The document summarizes a presentation given by Bill Slawski at the Semantic Technology & Business Conference in San Jose. The presentation discussed how adding semantic information and structuring content around entities can help websites better optimize for search engines and provide more relevant experiences for users. It also provided several examples of how search engines are using entities and knowledge graphs to enhance search results and anticipate related queries.
Semantic Content Networks - Ranking Websites on Google with Semantic SEOKoray Tugberk GUBUR
Semantic Content Networks are the semantic networks of things with relations, directed graphs, attributes and facts. Every declaration, and proposition for semantic search represent a factual repository. Open Information Extraction is a methodology for creation of a semantic network. The Knowledge Base and Knowledge Graph are connected things to each other in terms of factual repository usage. The Knowledge Base represents a factual repository with descriptions and triples. Knowledge Graph is the visualized version of the Knowledge Base. A semantic network is knowledge representation. Semantic Network is prominent to understand the value of an individual node, or the similar and distant members of the same semantic network. Semantic networks are implemented for the search engine result pages. Semantic networks are to create a factual and connected question and answer networks. A semantic network can be represented and consist of from textual and visual content. Semantic Network include lexical parts and lexical units.
Links, Nodes, and Labels are parts of the semantic networks. Procedural Parts are constructors, destructors, writers and readers. Procedural parts are to expand the semantic networks and refresh the information on it.
Structural Part has links and nodes. Semantic part has the associated meanings which are represented as the labels.
The semantic content networks have different types of relations and relation types.
Semantic content networks have "and/OR" trees.
Semantic Content Networks have "Relation Type Examples" with "is/A" hierarchies.
Semantic Content Networks have "is/Part" Hierarchy.
Inheritance, reification, multiple inheritance, range queries and values, intersection search, complex semantic networks, inferential distance, partial ordering, semantic distance, and semantic relevance are concepts from semantic networks.
Semantic networks help understanding semantic search engines and the semantic SEO. Because, it contains all of the related lexical relations, semantic role labels, entity-attribute pairs, or triples like entity, predicate and object. Search engines prefer to use semantic networks to understand the factuality of a website. Knowledge-based Trust is related to the semantic networks because it provides a factuality related trust score to balance the PageRank. The knowledge-based Trust is announced by Luna DONG. Ramanathan V. Guha is another inventor from the Google and Schema.org. He focuses on the semantic web and semantic search engine behaviors. He explored and invented the semantic search engine related facts.
Semantic Content Networks are used as a concept by Koray Tuğberk GÜBÜR who is founder of Holistic SEO & Digital. Expressing semantic content networks helps to shape the semantic networks via textual and visual content pieces. The semantic content networks are helpful to shape the truth on the open web, and help a search engine to rank a website even if there is no external PageRank flow.
Lexical Semantics, Semantic Similarity and Relevance for SEOKoray Tugberk GUBUR
Lexical semantics and relations between words include relations of superiority, inferiority, part, whole, opposition, and sameness between the meanings of words. The same word can be a meronymy, hyponym, or antonym of another word, depending on the word before or after it. The lexical relation value of the first word can affect the structure of the next word, affecting the context of the sentence and the Information Retrieval Score. Information Retrieval Score is the score that determines how much content is related to a query, how close the different variants of the related query are, and the structure processed by the search engine’s query processor to the relevant document. A higher information retrieval score represents better relevance and possible click satisfaction.
The problem with a semi-structured and distracting context for Information Retrieval Score is that, if a document is not configured for a single topic, the IR Score can be diluted by the two different contexts resulting in a relative rank lost to another textual document.
IR Score Dilution involves badly structured lexical relations, along with bad word proximity. The relevant words that complete each other within the meaning map should be used closely, within a paragraph or section of the document, to signal the context in a more clear way to increase the IR Score. A search engine can check whether the document contains the hyponym of the words within the query or not. A possible query prediction can be generated from the hypernyms of the query. A search engine can check only the anchor texts to see whether there is a word within the “hyponym distance” which represents the hyponym depth between two different words.
Lexical Relations can represent the semantic annotations for a document. A semantic annotation is a word that describes the document overall in terms of category and main context that carries the purpose of the document. A semantic annotation can contain the main entity of the document or a general concept for covering a broader meaning area (knowledge domain). Semantic Annotations can be generated with the lexical relations between words. A semantic annotation can be used to match the document to the query. Semantic annotations are factors for a better IR Score.
A search engine can generate phrase patterns from the lexical relationships between words within the queries or the documents. A phrase pattern contains sections that define a concept with qualifiers. Phrase patterns can contain a hyponym just after an adjective, or a hypernym with the antonym of the same adjective. Most of these connections and patterns are used within the Recurrent Neural Network (RNN) for the next word prediction. A phrase pattern helps a search engine to increase its confidence score for relating the document to the specific query, or the meaning of the query.
Semantic Search Engine: Semantic Search and Query Parsing with Phrases and En...Koray Tugberk GUBUR
Semantic Search Engines can understand human language to analyze the need behind a query. Instead of focusing, string, or word matching, a semantic search engine focuses on concepts, intents, and relations of named entities. Taxonomy, ontology, onomastics, semantic role labeling, relation detection, lexical semantics, entity extraction, recognition, resolution can be used by semantic search engines. In this PDF file, semantic search engines' evolution will be processed based on Google Search Engine's research papers, patents, and official announcements. From 1998 to 20021, search's and search engines' evolution, from strings to things, from phrases to entities will be told along with query processing, and parsing methodology changes.
As opposed to lexical search, semantic searching searches for meaning, not meaningless matches of the query words. Semantic search attempts to increase the relevancy of results by understanding searchers' intents and the context of terms in the searchable dataspace, whether online or within a closed system. The right semantic search content is a blend of natural language, focuses on the intent of the user, and considers other topics the user may be interested in.
Ontologies, XML, and other structured data sources can be used to retrieve knowledge using semantic search according to some authors. The use of such technologies provides a mechanism for creating formal expressions of domain knowledge that are highly expressive and may allow the user to express more detailed intent during query processing.
Search Query Processing: The Secret Life of Queries, Parsing, Rewriting & SEOKoray Tugberk GUBUR
Query Processing is the process of query term weight calculation, query augmentation, query context defining, and more. Query understanding and Query clustering are related to Information Retrieval tasks for the search engines. To provide a better search engine optimization effort and project result, the organic search performance optimizers need to implement query processing methodologies. Digital marketing and SEO are connected to each other. Understanding a query includes query parsing, query rewriting, question generation, and answer pairing. Multi-stages Query Processing, Candidate Answer Passages, or Candidate Answer Passages and Answer Term Weighting are some of the concepts from the Google Search Engine to parse the queries.
The presentation of The Secret Life of Queries, Parsing, Rewriting & SEO has been presented at the Brighton SEO Event in April 2022. The event speech focused on explaining the theoretical SEO and practical SEO examples together.
Query Processing methodologies are beyond synonym matching or synonym finding. It involves multiple aspects of the words, and meanings of the words. The theme of words, the centrality of words, attention windows, context windows, and word co-occurrence matrices, GloVe, Word2Vec, word embeddings, character embeddings, and more.
Themes of words contain the word probability like in Continues Bag of Window.
The search engine optimization community focuses on keyword research by matching the queries. Query processing involves query word order change, query word type change, query word combination change, query phrase synonym usage, query question generation, query clustering. Query processing and document processing are correlational. Query processing is to understand a query while document processing is to process a web document. Both of the processes are for ranking algorithms. Providing a better ranking algorithm requires a better query understanding. And providing better rankings as SEOs require better search engine understanding. Thus, understanding the methods of query processing is necessary.
Search Query Processing is implementing the query processing for thesearch engines. Search query refers to the phrase that search engine users use for searching. Search intent understanding and search intent grouping are two different things. But, query templates, questions templates, and document templates work together. Search query is for organic search behaviors. A web search engine answers millions of queries every day. Search query processing is a fundamental task for search engine optimization and search engine result page optimization.
The "Semantic Search Engine: Query Processing" slides from Koray Tuğberk GÜBÜR supported the presentation of "Search Query Processing: The Secret Life of Queries, Parsing, Rewriting & SEO". The presentation has been created by Dear Rebecca Berbel.
Many thanks to the Google engineers that created the Semantic Search Engine patents including Larry Page.
Semantic Content Networks - Ranking Websites on Google with Semantic SEOKoray Tugberk GUBUR
Semantic Content Networks are the semantic networks of things with relations, directed graphs, attributes and facts. Every declaration, and proposition for semantic search represent a factual repository. Open Information Extraction is a methodology for creation of a semantic network. The Knowledge Base and Knowledge Graph are connected things to each other in terms of factual repository usage. The Knowledge Base represents a factual repository with descriptions and triples. Knowledge Graph is the visualized version of the Knowledge Base. A semantic network is knowledge representation. Semantic Network is prominent to understand the value of an individual node, or the similar and distant members of the same semantic network. Semantic networks are implemented for the search engine result pages. Semantic networks are to create a factual and connected question and answer networks. A semantic network can be represented and consist of from textual and visual content. Semantic Network include lexical parts and lexical units.
Links, Nodes, and Labels are parts of the semantic networks. Procedural Parts are constructors, destructors, writers and readers. Procedural parts are to expand the semantic networks and refresh the information on it.
Structural Part has links and nodes. Semantic part has the associated meanings which are represented as the labels.
The semantic content networks have different types of relations and relation types.
Semantic content networks have "and/OR" trees.
Semantic Content Networks have "Relation Type Examples" with "is/A" hierarchies.
Semantic Content Networks have "is/Part" Hierarchy.
Inheritance, reification, multiple inheritance, range queries and values, intersection search, complex semantic networks, inferential distance, partial ordering, semantic distance, and semantic relevance are concepts from semantic networks.
Semantic networks help understanding semantic search engines and the semantic SEO. Because, it contains all of the related lexical relations, semantic role labels, entity-attribute pairs, or triples like entity, predicate and object. Search engines prefer to use semantic networks to understand the factuality of a website. Knowledge-based Trust is related to the semantic networks because it provides a factuality related trust score to balance the PageRank. The knowledge-based Trust is announced by Luna DONG. Ramanathan V. Guha is another inventor from the Google and Schema.org. He focuses on the semantic web and semantic search engine behaviors. He explored and invented the semantic search engine related facts.
Semantic Content Networks are used as a concept by Koray Tuğberk GÜBÜR who is founder of Holistic SEO & Digital. Expressing semantic content networks helps to shape the semantic networks via textual and visual content pieces. The semantic content networks are helpful to shape the truth on the open web, and help a search engine to rank a website even if there is no external PageRank flow.
Lexical Semantics, Semantic Similarity and Relevance for SEOKoray Tugberk GUBUR
Lexical semantics and relations between words include relations of superiority, inferiority, part, whole, opposition, and sameness between the meanings of words. The same word can be a meronymy, hyponym, or antonym of another word, depending on the word before or after it. The lexical relation value of the first word can affect the structure of the next word, affecting the context of the sentence and the Information Retrieval Score. Information Retrieval Score is the score that determines how much content is related to a query, how close the different variants of the related query are, and the structure processed by the search engine’s query processor to the relevant document. A higher information retrieval score represents better relevance and possible click satisfaction.
The problem with a semi-structured and distracting context for Information Retrieval Score is that, if a document is not configured for a single topic, the IR Score can be diluted by the two different contexts resulting in a relative rank lost to another textual document.
IR Score Dilution involves badly structured lexical relations, along with bad word proximity. The relevant words that complete each other within the meaning map should be used closely, within a paragraph or section of the document, to signal the context in a more clear way to increase the IR Score. A search engine can check whether the document contains the hyponym of the words within the query or not. A possible query prediction can be generated from the hypernyms of the query. A search engine can check only the anchor texts to see whether there is a word within the “hyponym distance” which represents the hyponym depth between two different words.
Lexical Relations can represent the semantic annotations for a document. A semantic annotation is a word that describes the document overall in terms of category and main context that carries the purpose of the document. A semantic annotation can contain the main entity of the document or a general concept for covering a broader meaning area (knowledge domain). Semantic Annotations can be generated with the lexical relations between words. A semantic annotation can be used to match the document to the query. Semantic annotations are factors for a better IR Score.
A search engine can generate phrase patterns from the lexical relationships between words within the queries or the documents. A phrase pattern contains sections that define a concept with qualifiers. Phrase patterns can contain a hyponym just after an adjective, or a hypernym with the antonym of the same adjective. Most of these connections and patterns are used within the Recurrent Neural Network (RNN) for the next word prediction. A phrase pattern helps a search engine to increase its confidence score for relating the document to the specific query, or the meaning of the query.
Semantic Search Engine: Semantic Search and Query Parsing with Phrases and En...Koray Tugberk GUBUR
Semantic Search Engines can understand human language to analyze the need behind a query. Instead of focusing, string, or word matching, a semantic search engine focuses on concepts, intents, and relations of named entities. Taxonomy, ontology, onomastics, semantic role labeling, relation detection, lexical semantics, entity extraction, recognition, resolution can be used by semantic search engines. In this PDF file, semantic search engines' evolution will be processed based on Google Search Engine's research papers, patents, and official announcements. From 1998 to 20021, search's and search engines' evolution, from strings to things, from phrases to entities will be told along with query processing, and parsing methodology changes.
As opposed to lexical search, semantic searching searches for meaning, not meaningless matches of the query words. Semantic search attempts to increase the relevancy of results by understanding searchers' intents and the context of terms in the searchable dataspace, whether online or within a closed system. The right semantic search content is a blend of natural language, focuses on the intent of the user, and considers other topics the user may be interested in.
Ontologies, XML, and other structured data sources can be used to retrieve knowledge using semantic search according to some authors. The use of such technologies provides a mechanism for creating formal expressions of domain knowledge that are highly expressive and may allow the user to express more detailed intent during query processing.
Search Query Processing: The Secret Life of Queries, Parsing, Rewriting & SEOKoray Tugberk GUBUR
Query Processing is the process of query term weight calculation, query augmentation, query context defining, and more. Query understanding and Query clustering are related to Information Retrieval tasks for the search engines. To provide a better search engine optimization effort and project result, the organic search performance optimizers need to implement query processing methodologies. Digital marketing and SEO are connected to each other. Understanding a query includes query parsing, query rewriting, question generation, and answer pairing. Multi-stages Query Processing, Candidate Answer Passages, or Candidate Answer Passages and Answer Term Weighting are some of the concepts from the Google Search Engine to parse the queries.
The presentation of The Secret Life of Queries, Parsing, Rewriting & SEO has been presented at the Brighton SEO Event in April 2022. The event speech focused on explaining the theoretical SEO and practical SEO examples together.
Query Processing methodologies are beyond synonym matching or synonym finding. It involves multiple aspects of the words, and meanings of the words. The theme of words, the centrality of words, attention windows, context windows, and word co-occurrence matrices, GloVe, Word2Vec, word embeddings, character embeddings, and more.
Themes of words contain the word probability like in Continues Bag of Window.
The search engine optimization community focuses on keyword research by matching the queries. Query processing involves query word order change, query word type change, query word combination change, query phrase synonym usage, query question generation, query clustering. Query processing and document processing are correlational. Query processing is to understand a query while document processing is to process a web document. Both of the processes are for ranking algorithms. Providing a better ranking algorithm requires a better query understanding. And providing better rankings as SEOs require better search engine understanding. Thus, understanding the methods of query processing is necessary.
Search Query Processing is implementing the query processing for thesearch engines. Search query refers to the phrase that search engine users use for searching. Search intent understanding and search intent grouping are two different things. But, query templates, questions templates, and document templates work together. Search query is for organic search behaviors. A web search engine answers millions of queries every day. Search query processing is a fundamental task for search engine optimization and search engine result page optimization.
The "Semantic Search Engine: Query Processing" slides from Koray Tuğberk GÜBÜR supported the presentation of "Search Query Processing: The Secret Life of Queries, Parsing, Rewriting & SEO". The presentation has been created by Dear Rebecca Berbel.
Many thanks to the Google engineers that created the Semantic Search Engine patents including Larry Page.
SEO Case Study - Hangikredi.com From 12 March to 24 September Core UpdateKoray Tugberk GUBUR
Start Summary:
"131% Organic Session Increase in 5 Months
62% Impression Increase in 5 Months
144% Clicks Increase in 5 Months"
This SEO Case study is about Google Core Updates and their impacts on biggest financial institution website in Turkey.
I have started to work in Hangikredi.com at 26 March 2019. But, the company's website had been affected by 12 March Google Core Update very negatively.
I had started to work in here while a crisis had been happening.
I had examined the web site and figured it out that the real problems were crawl budget, authority signasl and relevancy-entity connection. I have activated social media, Google My Bussiness accounts, I have entered financial forums, every other alternative channel. I created a news publisher network about us. I cleaned the misleading status codes, HTML and CSS mistakes, optimised meta tags, fixed the redirection chains, I used the image compressions and deleted lots of unnecessary URL and their contents, I created the internal link structure from scratch.
Until 5 June Google Core Update, we were winners again.
We had regained all of our traffic lost. Until 1 August server atack, we were okay, then in one day, everything went wrong.
I had started from 0 again...
I had been optimising web site's offpage signals for regain the trust of Google AI and I had been supporting this strategy with onpage elements.
After 24th September Google Core Update, there was another success. We breaked the crawl load/rate record, avarage site position, CTR and impression, click records for site history.
In this CASE Study, you are gonna find details of a SEO Success Story with graphics and also some funny cencor images from my life.
End Summary:
"12 March, 5 june and 24 September Google Core Updates with 1 August Server Atack are the milestones of this SEO Casse Study. You will find all details from our view of point. I hope you will like it."
The Reason Behind Semantic SEO: Why does Google Avoid the Word PageRank?Koray Tugberk GUBUR
This article delves into the concepts of Semantic SEO, Topical Authority, and PageRank, exploring their relationships and how they benefit both website owners and search engines. By leveraging Natural Language Processing (NLP) techniques, Semantic SEO improves search engine comprehension of content and enhances user experience, ultimately leading to better search results.
In the ever-evolving world of Search Engine Optimization (SEO), understanding the intricate connections between Semantic SEO, Topical Authority, and PageRank is crucial for webmasters, content creators, and marketers. These concepts play a vital role in enhancing the visibility and relevance of websites in search results.
Semantic SEO: Going Beyond Keywords
Semantic SEO involves optimizing content by focusing on the meaning and context of words, phrases, and sentences rather than merely targeting specific keywords. This is achieved through NLP techniques such as topic modeling, sentiment analysis, and entity recognition, which allow search engines to comprehend the true essence of content.
Topical Authority: Establishing Expertise and Trustworthiness
Topical Authority refers to the perceived expertise of a website or content creator in a specific subject area. By producing high-quality, relevant, and in-depth content, websites can establish themselves as authorities, earning the trust of both users and search engines. This translates into higher search rankings and increased visibility.
PageRank: Measuring the Importance of Webpages
PageRank is an algorithm used by Google to determine the significance of a webpage by analyzing the quality and quantity of its inbound links. A higher PageRank implies that a website is more authoritative and valuable, thus warranting a better position in search results.
The Interrelation of Semantic SEO, Topical Authority, and PageRank
Semantic SEO, Topical Authority, and PageRank are interconnected concepts that work in tandem to improve a website's search performance. By focusing on Semantic SEO, content creators can enhance their Topical Authority and establish a solid online presence. This, in turn, can lead to higher PageRank and improved search visibility.
The Benefits of Semantic SEO for Search Engines
Semantic SEO not only benefits website owners but also search engines by reducing the cost of understanding documents. With the help of NLP techniques, search engines can efficiently analyze and comprehend content, making it easier to identify and index relevant webpages. This ultimately leads to more accurate search results and a better user experience.
In conclusion, embracing Semantic SEO, Topical Authority, and PageRank is essential for achieving higher search rankings and increased online visibility. By leveraging NLP techniques, Semantic SEO offers a more sophisticated and efficient approach to understanding and optimizing content, ultimately benefiting both website owners and search engines.
40 Deep #SEO Insights for 2023:
-In 2022, I told to focus on Natural Language Generation, and it happened.
-In 2023, F-O-C-U-S on "Information Density, Richness, and Unique Added Value" with Microsemantics.
I call the collection of these, "Information Responsiveness".
1/40 🧵.
1. PageRank Increases its Prominence for Weighting Sources
Reason: #AI and automation will bloat the web, and the real authority signals will come from PageRank, and Exogenous Factors.
The expert-like AI content and real expertise are differentiated with historical consistency.
2. Indexing and relevance thresholds will increase.
Reason: A bloated web creates the need for unique value to be added to the web with real-world expertise and organizational signals. The knowledge domain terms, or #PageRank, will be important in the future of a web source.
3. AI and #automation filters will be created.
Reason: Google needs to filter the websites that publish 500 articles a day on multiple topics to find non-expert websites. This is already happening.
4. #Google will start to make mistakes in filtering websites that use spam and AI.
Reason: The need for AI-generated content filtration forced Google to check and audit "momentum", in other words, content publication frequency.
I used the "momentum" first in TA Case Study.
5. Google uses #Author Vectors, and Author Recognition.
Reason: LLMs use certain types of language styles and word sequences by leaving a watermark behind them. It is easy to understand which websites do not use a real expert for their articles, and content to differentiate.
6. #Microsemantics will be the name of the next game.
Reason: The bloating on the web will create bigger web document clusters, and being a representative source will be more important.
Thus, micro-differences inside the content will create higher unique value.
7. Custom #LLMs will be rented.
Reason: Custom and unique LLMs will be trained and rented to the people who try to create 100 websites with 100,000 content items per website.
NLP in SEO will show its true monetary value in mid-2023.
8. Advanced Semantic SEO will be a must for every SEO.
Reason: 20 years of websites will lose their rankings to the new websites that come with 60,000 articles. This creates the need for advanced #Semantics and Lingusitics capabilities for SEOs.
9. Cost-of-retrieval will be a base concept for #SEO, as TA.
Reason: TA explains a big portion of how the web works. Information Responsiveness and Cost-of-retrieval will complete it further.
For two books, I will be publishing only these two concepts.
10. Google Keys
Reason: The biggest Google leak after Quality Rater Guidelines will happen in 2023. And, I will be involved, but no more information, for now, I am not allowed to share more.
Check the slides for the next SEO Insights for 2023.
#searchengineoptimization #future #nlp #semantic #chatgpt #ai #content #quality #publishing #trend #seotrend #seo #searchengineoptimisation
Slawski New Approaches for Structured Data:Evolution of Question Answering Bill Slawski
Google has moved from Search to Knowledge, and Focusing on Answering questions with knowledge graph entity information provides has led to answering queries with Knowledge graphs for those questions, with confidence scores between entities and other entities or attributes of entities, based upon freshness, reliabilillity, popularity, and proximity between an entity and another entity or an attribute.
Coronavirus and Future of SEO: Digital Marketing and Remote CultureKoray Tugberk GUBUR
I have attended a great SEO and Digital Marketing webinar with Founder of Stradiji and SEMRush Turkey Lead Mr. Mert Erkal and My Dearest Friend and SEO Consultant Atakan Erdoğan.
Small Note: After I uploaded the presentation, Google launched a new Covid-19 news address like Bing/covid-19. You may want to look at it -> https://www.google.com/covid-19
I have prepared a Presentation about Coronavirus's Effects on Search Engine Optimization (SEO).
You will find Coronavirus's changing effects on Digital Marketing and psychology of global society while using Search Engines.
I also have focused on Search Engine's and Social Media Brands, E-commerce Site's reflexes against Coronavirus Pandemic.
You will see the web sites and categories who earn more traffic and lose traffic. You will also see conversion rate differences because of Coronavirus.
Also, I have told about Search Engine's differences and their attitude against the Coronavirus Pandemic, their future, their updates during the pandemic.
In the last part, you will see some new 2020 Web Technology and Design Trends with AI.
There are also Google Researches for better Search Engine technologies.
Questions:
1- What are the differences between Yandex, Google, Bing, and Duckduckgo for Coronavirus Pandemic?
2- Twitter, Instagram, Amazon or Apple, what are they doing?
3- What do people search most for during the Coronavirus Crisis?
4- What changes from country to country?
5- What are the future technologies of Web and App?
6- How and why do Search Engines improve AI, what is the last events?
7- Which sites loose traffic and which earn more?
8- Lots of quotes from International SEOs about the pandemic.
And more...
I am Koray Tuğberk GÜBÜR and a Holistic SEO Expert.
I sincerely thank you for my Dearest Friend Atakan Erdoğan and Mr. Mert Erkal for this awesome webinar opportunity and experience.
To watch the webinar, please visit Stradiji's Official Youtube Channel.
https://www.youtube.com/watch?v=V4sJTNcRqaM&t=100s
Simple guidelines for your online presence and marketing, utilizing basics of digital marketing, as well as the main ideas behind antifragility and asymmetry .
The main online marketing strategy discussed is establishing yourself (company) as an authority/expert in your field. You do this very well and consistently such that people want to work with you (customers, investors, collaborators, etc.).
The main tool for establishing your presence is creating useful content that demonstrates your expertise. The main focus is on timeless topics to have time work with you, not against you.
Good luck!
How to approach SEO in a world where Google has moved from strings and keywords to things, topics and entities. Dixon JOnes is the CEO of InLinks, who have build a proprietory NLP algorithm and Knowledge Graph designed for the SEO Industry.
Opinion-based Article Ranking for Information Retrieval Systems: Factoids and...Koray Tugberk GUBUR
How Search Engines Leverage Opinion-based Articles for Ranking?
Search engines use opinions, and factoids to understand the consensus. News search engines use different reports, and opinions in their search results to satisfy the urgent news information needed by the newsreaders. The news search engines differentiate disinformation from information to protect the newsreaders. Google, Microsoft Bing, Yandex, and DuckDuckGo have different algorithms and prioritization for classifications of the news sources, or prioritization of the news, and newsworthy topics.
Corroboration of the Web Answers from the Open Web is a research paper from Amelia Marian and Minji Wu explaining how a search engine can rank information according to its accuracy.
Google started to explain that the Expertise-Authoriteveness-Trustworthiness is the most important group of signals to be sure that a result won't shame the search engine. Embarrassment factors for the search engines involve wrong information on a news title on the news story, or a wrong featured snippet. A search engine might be shame due to the bad result that is ranking on the SERP.
Dense-retrieval, context scoring, named entity recognition, semantic role labeling, truth ranges, fix points, confidence score, query processing, and parsing.
Context understanding requires processing the text, and tokenizing the words by recognizing the word sense. Processing the text of the news articles requires time. And, most of the time, news search engines do not have enough time for processing the text. Thus, PageRank provides a sustainable timeline for the news sources for rankings.
PageRank is a quick signal for search engines to show the authenticity of the news web source. The highly cited sources are ranked higher, and longer on the top stories. Usually, Google protects the high PageRank sources by trusting the judgment of the websites. But, fact-finding algorithms do not use PageRank mostly, unless they couldn't decide by looking at other factors, or they do not have enough resources to process the text among the hundreds of sources.
News ranking algorithms differentiate opinions, reports, and breaking news from each other. News-related entities, their co-existence, and contextual relations change. Google inventors suggest differentiation of these entities from each other for a proper news categorization.
News categorization is important to match the interested topics of the users in queryless news feeds such as Google Discover. Google Discover is a queryless news feed that serves news stories according to the users' interest areas.
An opinion for news might be misleading. Some news titles might be too harsh, or strict. Search engines use these headlines to differentiate the non-trustworthy news sources from the trustworthy ones. And, opinions of journalists or their different interpretations of the events might change the rankings of a document according to the fact-finding algorithms.
The Python Cheat Sheet for the Busy MarketerHamlet Batista
What percentage of an Inbound marketer's day doesn't involve working with spreadsheets? How much of this work is time-consuming and repetitive? In this interactive session, you will learn how to manipulate Google Sheets to automate common data analysis workflows using Python, a very easy to use programming language.
Google Lighthouse is super valuable but it only checks one page at a time.
Hamlet will show you how to get it to check all pages of a site, and how to run automated Lighthouse checks on-demand at scheduled intervals and from automated tests.
He'll also cover how to set performance budgets, how to get alerts when budgets are exceeded, and how to aggregate page reports using BigQuery and Google Data Studio.
SEO in the Age of Artificial Intelligence | How AI influences SearchPhilipp Klöckner
SEO hast changes over the past decade. Understand how classical ranking factors become less important, while user experience dominates the top rankings.
As seen live on stage at @ProjectAcom #PakCon2018 in Berlin.
A pipeline of reading, parsing, optimizing, and storing a log file to parquet.
This script uses the Python pandas library, utilizing the efficient Apache Parquet format for a big speed up and efficient storage.
Thank U (Rel) Next - State of Retail Pagination 1Y Later - Orit Mutznik - Bri...Orit Mutznik
BrightonSEO April 2019 - I attended Adam Gent's presentation, in which he presented pagination best practices given Google's recent announcement of rel next/prev deprecation. After not being sure about what to do with our pagination, this talk came right on time, and drove us to act based on those best practices, as I believed (and still believe) that pagination is critical to get right by retailers & ecommerce businesses. By the time BrightonSEO 2020 arrives, it will be over a year since the announcement, and I'd like to review the UK retail ecommerce sector, to see how many did something about it, best practices, bad practices, ugly practices, and give some tips based on what we did at SilkFred including how that worked out for us.
Download the full write up with embedded slides here: https://bit.ly/ecom-pagination
SEO Case Study - Hangikredi.com From 12 March to 24 September Core UpdateKoray Tugberk GUBUR
Start Summary:
"131% Organic Session Increase in 5 Months
62% Impression Increase in 5 Months
144% Clicks Increase in 5 Months"
This SEO Case study is about Google Core Updates and their impacts on biggest financial institution website in Turkey.
I have started to work in Hangikredi.com at 26 March 2019. But, the company's website had been affected by 12 March Google Core Update very negatively.
I had started to work in here while a crisis had been happening.
I had examined the web site and figured it out that the real problems were crawl budget, authority signasl and relevancy-entity connection. I have activated social media, Google My Bussiness accounts, I have entered financial forums, every other alternative channel. I created a news publisher network about us. I cleaned the misleading status codes, HTML and CSS mistakes, optimised meta tags, fixed the redirection chains, I used the image compressions and deleted lots of unnecessary URL and their contents, I created the internal link structure from scratch.
Until 5 June Google Core Update, we were winners again.
We had regained all of our traffic lost. Until 1 August server atack, we were okay, then in one day, everything went wrong.
I had started from 0 again...
I had been optimising web site's offpage signals for regain the trust of Google AI and I had been supporting this strategy with onpage elements.
After 24th September Google Core Update, there was another success. We breaked the crawl load/rate record, avarage site position, CTR and impression, click records for site history.
In this CASE Study, you are gonna find details of a SEO Success Story with graphics and also some funny cencor images from my life.
End Summary:
"12 March, 5 june and 24 September Google Core Updates with 1 August Server Atack are the milestones of this SEO Casse Study. You will find all details from our view of point. I hope you will like it."
The Reason Behind Semantic SEO: Why does Google Avoid the Word PageRank?Koray Tugberk GUBUR
This article delves into the concepts of Semantic SEO, Topical Authority, and PageRank, exploring their relationships and how they benefit both website owners and search engines. By leveraging Natural Language Processing (NLP) techniques, Semantic SEO improves search engine comprehension of content and enhances user experience, ultimately leading to better search results.
In the ever-evolving world of Search Engine Optimization (SEO), understanding the intricate connections between Semantic SEO, Topical Authority, and PageRank is crucial for webmasters, content creators, and marketers. These concepts play a vital role in enhancing the visibility and relevance of websites in search results.
Semantic SEO: Going Beyond Keywords
Semantic SEO involves optimizing content by focusing on the meaning and context of words, phrases, and sentences rather than merely targeting specific keywords. This is achieved through NLP techniques such as topic modeling, sentiment analysis, and entity recognition, which allow search engines to comprehend the true essence of content.
Topical Authority: Establishing Expertise and Trustworthiness
Topical Authority refers to the perceived expertise of a website or content creator in a specific subject area. By producing high-quality, relevant, and in-depth content, websites can establish themselves as authorities, earning the trust of both users and search engines. This translates into higher search rankings and increased visibility.
PageRank: Measuring the Importance of Webpages
PageRank is an algorithm used by Google to determine the significance of a webpage by analyzing the quality and quantity of its inbound links. A higher PageRank implies that a website is more authoritative and valuable, thus warranting a better position in search results.
The Interrelation of Semantic SEO, Topical Authority, and PageRank
Semantic SEO, Topical Authority, and PageRank are interconnected concepts that work in tandem to improve a website's search performance. By focusing on Semantic SEO, content creators can enhance their Topical Authority and establish a solid online presence. This, in turn, can lead to higher PageRank and improved search visibility.
The Benefits of Semantic SEO for Search Engines
Semantic SEO not only benefits website owners but also search engines by reducing the cost of understanding documents. With the help of NLP techniques, search engines can efficiently analyze and comprehend content, making it easier to identify and index relevant webpages. This ultimately leads to more accurate search results and a better user experience.
In conclusion, embracing Semantic SEO, Topical Authority, and PageRank is essential for achieving higher search rankings and increased online visibility. By leveraging NLP techniques, Semantic SEO offers a more sophisticated and efficient approach to understanding and optimizing content, ultimately benefiting both website owners and search engines.
40 Deep #SEO Insights for 2023:
-In 2022, I told to focus on Natural Language Generation, and it happened.
-In 2023, F-O-C-U-S on "Information Density, Richness, and Unique Added Value" with Microsemantics.
I call the collection of these, "Information Responsiveness".
1/40 🧵.
1. PageRank Increases its Prominence for Weighting Sources
Reason: #AI and automation will bloat the web, and the real authority signals will come from PageRank, and Exogenous Factors.
The expert-like AI content and real expertise are differentiated with historical consistency.
2. Indexing and relevance thresholds will increase.
Reason: A bloated web creates the need for unique value to be added to the web with real-world expertise and organizational signals. The knowledge domain terms, or #PageRank, will be important in the future of a web source.
3. AI and #automation filters will be created.
Reason: Google needs to filter the websites that publish 500 articles a day on multiple topics to find non-expert websites. This is already happening.
4. #Google will start to make mistakes in filtering websites that use spam and AI.
Reason: The need for AI-generated content filtration forced Google to check and audit "momentum", in other words, content publication frequency.
I used the "momentum" first in TA Case Study.
5. Google uses #Author Vectors, and Author Recognition.
Reason: LLMs use certain types of language styles and word sequences by leaving a watermark behind them. It is easy to understand which websites do not use a real expert for their articles, and content to differentiate.
6. #Microsemantics will be the name of the next game.
Reason: The bloating on the web will create bigger web document clusters, and being a representative source will be more important.
Thus, micro-differences inside the content will create higher unique value.
7. Custom #LLMs will be rented.
Reason: Custom and unique LLMs will be trained and rented to the people who try to create 100 websites with 100,000 content items per website.
NLP in SEO will show its true monetary value in mid-2023.
8. Advanced Semantic SEO will be a must for every SEO.
Reason: 20 years of websites will lose their rankings to the new websites that come with 60,000 articles. This creates the need for advanced #Semantics and Lingusitics capabilities for SEOs.
9. Cost-of-retrieval will be a base concept for #SEO, as TA.
Reason: TA explains a big portion of how the web works. Information Responsiveness and Cost-of-retrieval will complete it further.
For two books, I will be publishing only these two concepts.
10. Google Keys
Reason: The biggest Google leak after Quality Rater Guidelines will happen in 2023. And, I will be involved, but no more information, for now, I am not allowed to share more.
Check the slides for the next SEO Insights for 2023.
#searchengineoptimization #future #nlp #semantic #chatgpt #ai #content #quality #publishing #trend #seotrend #seo #searchengineoptimisation
Slawski New Approaches for Structured Data:Evolution of Question Answering Bill Slawski
Google has moved from Search to Knowledge, and Focusing on Answering questions with knowledge graph entity information provides has led to answering queries with Knowledge graphs for those questions, with confidence scores between entities and other entities or attributes of entities, based upon freshness, reliabilillity, popularity, and proximity between an entity and another entity or an attribute.
Coronavirus and Future of SEO: Digital Marketing and Remote CultureKoray Tugberk GUBUR
I have attended a great SEO and Digital Marketing webinar with Founder of Stradiji and SEMRush Turkey Lead Mr. Mert Erkal and My Dearest Friend and SEO Consultant Atakan Erdoğan.
Small Note: After I uploaded the presentation, Google launched a new Covid-19 news address like Bing/covid-19. You may want to look at it -> https://www.google.com/covid-19
I have prepared a Presentation about Coronavirus's Effects on Search Engine Optimization (SEO).
You will find Coronavirus's changing effects on Digital Marketing and psychology of global society while using Search Engines.
I also have focused on Search Engine's and Social Media Brands, E-commerce Site's reflexes against Coronavirus Pandemic.
You will see the web sites and categories who earn more traffic and lose traffic. You will also see conversion rate differences because of Coronavirus.
Also, I have told about Search Engine's differences and their attitude against the Coronavirus Pandemic, their future, their updates during the pandemic.
In the last part, you will see some new 2020 Web Technology and Design Trends with AI.
There are also Google Researches for better Search Engine technologies.
Questions:
1- What are the differences between Yandex, Google, Bing, and Duckduckgo for Coronavirus Pandemic?
2- Twitter, Instagram, Amazon or Apple, what are they doing?
3- What do people search most for during the Coronavirus Crisis?
4- What changes from country to country?
5- What are the future technologies of Web and App?
6- How and why do Search Engines improve AI, what is the last events?
7- Which sites loose traffic and which earn more?
8- Lots of quotes from International SEOs about the pandemic.
And more...
I am Koray Tuğberk GÜBÜR and a Holistic SEO Expert.
I sincerely thank you for my Dearest Friend Atakan Erdoğan and Mr. Mert Erkal for this awesome webinar opportunity and experience.
To watch the webinar, please visit Stradiji's Official Youtube Channel.
https://www.youtube.com/watch?v=V4sJTNcRqaM&t=100s
Simple guidelines for your online presence and marketing, utilizing basics of digital marketing, as well as the main ideas behind antifragility and asymmetry .
The main online marketing strategy discussed is establishing yourself (company) as an authority/expert in your field. You do this very well and consistently such that people want to work with you (customers, investors, collaborators, etc.).
The main tool for establishing your presence is creating useful content that demonstrates your expertise. The main focus is on timeless topics to have time work with you, not against you.
Good luck!
How to approach SEO in a world where Google has moved from strings and keywords to things, topics and entities. Dixon JOnes is the CEO of InLinks, who have build a proprietory NLP algorithm and Knowledge Graph designed for the SEO Industry.
Opinion-based Article Ranking for Information Retrieval Systems: Factoids and...Koray Tugberk GUBUR
How Search Engines Leverage Opinion-based Articles for Ranking?
Search engines use opinions, and factoids to understand the consensus. News search engines use different reports, and opinions in their search results to satisfy the urgent news information needed by the newsreaders. The news search engines differentiate disinformation from information to protect the newsreaders. Google, Microsoft Bing, Yandex, and DuckDuckGo have different algorithms and prioritization for classifications of the news sources, or prioritization of the news, and newsworthy topics.
Corroboration of the Web Answers from the Open Web is a research paper from Amelia Marian and Minji Wu explaining how a search engine can rank information according to its accuracy.
Google started to explain that the Expertise-Authoriteveness-Trustworthiness is the most important group of signals to be sure that a result won't shame the search engine. Embarrassment factors for the search engines involve wrong information on a news title on the news story, or a wrong featured snippet. A search engine might be shame due to the bad result that is ranking on the SERP.
Dense-retrieval, context scoring, named entity recognition, semantic role labeling, truth ranges, fix points, confidence score, query processing, and parsing.
Context understanding requires processing the text, and tokenizing the words by recognizing the word sense. Processing the text of the news articles requires time. And, most of the time, news search engines do not have enough time for processing the text. Thus, PageRank provides a sustainable timeline for the news sources for rankings.
PageRank is a quick signal for search engines to show the authenticity of the news web source. The highly cited sources are ranked higher, and longer on the top stories. Usually, Google protects the high PageRank sources by trusting the judgment of the websites. But, fact-finding algorithms do not use PageRank mostly, unless they couldn't decide by looking at other factors, or they do not have enough resources to process the text among the hundreds of sources.
News ranking algorithms differentiate opinions, reports, and breaking news from each other. News-related entities, their co-existence, and contextual relations change. Google inventors suggest differentiation of these entities from each other for a proper news categorization.
News categorization is important to match the interested topics of the users in queryless news feeds such as Google Discover. Google Discover is a queryless news feed that serves news stories according to the users' interest areas.
An opinion for news might be misleading. Some news titles might be too harsh, or strict. Search engines use these headlines to differentiate the non-trustworthy news sources from the trustworthy ones. And, opinions of journalists or their different interpretations of the events might change the rankings of a document according to the fact-finding algorithms.
The Python Cheat Sheet for the Busy MarketerHamlet Batista
What percentage of an Inbound marketer's day doesn't involve working with spreadsheets? How much of this work is time-consuming and repetitive? In this interactive session, you will learn how to manipulate Google Sheets to automate common data analysis workflows using Python, a very easy to use programming language.
Google Lighthouse is super valuable but it only checks one page at a time.
Hamlet will show you how to get it to check all pages of a site, and how to run automated Lighthouse checks on-demand at scheduled intervals and from automated tests.
He'll also cover how to set performance budgets, how to get alerts when budgets are exceeded, and how to aggregate page reports using BigQuery and Google Data Studio.
SEO in the Age of Artificial Intelligence | How AI influences SearchPhilipp Klöckner
SEO hast changes over the past decade. Understand how classical ranking factors become less important, while user experience dominates the top rankings.
As seen live on stage at @ProjectAcom #PakCon2018 in Berlin.
A pipeline of reading, parsing, optimizing, and storing a log file to parquet.
This script uses the Python pandas library, utilizing the efficient Apache Parquet format for a big speed up and efficient storage.
Thank U (Rel) Next - State of Retail Pagination 1Y Later - Orit Mutznik - Bri...Orit Mutznik
BrightonSEO April 2019 - I attended Adam Gent's presentation, in which he presented pagination best practices given Google's recent announcement of rel next/prev deprecation. After not being sure about what to do with our pagination, this talk came right on time, and drove us to act based on those best practices, as I believed (and still believe) that pagination is critical to get right by retailers & ecommerce businesses. By the time BrightonSEO 2020 arrives, it will be over a year since the announcement, and I'd like to review the UK retail ecommerce sector, to see how many did something about it, best practices, bad practices, ugly practices, and give some tips based on what we did at SilkFred including how that worked out for us.
Download the full write up with embedded slides here: https://bit.ly/ecom-pagination
Leveraging Social Media to Engage Alumni presented to Johns Hopkins UniversityGoody PR and Goody Awards
This social media marketing training deck is a high level overview of how you can leverage social media (Facebook, Twitter and YouTube) to engage alumni that was presented by Liz H Kelly, Goody PR & Goody Awards Founder, to the Johns Hopkins University Alumni Council on the Homewood Campus in Baltimore, MD,
October 2013. Liz is based in CA, a graduate of the Johns Hopkins Carey Business School, worked for Fox Interactive Media/MySpace, and has managed $million advertising campaigns for clients such as Toyota, University of Phoenix and Southern California Edison. If you'd like to discuss how to leverage social media for your business with an engagement strategy and compelling content, email liz AT goodypr DOT com.
DTEK50 secure smartphone powered by Android combines BlackBerry’s unique security, privacy, and productivity with device design, specs, and an accessible price point for professionals and ideal for enterprise deployments. This Buyer’s Guide describes DTEK50’s slim, sleek design, robust specs, and key features, such as: BlackBerry Intelligent Keyboard, the 5.2” display, BlackBerry Hub, Convenience Key, Stunning Camera, DTEK, and other great, best-in-class security features, plus access to the world’s largest app ecosystem.
Getting Started - Creating products and services that make life betterSagar Arlekar
Foodlets Team at SVS College of Management Studies, Goa.
An interactive session where we shared the Foodlets story and engaged with the students on different aspects of Entrepreneurship and how to look for opportunities around.
Rethinking Annual Performance as WorkshopsEric Tachibana
Each year millions of line managers do performance reviews with each member of their team. Reviews are tricky, risky, and difficult to run as structured conversations, which is how they have always been run. This deck proposes that managers replace conversations with workshops, which are easier to run, generate more insights, and less likely to cause bad feelings as focus turns from attack-defend to collaborative problem solving.
Driving Sales Effectiveness with Great ContentSAVO
In this session, you'll learn what world-class sales enablement teams are doing to drive seller effectiveness with great content that aligns to the sales motion.
AMA in the AM-An Analytical Approach to Web Redesign & Overhaul 022311Sharon Mostyn
Web Redesign by the Numbers - A site redesign shouldn't be about making the new website "prettier." Instead, by using web analytics and
online marketing best practices, guide your website redesign in order to produce optimal conversions and revenue. Make sense of the who, what, when where, why, and how of your site to create a new, highly-optimized site.
Talk at the Schulich School of Business to marketing professionals on December 1, 2009. Slidecast (audio) edited to omit Q&A and interactive activities.
Basics of SEO and reputation management online using a 4 step process: Dedication, Collaboration, Mitigation, and Litigation.
SearchLove San Diego 2018 | Will Critchlow | From the Horse’s Mouth: What We ...Distilled
If you pay close enough attention, you can learn all kinds of things from what Google does and doesn’t say in public. From patents to official statements, to comments that Googlers leave on message boards, there is a wealth of information out there that hints at what they really think.
In this presentation, Will is going to work through some of the most significant official announcements and the most insight-heavy comments and leaks of Google’s first 20 years. You’ll come away from this presentation not only with a deeper understanding of the search giant, but also with the tools to understand and interpret future statements and leaks.
Search Engine Optimization (SEO) Training PresentationAaron Bramley
David J. Neff, of Ridgewood : Ingenious Communication Strategies, delivered this search engin optimization workshop for the Communications and Web Teams at Best Friends Animal Society.
The astute group quickly grasped how link building, site structure, alt tags, social media, and much more can improve your Google ranking.
Whitehat Linkbuilding Strategies Beginner To AdvancedAffiliate Summit
It's no secret, links matter. We'll present everything from basic directory linkbuilding to advanced tactics like .edu & authority linkbuilding. Discussion will include all above board, clean tactics.
y Keynote Presentation from today at SMXL Milan 2019 - Loving Italy about entities and augmentation queries and question answer through building knowledge graphs.
Keyword Research requires knowing your audiences and tasks for each of them. It can include taxonomies, Ontologies, context terms and disambiguation and optimizing for a knowledge graph and finding related entities.
Changes in Structured Data at Google (SEO Camp 'us in Paris)Bill Slawski
The history of structured data at Google and a look at some of the ways it is being applied. #KnowledgeGraph #Entities #StructuredData #AnnotationFramework #Patents
Guidelines and best practices for successful seo william slawski smxl milan...Bill Slawski
About How I started using Entities in Optimizing sites, and How Google has been adding Entities to Search Results and working on Updating its Knowledge Graph.
Knowledge Panels, Rich Snippets and Semantic MarkupBill Slawski
My 2016 Pubcon Presentation showing how I incorporate Knowledge Panels, Entities, the Knowledge Graph API, Rich Snippets, Featured Snippets and Structured Snippets in SEO site Audits.
Ranking in Google Since The Advent of The Knowledge GraphBill Slawski
A Two Person Panel Discussion/Presentation by Bill Slawski and Barbara Starr On June 23, 2015
The Lotico Semantic Web of San Diego
The SEO San Diego Meetup
The SEM San Diego Meetup
http://www.meetup.com/InternetMarketingSanDiego/events/222788495/
User experience drives search engines, and hence their results. Search Engine Result Presentation/Placements naturally follow that route.
This means that search results are no longer exclusively based on just ranking criteria. Amongst other critical factors is understanding the notion of 'ordering vs ranking', the impact of context and many others.
Content Audits for SEO & Site Migration: Picking a website up on your back an...Bill Slawski
Once I was tasked as part of a team moving a large Public Courthouse to a new location. It's something I'll always remember, and I'm reminded of it every time I'm involved in the migration of a new site to a new domain. Success is in the planning, and in successfully tackling small details.
First question I asked everyone is, "How many of you have never moved to a new home? Moving a courthouse is a whole lot more work." No one raised their hand. They can related to the challege.
Google Will Not Go Gentle into That Good Night: Project GlassBill Slawski
My presentation slides from SMX East on future search interfaces on a conceptual level, and how spoken, visual, and even parameterless searches may impact seo and online marketing.
Promoting the blog with search engine optimization
Semantic search
1. Semantic Technology & Business Conference
San Jose Convention Center
Bill Slawski
Barbara Starr
2. English & Law degrees, former court admin
Web Promotion & SEO since 1996
Director of Search Marketing at Go Fish Digital
Writes about the latest search patents/papers at
SEO by the Sea
Twitter: @bill_slawski
9. …meaning to a big field and a copse of trees,
as in the first major battle of the American
Civil War, in Manassas, Virginia.
…mystery, as Jackson either stood there like a
stonewall not relieving troops, or stood there
like a stonewall, unflappable.
He was known as Stonewall Jackson after this
battle.
10. Adding a Semantic Layer to the Web adds to the
stories that sites tell, and the experiences that
we have with them.
11. In 2005,
the way I
was doing
SEO
changed
when I
decided to
make a
page as
much
about
Baltimore
as I
possibly
could…..
13. To help people learn about the City of
Baltimore
To attract people to Baltimore, visit hotels
and restaurants, and to hold conferences
there
To share information about events so people
could attend
14. An 8’6” tall monument to Billie Holiday
(Lafayette and Pennsylvania avenues)
Bethel African Methodist Episcopal Church
Coppin State University
Morgan State University
Frederick Douglass
Thurgood Marshal
Thomas R. Smith
15.
16. Site URL Structure
Site Information Architecture
Use of Keywords on Pages
Site Speed Issues
Use of Canonical URLs
Many More
17. In addition to SEO audits for clients, we often
do audits for them focusing upon entities that
appear on a site or should ideally show up
there.
18.
19. What Entities Appear on the Site
How the site appears in
(Snippet/Sitelinks/Knowledge Panel/OneBox)
for company name/domain name
Related Entities
Emerging (New) Entities
Unrelated Entities (same/similar name) on the
Web
Knowledge Base Listings
22. What Movie does Robert Duvall love the Smell
of Napalm in the Morning in?
Actor: Robert Duvall
Known for: “…love the smell of napalm in the
morning”
Is A: Movie
Show Results from Apocalypse Now
Identifying entities using search results (July 8, 2014)
23.
24.
25. Top 4 Results from
spaceneedle.com
Query rewriting with entity detection (August 12, 2014)
27. Google anticipates what other
things people search for, & may
include them in a knowledge
panel.
Consider creating pages for
those follow-up queries…
“We can use the Knowledge
Graph to answer questions you
never thought to ask and help
you discover more.”
~
http://www.google.com/inside
search/features/search/knowle
dge.html
29. Aspect-based sentiment summarization,
August 5, 2014)
Phrases in the reviews that express
sentiment about a particular aspect are
identified. Reviewable aspects of the
entity are also identified.
The reviewable aspects include static
aspects that are specific to particular
types of entities and dynamic aspects that
are extracted from the reviews of a
specific entity instance.
36. Entities may be identified in sources such as news articles, where
those
Entities are new or not well known, and can be added to knowledge
bases.
Anticipate this discovery.
37.
38.
39. This site is very light on
Information about its location.
40.
41.
42.
43.
44.
45.
46. The Business name only returns 2 Sitelinks; the domain name
Returns 6 site links. Do people search for the domain name at all?
51. Focus on the Business Model Better (Apartments
or Hotel?)
Aid in Content Creation
Aid in Site Organization (Information Architecture
& Navigation)
Frequently Asked Questions Topics (Where are
nearby Schools/Daycare?)
Understand Other Potential Benefits and
Problems (Transportation and Metro Benefits)
52. Entity Confusion Resolved – Hotel Listings to
be removed.
More Location information to be added (big
selling point.)
Metro Elevator makes DC area very accessible
– to be shown!
Need a FAQ for things like School and
Daycare information
Quiet about Pool & other attributes of the
apartments – will be added.