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.
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.
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.
Disambiguating Equiprobability in SEO Dawn Anderson Friends of Search 2020Dawn Anderson MSc DigM
Connecting the probable dots in content and data can help significantly and improve your search strategy. Ambiguity in SEO comes in many form too, going beyond content and into entities and locations. This talk touches on some of the areas where ambiguity can impact and hinder your performance
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.
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.
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.
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.
Disambiguating Equiprobability in SEO Dawn Anderson Friends of Search 2020Dawn Anderson MSc DigM
Connecting the probable dots in content and data can help significantly and improve your search strategy. Ambiguity in SEO comes in many form too, going beyond content and into entities and locations. This talk touches on some of the areas where ambiguity can impact and hinder your performance
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.
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.
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
SEO Başarı Hikayesi - Hangikredi.com 12 Mart'tan 24 Eylül Google Core Güncell...Koray Tugberk GUBUR
Özeti Başlat:
"5 Ayda% 131 Organik Oturum Artışı
5 Ay İçinde% 62 Gösterim Artışı
5 Aylık% 144 Tıklama Artışı "
Bu SEO Vaka çalışması, Google Çekirdek Güncellemeleri ve Türkiye'deki en büyük finansal kurum web sitesine etkileri ile ilgili.
Hangikredi.com'da 26 Mart 2019'da çalışmaya başladım. Ancak şirketin web sitesi 12 Mart Google Çekirdek Güncellemesi'nden çok olumsuz etkilendi.
Bir kriz yaşanırken burada çalışmaya başladım.
Web sitesini inceledim ve asıl sorunların tarama bütçesi, otorite işareti ve alaka düzeyi-işletme ilişkisi olduğunu anladım. Sosyal medyayı, Google İş Hesaplarım'ı etkinleştirdim, diğer tüm alternatif kanallardan finansal forumlara girdim. Bizimle ilgili bir haber yayıncı ağı yarattım. Yanıltıcı durum kodlarını, HTML ve CSS hatalarını temizledim, meta etiketleri optimize ettim, yönlendirme zincirlerini düzelttim, görüntü sıkıştırma kullandım ve birçok gereksiz URL ve içeriğini sildim, iç bağlantı yapısını sıfırdan yarattım.
5 Haziran’dan Google Çekirdek Güncelleme’ye kadar tekrar kazandık.
Kaybettiğimiz bütün trafiğimizi geri kazandık. 1 Ağustos sunucu atack kadar, biz iyiydik, sonra bir gün, her şey ters gitti.
Yine 0'dan başladım ...
Web sitemizin offpage sinyallerini Google AI’ın güvenini yeniden kazanmak için optimize ediyordum ve bu stratejiyi onpage öğeleriyle destekliyordum.
24 Eylül Google Çekirdek Güncellemesi’nden sonra başka bir başarı daha oldu. Tarama yükü / oranı kaydını, ortalama site konumunu, TO'yu ve gösterimini, site geçmişi için tıklama kayıtlarını kırdık.
Bu CASE Çalışmasında grafikler içeren bir SEO Başarı Hikayesinin ayrıntılarını ve hayatımdaki bazı komik cencor resimlerini bulacaksınız.
Bitiş Özeti:
"12 Mart, 5 Haziran ve 24 Eylül 1 Ağustos Sunucu ile Google Çekirdek Güncellemeleri Atack, bu SEO Casse Çalışmasının kilometre taşlarıdır. Tüm detayları bizim açımızdan göreceksiniz. Umarım beğenirsiniz."
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
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.
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.
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.
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.
PubCon, Lazarina Stoy. - Machine Learning in Search: Google's ML APIs vs Open...LazarinaStoyanova
In this presentation, I go through the different use cases of machine learning APIs for search marketers and digital marketers. Specifically, I look at APIs by Google Cloud and OpenAI and identify the best to use for your SEO projects, based on the task at hand.
Crafting Expertise, Authority and Trust with Entity-Based Content Strategy - ...Jamie Indigo
At SMXL, I presented a talk about crafting effective, authoritative content by understanding entities. People, places, objects, and ideas have facets. Human users have unique perspectives and their language changes as their relationship to an entity changes. It's time we stop chasing keywords-- a byproduct of search intent-- in favor of strategic entity-based strategy.
This deck includes insights into how to access the data behind Google's knowledge graph, use external links to boost the search engine's understanding, and ways to become an authoritative and trusted source.
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.
BrightonSEO March 2021 | Dan Taylor, Image Entity TagsDan Taylor
My talk from BrightonSEO 2021; focusing on using Google's image category labels (glancing into the Knowledge Graph and Google's image annotation processes) for better topic research and content optimization.
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.
This is a presentation that I did for the Enterprise Search Summit West 2008 that has been amended for a Web Project Management class at the University of Washington
VoiceSummit.AI: Marketers! Prepare Now for the Voice Search Present & FutureWO Strategies
Presentation on Voice SEO at the VoiceSummit.ai by Katherine Watier Ong, the founder of WO Strategies LLC.
Curious as to how fast you need to adjust to capture voice search queries? You need to check out this presentation.
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.
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
SEO Başarı Hikayesi - Hangikredi.com 12 Mart'tan 24 Eylül Google Core Güncell...Koray Tugberk GUBUR
Özeti Başlat:
"5 Ayda% 131 Organik Oturum Artışı
5 Ay İçinde% 62 Gösterim Artışı
5 Aylık% 144 Tıklama Artışı "
Bu SEO Vaka çalışması, Google Çekirdek Güncellemeleri ve Türkiye'deki en büyük finansal kurum web sitesine etkileri ile ilgili.
Hangikredi.com'da 26 Mart 2019'da çalışmaya başladım. Ancak şirketin web sitesi 12 Mart Google Çekirdek Güncellemesi'nden çok olumsuz etkilendi.
Bir kriz yaşanırken burada çalışmaya başladım.
Web sitesini inceledim ve asıl sorunların tarama bütçesi, otorite işareti ve alaka düzeyi-işletme ilişkisi olduğunu anladım. Sosyal medyayı, Google İş Hesaplarım'ı etkinleştirdim, diğer tüm alternatif kanallardan finansal forumlara girdim. Bizimle ilgili bir haber yayıncı ağı yarattım. Yanıltıcı durum kodlarını, HTML ve CSS hatalarını temizledim, meta etiketleri optimize ettim, yönlendirme zincirlerini düzelttim, görüntü sıkıştırma kullandım ve birçok gereksiz URL ve içeriğini sildim, iç bağlantı yapısını sıfırdan yarattım.
5 Haziran’dan Google Çekirdek Güncelleme’ye kadar tekrar kazandık.
Kaybettiğimiz bütün trafiğimizi geri kazandık. 1 Ağustos sunucu atack kadar, biz iyiydik, sonra bir gün, her şey ters gitti.
Yine 0'dan başladım ...
Web sitemizin offpage sinyallerini Google AI’ın güvenini yeniden kazanmak için optimize ediyordum ve bu stratejiyi onpage öğeleriyle destekliyordum.
24 Eylül Google Çekirdek Güncellemesi’nden sonra başka bir başarı daha oldu. Tarama yükü / oranı kaydını, ortalama site konumunu, TO'yu ve gösterimini, site geçmişi için tıklama kayıtlarını kırdık.
Bu CASE Çalışmasında grafikler içeren bir SEO Başarı Hikayesinin ayrıntılarını ve hayatımdaki bazı komik cencor resimlerini bulacaksınız.
Bitiş Özeti:
"12 Mart, 5 Haziran ve 24 Eylül 1 Ağustos Sunucu ile Google Çekirdek Güncellemeleri Atack, bu SEO Casse Çalışmasının kilometre taşlarıdır. Tüm detayları bizim açımızdan göreceksiniz. Umarım beğenirsiniz."
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
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.
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.
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.
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.
PubCon, Lazarina Stoy. - Machine Learning in Search: Google's ML APIs vs Open...LazarinaStoyanova
In this presentation, I go through the different use cases of machine learning APIs for search marketers and digital marketers. Specifically, I look at APIs by Google Cloud and OpenAI and identify the best to use for your SEO projects, based on the task at hand.
Crafting Expertise, Authority and Trust with Entity-Based Content Strategy - ...Jamie Indigo
At SMXL, I presented a talk about crafting effective, authoritative content by understanding entities. People, places, objects, and ideas have facets. Human users have unique perspectives and their language changes as their relationship to an entity changes. It's time we stop chasing keywords-- a byproduct of search intent-- in favor of strategic entity-based strategy.
This deck includes insights into how to access the data behind Google's knowledge graph, use external links to boost the search engine's understanding, and ways to become an authoritative and trusted source.
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.
BrightonSEO March 2021 | Dan Taylor, Image Entity TagsDan Taylor
My talk from BrightonSEO 2021; focusing on using Google's image category labels (glancing into the Knowledge Graph and Google's image annotation processes) for better topic research and content optimization.
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.
This is a presentation that I did for the Enterprise Search Summit West 2008 that has been amended for a Web Project Management class at the University of Washington
VoiceSummit.AI: Marketers! Prepare Now for the Voice Search Present & FutureWO Strategies
Presentation on Voice SEO at the VoiceSummit.ai by Katherine Watier Ong, the founder of WO Strategies LLC.
Curious as to how fast you need to adjust to capture voice search queries? You need to check out this presentation.
Basic SEO by Andrea H. Berberich @webpresenceoptiAndrea Berberich
SEO stands for Search Engine Optimization and everyone who uses the Internet will eventually use a search engine. SEO is a huge field and everyone who works in the digital sphere is impacted by the guidelines, findings and rules of SEO.
Search engines have changed a lot over the last 15 years and optimizing Websites for them must keep up. This presentation looks at the search landscape and present strategies and tactics for optimizing for today's search.
In the last weeks of 2017, Google released a Rich Results Testing Tool to help webmasters understand what pages can generate rich results, based on their structured data implementation.
This new tool, coming from the search giant, is just one of the many recent affirmations of structured data’s continued and growing importance to search optimization in 2018 and beyond.
But why is structured data important to search? How does it impact your SEO strategy? And most importantly, what can you do to optimize structured data and maximize your potential in the SERPs?
This presentation hopes to illuminate how Search, Content Strategy, Information Architecture, User Experience, Interaction Design can break down silos to take back relevance. Because, in the end, we, the people, should be the arbiters of experience, not machines and certainly not math.
The goal of this presentation is to allow researchers to understand the possibilities of Social Media as a research field on the fields related to NLP/IR/DM.
Licensed to Analyze? Strata Data NY 2019 IADSS Session - Usama Fayyad, Hamit ...IADSS
The latest insight into IADSS Research was shared with analytics community at Strata Data NY 2019 by O'reilly. IADSS Co-founders Usama Fayyad and Hamit Hamutcu talked about the current status of data science job market, the wasted cost of data science recruitment and role definitions & required skill-sets for most common roles in data science.
Please check out IADSSglobal on Twitter and visit www.iadss.org for more information.
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.
My presentation at the Semantic Technology and Business Conference in San Jose on August 19, 2014, with Barbara Starr (Her slides are separate, and cover a vast array of semantic tools and approaches for assessing and understanding your pages).
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.
Discover the innovative and creative projects that highlight my journey throu...dylandmeas
Discover the innovative and creative projects that highlight my journey through Full Sail University. Below, you’ll find a collection of my work showcasing my skills and expertise in digital marketing, event planning, and media production.
Cracking the Workplace Discipline Code Main.pptxWorkforce Group
Cultivating and maintaining discipline within teams is a critical differentiator for successful organisations.
Forward-thinking leaders and business managers understand the impact that discipline has on organisational success. A disciplined workforce operates with clarity, focus, and a shared understanding of expectations, ultimately driving better results, optimising productivity, and facilitating seamless collaboration.
Although discipline is not a one-size-fits-all approach, it can help create a work environment that encourages personal growth and accountability rather than solely relying on punitive measures.
In this deck, you will learn the significance of workplace discipline for organisational success. You’ll also learn
• Four (4) workplace discipline methods you should consider
• The best and most practical approach to implementing workplace discipline.
• Three (3) key tips to maintain a disciplined workplace.
Enterprise Excellence is Inclusive Excellence.pdfKaiNexus
Enterprise excellence and inclusive excellence are closely linked, and real-world challenges have shown that both are essential to the success of any organization. To achieve enterprise excellence, organizations must focus on improving their operations and processes while creating an inclusive environment that engages everyone. In this interactive session, the facilitator will highlight commonly established business practices and how they limit our ability to engage everyone every day. More importantly, though, participants will likely gain increased awareness of what we can do differently to maximize enterprise excellence through deliberate inclusion.
What is Enterprise Excellence?
Enterprise Excellence is a holistic approach that's aimed at achieving world-class performance across all aspects of the organization.
What might I learn?
A way to engage all in creating Inclusive Excellence. Lessons from the US military and their parallels to the story of Harry Potter. How belt systems and CI teams can destroy inclusive practices. How leadership language invites people to the party. There are three things leaders can do to engage everyone every day: maximizing psychological safety to create environments where folks learn, contribute, and challenge the status quo.
Who might benefit? Anyone and everyone leading folks from the shop floor to top floor.
Dr. William Harvey is a seasoned Operations Leader with extensive experience in chemical processing, manufacturing, and operations management. At Michelman, he currently oversees multiple sites, leading teams in strategic planning and coaching/practicing continuous improvement. William is set to start his eighth year of teaching at the University of Cincinnati where he teaches marketing, finance, and management. William holds various certifications in change management, quality, leadership, operational excellence, team building, and DiSC, among others.
As a business owner in Delaware, staying on top of your tax obligations is paramount, especially with the annual deadline for Delaware Franchise Tax looming on March 1. One such obligation is the annual Delaware Franchise Tax, which serves as a crucial requirement for maintaining your company’s legal standing within the state. While the prospect of handling tax matters may seem daunting, rest assured that the process can be straightforward with the right guidance. In this comprehensive guide, we’ll walk you through the steps of filing your Delaware Franchise Tax and provide insights to help you navigate the process effectively.
India Orthopedic Devices Market: Unlocking Growth Secrets, Trends and Develop...Kumar Satyam
According to TechSci Research report, “India Orthopedic Devices Market -Industry Size, Share, Trends, Competition Forecast & Opportunities, 2030”, the India Orthopedic Devices Market stood at USD 1,280.54 Million in 2024 and is anticipated to grow with a CAGR of 7.84% in the forecast period, 2026-2030F. The India Orthopedic Devices Market is being driven by several factors. The most prominent ones include an increase in the elderly population, who are more prone to orthopedic conditions such as osteoporosis and arthritis. Moreover, the rise in sports injuries and road accidents are also contributing to the demand for orthopedic devices. Advances in technology and the introduction of innovative implants and prosthetics have further propelled the market growth. Additionally, government initiatives aimed at improving healthcare infrastructure and the increasing prevalence of lifestyle diseases have led to an upward trend in orthopedic surgeries, thereby fueling the market demand for these devices.
Improving profitability for small businessBen Wann
In this comprehensive presentation, we will explore strategies and practical tips for enhancing profitability in small businesses. Tailored to meet the unique challenges faced by small enterprises, this session covers various aspects that directly impact the bottom line. Attendees will learn how to optimize operational efficiency, manage expenses, and increase revenue through innovative marketing and customer engagement techniques.
Unveiling the Secrets How Does Generative AI Work.pdfSam H
At its core, generative artificial intelligence relies on the concept of generative models, which serve as engines that churn out entirely new data resembling their training data. It is like a sculptor who has studied so many forms found in nature and then uses this knowledge to create sculptures from his imagination that have never been seen before anywhere else. If taken to cyberspace, gans work almost the same way.
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Explore our most comprehensive guide on lookback analysis at SafePaaS, covering access governance and how it can transform modern ERP audits. Browse now!
Affordable Stationery Printing Services in Jaipur | Navpack n PrintNavpack & Print
Looking for professional printing services in Jaipur? Navpack n Print offers high-quality and affordable stationery printing for all your business needs. Stand out with custom stationery designs and fast turnaround times. Contact us today for a quote!
RMD24 | Debunking the non-endemic revenue myth Marvin Vacquier Droop | First ...BBPMedia1
Marvin neemt je in deze presentatie mee in de voordelen van non-endemic advertising op retail media netwerken. Hij brengt ook de uitdagingen in beeld die de markt op dit moment heeft op het gebied van retail media voor niet-leveranciers.
Retail media wordt gezien als het nieuwe advertising-medium en ook mediabureaus richten massaal retail media-afdelingen op. Merken die niet in de betreffende winkel liggen staan ook nog niet in de rij om op de retail media netwerken te adverteren. Marvin belicht de uitdagingen die er zijn om echt aansluiting te vinden op die markt van non-endemic advertising.
Remote sensing and monitoring are changing the mining industry for the better. These are providing innovative solutions to long-standing challenges. Those related to exploration, extraction, and overall environmental management by mining technology companies Odisha. These technologies make use of satellite imaging, aerial photography and sensors to collect data that might be inaccessible or from hazardous locations. With the use of this technology, mining operations are becoming increasingly efficient. Let us gain more insight into the key aspects associated with remote sensing and monitoring when it comes to mining.
Putting the SPARK into Virtual Training.pptxCynthia Clay
This 60-minute webinar, sponsored by Adobe, was delivered for the Training Mag Network. It explored the five elements of SPARK: Storytelling, Purpose, Action, Relationships, and Kudos. Knowing how to tell a well-structured story is key to building long-term memory. Stating a clear purpose that doesn't take away from the discovery learning process is critical. Ensuring that people move from theory to practical application is imperative. Creating strong social learning is the key to commitment and engagement. Validating and affirming participants' comments is the way to create a positive learning environment.
2. Bill Slawski
● Author at https://www.seobythesea.com
● Director of SEO Research at
https://gofishdigital.com
● Twitter: https://twitter.com/bill_slawski
● LinkedIn: https://www.linkedin.com/in/slawski/
● Jurisdoctor Degree: Widener University School of
Law
● SEO since 1996
3. Why Patents?
1. Protect a Search Engine’s Intellectual Property
2. Exclude other search engines from using technology
3. Not for marketing
4. Related Patents and white papers exist
5. Continuation Patents have updated claims sections.
6. The Purpose Behind Patents are to Spur Innovation.
4. In Patents Live Algorithms
● Algorithms solve problems
● A patent shows the problems it solves
● Prior history is in the patent
● Patent must be novel, non-obvious, and useful
● A patent must be understandable to someone “learned in
the art.”
● Patents show what might be used.
5. Knowledge in Search Results!
Augmented Search Queries Using Knowledge Graph
Information
Providing search results using augmented search
queries
Inventors: Emily Moxley and Sean Liu
Assignee: Google LLC
US Patent: 10,055,462
Granted: August 21, 2018
Filed: March 15, 2013
6. A Query w/an Entity Shows Knowledge SERPs
Rich Snippets
Featured Snippets
Structured Snippets
Carousels
Local Pack
People Also Ask Questions
People Also Search For
Related Entities
Knowledge Panels
7. 4th Updated Universal Search has Knowledge
Google’s New Universal Search Results
Interface for a universal search
Inventors: Bret S. Taylor, Marissa Ann
Mayer, Orkut Buyukkokten
Assignee: Google LLC (Mountain View, CA)
US Patent: 10,409,865
Granted: September 10, 2019
Filed: April 15, 2016
From the Version Granted in 2009:
9. The method of claim 1, where the
Document categories include at
least one of a news category,
an image category,
or a product category.
9. Entity Extraction
Entity Extractions for Knowledge Graphs at Google
Computerized systems and methods for extracting
and storing information regarding entities
Inventors: Christopher Semturs, Lode Vandevenne,
Danila Sinopalnikov, Alexander Lyashuk, Sebastian
Steiger, Henrik Grimm, Nathanael Martin Scharli
and David Lecomte
Assignee: GOOGLE LLC
US Patent: 10,198,491
Granted: February 5, 2019
Filed: July 6, 2015
In web crawling, a node is
a page, and an edge is a
link between pages; in data
crawling, a node is an
entity, and an edge is a
relationship between
entities.
It's an evolution
in thinking about the web.
10.
11. A Move Away From Wikipedia
Extracting Entities from News
and Authoritative sites means less
reliance on manually edited
Knowledge bases such as
Wikipedia or IMDB
12. Answering Questions Using Knowledge Graphs
Answering Questions Using Knowledge
Graphs
Natural Language Processing With An N-Gram
Machine
Pub. No.: WO2019083519A1
Publication Date: May 2, 2019
International Filing Date: October 25, 2017
Inventors: Ni Lao, Jiazhong Nie, Fan Yang
Business books 2020
15. Ranked Entities in Search Results
Ranked Entities in Search Results
at Google
Generating ranked lists of entities
Inventors: Toshiaki Fujiki, Slaven Bilac,
Kavi J. Goel, Shuhei Takahashi,
Tomohiko Kimura
Assignee: Google LLC
US Patent: 10,691,702
Granted: June 23, 2020
Filed: August 31, 2017
Best Science Fiction Books of 2020
Best Houseplants for air quality
17. Quote Search: Knowledge Bases to Videos
● Google Has Updated Quote Searching
to Focus on Videos
Systems and methods for searching
quotes of entities using a database
● Inventors: Eyal Segalis, Gal Chechik,
Yossi Matias, Yaniv Leviathan, and
Yoav Tzur
● Assignee: Google LLC
● US Patent: 10,198,508
● Granted: February 5, 2019
● Filed: June 26, 2017
18. Claims Updated in Continuation Patent
2017 Version:
match the one or more keywords to knowledge graph items associated with candidate
subject entities in a knowledge graph stored in one or more databases,
2019 Version
performing audio analysis on the audio content to identify a quote in the audio content;
determining the user as an author of the audio content based on recognizing the user as the
speaker of the audio content; identifying, based on words or phrases extracted from the
quote, one or more subject entities associated with the quote;
19. Google Learning from Images on the Web
● How Google May Annotate Images to
Improve Search Results
● Computerized systems and methods
for enriching a knowledge base for
search queries
Inventors: Ran El Manor and Yaniv
Leviathan
Assignee: Google LLC
US Patent: 10,534,810
Granted: January 14, 2020
Filed: February 29, 2016
20. Bears Hunt Fish in Rivers
Main Objects: Bears
Secondary Objects: Fish
22. Image Search Categories: Entities/Ontologies
● Google Image Search Labels Becoming More
Semantic?
● System and method for associating images with
semantic entities
Inventors: Maks Ovsjanikov, Yuan Li, Hartwig
Adam and Charles Joseph Rosenberg;
Assignee: Google LLC
US Patent: 10,268,703
Granted: April 23, 2019
Filed: December 8, 2016
23. George Washington Image Categories
Catgegories contain Semantically Related Entities which are an ontology,
Useful if you want to visually learn about an entity
25. Classification of Websites
● Google Using Website Representation Vectors to
Classify with Expertise and Authority
● Website Representation Vector to Generate Search
Results and Classify Website
Publication number: WO2020033805
Applicants: GOOGLE LLC
Inventors: Yevgen Tsykynovskyy
Publication Number WO/2020/033805
Filed: August 10, 2018
Publication Date February 13, 2020
26. Categories Based on Industry/Expertise Levels
● For instance, the website classifications may include the first category of
websites authored by experts in the knowledge domain, e.g., doctors, the
second category of websites authored by apprentices in the knowledge
domain, e.g., medical students, and a third category of websites authored by
laypersons in the knowledge domain.
27. Queries are Categorized using Query logs, and return results only from
the appropriate Category of Website…
A Query such as “What are the Symptoms of Diabetes Type 2,” would
be answered with An expert medical site run by Doctors.
Sites need to meet Thresholds of Quality to Rank for a query.
Because the Categories for Queries and for Websites need to match, this
Means Google has less pages to sort and rank to respond to a query with,
Making it more efficient
28. Thank you very much!
Any Questions? You can always ask later on Twitter: https://twitter.com/bill_slawski
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