누구도 알려주지 않는 크롤링의 함정
•Download as PPTX, PDF•
1 like•18,005 views
누구도 알려주지 않는 크롤링의 함정 by Hong3
Report
Share
Report
Share
Recommended
파이썬을 활용한 웹 크롤링
* 국민대학교 빅데이터 분석학회 D&A Session에서 진행한 자료입니다. 웹 크롤링에 대한 기본적인 개념과 파이썬과 관련된 소스 코드를 담았습니다.
* 아래는 PPT에 포함된 크롤링 예제 코드입니다. https://drive.google.com/file/d/1ty7JLz8ccicPTrpry4dpkqCGuTclA68M/view?usp=sharing
성장을 좋아하는 사람이, 성장하고 싶은 사람에게
The document discusses deep learning paper reading roadmaps and lists several github repositories that aggregate deep learning papers. It also discusses developing mobile applications that utilize machine learning and the differences between developing for iOS versus Android. Lastly, it mentions continuing to learn through practice and experimentation with deep learning techniques.
Little Big Data #1. 바닥부터 시작하는 데이터 인프라
This document discusses using BigQuery and Dataflow for ETL processes. It explains loading raw data from databases into BigQuery, transforming the data with Dataflow, and writing the results. It also mentions pricing of $5 per terabyte for BigQuery storage and notes that Dataflow provides virtual CPUs and RAM. Finally, it includes a link about performing ETL from relational databases to BigQuery.
제 15회 보아즈(BOAZ) 빅데이터 컨퍼런스 - [YouPlace 팀] : 카프카와 스파크를 활용한 유튜브 영상 속 제주 명소 검색
데이터 엔지니어링 프로젝트를 진행한 YouPlace팀에서는 아래와 같은 프로젝트를 진행했습니다.
<aside>
이젠 검색도 유튜브 시대
제주여행을 계획할 때 브이로그 영상을 많이 참고하실텐데요
수많은 영상들과 영상 속 분산된 명소들을 하나 하나 찾으려 생각하면 막막하지 않으셨나요?
이러한 고민을 갖고 계신 분들을 위해, 유튜브 브이로거들이 찾아간 여행 명소들을 지도에서 한 눈에 파악할 수 있도록 만들었어요
(github : https://github.com/Boaz-Youplace)
16기 엔지니어링 고은서 | 중앙대학교 소프트웨어학부
16기 엔지니어링 류정화 | 성신여자대학교 융합보안공학과
16기 엔지니어링 송경민 | 국민대학교 소프트웨어학과
개발자를 위한 (블로그) 글쓰기 intro
2018년 6월 24일 "백수들의 Conference"에서 발표한 개발자를 위한 (블로그) 글쓰기 intro입니다
좋은 글을 많이 보는 노하우 + 꾸준히 글을 작성하는 노하우에 대해 주로 이야기했습니다! (어떻게 글을 작성하는가는 없어요!)
피드백은 언제나 환영합니다 :)
BigQuery의 모든 것(기획자, 마케터, 신입 데이터 분석가를 위한) 입문편
The document contains log data from user activities on a platform. There are three columns - user_id, event, and event_date. It logs the activities of 5 users over several days, including events like logins, posts, comments, views. It also includes some aggregated data on unique events and totals by user.
AI-powered Semantic SEO by Koray GUBUR
This document discusses optimizing websites and search engines using semantic techniques. It suggests that Website B, with more content, triples, accuracy and connected topics, would be more successful at satisfying search queries. It introduces the concept of topical authority to lower retrieval costs. Several techniques are proposed for language model optimization including fine-tuning, creating topical maps and semantic networks, and generating content informed by human effort and microsemantics. Cross-lingual embeddings and understanding word relationships are also discussed as ways to improve semantic search.
Recommended
파이썬을 활용한 웹 크롤링
* 국민대학교 빅데이터 분석학회 D&A Session에서 진행한 자료입니다. 웹 크롤링에 대한 기본적인 개념과 파이썬과 관련된 소스 코드를 담았습니다.
* 아래는 PPT에 포함된 크롤링 예제 코드입니다. https://drive.google.com/file/d/1ty7JLz8ccicPTrpry4dpkqCGuTclA68M/view?usp=sharing
성장을 좋아하는 사람이, 성장하고 싶은 사람에게
The document discusses deep learning paper reading roadmaps and lists several github repositories that aggregate deep learning papers. It also discusses developing mobile applications that utilize machine learning and the differences between developing for iOS versus Android. Lastly, it mentions continuing to learn through practice and experimentation with deep learning techniques.
Little Big Data #1. 바닥부터 시작하는 데이터 인프라
This document discusses using BigQuery and Dataflow for ETL processes. It explains loading raw data from databases into BigQuery, transforming the data with Dataflow, and writing the results. It also mentions pricing of $5 per terabyte for BigQuery storage and notes that Dataflow provides virtual CPUs and RAM. Finally, it includes a link about performing ETL from relational databases to BigQuery.
제 15회 보아즈(BOAZ) 빅데이터 컨퍼런스 - [YouPlace 팀] : 카프카와 스파크를 활용한 유튜브 영상 속 제주 명소 검색
데이터 엔지니어링 프로젝트를 진행한 YouPlace팀에서는 아래와 같은 프로젝트를 진행했습니다.
<aside>
이젠 검색도 유튜브 시대
제주여행을 계획할 때 브이로그 영상을 많이 참고하실텐데요
수많은 영상들과 영상 속 분산된 명소들을 하나 하나 찾으려 생각하면 막막하지 않으셨나요?
이러한 고민을 갖고 계신 분들을 위해, 유튜브 브이로거들이 찾아간 여행 명소들을 지도에서 한 눈에 파악할 수 있도록 만들었어요
(github : https://github.com/Boaz-Youplace)
16기 엔지니어링 고은서 | 중앙대학교 소프트웨어학부
16기 엔지니어링 류정화 | 성신여자대학교 융합보안공학과
16기 엔지니어링 송경민 | 국민대학교 소프트웨어학과
개발자를 위한 (블로그) 글쓰기 intro
2018년 6월 24일 "백수들의 Conference"에서 발표한 개발자를 위한 (블로그) 글쓰기 intro입니다
좋은 글을 많이 보는 노하우 + 꾸준히 글을 작성하는 노하우에 대해 주로 이야기했습니다! (어떻게 글을 작성하는가는 없어요!)
피드백은 언제나 환영합니다 :)
BigQuery의 모든 것(기획자, 마케터, 신입 데이터 분석가를 위한) 입문편
The document contains log data from user activities on a platform. There are three columns - user_id, event, and event_date. It logs the activities of 5 users over several days, including events like logins, posts, comments, views. It also includes some aggregated data on unique events and totals by user.
AI-powered Semantic SEO by Koray GUBUR
This document discusses optimizing websites and search engines using semantic techniques. It suggests that Website B, with more content, triples, accuracy and connected topics, would be more successful at satisfying search queries. It introduces the concept of topical authority to lower retrieval costs. Several techniques are proposed for language model optimization including fine-tuning, creating topical maps and semantic networks, and generating content informed by human effort and microsemantics. Cross-lingual embeddings and understanding word relationships are also discussed as ways to improve semantic search.
딥 러닝 자연어 처리를 학습을 위한 파워포인트. (Deep Learning for Natural Language Processing)
딥 러닝 자연어 처리를 공부하면서 여러가지 기법들을 파워 포인트 그림으로 그려보았습니다. 참고하시라고 업로드합니다.
감사합니다.
대용량 로그분석 Bigquery로 간단히 사용하기 (20170215 T아카데미)
T아카데미 강연 자료 :
https://tacademy.sktechx.com/front/community/techclinic/viewTechClinic.action?seq=131
Elasticsearch 엘라스틱서치 (검색서비스) 에 대해 알아보자.txt
elasticsearch의 기본적인 working에 대한 발표자료입니다.
특히나 logging보다는 '검색 서비스'에 포커싱된 자료이기 때문에 '한글검색' 으로 고통받으실 분들을 위한 기초 자료라 생각해주시면 감사하겠습니다.
맞지않는 정보와 오탈자 그리고 의문점이 든다면 dydwls121200@gmail.com으로 언제든지 가벼운 마음으로 메일주세요. 저 또한 성장시키는 일이기도 하니까요. 환영합니다.
Semantic search Bill Slawski DEEP SEA Con
1) Google uses various techniques to extract structured information like entities, relationships, and properties from unstructured text on the web and databases. This extracted information is then used to generate knowledge graphs and provide augmented responses to user queries.
2) One key technique is to identify patterns in which tuples of information are stored in databases, and then extract additional tuples by repeating the process and utilizing the identified patterns.
3) Google also extracts entities from user queries and may generate a knowledge graph to answer questions by providing information about the entities from sources like its own knowledge graph and information extracted from the web.
40 Deep #SEO Insights for 2023
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 SEO
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.
Naver속도의, 속도에 의한, 속도를 위한 몽고DB (네이버 컨텐츠검색과 몽고DB) [Naver]
네이버 검색에서 몽고DB 사용에 대한 사용자 경험 공유
- 컨텐츠검색 설명
- 몽고DB인덱스의 이해
- 몽고DB 속도 올리기
책 읽어주는 딥러닝: 배우 유인나가 해리포터를 읽어준다면 DEVIEW 2017
발표 영상 : https://youtu.be/klnfWhPGPRs
코드 : https://github.com/carpedm20/multi-speaker-tacotron-tensorflow
음성 합성 데모 : http://carpedm20.github.io/tacotron
발표 소개 : https://deview.kr/2017/schedule/182
딥러닝을 활용한 음성 합성 기술을 소개하고 개발 경험과 그 과정에서 얻었던 팁을 공유하고자 합니다.
Attacking and defending GraphQL applications: a hands-on approach
DevSecCon Seatlle 2019 - Workshop
The workshop is meant for developers, architects and security folks. During the workshop we will learn how to setup a GraphQL project, define a schema, create Query, Mutation and Subscription for a "fake" social network. We will learn what are the main security issues to consider when developing a GraphQL application:
Introspection: information disclosure
/graphql as a single point of failure (DoS attacks)
IDOR
Broken Access control
Injections
Once we get familiar with the issues, we will explain how to avoid it and/or fix it.
Log file analysis with advertools
This describes a Python workflow script for preparing log files for analysis. It works with any log format (you need to provide a regex), and compresses files to the extremely efficient parquet format.
https://advertools.readthedocs.io/en/master/advertools.logs.html
어떻게 하면 데이터 사이언티스트가 될 수 있나요?
'꿈꾸는 데이터 디자이너' 과정의 학생들에게 소개한
'직업의 세계(?)' 강의 자료 입니다.
https://www.facebook.com/datadesigner2015
데이터를 다루기 위한 스킬과
보면 유용한 책들을 간단히 소개하였습니다.
The Reason Behind Semantic SEO: Why does Google Avoid the Word PageRank?
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.
PoolParty Semantic Classifier
The PoolParty Semantic Classifier is a component of the Semantic Suite, which makes use of machine learning in combination with Knowledge Graphs.
We discuss the potential of the fusion of machine learning, neuronal networks, and knowledge graphs based on use cases and this concrete technology offering.
We introduce the term 'Semantic AI' that refers to the combined usage of various AI methods.
Semantic Search Engine: Semantic Search and Query Parsing with Phrases and En...
This document summarizes several patents related to query parsing and semantic search. It describes patents for multi-stage query processing, query breadth, query analysis, midpage query refinements (search suggestions), context vectors, and categorical quality (re-ranking search results based on the category of the query). Each patent is briefly described, including inventors, filing dates, and some technical details. The document aims to provide an overview of the evolution of semantic search and query understanding technologies at Google.
Opinion-based Article Ranking for Information Retrieval Systems: Factoids and...
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.
BrightonSEO Structured Data by Alexis Sanders
This document provides steps for implementing structured data markup on a website at different levels from beginner to advanced. At a beginner level, it recommends using a structured data generator to automatically output JSON-LD markup. At an intermediate level, it advises reviewing Google's documentation to understand required properties and view examples. For advanced implementation, it suggests exploring Schema.org's full documentation to understand type hierarchies and property descriptions. The document stresses the importance of validating markup using Google's structured data testing tool.
More Related Content
What's hot
딥 러닝 자연어 처리를 학습을 위한 파워포인트. (Deep Learning for Natural Language Processing)
딥 러닝 자연어 처리를 공부하면서 여러가지 기법들을 파워 포인트 그림으로 그려보았습니다. 참고하시라고 업로드합니다.
감사합니다.
대용량 로그분석 Bigquery로 간단히 사용하기 (20170215 T아카데미)
T아카데미 강연 자료 :
https://tacademy.sktechx.com/front/community/techclinic/viewTechClinic.action?seq=131
Elasticsearch 엘라스틱서치 (검색서비스) 에 대해 알아보자.txt
elasticsearch의 기본적인 working에 대한 발표자료입니다.
특히나 logging보다는 '검색 서비스'에 포커싱된 자료이기 때문에 '한글검색' 으로 고통받으실 분들을 위한 기초 자료라 생각해주시면 감사하겠습니다.
맞지않는 정보와 오탈자 그리고 의문점이 든다면 dydwls121200@gmail.com으로 언제든지 가벼운 마음으로 메일주세요. 저 또한 성장시키는 일이기도 하니까요. 환영합니다.
Semantic search Bill Slawski DEEP SEA Con
1) Google uses various techniques to extract structured information like entities, relationships, and properties from unstructured text on the web and databases. This extracted information is then used to generate knowledge graphs and provide augmented responses to user queries.
2) One key technique is to identify patterns in which tuples of information are stored in databases, and then extract additional tuples by repeating the process and utilizing the identified patterns.
3) Google also extracts entities from user queries and may generate a knowledge graph to answer questions by providing information about the entities from sources like its own knowledge graph and information extracted from the web.
40 Deep #SEO Insights for 2023
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 SEO
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.
Naver속도의, 속도에 의한, 속도를 위한 몽고DB (네이버 컨텐츠검색과 몽고DB) [Naver]
네이버 검색에서 몽고DB 사용에 대한 사용자 경험 공유
- 컨텐츠검색 설명
- 몽고DB인덱스의 이해
- 몽고DB 속도 올리기
책 읽어주는 딥러닝: 배우 유인나가 해리포터를 읽어준다면 DEVIEW 2017
발표 영상 : https://youtu.be/klnfWhPGPRs
코드 : https://github.com/carpedm20/multi-speaker-tacotron-tensorflow
음성 합성 데모 : http://carpedm20.github.io/tacotron
발표 소개 : https://deview.kr/2017/schedule/182
딥러닝을 활용한 음성 합성 기술을 소개하고 개발 경험과 그 과정에서 얻었던 팁을 공유하고자 합니다.
Attacking and defending GraphQL applications: a hands-on approach
DevSecCon Seatlle 2019 - Workshop
The workshop is meant for developers, architects and security folks. During the workshop we will learn how to setup a GraphQL project, define a schema, create Query, Mutation and Subscription for a "fake" social network. We will learn what are the main security issues to consider when developing a GraphQL application:
Introspection: information disclosure
/graphql as a single point of failure (DoS attacks)
IDOR
Broken Access control
Injections
Once we get familiar with the issues, we will explain how to avoid it and/or fix it.
Log file analysis with advertools
This describes a Python workflow script for preparing log files for analysis. It works with any log format (you need to provide a regex), and compresses files to the extremely efficient parquet format.
https://advertools.readthedocs.io/en/master/advertools.logs.html
어떻게 하면 데이터 사이언티스트가 될 수 있나요?
'꿈꾸는 데이터 디자이너' 과정의 학생들에게 소개한
'직업의 세계(?)' 강의 자료 입니다.
https://www.facebook.com/datadesigner2015
데이터를 다루기 위한 스킬과
보면 유용한 책들을 간단히 소개하였습니다.
The Reason Behind Semantic SEO: Why does Google Avoid the Word PageRank?
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.
PoolParty Semantic Classifier
The PoolParty Semantic Classifier is a component of the Semantic Suite, which makes use of machine learning in combination with Knowledge Graphs.
We discuss the potential of the fusion of machine learning, neuronal networks, and knowledge graphs based on use cases and this concrete technology offering.
We introduce the term 'Semantic AI' that refers to the combined usage of various AI methods.
Semantic Search Engine: Semantic Search and Query Parsing with Phrases and En...
This document summarizes several patents related to query parsing and semantic search. It describes patents for multi-stage query processing, query breadth, query analysis, midpage query refinements (search suggestions), context vectors, and categorical quality (re-ranking search results based on the category of the query). Each patent is briefly described, including inventors, filing dates, and some technical details. The document aims to provide an overview of the evolution of semantic search and query understanding technologies at Google.
Opinion-based Article Ranking for Information Retrieval Systems: Factoids and...
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.
BrightonSEO Structured Data by Alexis Sanders
This document provides steps for implementing structured data markup on a website at different levels from beginner to advanced. At a beginner level, it recommends using a structured data generator to automatically output JSON-LD markup. At an intermediate level, it advises reviewing Google's documentation to understand required properties and view examples. For advanced implementation, it suggests exploring Schema.org's full documentation to understand type hierarchies and property descriptions. The document stresses the importance of validating markup using Google's structured data testing tool.
What's hot (20)
딥 러닝 자연어 처리를 학습을 위한 파워포인트. (Deep Learning for Natural Language Processing)
딥 러닝 자연어 처리를 학습을 위한 파워포인트. (Deep Learning for Natural Language Processing)
대용량 로그분석 Bigquery로 간단히 사용하기 (20170215 T아카데미)
대용량 로그분석 Bigquery로 간단히 사용하기 (20170215 T아카데미)
Semantic Content Networks - Ranking Websites on Google with Semantic SEO
Semantic Content Networks - Ranking Websites on Google with Semantic SEO
Naver속도의, 속도에 의한, 속도를 위한 몽고DB (네이버 컨텐츠검색과 몽고DB) [Naver]
Naver속도의, 속도에 의한, 속도를 위한 몽고DB (네이버 컨텐츠검색과 몽고DB) [Naver]
Attacking and defending GraphQL applications: a hands-on approach
Attacking and defending GraphQL applications: a hands-on approach
The Reason Behind Semantic SEO: Why does Google Avoid the Word PageRank?
The Reason Behind Semantic SEO: Why does Google Avoid the Word PageRank?
Semantic Search Engine: Semantic Search and Query Parsing with Phrases and En...
Semantic Search Engine: Semantic Search and Query Parsing with Phrases and En...
Opinion-based Article Ranking for Information Retrieval Systems: Factoids and...
Opinion-based Article Ranking for Information Retrieval Systems: Factoids and...
Similar to 누구도 알려주지 않는 크롤링의 함정
불법 스포츠 도박 근절 해커톤
"HTML 태그 순서를 이용한 불법 사이트 탐지 자동화 기술" (이기룡, 이희조. (2016). HTML 태그 순서를 이용한 불법 사이트 탐지 자동화 기술. 정보과학회논문지, 43(10), 1173-1178.)
기반 불법 스포츠도박 사이트 탐지기의 구현
불법 스포츠도박 근절 해커톤에 참가해서 만들어낸 성과를 공유합니다.
후기는
https://byeongsupark.github.io/blog/sportshackathon/hackathon
에서 보실 수 있습니다.
[법무법인 민후 | 김경환 변호사]인터넷 공간의 잘못된 기사와 새로운 피해 구제 방안 (인터넷기사)
2014. 12. 4 언론중재위 정책심포지엄 / 인터넷 기사의 현실과 법적 문제, 구제수단 등
[제2회 신기술 경영과 법 세미나] 블록체인과 법률 이슈
*일시 : 2017. 7. 14 (금)
*제2회 신기술 경영과 법 세미나 (가상화폐·블록체인과 법적 이슈)
*지난 7월 14일 법무법인 민후의 주최로 개최된 제2회 신기술 경영과 법 세미나에서 법무법인 민후 김경환 대표 변호사가 강연한 자료입니다.
블록체인의 개념에서부터 블록체인 기술의 유형과 진화, 실제 사례는 물론 블록체인과 개인정보, 지식재산권과의 관계에 대해 상세히 설명합니다.
Similar to 누구도 알려주지 않는 크롤링의 함정 (7)
[법무법인 민후 | 김경환 변호사]인터넷 공간의 잘못된 기사와 새로운 피해 구제 방안 (인터넷기사)
[법무법인 민후 | 김경환 변호사]인터넷 공간의 잘못된 기사와 새로운 피해 구제 방안 (인터넷기사)
More from Herren
Javascript 생태계
JavaScript ecosystem by Seha
자바스크립트의 역사, 특징 및 라이브러리, 프레임워크, 기타도구에 대한 정리
문제가 될 시 삭제하겠습니다(__)
UI UX by luna
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive functioning. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms.
More from Herren (15)
누구도 알려주지 않는 크롤링의 함정
- 1. 누구도 알려주지 않는 크롤링의 함정 (주)헤렌 1타강사 Hong3
- 2. 크롤링(Crawling) 크롤링이란 단어는 웹 크롤러(crawler)라는 단어 에서 시작 크롤러란 조직적, 자동화된 방법으로 웹을 탐색, 수집하는 행위 또는 프로그램 ex) 구글,네이버등의 검색엔진 결과 데이터를 수 집하기 위한 봇(bot)
- 3. 스크래핑(Scraping) 웹 사이트의 내용을 긁어다 원하는 형태로 가공 하는 것. 웹 사이트에서 정보를 추출하는 행위 또는 프로 그램 ex) 상품별 가격을 알기 위해 해당 상품을 파는 페이지 들의 가격을 추출 하는 행위
- 4. 크롤링과 스크래핑은 다릅니다. 하지만 우리는 보편적으로 크롤링 = 스크래핑 으 로 이해하고 사용합니다. 이번 자료에서는 설명을 위해 크롤링 = 스크래핑 으로 통칭합니다.
- 5. 이번 스터디에서는 누구도 알려주지 않는 크롤 링의 함정 합법적인 크롤링과 불법적인 크롤링에 대해 사례를 통해 알아보겠습니다.
- 6. 합법적인 크롤링과 불법적인 크 롤링
- 7. 합법? 불법? 합법의 부정어는 불법 합법 : 법령이나 규범에 적합 -> 비범죄 불법 : 법에 어긋남 -> 범죄 TMI) 합법과 불법의 사이에는 탈법이라는게 존재
- 8. 합법적인 크롤링 사이트 운영자의 의사에 반하지 않은 크롤링을 의미
- 9. 불법적인 크롤링 불법적인 크롤링은 사이트 운영자의 의사에 반 하거나 또는 실정법을 어긴 크롤링을 의미
- 10. 그럼 구분은 어떻게?웹 사이트에서 웹서버의 홈디렉토리에 위치한 robots.txt 파일에 포괄 적인 크롤링 금지 또는 특정 검색엔진의 크롤링 금지, 특정 디렉토리 에 대한 크롤링 금지 등을 표시하였음에도 불구하고, 그 표시를 무시 하고 크롤링을 하였다면 이는 사이트 운영자의 의사에 반한 크롤링에 해당함 웹사이트 운영자는 robots.txt 외에 메인페이지의 하단, 약관 등에 크롤 링 금지를 표시할 수도 있다. 이를 무시하고 크롤링이 이루어졌다면 이 역시 사이트 운영자의 의사 에 반한 크롤링이라고 할 수 있다.
- 12. 크롤링으로 인한 분쟁1.엠파스 열린검색 (검색하면 다 검색엔진의 결과도 다 나왔음 -> robots.txt 를 무시) -> 법적 분쟁 X 2.리그베다위키와 엔하위키의 법적 분쟁 (엔하위키가 리그베다위키 를 미러링 하여 정보를 수집) -> 리그베다위키 승소 3. 잡코리아와 사람인의 법적 분쟁 -> 사람인 승소 4. 여기어때와 야놀자의 니가가라 2위싸움 -> 19년 현재 진행중 TMI) 2,3번의 승소를 이끈건 법무법인 민후 여러분도 법적분쟁에 말리게 된다면…
- 13. 잡코리아와 사람인의 법적 분쟁 사건의 개요
- 14. 잡코리아와 사람인의 법적 분쟁 1.사람인은 잡코리아의 정보를 크롤링하여 정보를 수집 수집도중 사람인에 게시되지 않은 정보를 발견하였을 때 해당 회사에 연락하여 자신의사이트에 게시해도 되냐고 물어보고 허락했을 경우 해당 구인공고를 자신의 사이트에 등록함
- 15. 잡코리아와 사람인의 법적 분쟁 2. 잡코리아는 기분이 너무 나쁨 x나 힘들게 구했는데 경쟁사는 너무나도 쉽게 구인공고를 구함 코리아는 robots.txt에 네이버,구글등의 검색엔진에게는 크롤링을 허가한 상 모든정보를 가져가라고 허락하지는 않았다고함
- 16. 잡코리아와 사람인의 법적 분쟁 3. 문제는 사람인의 크롤러는 잡코리아의 robots.txt에 접근 한적이 없었음 다지를 캐는 사람인의 크롤러를 보다 못한 잡코리아는 사람인의 IP를 차단 하지만 사람인은 VPN을 사용하여 또 다시 크롤링을 함
- 18. 잡코리아와 사람인의 법적 분쟁 잡코리아는 2008년 사람인을 고소함 그리고 9년이 지난 2017년에 법적분쟁은 마무리되었음
- 19. 잡코리아와 사람인의 법적 분쟁 결과는 잡코리아의 승소 법정은 사람인의 행위는 부정경쟁행위에 해당 구인공고 396건을 폐기하고 건당 50만원씩 1억9천800만원을 배상하라고 판결 사람인은 항소 했으나 오히려 죄와 벌금만 늘어나고 또 패배 물론 3심도 신청했지만 기각 되어 법적분쟁이 종료됨
- 20. 잡코리아와 사람인의 법적 분쟁 사람인의 주장 1. 웹크롤링은 불법이 아님 2. 수집한 정보를 무작정 올린것도 아니고 구인회사의 허락을 받았음 3. 목적글은 사이트 운영자가 저작권 행사할 수 없음 의 저작권행사는 글을 작성한 사람이 본문내용에 저작권 권리 행사글을 첨부 따라서 저작권 권리 행사글이 없는 목적글은 긁어도 불법이 되지 않음
- 21. 잡코리아와 사람인의 법적 분쟁 당시 법원의 판례(1심) “원고(잡코리아)는 자신의 정체를 명시하고 원고 웹사이트를 출처로 표시하는 아웃링크 기능을 통해 이용자를 원고 웹 “피고는 가상사설망을 쓰는 VPN 업체를 통해 IP를 여러 개 로 분산한 뒤 검색로봇의 User-Agent에 피고의 정체를 명 “피고가 원고 웹사이트의 HTML 소스를 기계적인 방법 을 사용해 대량복제하여 피고 웹사이트에 게재하고 자신의 영
- 22. 잡코리아와 사람인의 법적 분쟁 2심에서 사람인은 네임드 법무법인 김앤장을 데리고옴 하지만 잡코리아는 데이터베이스권 침해라는 내용을 추가하여 대응
- 23. 잡코리아와 사람인의 법적 분쟁 잡코리아의 주장 코리아 웹사이트는 저작권법상 데이터베이스에 해당하고, 잡코리아는 그 웹 트의 제작이나 그 소재(채용정보)의 갱신/검증 또는 보충에 인적 또는 물적으 했으므로 잡코리아 웹사이트에 대해 데이터베이스 제작자의 지위와 권리를
- 24. 잡코리아와 사람인의 법적 분쟁 당시 법원의 판례(항소심) 인적 또는 물적으로 상당한 투자를 했고 그 소재의 갱신/검증 또는 보충을 위하여도 인적 또는 물적으로 상당한 투자를 게재행위에 의해 저작권법 제93조 제2항, 제1항에서 정하고 있는 원고의 데이터베이스 제작자의 권리가 침해됐다고 보 따라서 피고인 사람인HR은 잡코리아 웹사이트의 채용정보들을 모두 폐기할 의무가 있다"
- 25. 잡코리아와 사람인의 법적 분쟁 사람인은 1심에서 끝나면 약 2억원에 퉁칠수 있었으나 데이터베이스권 침해까지 포함하여 4억5천만원을 배상하게 됨
- 26. 잡코리아와 사람인의 법적 분쟁 소송을 담당한 법무법인 민후 김경환 대표변호사는 "이번 사건은 사용자제
- 27. 불법적인 크롤링 자체로 처벌을 받지는 않았음 하지만 크롤링을 통하여 얻은 데이터로 인한 법적분쟁이 발생하였고 처벌을 받았음(불공정경쟁행위,데이터베이스권 침해,저작권)
- 28. 결론 1. 덮어놓고 긁다보면 거지꼴을 못면한다. 2. 법적분쟁이 발생한다면(했다면) 법무법인 민후를 찾아가자
- 30. :wq