Home
Search results “Semantic web mining techniques wikipedia”
Intelligence in Wikipedia
 
51:03
Google Tech Talks November 11, 2008 ABSTRACT Berners-Lee's vision of the Semantic Web is hindered by a chicken-and-egg problem, which can be best solved by a bootstrapping method: creating enough structured data to motivate the development of applications. We believe that autonomously `Semantifying Wikipedia' is the best way to bootstrap. We choose Wikipedia as an initial data source, because it is comprehensive, high-quality, modestly sized, and contains enough manually-derived structure to bootstrap an autonomous, self-supervised process. In this talk I will present our success to date in this endeavor: A novel approach for self-supervised learning of CRF information extractors Automatic construction of a comprehensive ontology via statistical-relational learning Vast improvements in extraction recall through shrinkage over this ontology and retraining The stimulation of a virtuous feedback cycle between communal content creation and information extraction We aim to construct a knowledge base of outstanding size to support inference, automatic question answering, faceted browsing, and potentially to bootstrap the Semantic Web. Speaker: Daniel S. Weld Daniel S. Weld is Thomas J. Cable / WRF Professor of Computer Science and Engineering at the University of Washington. After formative education at Phillips Academy, he received bachelor's degrees in both Computer Science and Biochemistry at Yale University in 1982. He landed a Ph.D. from the MIT Artificial Intelligence Lab in 1988, received a Presidential Young Investigator's award in 1989, an Office of Naval Research Young Investigator's award in 1990, was named AAAI Fellow in 1999 and deemed ACM Fellow in 2005. Dan is an area editor for the Journal of the ACM, on the editorial board of Artificial Intelligence, was a founding editor and member of the advisory board for the Journal of AI Research, was guest editor for Computational Intelligence and Artificial Intelligence, edited the AAAI report on the Role of Intelligent Systems in the National Information Infrastructure, and was Program Chair for AAAI-96. Dan has published two books and scads of technical papers. Dan is an active entrepreneur with several patents and technology licenses. In May 1996, he co-founded Netbot Incorporated, creator of Jango Shopping Search and later acquired by Excite. In October 1998, Dan co-founded AdRelevance, a revolutionary monitoring service for internet advertising which was acquired by Media Metrix and subsequently by Nielsen NetRatings. In June 1999, Dan co-founded data integration company Nimble Technology which was acquired by the Actuate Corporation. In January 2001, Dan joined the Madrona Venture Group as a Venture Partner and member of the Technical Advisory Board.
Views: 32189 GoogleTechTalks
Wikidata - Semantic Wikipedia
 
42:40
Denny Vrandečić, one of the original authors of Semantic MediaWiki and the project lead of Wikidata describes how to make Wikipedia machine readable. In his talk Denny explains the need of semantic data in Wikimedia projects, the stages of the Wikidata and its possible use cases in the future.
Views: 1530 Yury Katkov
Natural Language Processing (NLP) & Text Mining Tutorial Using NLTK | NLP Training | Edureka
 
40:29
** NLP Using Python: - https://www.edureka.co/python-natural-language-processing-course ** This Edureka video will provide you with a comprehensive and detailed knowledge of Natural Language Processing, popularly known as NLP. You will also learn about the different steps involved in processing the human language like Tokenization, Stemming, Lemmatization and much more along with a demo on each one of the topics. The following topics covered in this video : 1. The Evolution of Human Language 2. What is Text Mining? 3. What is Natural Language Processing? 4. Applications of NLP 5. NLP Components and Demo Do subscribe to our channel and hit the bell icon to never miss an update from us in the future: https://goo.gl/6ohpTV --------------------------------------------------------------------------------------------------------- Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Instagram: https://www.instagram.com/edureka_learning/ --------------------------------------------------------------------------------------------------------- - - - - - - - - - - - - - - How it Works? 1. This is 21 hrs of Online Live Instructor-led course. Weekend class: 7 sessions of 3 hours each. 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training you will have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Natural Language Processing using Python Training focuses on step by step guide to NLP and Text Analytics with extensive hands-on using Python Programming Language. It has been packed up with a lot of real-life examples, where you can apply the learnt content to use. Features such as Semantic Analysis, Text Processing, Sentiment Analytics and Machine Learning have been discussed. This course is for anyone who works with data and text– with good analytical background and little exposure to Python Programming Language. It is designed to help you understand the important concepts and techniques used in Natural Language Processing using Python Programming Language. You will be able to build your own machine learning model for text classification. Towards the end of the course, we will be discussing various practical use cases of NLP in python programming language to enhance your learning experience. -------------------------- Who Should go for this course ? Edureka’s NLP Training is a good fit for the below professionals: From a college student having exposure to programming to a technical architect/lead in an organisation Developers aspiring to be a ‘Data Scientist' Analytics Managers who are leading a team of analysts Business Analysts who want to understand Text Mining Techniques 'Python' professionals who want to design automatic predictive models on text data "This is apt for everyone” --------------------------------- Why Learn Natural Language Processing or NLP? Natural Language Processing (or Text Analytics/Text Mining) applies analytic tools to learn from collections of text data, like social media, books, newspapers, emails, etc. The goal can be considered to be similar to humans learning by reading such material. However, using automated algorithms we can learn from massive amounts of text, very much more than a human can. It is bringing a new revolution by giving rise to chatbots and virtual assistants to help one system address queries of millions of users. NLP is a branch of artificial intelligence that has many important implications on the ways that computers and humans interact. Human language, developed over thousands and thousands of years, has become a nuanced form of communication that carries a wealth of information that often transcends the words alone. NLP will become an important technology in bridging the gap between human communication and digital data. --------------------------------- For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free).
Views: 34000 edureka!
What is SEMANTIC WEB? What does SEMANTIC WEB mean? SEMANTIC WEB meaning & explanation
 
01:35
What is SEMANTIC WEB? What does SEMANTIC WEB mean? SEMANTIC WEB meaning - SEMANTIC WEB explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. The Semantic Web is an extension of the Web through standards by the World Wide Web Consortium (W3C). The standards promote common data formats and exchange protocols on the Web, most fundamentally the Resource Description Framework (RDF). According to the W3C, "The Semantic Web provides a common framework that allows data to be shared and reused across application, enterprise, and community boundaries". The term was coined by Tim Berners-Lee for a web of data that can be processed by machines. While its critics have questioned its feasibility, proponents argue that applications in industry, biology and human sciences research have already proven the validity of the original concept. The 2001 Scientific American article by Berners-Lee, Hendler, and Lassila described an expected evolution of the existing Web to a Semantic Web. In 2006, Berners-Lee and colleagues stated that: "This simple idea…remains largely unrealized". In 2013, more than four million Web domains contained Semantic Web markup.
Views: 3246 The Audiopedia
Final Year Projects | Semantic Web Service Discovery Using Natural Language Processing Techniques
 
07:04
Including Packages ======================= * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/lGybbe Chat Now @ http://goo.gl/snglrO Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected]
Views: 1147 Clickmyproject
PoolParty Semantic Classifier - Bringing Machine Learning, NLP and Knowledge Graphs together
 
56:56
PoolParty Semantic Suite (https://www.poolparty.biz/) combines technologies based on Semantic Web, NLP, and Machine Learning. From version 6.2, users benefit from the Semantic Classifer, which is based on ML (Deep Learning, SVM, Bayes, ...) making use of semantically enriched training documents. This fusion of technologies, which is called 'Semantic AI', delivers higher F1 scores (precision and recall) than ML based on simple text input. In this video we discuss several AI technologies and how they are currently linked to each other.
Final Year Projects | Semantic Web service discovery using natural language processing techniques
 
09:40
Including Packages ======================= * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/lGybbe Chat Now @ http://goo.gl/snglrO Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected]
Views: 620 Clickmyproject
Wikitag: Generating in-text links to Wikipedia (part 1)
 
08:39
Tomaž Šolc at Wikimania 2008, Alexandria, Egypt A common use of Wikipedia in web publishing is to provide explanations for various terms in published texts with which the reader may not be familiar. This is usually done in form of in-text hyperlinks to relevant pages in Wikipedia. Building on the existing research we have created a system that automatically adds such explanatory links to a plain text article. Combined with structured data extracted from linked Wikipedia articles, the system can also provide links to other websites concerning the subject and semantic tagging that can be used in any further processing. This talk is about the research that resulted in Wikitag, a system that is currently running as part of Zemanta (www.zemanta.com) service. An overview of the algorithm is given with descriptions of its basic building blocks and discussion of the primary problems we encountered: how to get link candidates, automatically disambiguate terms, estimate link desirability and select only the most appropriate links for the final result.
Views: 625 avian6
Deepti Ameta | Relation Extraction from Wikipedia articles using DeepDive | PyData Meetup 2
 
11:33
PyData meetups are a forum for members of the PyData community to meet and share new approaches and emerging technologies for data management and analytics. This was the second meet-up of PyData Gandhinagar hosted at IIT Gandhinagar on October 27, 2018. Speaker – Deepti Ameta Bio: Junior Research Fellow at DAIICT Title – Relation Extraction from Wikipedia articles using DeepDive Short Description – Information Extraction is one of the challenging research areas of Computer Science today. The talk focuses on three problems: how to extract the information (relations between two named entities) from unstructured or semi-structured text documents (Wikipedia); to recognize the techniques of storage in Knowledge Base so that the information can be easily utilized and how to construct end to end data pipelines using a tool: DeepDive. A simple example will be used to understand the tool functionality and working. Further focus is on its real world applications.
Views: 348 IIT Gandhinagar
What is ontology? Introduction to the word and the concept
 
03:58
In a philosophical context 0:28 Why ontology is important 1:08 Ontological materialism 1:34 Ontological idealism 1:59 In a non-philosophical context 2:24 Information systems 2:40 Social ontology 3:25 The word ontology comes from two Greek words: "Onto", which means existence, or being real, and "Logia", which means science, or study. The word is used both in a philosophical and non-philosophical context. ONTOLOGY IN A PHILOSOPHICAL CONTEXT In philosophy, ontology is the study of what exists, in general. Examples of philosophical, ontological questions are: What are the fundamental parts of the world? How they are related to each other? Are physical parts more real than immaterial concepts? For example, are physical objects such as shoes more real than the concept of walking? In terms of what exists, what is the relationship between shoes and walking? Why is ontology important in philosophy? Philosophers use the concept of ontology to discuss challenging questions to build theories and models, and to better understand the ontological status of the world. Over time, two major branches of philosophical ontology has developed, namely: Ontological materialism, and ontological idealism. Ontological materialism From a philosophical perspective, ontological materialism is the belief that material things, such as particles, chemical processes, and energy, are more real, for example, than the human mind. The belief is that reality exists regardless of human observers. Ontological idealism Idealism is the belief that immaterial phenomenon, such as the human mind and consciousness, are more real, for example, than material things. The belief is that reality is constructed in the mind of the observer. ONTOLOGY IN A NON-PHILOSOPHICAL CONTEXT Outside philosophy, ontology is used in a different, more narrow meaning. Here, an ontology is the description of what exist specifically within a determined field. For example, every part that exists in a specific information system. This includes the relationship and hierarchy between these parts. Unlike the philosophers, these researchers are not primarily interested in discussing if these things are the true essence, core of the system. Nor are they discussing if the parts within the system are more real compared to the processes that take place within the system. Rather, they are focused on naming parts and processes and grouping similar ones together into categories. Outside philosophy, the word ontology is also use, for example, in social ontology. Here, the idea is to describe society and its different parts and processes. The purpose of this is to understand and describe the underlying structures that affect individuals and groups. Suggested reading You can read more about ontology in some of the many articles available online, for example: http://www.streetarticles.com/science/what-is-ontology Copyright Text and video (including audio) © Kent Löfgren, Sweden
Views: 284162 Kent Löfgren
Sentiment Analysis - Sirisha
 
04:25
This video describes the implementation of sentimental analysis using Naive Bayes algorithm. This is part of final project of AI course @ UW Instructor: Jeff Clune References: https://www.youtube.com/watch?v=EGKeC2S44Rs https://en.wikipedia.org/wiki/Sentiment_analysis
Views: 17663 UW-AI Class
Semantic Web - Semantic Data Generator with Fortunata Part I
 
03:45
This video show the instalation of Fortunata Framework (created for the UAM and UPM university). It function is simplify the creation of Web Page with Semantic data.
How to use the iGlue semantic web database part1 - Facetted search
 
01:01
How to use the iGlue semantic web database part1 - Facetted search
Views: 2037 iglueteam
How to Make a Text Summarizer - Intro to Deep Learning #10
 
09:06
I'll show you how you can turn an article into a one-sentence summary in Python with the Keras machine learning library. We'll go over word embeddings, encoder-decoder architecture, and the role of attention in learning theory. Code for this video (Challenge included): https://github.com/llSourcell/How_to_make_a_text_summarizer Jie's Winning Code: https://github.com/jiexunsee/rudimentary-ai-composer More Learning resources: https://www.quora.com/Has-Deep-Learning-been-applied-to-automatic-text-summarization-successfully https://research.googleblog.com/2016/08/text-summarization-with-tensorflow.html https://en.wikipedia.org/wiki/Automatic_summarization http://deeplearning.net/tutorial/rnnslu.html http://machinelearningmastery.com/text-generation-lstm-recurrent-neural-networks-python-keras/ Please subscribe! And like. And comment. That's what keeps me going. Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 154846 Siraj Raval
Discovering Content by Mining the Entity Web - Part 1 of 6
 
09:58
Deep Dhillon, CTO of Evri.com presents Evri's technology to UW students at the Paul G. Allen Center for Computer Science & Engineering. Talk abstract: Unstructured natural language text found in blogs, news and other web content is rich with semantic relations linking entities (people, places and things). At Evri, we are building a system which automatically reads web content similar to the way humans do. The system can be thought of as an army of 7th grade grammar students armed with a really large dictionary. The dictionary, or knowledge base, consists of relatively static information mined from structured and semi-structured publicly available information repositories like Freebase, Wikipedia, and Amazon. This large knowledge base is in turn used by a highly distributed search and indexing infrastructure to perform a deep linguistic analysis of many millions of documents ultimately culminating in a large set of semantic relationships expressing grammatical SVO style clause level relationships. This highly expressive, exacting, and scalable index makes possible a new generation of content discovery applications. Need a custom machine learning solution like this one? Visit http://www.xyonix.com.
Views: 2167 zang0
What is SEMANTIC SEARCH? What does SEMANTIC SEARCH mean? SEMANTIC SEARCH meaning & explanation
 
03:17
What is SEMANTIC SEARCH? What does SEMANTIC SEARCH mean? SEMANTIC SEARCH meaning - SEMANTIC SEARCH definition - SEMANTIC SEARCH explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ Semantic search seeks to improve search accuracy by understanding the searcher's intent and the contextual meaning of terms as they appear in the searchable dataspace, whether on the Web or within a closed system, to generate more relevant results. Semantic search systems consider various points including context of search, location, intent, variation of words, synonyms, generalized and specialized queries, concept matching and natural language queries to provide relevant search results. Major web search engines like Google and Bing incorporate some elements of semantic search. In vertical search, LinkedIn publishes their semantic search approach to job search by recognizing and standardizing entities in both queries and documents, e.g., companies, titles and skills, then constructing various entity-awared features based on the entities. Guha et al. distinguish two major forms of search: navigational and research. In navigational search, the user is using the search engine as a navigation tool to navigate to a particular intended document. Semantic search is not applicable to navigational searches. In research search, the user provides the search engine with a phrase which is intended to denote an object about which the user is trying to gather/research information. There is no particular document which the user knows about and is trying to get to. Rather, the user is trying to locate a number of documents which together will provide the desired information. Semantic search lends itself well with this approach that is closely related with exploratory search. Rather than using ranking algorithms such as Google's PageRank to predict relevancy, semantic search uses semantics, or the science of meaning in language, to produce highly relevant search results. In most cases, the goal is to deliver the information queried by a user rather than have a user sort through a list of loosely related keyword results. However, Google itself has subsequently also announced its own Semantic Search project. Author Seth Grimes lists "11 approaches that join semantics to search", and Hildebrand et al. provide an overview that lists semantic search systems and identifies other uses of semantics in the search process. Other authors primarily regard semantic search as a set of techniques for retrieving knowledge from richly structured data sources like ontologies and XML as found on the Semantic Web. Such technologies enable the formal articulation of domain knowledge at a high level of expressiveness and could enable the user to specify their intent in more detail at query time.
Views: 1042 The Audiopedia
Deciphering the Semantic Web
 
43:54
What is the Semantic Web? Technology Voice recently interviewed some leading Semantic Web researchers with both academic and industrial experience to find out what it is, why it is needed, and what are the exciting applications of semantic technologies. The results of these interviews are captured in our exclusive 44-minute video, "Deciphering the Semantic Web". We speak to Professor Stefan Decker of the INSIGHT Data Analytics Centre at NUI Galway. Established ten years ago on what was effectively a greenfield site in terms of Semantic Web research, the Digital Enterprise Research Institute (DERI) at NUI Galway created a Semantic Web powerhouse with research linkages to multinational corporations and SMEs in the Galway area. Now part of the new national INSIGHT Data Analytics Centre funded by Science Foundation Ireland (SFI), this Semantic Web research group at NUI Galway continues to tackle challenging problems around networked knowledge and linked data. We interview Dr John Breslin, Lecturer at NUI Galway and creator of the SIOC project, about sourcing semantic data for interesting applications. Also with a base in Galway, Avaya has collaborated with DERI on Semantic Web research co-funded by SFI to examine the future of business telecommunications. We speak to Dr Ronan Fox, now Software Engineering Manager at Avaya, about how third parties can gain an understanding of what the Semantic Web can do for them. Seevl is one of the spin-out companies from DERI, providing music recommendations on YouTube and Deezer using linked music data. We interview Dr Alexandre Passant, CEO and co-founder of Seevl, to find out about some of the practical uses of the Semantic Web. We also interview Professor Axel Polleres from the Vienna University of Economics and Business (WU Vien) about standardisation and the importance of common agreement between organisations. Finally, we speak to Dr Michael Hausenblas, Chief Data Engineer EMEA with big data platform solutions company MapR Technologies, to find out about linked data and the possibilities it can enable. Written and produced by Tom Murphy. Produced and directed by Julie Letierce.
Views: 5052 technologyvoice
International Journal of Web & Semantic Technology (IJWesT)
 
00:16
International Journal of Web & Semantic Technology (IJWesT) ISSN : 0975 - 9026 ( Online ) 0976- 2280 ( Print ) http://www.airccse.org/journal/ijwest/ijwest.html Scope & Topics International journal of Web & Semantic Technology (IJWesT) is a quarterly open access peer-reviewed journal that provides excellent international forum for sharing knowledge and results in theory, methodology and applications of web & semantic technology. The growth of the World-Wide Web today is simply phenomenal. It continues to grow rapidly and new technologies, applications are being developed to support end users modern life. Semantic Technologies are designed to extend the capabilities of information on the Web and enterprise databases to be networked in meaningfulways. Semantic web is emerging as a core discipline in the field of Computer Science & Engineering from distributed computing, web engineering, databases, social networks, Multimedia, information systems, artificial intelligence, natural language processing, soft computing, and human-computer interaction. The adoption of standards like XML, Resource Description Framework and Web Ontology Language serve as foundation technologies to advancing the adoption of semantic technologies. Topics of Interest Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to • Semantic Query & Search • Semantic Advertising and Marketing • Linked Data, Taxonomies • Collaboration and Social Networks • Semantic Web and Web 2.0/AJAX, Web 3.0 • Semantic Case Studies • Ontologies (creation , merging, linking and reconciliation) • Semantic Integration, Rules • Data Integration and Mashups • Unstructured Information • Developing Semantic Applications • Semantics for Enterprise Information Management (EIM) • Knowledge Engineering and Management • Semantic SOA (Service Oriented Architectures) • Database Technologies for the Semantic Web • Semantic Web for e-Business, Governance and e-Learning • Semantic Brokering, Semantic Interoperability, Semantic Web Mining • Semantic Web Services (service description, discovery, invocation, composition) • Semantic Web Inference Schemes • Semantic Web Trust, Privacy, Security and Intellectual Property Rights • Information discovery and retrieval in semantic web; • Web services foundation, Architectures and frameworks. • Web languages & Web service applications. • Web Services-driven Business Process Management. • Collaborative systems Techniques. • Communication, Multimedia applications using web services • Virtualization • Federated Identity Management Systems • Interoperability and Standards • Social and Legal Aspect of Internet Computing • Internet and Web-based Applications and Services Paper Submission Authors are invited to submit papers for this journal through E-mail : [email protected] or [email protected] Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal.
Views: 101 IJWEST JOURNAL
1 - Intro to the Open Semantic Framework (OSF)
 
25:26
This kick-off video to the OSF Academy overviews the Open Semantic Framework platform and describes it in terms of the 5 Ws (welcome, why, what, when, where) and the 1 H (how).
Information Retrieval and Extraction.
 
01:55
Mining Name Entity From Wikipedia In many search domains, both contents and searches are frequently tied to named entities such as a person, a company or similar.One challenge from an information retrieval point of view is that a single entity can have more than one way of referring to it. In this project we describe how to use Wikipedia contents to automatically generate a dictionary of named entities and synonyms that are all referring to the same entity. Contact:- Nikhil Barote([email protected]) kunj Thakker([email protected]) shivani Poddar([email protected]) Ankit Sharma([email protected]).
Views: 536 Ankit Sharma
Semantic Web - Semantic Data Generator with Fortunata Part II
 
06:45
This video show the use of Fortunata Framework (created for the UAM and UPM university). It function is simplify the creation of Web Page with Semantic data. Here I show a little example, creating a wiki of the example and inserting the code for the generate the pages's OWL and RDF files.
Wikipedia MWdumper
 
25:28
Wikipedia has over 4.45 million articles in about 32 million pages. This VM has been running for over 1 week now, taking gaps in between. Now is the time to break this process, as it is likely to take another few days / weeks if continued like this. Lets pause the VM and take a final snapshot ! VMware VM snapshots sometimes require immense hardware resources and time, especially on a huge VM like this one, Wikipedia. As we see 8 GB RAM is given to the VM, the Disk contention has suffered greatly during this process... CPU and RAM were relatively free, but disk was highly occupied with disk I/0 activity ranging between 1 to MB/sec throughout. Therefore, we shall look at installing local Wikipedia through a Big Data subsystem in the next activity. We shall bring in a "Mahout library", that works with HADOOP and HDFS, and then perform similar activity with parallel processing. To see how our local wikipedia looks as of now, lets open the web browser, and open the web page. Mahout is a scalable machine learning library that implements many different approaches to machine learning. The project currently contains implementations of algorithms for classification, clustering, frequent item set mining, genetic programming and collaborative filtering. Mahout is scalable along three dimensions: It scales to reasonably large data sets by leveraging algorithm properties or implementing versions based on Apache Hadoop. Snapshot is 85% complete now, and after this finishes, lets have a look at our local Wikipedia page. The whole idea is to manage huge sums of information. In this example, we saw that MediaWiki Inc. allows the public to download its database dumps. The english version of Wikipedia consists of a compressed file of 9.9 GB, which decompresses to over 44 GB XML file. This XML file has the structure and content of entire Wikipedia english TEXT pages. There is a seperate database for images, diagrams and photos. Alright, the FINAL snapshot is over, let see the state our VM now, and connect to it through the web browser. That is the URL, and we have the main page. Let give a search... Wikipedia on the internet is extensively CACHED, hence we get responses almost immediately. In a Virtualization environment, this may be slow. So lets stop the MWdumper from reading the wiki-dump. Now this is your local wikipedia. It doesn't end here. This ought to be used later for Data mining, and other project purposes. Thanks for Watching !!!
Towards the Natural Ontology of Wikipedia
 
01:01
In this video we present preliminary results on the extraction of ORA: the Natural Ontology of Wikipedia. ORA is obtained through an automatic process that analyses the natural language definitions of DBpedia entities provided by their Wikipedia pages. Hence, this ontology reflects the richness of terms used and agreed by the crowds, and can be updated periodically according to the evolution of Wikipedia.
Enipedia-A Semantic Wiki for Energy and Industry Data
 
01:16
Finalist Delft Innovation Award 2011
Views: 841 TU Delft
What is WEB INTELLIGENCE? What does WEB INTELLIGENCE mean? WEB INTELLIGENCE meaning & explanation
 
01:10
What is WEB INTELLIGENCE? What does WEB INTELLIGENCE mean? WEB INTELLIGENCE meaning - WEB INTELLIGENCE definition - WEB INTELLIGENCE explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ Web intelligence is the area of scientific research and development that explores the roles and makes use of artificial intelligence and information technology for new products, services and frameworks that are empowered by the World Wide Web. The term was coined in a paper written by Ning Zhong, Jiming Liu Yao and Y.Y. Ohsuga in the Computer Software and Applications Conference in 2000. The research about the web intelligence covers many fields – including data mining (in particular web mining), information retrieval, pattern recognition, predictive analytics, the semantic web, web data warehousing – typically with a focus on web personalization and adaptive websites.
Views: 45 The Audiopedia
Text Search Engine | Wikipedia Corpus | Wiki_Search_Machine
 
17:05
To build a prototype of a search engine which works on millions of Wikipedia pages and retrieves the top 10 relevant results: System Capabilities: Top 10 results (title of wikipedia document) Support for field queries Search results displayed within 1s Crawler: Capable of crawling entire website and get all it’s links. Indexing System: Makes use of Inverted Index. Searching: A CLI/Terminal based result-retrieval system. = Entire Search Mechanism on Wikipedia Corpus Facebook: https://www.facebook.com/ankit.pahuja05 Instagram: Gmail: [email protected]
Views: 90 Ankit Pahuja
Wikidata- making a Semantic Wikipedia a reality
 
50:45
Presentation by Denny Vrandečić and Daniel Kinzler from SMWCon Fall 2011 For more information about Semantic MediaWiki, visit http://semantic-mediawiki.org/ For more information about SMWCon Fall 2011 in Berlin, including slides from many of the presentations, see the conference page at http://semantic-mediawiki.org/wiki/SMWCon_Fall_2011
Views: 442 Christopher Wilson
Linking Library Data to Wikipedia, Part II
 
07:51
OCLC Research Wikipedian in Residence Max Klein (twitter @notconfusing) and Senior Program Officer Merrilee Proffitt (@merrileeIAm) discuss the impact of Max's new "VIAFbot" that is linking Virtual International Authority File records to Wikipedia references.
Views: 753 OCLCResearch
How to create wikipedia search panel | Part 2| Javascript | Php | HTML5
 
03:41
This video will show you how to create wikipedia search panel through wikipedia Api. -~-~~-~~~-~~-~- Please watch: "How to enable Developer Option in android EASILY" https://www.youtube.com/watch?v=QJth01qsv5I -~-~~-~~~-~~-~-
Views: 196 Solutions Nerds
2014 IEEE DATA MINING Evaluating Wiki Collaborative Features in Ontology Authoring
 
00:47
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - [email protected] Our Website: www.globalsofttechnologies.org
What is RELATIONSHIP EXTRACTION? What does RELATIONSHIP EXTRACTION mean?
 
02:14
What is RELATIONSHIP EXTRACTION? What does RELATIONSHIP EXTRACTION mean? RELATIONSHIP EXTRACTION meaning - RELATIONSHIP EXTRACTION definition - RELATIONSHIP EXTRACTION explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ A relationship extraction task requires the detection and classification of semantic relationship mentions within a set of artifacts, typically from text or XML documents. The task is very similar to that of information extraction (IE), but IE additionally requires the removal of repeated relations (disambiguation) and generally refers to the extraction of many different relationships. Application domains where relationship extraction is useful include gene-disease relationships, protein-protein interaction etc. Never-Ending Language Learning is a semantic machine learning system developed by a research team at Carnegie Mellon University that extracts relationships from the open web. One approach to this problem involves the use of domain ontologies. Another approach involves visual detection of meaningful relationships in parametric values of objects listed on a data table that shift positions as the table is permuted automatically as controlled by the software user. The poor coverage, rarity and development cost related to structured resources such as semantic lexicons (e.g. WordNet, UMLS) and domain ontologies (e.g. the Gene Ontology) has given rise to new approaches based on broad, dynamic background knowledge on the Web. For instance, the ARCHILES technique uses only Wikipedia and search engine page count for acquiring coarse-grained relations to construct lightweight ontologies. The relationships can be represented using a variety of formalisms/languages. One such representation language for data on the Web is RDF.
Views: 23 The Audiopedia
Fang Xu - Connecting Keywords to Knowledge Base Using Search Keywords and Wikidata
 
30:32
PyData Berlin 2016 The development of large-scale Knowledge Base (KB) has drawn lots of attentions and efforts from both academy and industries recently . In this talk I will introduce how to use keywords and public available data to build our structural KB, and build knowledge retrieval system for different languages using python. Many large-scale Knowledge Bases (KB), such as Yago, Wikidata, Freebase, and Google’s Knowledge Graph, have been build by extracting facts fro structural Wikipedia and/or natural language Web documents. The main observation of using knowledge base is that not all facts are useful and have enough information. To tackle this problem I will introduce how we build various data sources to help facts and keywords selection. We will also discuss important questions of KB applications including, - architecture of a KB processing and extraction system using Wikipedia and two public available KB including Wikidata and Yago; - method for calculating contextual relevance between facts. - how to present different facts to users. Yago: https://www.mpi-inf.mpg.de/departments/databases-and-information-systems/research/yago-naga/yago/ Wikidata: https://www.wikidata.org/wiki/Wikidata:Main_Page Freebase: https://developers.google.com/freebase/ Google’s Knowledge Graph: https://developers.google.com/knowledge-graph/
Views: 1779 PyData
fuzzy logic in artificial intelligence in hindi | introduction to fuzzy logic example | #28
 
06:10
fuzzy logic in artificial intelligence in hindi | fuzzy logic example | #28 Fuzzy Logic (FL) is a method of reasoning that resembles human reasoning. The approach of FL imitates the way of decision making in humans that involves all intermediate possibilities between digital values YES and NO. The conventional logic block that a computer can understand takes precise input and produces a definite output as TRUE or FALSE, which is equivalent to human’s YES or NO. The inventor of fuzzy logic, Lotfi Zadeh, observed that unlike computers, the human decision making includes a range of possibilities between YES and NO, such as − CERTAINLY YES POSSIBLY YES CANNOT SAY POSSIBLY NO CERTAINLY NO well,academy,Fuzzy logic in hindi,fuzzy logic in artificial intelligence in hindi,artificial intelligence fuzzy logic,fuzzy logic example,fuzzy logic in artificial intelligence,fuzzy logic with example,fuzzy logic in artificial intelligence in hindi with exapmle,fuzzy logic,what is fuzzy logic in hindi,what is fuzzy logic with example,introduction to fuzzy logic
Views: 126486 Well Academy
Collaborative Financial Analysis using Semantic MediaWiki
 
22:23
A researcher from KIT Benedikt Kämpgen is describing how to use Semantic MediaWiki to analyse the finances of the small organisation. The description of the talk and the slides are here: http://semantic-mediawiki.org/wiki/SMWCon_Fall_2012/Collaborative_Financial_Analysis_using_Semantic_MediaWiki
Views: 119 Yury Katkov
From SKOS Over SKOS-XL to Custom Ontologies
 
55:12
This webinar is about PoolParty Semantic Suite (http://www.poolparty.biz/), especially about features included by releases 5.2 and 5.3. See how taxonomy management based on SKOS can be extended with SKOS-XL, all based on W3C's Semantic Web standards. See how SKOS-XL can be combined with ontologies like FIBO. PoolParty's built in reference corpus analysis based on powerful text mining helps to continuously extend taxonomies. Its built-in co-occurence analysis supports taxonomists with the identification of candidate concepts. PoolParty Semantic Integrator can be used for deep data analytics tasks and semantic search. See how this can be integrated with various graph databases and search engines.
multitouch knowledge browser
 
00:14
At Free University's Institute of Computer Science we are developing a medical knowledge browser. This video shows the multitouch gestures already implemented to navigate in 3D space. The semantic network was extracted entirely from wikipedia.
Views: 247 synthbath
Discovering Content by Mining the Entity Web - Part 6 of 6
 
01:26
Deep Dhillon, CTO of Evri.com presents Evri's technology to UW students at the Paul G. Allen Center for Computer Science & Engineering. Talk abstract: Unstructured natural language text found in blogs, news and other web content is rich with semantic relations linking entities (people, places and things). At Evri, we are building a system which automatically reads web content similar to the way humans do. The system can be thought of as an army of 7th grade grammar students armed with a really large dictionary. The dictionary, or knowledge base, consists of relatively static information mined from structured and semi-structured publicly available information repositories like Freebase, Wikipedia, and Amazon. This large knowledge base is in turn used by a highly distributed search and indexing infrastructure to perform a deep linguistic analysis of many millions of documents ultimately culminating in a large set of semantic relationships expressing grammatical SVO style clause level relationships. This highly expressive, exacting, and scalable index makes possible a new generation of content discovery applications. Need a custom machine learning solution like this one? Visit http://www.xyonix.com.
Views: 177 zang0
Applying Semantic Analyses to Content-based Recommendation and Document Clustering
 
43:16
This talk will present the results of my research on feature generation techniques for unstructured data sources. We apply Probase, a Web-scale knowledge base developed by Microsoft Research Asia, which is generated from the Bing index, search query logs and other sources, to extract concepts from text. We compare the performance of features generated from Probase and two other forms of semantic analysis, Explicit Semantic Analysis using Wikipedia and Latent Dirichlet Allocation. We evaluate the semantic analysis techniques on two tasks, recommendation using Matchbox, which is a platform for probabilistic recommendations from Microsoft Research Cambridge, and clustering using K-Means.
Views: 767 Microsoft Research
Semantic Web - Leapset Innovation Session
 
12:03
For this week's innovation session, Ananda Subasinghe from our engineering team spoke about Semantic web, the extension of the World Wide Web that enables people to share content beyond the boundaries of applications and websites. He further went on to explain the topic of ontology matching, the process of determining correspondences between data labels. Ananda concluded the session by introducing us to DBpedia; a part of the Wikipedia, which aims to extract structured content from the information.
Netlytic Text Analysis Keywords (Part 1)
 
11:21
A look at using Neltytic’s text analysis features. This tutorial covers analysis and visualizations for keyword. Information about using text analysis category features will be covered in Part 2. --------------------------------------------------------- Additional Resources List of Stop Words: https://code.google.com/p/stop-words/ --------------------------------------------------------- Works Cited Claude Monet. Haystack at sunset frosty winter. [Public Domain] via Wikimedia Commons. Retrieved from https://commons.wikimedia.org/wiki/File%3AMonet_haystacks-at-sunset-frosty-weather-1891_W1282.jpg TikiGiki. (2012). People Silhouette 1. Retrieved from https://openclipart.org/detail/173496… Tom Thomson. The Jack Pine [Public domain], via Wikimedia Commons. Retrieved from https://commons.wikimedia.org/wiki/File%3AThe_Jack_Pine%2C_by_Tom_Thomson.jpg Vincent van Gogh. Starry Night. [Public domain], via Wikimedia Commons. Retrieved from https://commons.wikimedia.org/wiki/File%3AVan_Gogh_-_Starry_Night_-_Google_Art_Project.jpg
Views: 3570 Netlytic
Discovering Content by Mining the Entity Web - Part 2 of 6
 
09:56
Deep Dhillon, CTO of Evri.com presents Evri's technology to UW students at the Paul G. Allen Center for Computer Science & Engineering. Talk abstract: Unstructured natural language text found in blogs, news and other web content is rich with semantic relations linking entities (people, places and things). At Evri, we are building a system which automatically reads web content similar to the way humans do. The system can be thought of as an army of 7th grade grammar students armed with a really large dictionary. The dictionary, or knowledge base, consists of relatively static information mined from structured and semi-structured publicly available information repositories like Freebase, Wikipedia, and Amazon. This large knowledge base is in turn used by a highly distributed search and indexing infrastructure to perform a deep linguistic analysis of many millions of documents ultimately culminating in a large set of semantic relationships expressing grammatical SVO style clause level relationships. This highly expressive, exacting, and scalable index makes possible a new generation of content discovery applications. Need a custom machine learning solution like this one? Visit http://www.xyonix.com.
Views: 373 zang0
Engineering Intelligence: Semantic Process for mechatronic design data
 
04:15
Engineering Intelligence: Semantic Process for mechatronic design data, Case Examples from Semogen Smart Simulators Research Group, Tampere University of Technology, 2012, Project SeMoGen: https://wiki.tut.fi/SmartSimulators/Semogen For a related publication, see "An Implementation of a Semantic, Web-Based Virtual Machine Laboratory Prototyping Environment" http://iswc2011.semanticweb.org/fileadmin/iswc/Papers/In-Use/70320225.pdf "What do information reuse and automated processing require in engineering design? Semantic process", http://www.jiem.org/index.php/jiem/article/view/329
Views: 272 smartsimulators
Mass edits on Wikidata - how to use Google spreadsheets and Quickstatements
 
09:43
Wikidata is a free linked database of secondary data that can be read and edited by both humans and machines. Wikidata acts as central storage for the structured data of its Wikimedia sister projects including Wikipedia, Wikivoyage, Wikisource, and others. Essential tools for mass editing Wikidata: Quickstatements v.2 For importing data from a spreadsheet into Wikidata. The syntax you need to use is explained in QuickStatements v.1 Wikipedia and Wikidata Tools for Google sheets (Demo) Google sheets add-on for pulling data from Wikidata and Wikipedia directly into a spreadsheet (Note: you need a Google account to install this) This video tutorial by Navino Evans from Histropedia demos how to add UNESCO data to Wikidata in bulk using the Quickstatements tool version 2 as follows: Practical - mass editing using QuickStatements 1.Go to the batches spreadsheet, then click the link with your selected batch number 2.Select all cells highlighted orange - the QuickStatements commands - then copy them to your clipboard. Click edit then copy 3.Go to QuickStatements and click 4.Click 'Import commands' - 'Version 1 format' 5.Paste in the commands copied in step 2, then click ‘import’ 6.Check a selection of the commands to make sure they have imported correctly 7.Click the “RUN” button at the bottom to launch your first mass edit!
Views: 313 Ewan McAndrew
11-411 NLP Project: Wikipedia Article Q&A System
 
06:29
Team Hydra's semester project for 11-411 Natural Language Processing at Carnegie Mellon University, explained in 6.5 minutes. http://github.com/jchang504/11411-project Contact: [email protected]
Views: 2115 Jemmin Chang
Knowledge Graph Search with Elasticsearch — L. Misquitta and A. Negro, GraphAware
 
38:34
Emil Eifrem highlighted knowledge graphs in his keynote at GraphConnect Europe. Last year, Forrester said "Knowledge graphs provide contextual windows into master data domains and the links between domains" (The Forrester Wave, Master Data Management). Knowledge graphs are the key to providing the semantic and link analysis capabilities required by modern applications. Providing relevant information to the user performing search queries or navigating a site is a complex task. It requires a huge set of data, a process of progressive improvements, and self tuning parameters together with infrastructure that can support them. To add to the complexity, this search infrastructure must be introduced seamlessly into the existing platform, with access to relevant data flows to provide always up-to-date data. Moreover, it should allow for easy addition of new data sources to cater to new requirements, without affecting the entire system or the current relevance. In all e-commerce sites, text search and catalog navigation are not only the entry points for users but they are also the main salespeople. Compared with web search engines, this use case has the advantage that the set of items to be searched is more controlled and regulated. In this talk, Luanne will share insights about the business value of knowledge graphs and their contribution to relevant search in an e-commerce domain for a Neo4j customer. With text search and catalog navigation being the entry point of users to the system and in fact, driving revenue, the talk will explain the challenges of relevant search and how graph models address them. Dr. Alessandro will then talk about various techniques used for information extraction and graph modelling. He will also demonstrate how to seamlessly introduce knowledge graphs into an existing infrastructure and integrate with other tools such as ElasticSearch, Kafka, Apache Spark, OpenNLP and Stanford NLP. Speakers: Luanne Misquitta; Alessandro Negro Location: GraphConnect NYC 2017
Views: 3113 Neo4j
Danny Hillis, Connection Machine Legacy 2: Neural Nets and Semantic Networks , August 2016
 
04:31
In this second of an informal five part interview with Danny Hillis, inventor of the Connection Machine and founder of Thinking Machines Corporation, Danny talks about why neural networks now work, and how semantic networks Danny's bio: http://longnow.org/people/board/danny0/ Connection Machine: https://en.wikipedia.org/wiki/Connection_Machine Google’s MapReduce: https://en.wikipedia.org/wiki/MapReduce For mapping and reduction on the Connection Machine, see: Hillis, W. Daniel. Chapter 2.2 Alpha Notation (mapping), p37, and 2.3 Beta Reduction, p41 and 2.7 "A Connection Machine is the direct hardware embodiment of the alpha (mapping) and beta (reduction) operators, p.47-48, The Connection Machine, MIT Press, 1985. Neural networks: https://en.wikipedia.org/wiki/Artificial_neural_network Semantic networks: https://en.wikipedia.org/wiki/Semantic_network Metaweb, sold to Google in 2010: https://en.wikipedia.org/wiki/Metaweb Geoff Hinton: https://en.wikipedia.org/wiki/Geoffrey_Hinton Marvin Minsky: https://en.wikipedia.org/wiki/Marvin_Minsky The Cloud: http://gizmodo.com/what-is-the-cloud-and-where-is-it-1682276210 GPUs (Graphic Processing Units): https://en.wikipedia.org/wiki/Graphics_processing_unit Pattern recognition: https://en.wikipedia.org/wiki/Pattern_recognition Semantic networks: https://en.wikipedia.org/wiki/Semantic_network The Connection Machine 30th anniversary project is an investigation into its legacy for artificial intelligence and parallel processing 30 years after the launch of the CM-1 in 1986. Tamiko Thiel was in charge of product design for the Connection Machine CM-1/CM-2 package. This is her personal exploration of what has happened with the concepts and the people she knew in the early/mid 1980s at Thinking Machine Corporation. More on the CM: http://www.tamikothiel.com/cm
Views: 225 Tamiko Thiel
PoolParty 6.0 - The Most Complete Semantic Middleware on the Market
 
01:02:30
PoolParty 6.0 (https://www.poolparty.biz/poolparty-6-0-release/) as enterprise software platform means: a rich set of semantic services at your fingertips! Key Improvements in release 6.0: more agile data integration, high-precision text mining, configurable semantic search and graph-based analytics dashboards. PoolParty’s new features also include a broad range of extensions and improvements in the areas of linked data, knowledge engineering and text mining.
DevCon 2012 Berlin: Semantic Web, Ezequiel Foncubierta
 
46:11
Slides are available at: https://devcon.alfresco.com/berlin/sessions/alfresco-semantic-web Audio improves after the first two minutes. 99% of the information stored in Alfresco repositories is unstructured. It is normally a bunch of documents with a set of metadata that describes themselves. With more and more content within the repository, key word search can begin to require optimization and tuning. Since documents are not linked to each other like on the internet, algorithms like page rank are useless in finding the relevant content. Since documents tends to be organized or filed by company structure it is not easy to discover related and relevant content. In this technical demo / case study we will demonstrate how you can turn a large Alfresco repository into an intelligent knowledge repository through the integration of the open source Apache Stanbol semantic stack. This will include: * Enhanced metadata modelling in Alfresco by support RDF triples in Alfresco * Auto-classification of content * Use of third party entities like DBPedia, Geonames and local Active Directory. * Enhanced taxonomy management within Alfresco * SPARQL query interface in Alfresco * Overview of the Apache Stanbol project & functionality. * Building Semantic Web Applications on top of Alfresco. Demos given during this talk: * Language detection: http://vimeo.com/53162495 * Entity extraction: http://vimeo.com/53162494 * Semantic annotations: http://vimeo.com/53162492 * Intelligent search: http://vimeo.com/53162493 Ezequiel has also written a blog about this talk here: http://www.zaizi.com/blog/semantic-technologies-in-alfresco
Views: 404 Alfresco