How is organized data used by some web players having not the best intentions?
How can tools that try to help individual authors be subverted by spammers?
Also, how does Zemanta work and why are we interested in this topic.
The document discusses search engines and how they have evolved over time. It explains that early search engines ranked results based mainly on content, while modern engines also consider factors like page structure, popularity, and reputation. The document provides definitions of key search-related terms and outlines some of the main components and processes involved in how search engines work, such as crawling websites, indexing pages, and ranking results. It also discusses different types of search tools and how to choose the best one depending on your information needs.
Semantic search uses language processing to analyze the meaning of content and search queries to return more relevant results. It involves classifying content using taxonomies, identifying named entities, extracting relationships between entities, and matching these based on meaning. Implementing semantic search requires preparing content through classification, metadata, and information architecture, as well as technologies for semantic tagging, entity extraction, triple stores, and integrating these capabilities with existing search and content management systems.
Leveraging Lucene/Solr as a Knowledge Graph and Intent EngineTrey Grainger
Search engines frequently miss the mark when it comes to understanding user intent. This talk will describe how to overcome this by leveraging Lucene/Solr to power a knowledge graph that can extract phrases, understand and weight the semantic relationships between those phrases and known entities, and expand the query to include those additional conceptual relationships. For example, if a user types in (Senior Java Developer Portland, OR Hadoop), you or I know that the term “senior” designates an experience level, that “java developer” is a job title related to “software engineering”, that “portland, or” is a city with a specific geographical boundary, and that “hadoop” is a technology related to terms like “hbase”, “hive”, and “map/reduce”. Out of the box, however, most search engines just parse this query as text:((senior AND java AND developer AND portland) OR (hadoop)), which is not at all what the user intended. We will discuss how to train the search engine to parse the query into this intended understanding, and how to reflect this understanding to the end user to provide an insightful, augmented search experience. Topics: Semantic Search, Finite State Transducers, Probabilistic Parsing, Bayes Theorem, Augmented Search, Recommendations, NLP, Knowledge Graphs
The document discusses intelligent information retrieval techniques for web search, including:
- Using web crawlers or spiders to index pages and follow links to discover new pages
- Analyzing links and citations between pages to determine authoritative pages or related topics
- Updating indexes as pages change and employing focused crawlers to prioritize certain types of pages
It provides examples of early search engines and discusses challenges of searching the large, heterogeneous and constantly changing web corpus.
Data.Mining.C.8(Ii).Web Mining 570802461Margaret Wang
This document provides an overview of web mining and algorithms for analyzing web structure like PageRank and HITS. It discusses how web pages can be viewed as nodes in a network and hyperlinks as connections between nodes. PageRank determines importance based on the number and quality of inbound links to a page, while HITS identifies authoritative pages that many hubs point to and vice versa in a mutually reinforcing relationship. The document explains how these algorithms were inspired by models of prestige and authority in social networks.
This document discusses semantic search and how thesauri can improve search experiences. It describes different types of semantic searches and demands for smarter searches. PoolParty Semantic Search is presented as a solution that leverages thesauri to provide auto-complete, query expansion, faceted search, and integration of linked data from multiple sources. A live demo of PoolParty Semantic Search is available online.
Hypermedia In Practice - FamilySearch Developers Conference 2014Ryan Heaton
The document discusses how hypermedia principles can make web APIs more robust and adaptable to changes. It provides examples of how resources and their representations may change, such as property order, whitespace, namespaces, caching policies, available resources, domain names, resource locations, and inclusion of subresources. The document advocates using hypermedia to link related resources and handle changes gracefully.
The document outlines the agenda for a taxonomy design best practices workshop. The 6-part outline includes: 1) Introduction to taxonomies and other knowledge organization systems, 2) Taxonomies in support of search, 3) Term creation, 4) Term relationships, 5) Structural design, and 6) User displays. The workshop will cover topics such as different types of controlled vocabularies, standards and models, searching and browsing taxonomies, creating taxonomy terms and relationships, and designing taxonomy structures and user interfaces.
The document discusses search engines and how they have evolved over time. It explains that early search engines ranked results based mainly on content, while modern engines also consider factors like page structure, popularity, and reputation. The document provides definitions of key search-related terms and outlines some of the main components and processes involved in how search engines work, such as crawling websites, indexing pages, and ranking results. It also discusses different types of search tools and how to choose the best one depending on your information needs.
Semantic search uses language processing to analyze the meaning of content and search queries to return more relevant results. It involves classifying content using taxonomies, identifying named entities, extracting relationships between entities, and matching these based on meaning. Implementing semantic search requires preparing content through classification, metadata, and information architecture, as well as technologies for semantic tagging, entity extraction, triple stores, and integrating these capabilities with existing search and content management systems.
Leveraging Lucene/Solr as a Knowledge Graph and Intent EngineTrey Grainger
Search engines frequently miss the mark when it comes to understanding user intent. This talk will describe how to overcome this by leveraging Lucene/Solr to power a knowledge graph that can extract phrases, understand and weight the semantic relationships between those phrases and known entities, and expand the query to include those additional conceptual relationships. For example, if a user types in (Senior Java Developer Portland, OR Hadoop), you or I know that the term “senior” designates an experience level, that “java developer” is a job title related to “software engineering”, that “portland, or” is a city with a specific geographical boundary, and that “hadoop” is a technology related to terms like “hbase”, “hive”, and “map/reduce”. Out of the box, however, most search engines just parse this query as text:((senior AND java AND developer AND portland) OR (hadoop)), which is not at all what the user intended. We will discuss how to train the search engine to parse the query into this intended understanding, and how to reflect this understanding to the end user to provide an insightful, augmented search experience. Topics: Semantic Search, Finite State Transducers, Probabilistic Parsing, Bayes Theorem, Augmented Search, Recommendations, NLP, Knowledge Graphs
The document discusses intelligent information retrieval techniques for web search, including:
- Using web crawlers or spiders to index pages and follow links to discover new pages
- Analyzing links and citations between pages to determine authoritative pages or related topics
- Updating indexes as pages change and employing focused crawlers to prioritize certain types of pages
It provides examples of early search engines and discusses challenges of searching the large, heterogeneous and constantly changing web corpus.
Data.Mining.C.8(Ii).Web Mining 570802461Margaret Wang
This document provides an overview of web mining and algorithms for analyzing web structure like PageRank and HITS. It discusses how web pages can be viewed as nodes in a network and hyperlinks as connections between nodes. PageRank determines importance based on the number and quality of inbound links to a page, while HITS identifies authoritative pages that many hubs point to and vice versa in a mutually reinforcing relationship. The document explains how these algorithms were inspired by models of prestige and authority in social networks.
This document discusses semantic search and how thesauri can improve search experiences. It describes different types of semantic searches and demands for smarter searches. PoolParty Semantic Search is presented as a solution that leverages thesauri to provide auto-complete, query expansion, faceted search, and integration of linked data from multiple sources. A live demo of PoolParty Semantic Search is available online.
Hypermedia In Practice - FamilySearch Developers Conference 2014Ryan Heaton
The document discusses how hypermedia principles can make web APIs more robust and adaptable to changes. It provides examples of how resources and their representations may change, such as property order, whitespace, namespaces, caching policies, available resources, domain names, resource locations, and inclusion of subresources. The document advocates using hypermedia to link related resources and handle changes gracefully.
The document outlines the agenda for a taxonomy design best practices workshop. The 6-part outline includes: 1) Introduction to taxonomies and other knowledge organization systems, 2) Taxonomies in support of search, 3) Term creation, 4) Term relationships, 5) Structural design, and 6) User displays. The workshop will cover topics such as different types of controlled vocabularies, standards and models, searching and browsing taxonomies, creating taxonomy terms and relationships, and designing taxonomy structures and user interfaces.
The document discusses the semantic web and how it can potentially disrupt or benefit online commerce. It provides definitions and explanations of key concepts related to the semantic web including RDF, ontologies, linked data, and semantic search. It outlines how search engines and websites are increasingly adopting and leveraging semantic web technologies like RDFa to provide richer search results and experiences for users.
The document provides an overview of search engine optimization (SEO) concepts, including:
1) The importance of SEO for driving online and offline sales.
2) How search engines work and are composed of web crawlers and databases to index web pages.
3) Key factors search engines use to evaluate and rank pages, such as relevance, importance, links, and content.
4) Techniques for improving rankings, like optimizing titles, meta tags, and adding relevant and quality backlinks.
This document discusses various conventional indexing techniques used to improve the speed of data retrieval from databases and data warehouses. It describes dense indexing, sparse indexing, and multi-level or B-tree indexing. It explains that indexing provides pointers to the location of data, avoiding the need to sequentially scan entire data files. The document also covers hashing-based indexes and compares B-tree indexes, which support range queries, to hashing indexes, which are best for exact match queries.
Thought Vectors and Knowledge Graphs in AI-powered SearchTrey Grainger
While traditional keyword search is still useful, pure text-based keyword matching is quickly becoming obsolete; today, it is a necessary but not sufficient tool for delivering relevant results and intelligent search experiences.
In this talk, we'll cover some of the emerging trends in AI-powered search, including the use of thought vectors (multi-level vector embeddings) and semantic knowledge graphs to contextually interpret and conceptualize queries. We'll walk through some live query interpretation demos to demonstrate the power that can be delivered through these semantic search techniques leveraging auto-generated knowledge graphs learned from your content and user interactions.
Faceted Navigation of User-Generated Metadata (Calit2 Rescue Seminar Series 2...Bradley Allen
Faceted navigation relies on metadata to organize and navigate large collections of information. Users are becoming an important source of metadata in the form of user-generated tags and annotations. By combining user-generated metadata with traditional subject indexing, new applications of faceted navigation can be created that bridge folksonomies and taxonomies to provide more compelling ways to explore and discover online information.
The document provides guidance on how to conduct effective searches on the internet and evaluate the results. It discusses using specific search terms and operators like "+" and "-" to include or exclude terms. It also covers evaluating search results based on the accuracy, authority, objectivity, currency and coverage of websites. Formatting citations in APA and MLA styles is also addressed.
Semantic Search tutorial at SemTech 2012Peter Mika
This document provides an introduction to a semantic search tutorial given by Peter Mika and Tran Duc Thanh. The agenda covers semantic web data, including the RDF data model and publishing RDF data. It also covers query processing, ranking, result presentation, evaluation, and a question period. The document discusses why semantic search is needed to address poorly solved queries and enable novel search tasks using structured data and background knowledge.
Presentation of the Semantic Knowledge Graph research paper at the 2016 IEEE 3rd International Conference on Data Science and Advanced Analytics (Montreal, Canada - October 18th, 2016)
Abstract—This paper describes a new kind of knowledge representation and mining system which we are calling the Semantic Knowledge Graph. At its heart, the Semantic Knowledge Graph leverages an inverted index, along with a complementary uninverted index, to represent nodes (terms) and edges (the documents within intersecting postings lists for multiple terms/nodes). This provides a layer of indirection between each pair of nodes and their corresponding edge, enabling edges to materialize dynamically from underlying corpus statistics. As a result, any combination of nodes can have edges to any other nodes materialize and be scored to reveal latent relationships between the nodes. This provides numerous benefits: the knowledge graph can be built automatically from a real-world corpus of data, new nodes - along with their combined edges - can be instantly materialized from any arbitrary combination of preexisting nodes (using set operations), and a full model of the semantic relationships between all entities within a domain can be represented and dynamically traversed using a highly compact representation of the graph. Such a system has widespread applications in areas as diverse as knowledge modeling and reasoning, natural language processing, anomaly detection, data cleansing, semantic search, analytics, data classification, root cause analysis, and recommendations systems. The main contribution of this paper is the introduction of a novel system - the Semantic Knowledge Graph - which is able to dynamically discover and score interesting relationships between any arbitrary combination of entities (words, phrases, or extracted concepts) through dynamically materializing nodes and edges from a compact graphical representation built automatically from a corpus of data representative of a knowledge domain.
This document summarizes a lecture on data integration. It discusses key challenges in data integration including providing uniform access to multiple autonomous and heterogeneous data sources. It describes common solutions like data warehousing and the virtual integration approach. Research projects on data integration and current industry solutions are also mentioned. Key concepts in data integration like wrappers, mediated schemas, query reformulation, and optimization are covered at a high level.
Lexical Pattern- Based Approach for Extracting Name AliasesIJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
It's 2017, and I still want to sell you a graph databaseSwanand Pagnis
The aha!s and the oh-noe!s of over one year of building our product with a graph database, Neo4j, along with big brother PostgreSQL and hipster cousin Redis with Rails.
This talk will attempt to answer an important question, "when does using a graph database make sense?", through retrospection.
The document discusses PageRank and HITS algorithms for web structure mining. It provides an overview of key concepts like hubs, authorities, and link analysis. It then explains PageRank in detail, including how it is calculated iteratively based on the prestige of inbound links. Finally, it provides an example calculation and discusses how additional inbound links can increase a page's PageRank.
The document discusses semantic search capabilities at Yahoo. It describes how Yahoo has developed techniques to extract structured data and metadata from webpages to power enhanced search results. This includes information extraction, data fusion, and curating knowledge in a graph. Yahoo uses this knowledge to better understand search queries and present relevant entities and attributes in results. Semantic search remains an active area of research.
The document provides an overview of the Zemanta API, which processes text to extract semantic information like tags, categories, concepts, entities, and related articles and images. It analyzes natural language to identify meaningful data that can then be linked to external databases. The API aims to bridge human understandable text and computer-processable data through natural language processing and knowledge extraction techniques. It has a variety of uses including content discovery, automatic information delivery, and linking data across information networks.
Commande de Champagne Gaston Revolte au Carre Parisien les 12 et 20 decembre ...Agence Double Numérique
Champagne Gaston Révolte reviens vers vous avec trois
lieux parisiens où vous pourrez
nous trouver courant décembre 2012 et
faire vos achats pour les fêtes.
Afin d’être servi au mieux, il est
souhaitable que vous nous passiez
auparavant votre
commande.
Dans l’attente de vous revoir, recevez
toute notre gratitude.
Hélène et Hubert Révolte
contact@champagne-gaston-revolte.fr
Method and Apparatus for Tunneling by Melting (Patent US 3693731 A)swilsonmc
A machine and method for drilling bore holes and tunnels by melting in which a housing is provided for supporting a heat source and a heated end portion and in which the necessary melting heat is delivered to the walls of the end portion at a rate suft'lcient to melt rock and during operation of which the molten material may be disposed adjacent the boring zone in cracks in the rock and as a vitreous wail lining of the tunnel so formed. The heat source can be electrical or nuclear but for deep drilling is preferably a nuclear reactor.
Amit P. Sheth, “Relationships at the Heart of Semantic Web: Modeling, Discovering, Validating and Exploiting Complex Semantic Relationships,” Keynote at the 29th Conference on Current Trends in Theory and Practice of Informatics (SOFSEM 2002), Milovy, Czech Republic, November 22–29, 2002.
Keynote: http://www.sofsem.cz/sofsem02/keynote.html
Related paper: http://knoesis.wright.edu/?q=node/2063
SEMANTIC CONTENT MANAGEMENT FOR ENTERPRISES AND NATIONAL SECURITYAmit Sheth
Amit Sheth, SEMANTIC CONTENT MANAGEMENT FOR ENTERPRISES AND NATIONAL SECURITY, Keynote at:
CONTENT- AND SEMANTIC-BASED INFORMATION RETRIEVAL @ SCI 2002.
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
The document discusses the semantic web and how it can potentially disrupt or benefit online commerce. It provides definitions and explanations of key concepts related to the semantic web including RDF, ontologies, linked data, and semantic search. It outlines how search engines and websites are increasingly adopting and leveraging semantic web technologies like RDFa to provide richer search results and experiences for users.
The document provides an overview of search engine optimization (SEO) concepts, including:
1) The importance of SEO for driving online and offline sales.
2) How search engines work and are composed of web crawlers and databases to index web pages.
3) Key factors search engines use to evaluate and rank pages, such as relevance, importance, links, and content.
4) Techniques for improving rankings, like optimizing titles, meta tags, and adding relevant and quality backlinks.
This document discusses various conventional indexing techniques used to improve the speed of data retrieval from databases and data warehouses. It describes dense indexing, sparse indexing, and multi-level or B-tree indexing. It explains that indexing provides pointers to the location of data, avoiding the need to sequentially scan entire data files. The document also covers hashing-based indexes and compares B-tree indexes, which support range queries, to hashing indexes, which are best for exact match queries.
Thought Vectors and Knowledge Graphs in AI-powered SearchTrey Grainger
While traditional keyword search is still useful, pure text-based keyword matching is quickly becoming obsolete; today, it is a necessary but not sufficient tool for delivering relevant results and intelligent search experiences.
In this talk, we'll cover some of the emerging trends in AI-powered search, including the use of thought vectors (multi-level vector embeddings) and semantic knowledge graphs to contextually interpret and conceptualize queries. We'll walk through some live query interpretation demos to demonstrate the power that can be delivered through these semantic search techniques leveraging auto-generated knowledge graphs learned from your content and user interactions.
Faceted Navigation of User-Generated Metadata (Calit2 Rescue Seminar Series 2...Bradley Allen
Faceted navigation relies on metadata to organize and navigate large collections of information. Users are becoming an important source of metadata in the form of user-generated tags and annotations. By combining user-generated metadata with traditional subject indexing, new applications of faceted navigation can be created that bridge folksonomies and taxonomies to provide more compelling ways to explore and discover online information.
The document provides guidance on how to conduct effective searches on the internet and evaluate the results. It discusses using specific search terms and operators like "+" and "-" to include or exclude terms. It also covers evaluating search results based on the accuracy, authority, objectivity, currency and coverage of websites. Formatting citations in APA and MLA styles is also addressed.
Semantic Search tutorial at SemTech 2012Peter Mika
This document provides an introduction to a semantic search tutorial given by Peter Mika and Tran Duc Thanh. The agenda covers semantic web data, including the RDF data model and publishing RDF data. It also covers query processing, ranking, result presentation, evaluation, and a question period. The document discusses why semantic search is needed to address poorly solved queries and enable novel search tasks using structured data and background knowledge.
Presentation of the Semantic Knowledge Graph research paper at the 2016 IEEE 3rd International Conference on Data Science and Advanced Analytics (Montreal, Canada - October 18th, 2016)
Abstract—This paper describes a new kind of knowledge representation and mining system which we are calling the Semantic Knowledge Graph. At its heart, the Semantic Knowledge Graph leverages an inverted index, along with a complementary uninverted index, to represent nodes (terms) and edges (the documents within intersecting postings lists for multiple terms/nodes). This provides a layer of indirection between each pair of nodes and their corresponding edge, enabling edges to materialize dynamically from underlying corpus statistics. As a result, any combination of nodes can have edges to any other nodes materialize and be scored to reveal latent relationships between the nodes. This provides numerous benefits: the knowledge graph can be built automatically from a real-world corpus of data, new nodes - along with their combined edges - can be instantly materialized from any arbitrary combination of preexisting nodes (using set operations), and a full model of the semantic relationships between all entities within a domain can be represented and dynamically traversed using a highly compact representation of the graph. Such a system has widespread applications in areas as diverse as knowledge modeling and reasoning, natural language processing, anomaly detection, data cleansing, semantic search, analytics, data classification, root cause analysis, and recommendations systems. The main contribution of this paper is the introduction of a novel system - the Semantic Knowledge Graph - which is able to dynamically discover and score interesting relationships between any arbitrary combination of entities (words, phrases, or extracted concepts) through dynamically materializing nodes and edges from a compact graphical representation built automatically from a corpus of data representative of a knowledge domain.
This document summarizes a lecture on data integration. It discusses key challenges in data integration including providing uniform access to multiple autonomous and heterogeneous data sources. It describes common solutions like data warehousing and the virtual integration approach. Research projects on data integration and current industry solutions are also mentioned. Key concepts in data integration like wrappers, mediated schemas, query reformulation, and optimization are covered at a high level.
Lexical Pattern- Based Approach for Extracting Name AliasesIJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
It's 2017, and I still want to sell you a graph databaseSwanand Pagnis
The aha!s and the oh-noe!s of over one year of building our product with a graph database, Neo4j, along with big brother PostgreSQL and hipster cousin Redis with Rails.
This talk will attempt to answer an important question, "when does using a graph database make sense?", through retrospection.
The document discusses PageRank and HITS algorithms for web structure mining. It provides an overview of key concepts like hubs, authorities, and link analysis. It then explains PageRank in detail, including how it is calculated iteratively based on the prestige of inbound links. Finally, it provides an example calculation and discusses how additional inbound links can increase a page's PageRank.
The document discusses semantic search capabilities at Yahoo. It describes how Yahoo has developed techniques to extract structured data and metadata from webpages to power enhanced search results. This includes information extraction, data fusion, and curating knowledge in a graph. Yahoo uses this knowledge to better understand search queries and present relevant entities and attributes in results. Semantic search remains an active area of research.
The document provides an overview of the Zemanta API, which processes text to extract semantic information like tags, categories, concepts, entities, and related articles and images. It analyzes natural language to identify meaningful data that can then be linked to external databases. The API aims to bridge human understandable text and computer-processable data through natural language processing and knowledge extraction techniques. It has a variety of uses including content discovery, automatic information delivery, and linking data across information networks.
Commande de Champagne Gaston Revolte au Carre Parisien les 12 et 20 decembre ...Agence Double Numérique
Champagne Gaston Révolte reviens vers vous avec trois
lieux parisiens où vous pourrez
nous trouver courant décembre 2012 et
faire vos achats pour les fêtes.
Afin d’être servi au mieux, il est
souhaitable que vous nous passiez
auparavant votre
commande.
Dans l’attente de vous revoir, recevez
toute notre gratitude.
Hélène et Hubert Révolte
contact@champagne-gaston-revolte.fr
Method and Apparatus for Tunneling by Melting (Patent US 3693731 A)swilsonmc
A machine and method for drilling bore holes and tunnels by melting in which a housing is provided for supporting a heat source and a heated end portion and in which the necessary melting heat is delivered to the walls of the end portion at a rate suft'lcient to melt rock and during operation of which the molten material may be disposed adjacent the boring zone in cracks in the rock and as a vitreous wail lining of the tunnel so formed. The heat source can be electrical or nuclear but for deep drilling is preferably a nuclear reactor.
Amit P. Sheth, “Relationships at the Heart of Semantic Web: Modeling, Discovering, Validating and Exploiting Complex Semantic Relationships,” Keynote at the 29th Conference on Current Trends in Theory and Practice of Informatics (SOFSEM 2002), Milovy, Czech Republic, November 22–29, 2002.
Keynote: http://www.sofsem.cz/sofsem02/keynote.html
Related paper: http://knoesis.wright.edu/?q=node/2063
SEMANTIC CONTENT MANAGEMENT FOR ENTERPRISES AND NATIONAL SECURITYAmit Sheth
Amit Sheth, SEMANTIC CONTENT MANAGEMENT FOR ENTERPRISES AND NATIONAL SECURITY, Keynote at:
CONTENT- AND SEMANTIC-BASED INFORMATION RETRIEVAL @ SCI 2002.
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
JAVA 2013 IEEE DATAMINING PROJECT Annotating search results from web databasesIEEEGLOBALSOFTTECHNOLOGIES
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
E-commerce Search Engine with Apache Lucene/SolrVincenzo D'Amore
An introduction to the Search World with a special eye to E-Commerce by passing Apache Lucene and Solr. Explaining how and why to use a search engine, explaining what are the differences between rdbms and full text search, between the common search and the search applied to the e-commerce world. Also explaining what are the salient differences between Lucene and Solr.
How to build a data lake with aws glue data catalog (ABD213-R) re:Invent 2017Amazon Web Services
As data volumes grow and customers store more data on AWS, they often have valuable data that is not easily discoverable and available for analytics. The AWS Glue Data Catalog provides a central view of your data lake, making data readily available for analytics. We introduce key features of the AWS Glue Data Catalog and its use cases. Learn how crawlers can automatically discover your data, extract relevant metadata, and add it as table definitions to the AWS Glue Data Catalog. We will also explore the integration between AWS Glue Data Catalog and Amazon Athena, Amazon EMR, and Amazon Redshift Spectrum.
Smartlogic, Semaphore and Semantically Enhanced Search – For “Discovery”voginip
Smartlogic provides semantic search and content intelligence solutions to unlock business value from unstructured content. Their software, Semaphore, uses natural language processing and machine learning to build ontologies and automatically annotate content with metadata, enabling more sophisticated search and discovery of hidden knowledge within large volumes of documents. Semaphore integrates with various systems and delivers benefits such as cost savings from more efficient content exploration, risk reduction through improved compliance, and competitive advantages from making better use of organizational intelligence in content.
Smartlogic, Semaphore and Semantically Enhanced Search – For “Discovery”VOGIN-academie
Smartlogic provides semantic search and content intelligence solutions to unlock business value from unstructured content. Their solution, Semaphore, uses natural language processing and machine learning to automatically enrich content with metadata, extract entities and facts, and categorize content according to customizable semantic models or ontologies. This helps organizations more effectively search, discover, and leverage information across diverse content sources. Semaphore delivers enhanced search capabilities, automated categorization, and tools to build and manage semantic models collaboratively. Customers report benefits such as reduced time spent searching, lower classification costs, and reduced risk of non-compliance by making more information accessible.
Content Management, Metadata and Semantic WebAmit Sheth
The document discusses new challenges in content management, including information overload and the need for semantic metadata and ontologies to improve relevance and personalization. It proposes that next-generation content management should leverage semantic technologies like knowledge bases, classification, metadata extraction and semantic engines to organize content semantically rather than just structurally. This will help enterprises better distribute the right content to the right users.
Content Management, Metadata and Semantic WebAmit Sheth
Keynote given at NetObjectDays conference, Erfurt, September 11, 2001.
One of the earliest keynotes discussing commercial semantic web technologies, semantic web applications (including semantic search, semantic targeting, semantic content management). Prof. Sheth started a Semantic Web company Taalee, Inc. in 1999 (Product was MediaAnywhere A/V search engine),that merged to become Voquette in 2001 (product was called SCORE), Semagix in 2004 (product was called Semagix Freedom), and then Fortent in 2006 (products included Know Your Customers). Additional details can be found in U.S. Patent #6311194, 30 Oct. 2001 (filed 2000).
Note: the commercial system used "WorldModel" as at the time, business customers were not yet warm to "Ontology" - the concept/intent is the same. More recent information at http://knoesis.org
The document discusses how content analytics can enhance search capabilities. It provides examples of how key phrases, collocations, and statistically improbable phrases can be used to power related searches, cluster results, and enable faceted search. Beyond search, these content analytics techniques can be applied to applications like product recommendations, social media analysis, and customer experience analytics.
Humantics | Optimizing Your Content Strategy in an Entity-Driven WorldGrant Simmons
The document discusses strategies for creating content that both satisfies human readers and is well understood by search engines, referred to as "Humantics". It outlines the CARL framework for context, aim, relationships, and links and provides examples of tools that can be used to analyze content, find entities, connections, and questions to ensure the content will be salient for search engines. The goal is to build content that search engines can fully understand in order to rank well without relying solely on keywords.
This document discusses how search engines are leveraging artificial intelligence and machine learning to better understand user queries. It introduces concepts like Humantics, which refers to the intersection of human actions and search engine understanding. Various AI systems used by search engines are discussed, including BERT, RankBrain, and MUM. The document also covers how entities, relationships, and context are important for search engines to understand content. Various tools for identifying entities in text are also mentioned.
Crowdsourced query augmentation through the semantic discovery of domain spec...Trey Grainger
Talk Abstract: Most work in semantic search has thus far focused upon either manually building language-specific taxonomies/ontologies or upon automatic techniques such as clustering or dimensionality reduction to discover latent semantic links within the content that is being searched. The former is very labor intensive and is hard to maintain, while the latter is prone to noise and may be hard for a human to understand or to interact with directly. We believe that the links between similar user’s queries represent a largely untapped source for discovering latent semantic relationships between search terms. The proposed system is capable of mining user search logs to discover semantic relationships between key phrases in a manner that is language agnostic, human understandable, and virtually noise-free.
Smart Data Webinar: Choosing the Right Data Management Architecture for Cogni...DATAVERSITY
Once developers have a knowledge management model - covered in our August webinar - they still have to deal with real world implementation constraints. Big data is a fact of life for most modern AI/cognitive computing apps, which usually means ingesting, sampling, or analyzing large data sets from disparate sources, ranging from IOT sensors to social media streams to news feeds and weather forecasts. Frequently, historical data in legacy systems will also be required to generate new insights.
This webinar will present a framework to help participants evaluate streaming data management tools, IOT technology stacks, and graph databases as support tools for their modern AI/cognitive computing projects. They will also learn about emerging open source projects and ecosystems that can help kick start their projects today.
Making IA Real: Planning an Information Architecture StrategyChiara Fox Ogan
Presented at Internet Librarian conference in 2001. Provides an introduction to what information architecture is and how you can use the methods to develop a good website.
The need for sophistication in modern search engine implementationsBen DeMott
The need for more sophisticated search implementations is often at odds with the limited feature set available in modern out of the box open source search engines.
This presentation discusses the challenges associated with properly modeling information within a domain and why it's critically needed.
CSCI 340 Final Group ProjectNatalie Warden, Arturo Gonzalez, R.docxmydrynan
CSCI 340 Final Group Project
Natalie Warden, Arturo Gonzalez, Ricky Gaji
Introduction
As our world continues to rely on technology to store our information, issues concerning data storage and organization will arise
Association of Computing Machinery (ACM) has asked us to prepare a database through which they can easily and effectively access this information
In this project we have created a tier system of entities, established the relationships between them, and decreased redundancy by eliminating repeating attributes
Responsibility MatrixTask/PersonNatalieArturoRickyAnalysisMSER-DiagramSMRedundancySSSSQLMSLogical DesignMAnalysis DocMRelationships DocMReadMe DocSMDatabaseMSS
Software Used:
Analysis:
Google Docs - helped to bring the group together and organize all our information to make sure we were on the same page.
Google Slides- served as the main platform in which to come up with our presentation and visualize what we are going to do.
Draw.io- used to build our many ER diagrams
Database Design:
x10 web hosting- hosted our website and had the tools necessary to get started on the database
phpMyAdmin- here we created our database tables and made sure all the attribute’s data types and entity’s primary key, foreign keys, and attributes were correct.
mySQL Databases- used as relational database management system
generatedata.com-used to create “dummy” data to incorporate in the SQL testing
Analysis and Findings
Problems/Results
Final Decision
Decided to create entities for leadership
Took inspiration from University database setup
ER-Diagram
Tables
Tables
Building the ACM Database
Populated Tables
SQL/RESULTS
3
Name
Course
Date
Instructor
Benchmark - Gospel Essentials
In at least 150 words, complete your introductory paragraph with a thesis statement in which you will address each of the following six sections with at least one paragraph each.
God
In at least 150 words, respond thoroughly to the questions in the assignment. Be sure to include citations.
Humanity
In at least 150 words, respond thoroughly to the questions in the assignment. Be sure to include citations.
Jesus
In at least 150 words, respond thoroughly to the questions in the assignment. Be sure to include citations.
Restoration
In at least 150 words, respond thoroughly to the questions in the assignment. Be sure to include citations.
Analysis
In at least 150 words, respond thoroughly to the questions in the assignment. Be sure to include citations.
Reflection
In at least 150 words, respond thoroughly to the questions in the assignment. Be sure to include citations.
Conclusion
In at least 150 words, synthesize the main points, pulling the ideas of the paper together. Be sure to include citations.
References
Author, A. A., .
A Multifaceted Look At Faceting - Ted Sullivan, LucidworksLucidworks
This document discusses using facets in Solr to facilitate relevant search. It provides an overview of facet history and how facets represent metadata that provides context about search results. Facets can be used for visualization, analytics, and understanding language semantics from text. The document argues that facets are dynamic context discovery tools that can be leveraged to find similar items and enhance search in various ways such as query autofiltering, typeahead suggestions, and text analytics.
Similar to Quality, quantity, web and semantics (20)
How to force yourself to post more - how you need to behave and what tools might help you do that.
How to write blog posts regularly? What tools to use? How to use editorial calendar? What are other tools to be used - Zemanta and Blogspire.
Presentation at WordCamp NYC 2012.
The document discusses future trends in content marketing. It recommends creating engaging content that delivers value and builds relationships across multiple channels, including social media and a core owned destination. The key is to continuously publish fresh content for audiences while connecting content to other sources. Automated tools can help with recommendations, links, and related content to reduce barriers and connect content efficiently. The takeaway is to use technology as a force multiplier and not be an "content island" by building bridges to other sources.
This document summarizes a talk given by Andraz Tori about his company Zemanta. It discusses how Zemanta started as a system for closed captioning Slovenian television, which led Tori to start a startup. Zemanta provides a personal writing assistant that suggests images, related articles, in-text links and tags to bloggers as they write. It analyzes text using natural language processing and information retrieval against a database containing Wikipedia, Freebase and other web data. Tori discusses Zemanta's technology, growth serving over 80,000 bloggers monthly, and plans to open its API to more users. He emphasizes lessons like accelerators being beneficial, the importance of monetizing early, and focusing on one
The document discusses SQL versus NoSQL databases. It provides background on SQL databases and their advantages, then explains why some large tech companies have adopted NoSQL databases instead. Specifically, it describes how companies like Amazon, Facebook, and Google have such massive amounts of data that traditional SQL databases cannot adequately handle the scale, performance, and flexibility needs. It then summarizes some popular NoSQL databases like Cassandra, Hadoop, MongoDB that were developed to solve the challenges of scaling to big data workloads.
Semantic web user interfaces - Do they have to be ugly?Andraz Tori
The document discusses user interfaces for the semantic web and whether they have to be ugly. It notes that while semantic web technologies provide powerful abstractions, these abstractions can be a curse for user interaction and user interfaces if they are not tailored to specific use cases. It argues that semantic web development is missing frameworks and tools to build efficient and limited interfaces that concentrate on solving particular problems rather than trying to build unified interfaces for all data.
This document discusses recommendations and how they can benefit both readers and publishers/writers. Recommendations can drive monetization through contextual advertising and steering readers to more pages. For publishers, recommendations provide direct monetization, better service for readers, and reduce workload for writers. The document outlines some simple recommendation techniques like displaying random content, as well as ready-made solutions from third parties. It also discusses potential issues with recommendations and the future of understanding content and context on the web.
Skybuffer SAM4U tool for SAP license adoptionTatiana Kojar
Manage and optimize your license adoption and consumption with SAM4U, an SAP free customer software asset management tool.
SAM4U, an SAP complimentary software asset management tool for customers, delivers a detailed and well-structured overview of license inventory and usage with a user-friendly interface. We offer a hosted, cost-effective, and performance-optimized SAM4U setup in the Skybuffer Cloud environment. You retain ownership of the system and data, while we manage the ABAP 7.58 infrastructure, ensuring fixed Total Cost of Ownership (TCO) and exceptional services through the SAP Fiori interface.
Freshworks Rethinks NoSQL for Rapid Scaling & Cost-EfficiencyScyllaDB
Freshworks creates AI-boosted business software that helps employees work more efficiently and effectively. Managing data across multiple RDBMS and NoSQL databases was already a challenge at their current scale. To prepare for 10X growth, they knew it was time to rethink their database strategy. Learn how they architected a solution that would simplify scaling while keeping costs under control.
Taking AI to the Next Level in Manufacturing.pdfssuserfac0301
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
Your One-Stop Shop for Python Success: Top 10 US Python Development Providersakankshawande
Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectorsDianaGray10
Join us to learn how UiPath Apps can directly and easily interact with prebuilt connectors via Integration Service--including Salesforce, ServiceNow, Open GenAI, and more.
The best part is you can achieve this without building a custom workflow! Say goodbye to the hassle of using separate automations to call APIs. By seamlessly integrating within App Studio, you can now easily streamline your workflow, while gaining direct access to our Connector Catalog of popular applications.
We’ll discuss and demo the benefits of UiPath Apps and connectors including:
Creating a compelling user experience for any software, without the limitations of APIs.
Accelerating the app creation process, saving time and effort
Enjoying high-performance CRUD (create, read, update, delete) operations, for
seamless data management.
Speakers:
Russell Alfeche, Technology Leader, RPA at qBotic and UiPath MVP
Charlie Greenberg, host
zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...Alex Pruden
Folding is a recent technique for building efficient recursive SNARKs. Several elegant folding protocols have been proposed, such as Nova, Supernova, Hypernova, Protostar, and others. However, all of them rely on an additively homomorphic commitment scheme based on discrete log, and are therefore not post-quantum secure. In this work we present LatticeFold, the first lattice-based folding protocol based on the Module SIS problem. This folding protocol naturally leads to an efficient recursive lattice-based SNARK and an efficient PCD scheme. LatticeFold supports folding low-degree relations, such as R1CS, as well as high-degree relations, such as CCS. The key challenge is to construct a secure folding protocol that works with the Ajtai commitment scheme. The difficulty, is ensuring that extracted witnesses are low norm through many rounds of folding. We present a novel technique using the sumcheck protocol to ensure that extracted witnesses are always low norm no matter how many rounds of folding are used. Our evaluation of the final proof system suggests that it is as performant as Hypernova, while providing post-quantum security.
Paper Link: https://eprint.iacr.org/2024/257
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
Generating privacy-protected synthetic data using Secludy and MilvusZilliz
During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...Jason Yip
The typical problem in product engineering is not bad strategy, so much as “no strategy”. This leads to confusion, lack of motivation, and incoherent action. The next time you look for a strategy and find an empty space, instead of waiting for it to be filled, I will show you how to fill it in yourself. If you’re wrong, it forces a correction. If you’re right, it helps create focus. I’ll share how I’ve approached this in the past, both what works and lessons for what didn’t work so well.
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdfChart Kalyan
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
Dandelion Hashtable: beyond billion requests per second on a commodity serverAntonios Katsarakis
This slide deck presents DLHT, a concurrent in-memory hashtable. Despite efforts to optimize hashtables, that go as far as sacrificing core functionality, state-of-the-art designs still incur multiple memory accesses per request and block request processing in three cases. First, most hashtables block while waiting for data to be retrieved from memory. Second, open-addressing designs, which represent the current state-of-the-art, either cannot free index slots on deletes or must block all requests to do so. Third, index resizes block every request until all objects are copied to the new index. Defying folklore wisdom, DLHT forgoes open-addressing and adopts a fully-featured and memory-aware closed-addressing design based on bounded cache-line-chaining. This design offers lock-free index operations and deletes that free slots instantly, (2) completes most requests with a single memory access, (3) utilizes software prefetching to hide memory latencies, and (4) employs a novel non-blocking and parallel resizing. In a commodity server and a memory-resident workload, DLHT surpasses 1.6B requests per second and provides 3.5x (12x) the throughput of the state-of-the-art closed-addressing (open-addressing) resizable hashtable on Gets (Deletes).
Digital Banking in the Cloud: How Citizens Bank Unlocked Their MainframePrecisely
Inconsistent user experience and siloed data, high costs, and changing customer expectations – Citizens Bank was experiencing these challenges while it was attempting to deliver a superior digital banking experience for its clients. Its core banking applications run on the mainframe and Citizens was using legacy utilities to get the critical mainframe data to feed customer-facing channels, like call centers, web, and mobile. Ultimately, this led to higher operating costs (MIPS), delayed response times, and longer time to market.
Ever-changing customer expectations demand more modern digital experiences, and the bank needed to find a solution that could provide real-time data to its customer channels with low latency and operating costs. Join this session to learn how Citizens is leveraging Precisely to replicate mainframe data to its customer channels and deliver on their “modern digital bank” experiences.
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
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16. Profit
- publish as much content as possible
- quality is not (that) important
- get traffic or high page ranking for certain terms
- sell clicks, links or whole “fully built” sites to the
highest bidder
- users and search engines are necessary evil to
be tricked as cheaply as possible
19. Job opening
You will get a spreadsheet with 180 blog url’s and
logins. You will log into each blog and schedule 2
posts per week ...
You will spice up every post with images and/or
related links within the content, using a Wordpress
plugin called Zemanta
https://www.odesk.com/jobs/Wordpress-Blog-Poster_~~c8c04549b8e6b600
20. And why might you care?
- the organized information is great tool for those
that try to disorganize it
- they are poisoning “our web”, including twitter,
facebook
- and it's hard to see in the fog they are causing
- it is just matter of time when they start poisioning
linked data too
23. - is a “personal writing assistant”
- suggesting content while you write (your blog)
- analyzing your text
- connecting it with background knowledge, other
stories on the web, images
- you choose what suggestions to include
- to make your writing more informative, vivid and
useful
32. How it works
Content
suggestions
Plain text Semantic
Analysis
(article) search
Linked data RSS feeds
33. Main design goals
- Input is meaningful chunk of text (not a keyword
or a phrase)
- Input is (semi) English language
- Has to work across all domains in the open
world
- music, celebrities, finance, entertainment, politics,
gardening, parenting, …
34. Analysis pipeline
Known phrases
Named Entity
extraction
Extraction
(aho-corasick)
Triple store
Surface form features evaluation
Statistical comparison to
background knowledge
Semantic coherence
and hand-tuned
heuristics
etc.
Disambiguated entities
35. Analysis pipeline
Known phrases
Named Entity
extraction
Extraction
(aho-corasick)
Categorization to Dmoz
Triple store
Surface form features evaluation
Statistical comparison to
background knowledge
Semantic coherence
and hand-tuned
heuristics
etc.
Categories Ambigious named entities Disambiguated entities
36. Background knowledge
- Data from Wikipedia, MusicBrainz, Freebase…
and world wild web
- Includes linguistical and semantical properties
+ unstructured data
- Present in two forms:
- in “original” custom built triple store on top of MySQL
(150 GB)
- processed into 7 GB optimized “memory mapped
dump”
37. Background knowledge
- 7M mined and linked up entities and
concepts
Triple store
- 30M aliases
- Refreshed about once a month
- want to make it real-time
- Input data quality is really important
etc.
41. Solr
- We adapted Solr for “query by document”
- 52% precision (at 10) on internal evaluations
- plain Lucene MLT comes to 44%
- difference is from “bag of terms” approach over “bag
of words” (terms coming from analysis step)
- Our live index is 5M articles
- Solr is really not optimized to handle 50 terms in
a single query
43. Metrics & tests
- Every part of the system is being constantly
evaluted
- Precision/recall at 5 different points in the
system
- Mostly bi-weekly releases of new datasets and
the engine
44. Overview
- We do pretty deep processing to deliver simple
user experience of “personal authoring
assistant”
- And everything is available over the web API
- tagging
- named entity recognition and disambiguation to
Linked Open Data URIs
45. What API offers?
• Tags
Most used
• Categories
• Concepts and entities Most interesting
• Related articles
• Related images
49. We are just one of the many people offering
services based on large amounts of web data
each spending man-years trying to organize their
data, trying to offer best possible service
52. Job opening
You will get a spreadsheet with 180 blog url’s and
logins. You will log into each blog and schedule 2
posts per week ...
You will spice up every post with images and/or
related links within the content, using a Wordpress
plugin called Zemanta
https://www.odesk.com/jobs/Wordpress-Blog-Poster_~~c8c04549b8e6b600
54. Gather search terms Analyze → Find / create
(extensions, logs, guess) what people search for? such content
Pull additional content Use Zemanta or OpenCalais
Cover your tracks
from Freebase to add tags, images, links
Publish
Amazon Mechanical Turk
Use Zemanta to find
to post comments Profit?
similar blogs
and links back to your site
55. Warnings
- I've seen no single system using the whole
pipeline as described, however all parts were
found in the wild
- Examples used are from all kinds of sites –
good, bad and ugly
- I am not trying to imply that all of the steps in the
diagram are bad, but they can be used by bad
guys efficiently
56. Gather search terms Analyze → Find / create
(extensions, logs, guess) what people search for? such content
Pull additional content Use Zemanta or OpenCalais
Cover your tracks
from Freebase to add tags, images, links
Publish
Amazon Mechanical Turk
Use Zemanta to find
to post comments Profit?
similar blogs
and links back to your site
57. Finding their keywords, niches
- Domain expertise
- Users like to install extensions and say “yes”
- You observe referrers on sites you control
- You buy the data on the black market
58.
59. The sophisticated part of the market
“Demand Media relies on a proprietary algorithm
to help editors best determine what subjects
their writers should tackle.”
Factors:
- Keyword competition
- Revenue
- Driving traffic to/from existing conent
http://emediavitals.com/article/16/demand-media-s-content-assembly-line
60. Gather search terms Analyze → Find / create
(extensions, logs, guess) what people search for? such content
Pull additional content Use Zemanta or OpenCalais
Cover your tracks
from Freebase to add tags, images, links
Publish
Amazon Mechanical Turk
Use Zemanta to find
to post comments Profit?
similar blogs
and links back to your site
61. Find / create content
- Steal
- Take from “open article directories”
- Have your own “content assembly line” like
Demand Media
63. Gather search terms Analyze → Find / create
(extensions, logs, guess) what people search for? such content
Pull additional content Use Zemanta or OpenCalais
Cover your tracks
from Freebase to add tags, images, links
Publish
Amazon Mechanical Turk
Use Zemanta to find
to post comments Profit?
similar blogs
and links back to your site
66. Translate it to random language and back to English
Übersetzen sie zufällig Sprache und wieder auf Englisch
Language and translate it happen again in English
Μεταφράστε αυτό σε δειγματοληπτικούς γλώσσα και
πίσω στην αγγλική γλώσσα
Translate this random language back to English
Traduisez au langage aléatoire et revenir à l'anglais
Translate to random language to English and back
它翻译成随机的语言和回英文
Translate it back into the English language and random
67. Covering their tracks
- Trying to fool search engines or people?
- Search engines are catching up
- Google Translate API is being closed due to
“abuse”?
- The trend is “rewriting” by human editors,
procured on the global market
68.
69. Gather search terms Analyze → Find / create
(extensions, logs, guess) what people search for? such content
Pull additional content Use Zemanta, OpenCalais
Cover your tracks
from Freebase to add tags, images, links
Publish
Amazon Mechanical Turk
Use Zemanta to find
to post comments Profit?
similar blogs
and links back to your site
72. Gather search terms Analyze → Find / create
(extensions, logs, guess) what people search for? such content
Pull additional content Use Zemanta or OpenCalais
Cover your tracks
from Freebase to add tags, images, links
Publish
Amazon Mechanical Turk
Use Zemanta to find
to post comments Profit?
similar blogs
and links back to your site
73.
74. Remixing linked data and spam
- Currently mostly the good guys are using Linked
Data
- However, it's just too tempting to be left alone
- Fully synthetic articles using factual information
from linked data?
– Using advanced tools to form proper natural
language sentences and maybe even storyline?
75. Gather search terms Analyze → Find / create
(extensions, logs, guess) what people search for? such content
Pull additional content Use Zemanta or OpenCalais
Cover your tracks
from Freebase to add tags, images, links
Publish
Amazon Mechanical Turk
Use Zemanta to find
to post comments Profit?
similar blogs
and links back to your site
76. Publish
- On hosted third party platforms
- eating their resources
- Platforms have hard time killing spammers
- Smaller ones don't necessarily have the incentive
- If they remove spammer too fast, it is easier for
spammer to probe the limits
- Platforms use “kill with delay”
- Spam detection is resource intensive
77. Gather search terms Analyze → Find / create
(extensions, logs, guess) what people search for? such content
Pull additional content Use Zemanta or OpenCalais
Cover your tracks
from Freebase to add tags, images, links
Publish
Amazon Mechanical Turk
Use Zemanta to find
to post comments Profit?
similar blogs
and links back to your site
78. Valuable comments
As I write this post, Zemanta is showing me
5 different articles that are related to my
post. I could visit each one of these sites
and reach out to the owner to see if they
would be interested in linking to my post,
or I could leave a valuable comment on the
page and include a link back to my post.
http://www.mainelyseo.com/zemanta-review-seo-link-building-with-the-zemanta-plugin/
79. - Guy in previous slide is honest and well-
meaning
- But what if you automate that via Amazon
Mechanical Turk or oDesk?
80. Gather search terms Analyze → Find / create
(extensions, logs, guess) what people search for? such content
Pull additional content Use Zemanta or OpenCalais
Cover your tracks
from Freebase to add tags, images, links
Publish
Amazon Mechanical Turk
Use Zemanta to find
to post comments Profit?
similar blogs
and links back to your site
81. Profit?
- sell ads
- sell links
- sell “fully developed site”
- to the highest bidder
82. Search engines to the rescue?
- Mahalo cut 10% of the staff the day after
Google announced ranking changes
- Demand Media's stock isn't doing that well
anymore
- However this is a never-ending story, we'll have
co-evolution for foreseeable future
83. Ecosystem
- Very sophisticated, large players
- moving to more high quality content, video?
- Small time operations
- using more and more sophisticated tools available
on the market cheaply (modern asymmetric
warfare?)
- Dark industry specifically building tools to poison
the web and sell them to small time operators