which tells us that a review from a local expert might also increase the rankings of a local entity in local results. My post was At Google Local Expert Reviews May Boost Local Search Results on the patent Identifying local experts for local search 40. Similar Local Entities When you search for a local coffeehouse, Google may decide that it wants to show you similar local businesses, and may include some other coffee houses or other similar results in what you see also. I wrote a post on this called How Google May Determine Similar Local Entities, from the patent Detection of related local entities. 41. Distance from Mobile Location History Google to Use Distance from Mobile Location History for Ranking in Local Search 4239. What People Search for at Locations Searched Search for a place that you might visit, and the query refinements that you might see may be based upon what people at that location you are considering visiting may have searched for. This doesnât affect the rankings of the results you see, but instead the query refinements that you are shown. See Local Query Suggestions Based Upon Where People Search based on Local query suggestions. 42. Semantic Geotokens Better Organic Search Results at Google Involving Geographic Location Queries Voice Search44. Stressed Words in spoken queries This may not be something you can optimize a page for, but it does show that Google is paying attention to voice search and where that might take us. In the post Google and Spoken Queries: Understanding Stressed Pronouns based upon the patent Resolving pronoun ambiguity in voice queries, we see that Google may be listening for our voices to emphasize certain words when we ask for something. Here is an example from the patent: A voice query asks: âWho was Alexander Graham Bellâs father?â News Search45. Originality in News Search Originality Replaces Geography as Ranking Signal in Google News Copyright © 2018 SEO by the Sea â. This Feed is for personal non-commercial use only. If you are not reading this material in your news aggregator, the site you are looking at may be guilty of copyright infringement. Please contact SEO by the Sea, so we can take appropriate action immediately. Plugin by Taragana The post Five Years of Google Ranking Signals appeared first on SEO by the Sea â. from http://feedproxy.google.com/~r/seobythesea/Tesr/~3/1zWt3NZI2rQ/
If you are reading this article, you already know the importance of checking Google keyword rankings and how keywords are performing on the most popular search engine. It is mandatory to find out which keyword is performing well on Google to create a strategy to improve your keyword rankings. Keyword tracking not only boosts your...
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