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Search results “Towards semantic web mining components”
Towards the semantic web of science - P. Murray-Rust
 
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The development of CIF has allowed IUCr journals to capture the structured semantic content of research articles to a very high degree. Hyperlinking between components of the scholarly article (the text, structural data model, experimental data) provides a sound technical basis for mining, validating and reusing scientific data with the help of high-volume robotic tools.
Mod-01 Lec-28 PCA; SVD; Towards Latent Semantic Indexing(LSI)
 
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Natural Language Processing by Prof. Pushpak Bhattacharyya, Department of Computer science & Engineering,IIT Bombay.For more details on NPTEL visit http://nptel.iitm.ac.in
Views: 9906 nptelhrd
Final Year Projects | Domain Ontology based Semantic Search
 
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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: 793 Clickmyproject
Final Year Projects | An Ontology-Based Text-Mining Method to Cluster Proposals for Research
 
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Final Year Projects | An Ontology-Based Text-Mining Method to Cluster Proposals for Research Project Selection More Details: Visit http://clickmyproject.com/a-secure-erasure-codebased-cloud-storage-system-with-secure-data-forwarding-p-128.html 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: 3517 Clickmyproject
Semantic Web Technologies in Proteomics. An EBI overview
 
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http://togotv.dbcls.jp/20130627.html https://www.dropbox.com/s/bju0enplouqewi1/AF_Biohackathon_2013.pdf NBDC / DBCLS BioHackathon 2013 was held in Tokyo, Japan. Main focus of this BioHackathon is semantic interoperability and standardization of bioinformatics data and Web services. The participants discussed, explored and developed web applications and interoperability (DDBJ/UniProt, SADI, TogoGenome, Schema.org etc.), generation and standardization of RDF data (Open Bio* tools, SIO, FALDO, Identifiers.org etc.), text-mining, NLP and ontology mapping (LODQA, BioPortal, NanoPublication etc.), quality assessment of SPARQL endpoints (availability, contents, CORS etc.) and standardization of RDF data in specific domains. On the first day of the BioHackathon (Jun. 23), public symposium of the BioHackathon 2013 was held at Tokyo Skytree Space 634. In this talk, Antonio Fabregat makes a presentation entitled "Semantic Web Technologies in Proteomics. An EBI overview". (14:36)
Views: 204 togotv
What is Semantic Web?
 
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This video demonstrates how the internal vocabulary created with WordLift can be used by the Google Assistant as a reliable source for describing concepts. Is your website already talking with personal assistants? Try WordLift for free and play the magic of creating machine-friendly content to talk with your audience. Free 30-day trial ▶️ https://wordlift.io/ai-powered-seo/
Views: 90 WordLift
Deep Natural Language Semantics - Raymond Mooney
 
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Distinguished Lecture Series November 4, 2014 Raymond Mooney: "Deep Natural Language Semantics by Combining Logical and Distributional Methods using Probabilistic Logic" Traditional logical approaches to semantics and newer distributional or vector space approaches have complementary strengths and weaknesses.We have developed methods that integrate logical and distributional models by using a CCG-based parser to produce a detailed logical form for each sentence, and combining the result with soft inference rules derived from distributional semantics that connect the meanings of their component words and phrases. For recognizing textual entailment (RTE) we use Markov Logic Networks (MLNs) to combine these representations, and for Semantic Textual Similarity (STS) we use Probabilistic Soft Logic (PSL). We present experimental results on standard benchmark datasets for these problems and emphasize the advantages of combining logical structure of sentences with statistical knowledge mined from large corpora.
Final Year Projects| An Ontology-Based Text-Mining Method to Cluster Proposals for Research
 
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Final Year Projects | An Ontology-Based Text-Mining Method to Cluster Proposals for Research Project Selection More Details: Visit http://clickmyproject.com/a-secure-erasure-codebased-cloud-storage-system-with-secure-data-forwarding-p-128.html 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: 1377 Clickmyproject
AI and Semantic Web to power realtime customer insights
 
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Customers and Environments are evolving rapidly. Consumer behavior and expectations are getting influenced every second in this age of instant living. Social influences are determining this flux to a large extent. Corporations can leverage extremely powerful tools such as AI and Semantic web to decode the changing behavior and adapt their products, services and promotions on a real time basis.
Views: 96 xurmotech
An Ontology Based Text Mining Framework for R&D Project Selection
 
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An Ontology Based Text Mining Framework for R&D Project Selection ieee project in java
Views: 365 satya narayana
R tutorial: What is text mining?
 
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Learn more about text mining: https://www.datacamp.com/courses/intro-to-text-mining-bag-of-words Hi, I'm Ted. I'm the instructor for this intro text mining course. Let's kick things off by defining text mining and quickly covering two text mining approaches. Academic text mining definitions are long, but I prefer a more practical approach. So text mining is simply the process of distilling actionable insights from text. Here we have a satellite image of San Diego overlaid with social media pictures and traffic information for the roads. It is simply too much information to help you navigate around town. This is like a bunch of text that you couldn’t possibly read and organize quickly, like a million tweets or the entire works of Shakespeare. You’re drinking from a firehose! So in this example if you need directions to get around San Diego, you need to reduce the information in the map. Text mining works in the same way. You can text mine a bunch of tweets or of all of Shakespeare to reduce the information just like this map. Reducing the information helps you navigate and draw out the important features. This is a text mining workflow. After defining your problem statement you transition from an unorganized state to an organized state, finally reaching an insight. In chapter 4, you'll use this in a case study comparing google and amazon. The text mining workflow can be broken up into 6 distinct components. Each step is important and helps to ensure you have a smooth transition from an unorganized state to an organized state. This helps you stay organized and increases your chances of a meaningful output. The first step involves problem definition. This lays the foundation for your text mining project. Next is defining the text you will use as your data. As with any analytical project it is important to understand the medium and data integrity because these can effect outcomes. Next you organize the text, maybe by author or chronologically. Step 4 is feature extraction. This can be calculating sentiment or in our case extracting word tokens into various matrices. Step 5 is to perform some analysis. This course will help show you some basic analytical methods that can be applied to text. Lastly, step 6 is the one in which you hopefully answer your problem questions, reach an insight or conclusion, or in the case of predictive modeling produce an output. Now let’s learn about two approaches to text mining. The first is semantic parsing based on word syntax. In semantic parsing you care about word type and order. This method creates a lot of features to study. For example a single word can be tagged as part of a sentence, then a noun and also a proper noun or named entity. So that single word has three features associated with it. This effect makes semantic parsing "feature rich". To do the tagging, semantic parsing follows a tree structure to continually break up the text. In contrast, the bag of words method doesn’t care about word type or order. Here, words are just attributes of the document. In this example we parse the sentence "Steph Curry missed a tough shot". In the semantic example you see how words are broken down from the sentence, to noun and verb phrases and ultimately into unique attributes. Bag of words treats each term as just a single token in the sentence no matter the type or order. For this introductory course, we’ll focus on bag of words, but will cover more advanced methods in later courses! Let’s get a quick taste of text mining!
Views: 27358 DataCamp
The Semantic Bank with FIBO Presented by Shannon Walker
 
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Enterprise Data World April 20, 2016 Financial Industry Business Ontology
Views: 797 DATAVERSITY
Taxonomy Creator for SharePoint
 
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Taxonomy Creator for SharePoint is part of the 'Semantic SP' series (http://www.semantic-sharepoint.com/) of smart Web Parts and components to leverage the value of your SharePoint installation. To create, maintain and make use of even the largest taxonomies with your SharePoint Server you might seek for an alternative to the built-in Term Store Management Tool. We present PoolParty Thesaurus Manager as an option to create rich taxonomies and thesauri to be imported with a few mouse-clicks into your SharePoint Server.
Ontologies
 
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Dr. Michel Dumontier from Stanford University presents a lecture on "Ontologies." Lecture Description Ontology has its roots as a field of philosophical study that is focused on the nature of existence. However, today's ontology (aka knowledge graph) can incorporate computable descriptions that can bring insight in a wide set of compelling applications including more precise knowledge capture, semantic data integration, sophisticated query answering, and powerful association mining - thereby delivering key value for health care and the life sciences. In this webinar, I will introduce the idea of computable ontologies and describe how they can be used with automated reasoners to perform classification, to reveal inconsistencies, and to precisely answer questions. Participants will learn about the tools of the trade to design, find, and reuse ontologies. Finally, I will discuss applications of ontologies in the fields of diagnosis and drug discovery. View slides from this lecture: https://drive.google.com/open?id=0B4IAKVDZz_JUVjZuRVpMVDMwR0E About the Speaker Dr. Michel Dumontier is an Associate Professor of Medicine (Biomedical Informatics) at Stanford University. His research focuses on the development of methods to integrate, mine, and make sense of large, complex, and heterogeneous biological and biomedical data. His current research interests include (1) using genetic, proteomic, and phenotypic data to find new uses for existing drugs, (2) elucidating the mechanism of single and multi-drug side effects, and (3) finding and optimizing combination drug therapies. Dr. Dumontier is the Stanford University Advisory Committee Representative for the World Wide Web Consortium, the co-Chair for the W3C Semantic Web for Health Care and the Life Sciences Interest Group, scientific advisor for the EBI-EMBL Chemistry Services Division, and the Scientific Director for Bio2RDF, an open source project to create Linked Data for the Life Sciences. He is also the founder and Editor-in-Chief for a Data Science, a new IOS Press journal featuring open access, open review, and semantic publishing. Please join our weekly meetings from your computer, tablet or smartphone. Visit our website to learn how to join! http://www.bigdatau.org/data-science-seminars
The Text and Data mining functionalities of the PoolParty Semantic Suite
 
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This webinar introduces PoolParty Semantic Suite, the main software product of Semantic Web Company (SWC), one of the leading providers of graph-based metadata, search, and analytic solutions. PoolParty is a world-class semantic technology suite that offers sharply focused solutions to your knowledge organization and content business. PoolParty is the most complete semantic middleware on the global market. You can use it to enrich your information with valuable metadata to link your business and content assets automatically. This webinar focuses on the text-mining and entity- / text extraction capability of PoolParty Semantic Suite that is used for: • support of the continuous modelling of industrial knowledge graphs (as a supervised learning system) • for entity linking and data integration • for classification and semantic annotation mechanisms • and thereby for downstream applications like semantic search, recommender systems or intelligent agents The webinar presents and explains these features in the PoolParty software environment, shows demos based on real world use cases and finally showcases 3rd party integrations (e.g. into Drupal CMS).
Views: 170 AIMS CIARD
What is ontology? Introduction to the word and the concept
 
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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: 292576 Kent Löfgren
Natural Language Processing (NLP) & Text Mining Tutorial Using NLTK | NLP Training | Edureka
 
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** 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: 44051 edureka!
Social Network Analysis
 
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An overview of social networks and social network analysis. See more on this video at https://www.microsoft.com/en-us/research/video/social-network-analysis/
Views: 4960 Microsoft Research
Application of Semantic Web Enabled web-based Services
 
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Video demonstrating the three requirements (Ontology, Web services and Semantic annotation of web services) for achieving semantic web services. The goal is to enable users and software agents to automatically discover, invoke, compose, and monitor web resource offering services. Made for MSc. (Advanced Computer Science project) at Dept. of Informatics at the University of Leicester
Views: 71 Dipo Oyekanmi
Towards Ontology Based Data Access for Statoil. Part 1: Introduction
 
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Ontology Based Data Access (OBDA) is a prominent approach to provide end-users with high-level access to data via an ontology that is 'connected' to the data via mappings. State-of-the-art OBDA systems, however, suffer from limitations restricting their applicability in industry. In particular, development of necessary prerequisites to deploy an OBDA system, i.e., ontologies and mappings, as well as end-user oriented query interfaces, are poorly addressed. Moreover, solutions often focus on separate critical components of OBDA systems, while, to the best of our knowledge, there is no end-to-end OBDA solution. The Optique platform provides an integrated end-to-end OBDA system that addresses a number of practical challenges including support for development of deployment prerequisites and user-oriented query interfaces. During the demonstration one can try the platform with preconfigured scenarios from the petroleum industry and music domain, and try its end-to-end functionality: from deployment to query answering. In the first part we provide a general description of the OBDA approach in general and our system particularly.
Views: 887 Optique Project
IBM Watson Explorer: performing text analytics on scientific publications
 
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We use Watson Explorer to analyze unstructured text articles from PubMed as part of a machine learning project to study infectious diseases, specifically sepsis. For more information on IBM Watson Explorer, please visit the IBM Marketplace at https://www.ibm.com/us-en/marketplace/content-analytics Also, be sure to see the companion article on https://medium.com/@rbalduino/ab41315a6a37 which explains the data science behind this demonstration. The tools used in this video include: IBM Watson Explorer: https://www.ibm.com/us-en/marketplace/content-analytics NIH PubMed https://www.ncbi.nlm.nih.gov/pubmed/ NIH Medical Subject Headings (MeSH) https://www.ncbi.nlm.nih.gov/mesh/ Drugbank Database https://www.drugbank.ca Follow our presenters: Ricardo Balduino https://twitter.com/baldz70
Views: 8386 IBM Analytics
64 Cosine Similarity Example
 
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For Full Course Experience Please Go To http://mentorsnet.org/course_preview?course_id=1 Full Course Experience Includes 1. Access to course videos and exercises 2. View & manage your progress/pace 3. In-class projects and code reviews 4. Personal guidance from your Mentors
Views: 44070 Oresoft LWC
Context Ontology from Social Media
 
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Social networks are a real challenge in the ground of data mining. Most of social network’s users are active on several, and inquire different information on each of their profile. This project aims at defining a method which combines Data Mining and Semantic Web, to extract and store information on several social networks, including semantic links between them: under the form of an ontology (that is to say a graph including semantic relationship).
Views: 125 HumanTech
Text mining with correspondence analysis
 
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Here is an example of the use of correspondence analysis for textual data. Four methods of multivariate data analysis are descibed by words and compared with correspondence analysis.
Views: 5206 François Husson
bpmNEXT 2013: Extreme BPMN: Semantic Web Leveraging BPMN XML Serialization
 
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Lloyd Dugan, BPM, Inc. and Mohamed Keshk, Semantic BPMN This session demonstrates some of the most extreme work performed to date with BPMN -- extending it beyond the process view into semantic meaning and systems architecture. Completed inside the U.S. defense enterprise, BPMN is used for enterprise-level services modeling and within an ontology-based semantic search engine to automate search of process models. The resulting engine leverages the power of the Semantic Web to discover patterns and anomalies across now seamlessly linked repositories. This approach for the first time fully exploits the richness of the BPMN notation, uniquely enabling modeling of executable services as well as context-based retrieval of BPMN artifacts. Lloyd Dugan is the Chief Architect for Business Management, Inc., providing BPMN modeling, system design, and architectural advisory services for the Deputy Chief Management Office (DCMO) of the U.S. Department of Defense (DoD). He is also an Independent Consultant that designs executable BPMN processes that leverage Service Component Architecture (SCA) patterns (aka BPMN4SCA), principally on the Oracle BPMN/SOA platform. He has nearly 27 years of experience in providing IT consulting services to both private and public sector clients. He has an MBA from Duke University's Fuqua School of Business. Mohamed Keshk has been working with Semantic Technology since 2001, and Model Driven Architecture (MDA) since 2005. His most recent work focuses on bridging the gap between semantic technology and metamodel-based standards such as UML2 and BPMN 2.0, including the first ontology-based query engine for BPMN 2.0, based on XMI metamodel. As Sr. Semantic Architect, Mohamed is testing the engine in a production environment to let users instantly retrieve information in a model repository.
A Web Search Engine Based Approach to Measure Semantic Similarity between Words
 
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To get this project in ONLINE or through TRAINING Sessions, Contact: JP INFOTECH, 45, KAMARAJ SALAI, THATTANCHAVADY, PUDUCHERRY-9 Landmark: Opposite to Thattanchavady Industrial Estate, Next to VVP Nagar Arch. Mobile: (0) 9952649690 , Email: [email protected], web: www.jpinfotech.org Blog: www.jpinfotech.blogspot.com A Web Search EngineBased Approach to Measure Semantic Similarity between Words Measuring the semantic similarity between words is an important component in various tasks on the web such as relation extraction, community mining, document clustering, and automatic metadata extraction. Despite the usefulness of semantic similarity measures in these applications, accurately measuring semantic similarity between two words (or entities) remains a challenging task. We propose an empirical method to estimate semantic similarity using page counts and text snippets retrieved from a web search engine for two words. Specifically, we define various word co-occurrence measures using page counts and integrate those with lexical patterns extracted from text snippets. To identify the numerous semantic relations that exist between two given words, we propose a novel pattern extraction algorithm and a pattern clustering algorithm. The optimal combination of page counts-based co-occurrence measures and lexical pattern clusters is learned using support vector machines. The proposed method outperforms various baselines and previously proposed web-based semantic similarity measures on three benchmark data sets showing a high correlation with human ratings. Moreover, the proposed method significantly improves the accuracy in a community mining task.
Views: 1120 JPINFOTECH PROJECTS
How to Build an Imaging Ontology
 
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We will provide an introduction to the field of biomedical ontology with special reference to the field of pathology informatics. We will look at examples of existing ontologies especially the Ontology for Biomedical Investigations (OBI), the Ontology for Biological and Clinical Statistics (OBCS), and the Ontology for General Medical Science (OGMS). We will then draw lessons from these examples for an ontology of pathology imaging.
Views: 935 Barry Smith
Semantic Computing for Software Agents
 
01:09:27
Semantic Computing combines disciplines such as computational linguistics, artificial intelligence, multimedia, database and services computing together into an integrated theme while addressing their synergetic interactions. In this session presentations will show how semantic technologies coupled with machine learning approaches can address the meaning of large scale heterogeneous data and how they can help improve design for software engineering and develop intelligent software agents. In particular, the presentations will address challenge of correctly disambiguate entities and relationships in the merging process in order to be able to compose large knowledge bases with high coverage. Due to the high degree of uncertainty in the merging process it appears promising to use an approach based on probability, in particular, graphical models.
Views: 161 Microsoft Research
A Pragmatic Guide to Web Components
 
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Get a crash course on what Web Components are, how the current batch of JavaScript frameworks address the technology, and some good anti-patterns to avoid when building your own Web Components. More awesome HTML5 & JavaScript resources: http://crcl.to/nc Open Web Camp: http://openwebcamp.com
Views: 3987 InfoQ
Semantic Tagging and Support Vector Machines to Streamline the Analysis of Animal Accelerometry Data
 
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Increasingly, animal biologists are taking advantage of low cost micro-sensor technology, by deploying accelerometers to monitor the behaviour and movement of a broad range of species. The result is an avalanche of complex tri-axial accelerometer data streams that capture observations and measurements of a wide range of animal body motion and posture parameters. We present a system which supports storing, visualizing, annotating and automatic recognition of activities in accelerometer data streams by integrating semantic annotation and visualization services with Support Vector Machine techniques.
Views: 83 Microsoft Research
Towards Ontology Based Data Access for Statoil. Part 2: OptiqueVQS
 
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Ontology Based Data Access (OBDA) is a prominent approach to provide end-users with high-level access to data via an ontology that is 'connected' to the data via mappings. State-of-the-art OBDA systems, however, suffer from limitations restricting their applicability in industry. In particular, development of necessary prerequisites to deploy an OBDA system, i.e., ontologies and mappings, as well as end-user oriented query interfaces, are poorly addressed. Moreover, solutions often focus on separate critical components of OBDA systems, while, to the best of our knowledge, there is no end-to-end OBDA solution. The Optique platform provides an integrated end-to-end OBDA system that addresses a number of practical challenges including support for development of deployment prerequisites and user-oriented query interfaces. During the demonstration one can try the platform with preconfigured scenarios from the petroleum industry and music domain, and try its end-to-end functionality: from deployment to query answering. In the second part, we provide a general description of the OBDA approach in general and our system particularly.
Views: 761 Optique Project
IEEE 2013 JAVA Automatic Semantic Content Extraction in Videos Using a Fuzzy Ontology
 
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PG Embedded Systems #197 B, Surandai Road Pavoorchatram,Tenkasi Tirunelveli Tamil Nadu India 627 808 Tel:04633-251200 Mob:+91-98658-62045 General Information and Enquiries: [email protected] [email protected] PROJECTS FROM PG EMBEDDED SYSTEMS 2013 ieee projects, 2013 ieee java projects, 2013 ieee dotnet projects, 2013 ieee android projects, 2013 ieee matlab projects, 2013 ieee embedded projects, 2013 ieee robotics projects, 2013 IEEE EEE PROJECTS, 2013 IEEE POWER ELECTRONICS PROJECTS, ieee 2013 android projects, ieee 2013 java projects, ieee 2013 dotnet projects, 2013 ieee mtech projects, 2013 ieee btech projects, 2013 ieee be projects, ieee 2013 projects for cse, 2013 ieee cse projects, 2013 ieee it projects, 2013 ieee ece projects, 2013 ieee mca projects, 2013 ieee mphil projects, tirunelveli ieee projects, best project centre in tirunelveli, bulk ieee projects, pg embedded systems ieee projects, pg embedded systems ieee projects, latest ieee projects, ieee projects for mtech, ieee projects for btech, ieee projects for mphil, ieee projects for be, ieee projects, student projects, students ieee projects, ieee proejcts india, ms projects, bits pilani ms projects, uk ms projects, ms ieee projects, ieee android real time projects, 2013 mtech projects, 2013 mphil projects, 2013 ieee projects with source code, tirunelveli mtech projects, pg embedded systems ieee projects, ieee projects, 2013 ieee project source code, journal paper publication guidance, conference paper publication guidance, ieee project, free ieee project, ieee projects for students., 2013 ieee omnet++ projects, ieee 2013 oment++ project, innovative ieee projects, latest ieee projects, 2013 latest ieee projects, ieee cloud computing projects, 2013 ieee cloud computing projects, 2013 ieee networking projects, ieee networking projects, 2013 ieee data mining projects, ieee data mining projects, 2013 ieee network security projects, ieee network security projects, 2013 ieee image processing projects, ieee image processing projects, ieee parallel and distributed system projects, ieee information security projects, 2013 wireless networking projects ieee, 2013 ieee web service projects, 2013 ieee soa projects, ieee 2013 vlsi projects, NS2 PROJECTS,NS3 PROJECTS. DOWNLOAD IEEE PROJECTS: 2013 IEEE java projects,2013 ieee Project Titles, 2013 IEEE cse Project Titles, 2013 IEEE NS2 Project Titles, 2013 IEEE dotnet Project Titles. IEEE Software Project Titles, IEEE Embedded System Project Titles, IEEE JavaProject Titles, IEEE DotNET ... IEEE Projects 2013 - 2013 ... Image Processing. IEEE 2013 - 2013 Projects | IEEE Latest Projects 2013 - 2013 | IEEE ECE Projects2013 - 2013, matlab projects, vlsi projects, software projects, embedded. eee projects download, base paper for ieee projects, ieee projects list, ieee projectstitles, ieee projects for cse, ieee projects on networking,ieee projects. Image Processing ieee projects with source code, Image Processing ieee projectsfree download, Image Processing application projects free download. .NET Project Titles, 2013 IEEE C#, C Sharp Project Titles, 2013 IEEE EmbeddedProject Titles, 2013 IEEE NS2 Project Titles, 2013 IEEE Android Project Titles. 2013 IEEE PROJECTS, IEEE PROJECTS FOR CSE 2013, IEEE 2013 PROJECT TITLES, M.TECH. PROJECTS 2013, IEEE 2013 ME PROJECTS.
Views: 772 PG Embedded Systems
Components Of Natural Language Processing In Artificial Intelligence (HINDI)
 
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📚📚📚📚📚📚📚📚 GOOD NEWS FOR COMPUTER ENGINEERS INTRODUCING 5 MINUTES ENGINEERING 🎓🎓🎓🎓🎓🎓🎓🎓 SUBJECT :- Artificial Intelligence(AI) Database Management System(DBMS) Software Modeling and Designing(SMD) Software Engineering and Project Planning(SEPM) Data mining and Warehouse(DMW) Data analytics(DA) Mobile Communication(MC) Computer networks(CN) High performance Computing(HPC) Operating system System programming (SPOS) Web technology(WT) Internet of things(IOT) Design and analysis of algorithm(DAA) 💡💡💡💡💡💡💡💡 EACH AND EVERY TOPIC OF EACH AND EVERY SUBJECT (MENTIONED ABOVE) IN COMPUTER ENGINEERING LIFE IS EXPLAINED IN JUST 5 MINUTES. 💡💡💡💡💡💡💡💡 THE EASIEST EXPLANATION EVER ON EVERY ENGINEERING SUBJECT IN JUST 5 MINUTES. 🙏🙏🙏🙏🙏🙏🙏🙏 YOU JUST NEED TO DO 3 MAGICAL THINGS LIKE SHARE & SUBSCRIBE TO MY YOUTUBE CHANNEL 5 MINUTES ENGINEERING 📚📚📚📚📚📚📚📚
Views: 7811 5 Minutes Engineering
What is CORPORATE TAXONOMY? What does CORPORATE TAXONOMY mean? CORPORATE TAXONOMY meaning
 
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What is CORPORATE TAXONOMY? What does CORPORATE TAXONOMY mean? CORPORATE TAXONOMY meaning - CORPORATE TAXONOMY definition - CORPORATE TAXONOMY explanation. SUBSCRIBE to our Google Earth flights channel - http://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ?sub_confirmation=1 Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Corporate taxonomy is the hierarchical classification of entities of interest of an enterprise, organization or administration, used to classify documents, digital assets and other information. Taxonomies can cover virtually any type of physical or conceptual entities (products, processes, knowledge fields, human groups, etc.) at any level of granularity. Corporate taxonomies are increasingly used in information systems (particularly content management and knowledge management systems), as a way to promote discoverability and allow instant access to the right information within exponentially growing volumes of data in learning organizations. Relatively simple systems based on semantic networks and taxonomies proved to be a serious competitor to heavy data mining systems and behavior analysis software in contextual filtering applications used for routing customer requests, "pushing" content on a Web site or delivering product advertising in a targeted and pertinent way. A powerful approach to map and retrieve unstructured data, taxonomies allow efficient solutions in the management of corporate knowledge, in particular in complex organizational models for workflows, human resources or customer relations. As an extension of traditional thesauri and classifications used in a company, a corporate taxonomy is usually the fruit of a large harmonization effort involving most departments of the organization. It is often developed, deployed and fine tuned over the years, while setting up knowledge management systems, in order to assure the survival and good use of valuable corporate know-how. Enterprises have varying interest in the usage of taxonomies, from the usual enterprise information searches to the direct business benefits of taxonomies benefiting quicker and more accurate searches for the merchandise or the services of e-commerce or e-library sites. Such organisations may need to build large and complex vocabularies and deal with information assets that are largely in the public domain. Consequently, they are looking to shortcut their metadata schema development and avoid reinventing the wheel. Such shortcuts include the licensing of ready-built taxonomies and vocabularies with which to enhance their search results quickly.
Views: 79 The Audiopedia
OLAP Servers ll ROLAP, MOLAP, HOLAP Explained In Hindi
 
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ROLAP MOLAP HOLAP These OLAP SERVERS are explained in this video 📚📚📚📚📚📚📚📚 GOOD NEWS FOR COMPUTER ENGINEERS INTRODUCING 5 MINUTES ENGINEERING 🎓🎓🎓🎓🎓🎓🎓🎓 SUBJECT :- Artificial Intelligence(AI) Database Management System(DBMS) Software Modeling and Designing(SMD) Software Engineering and Project Planning(SEPM) Data mining and Warehouse(DMW) Data analytics(DA) Mobile Communication(MC) Computer networks(CN) High performance Computing(HPC) Operating system System programming (SPOS) Web technology(WT) Internet of things(IOT) Design and analysis of algorithm(DAA) 💡💡💡💡💡💡💡💡 EACH AND EVERY TOPIC OF EACH AND EVERY SUBJECT (MENTIONED ABOVE) IN COMPUTER ENGINEERING LIFE IS EXPLAINED IN JUST 5 MINUTES. 💡💡💡💡💡💡💡💡 THE EASIEST EXPLANATION EVER ON EVERY ENGINEERING SUBJECT IN JUST 5 MINUTES. 🙏🙏🙏🙏🙏🙏🙏🙏 YOU JUST NEED TO DO 3 MAGICAL THINGS LIKE SHARE & SUBSCRIBE TO MY YOUTUBE CHANNEL 5 MINUTES ENGINEERING
Views: 27740 5 Minutes Engineering
1. Information Retrieval - Introduction and Boolean Retrieval with example
 
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This video explains the Introduction to Information Retrieval with its basic terminology such as: Corpus, Information Need, Relevance etc. It also explains about the types of data i.e. Structured, Unstructured and Semi Structured. This video also contains the detailed explanation of How to create Term Document Incidence Matrix with the help of real world example, which is called as Boolean Retrieval.
Views: 15077 itechnica
Planetary Nervous System
 
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The Planetary Nervous System can be imagined as a global sensor network, where 'sensors' include anything able to provide static and dynamic data about socio-economic, environmental or technological systems which measure or sense the state and interactions of the components that make up our world. Such an infrastructure will enable real-time data mining - reality mining - using data from online surveys, web and lab experiments and the semantic web to provide aggregate information. FuturICT will closely collaborate with Sandy Pentland's team at MIT's Media Lab, to connect the sensors in today's smartphones (which comprise accelerometers, microphones, video functions, compasses, GPS, and more). One goal is to create better compasses than the gross national product (GDP), considering social, environmental and health factors. To encourage users to contribute data voluntarily, incentives and micropayment systems must be devised with privacy-respecting capabilities built into the data-mining, giving people control over their own data. This will facilitate collective and self-awareness of the implications of human decisions and actions. Two illustrative examples for smart-phone-based collective sensing applications are the open streetmap project and a collective earthquake sensing and warning concept.
Views: 1300 FuturICT
Web Crawler - CS101 - Udacity
 
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Help us caption and translate this video on Amara.org: http://www.amara.org/en/v/f16/ Sergey Brin, co-founder of Google, introduces the class. What is a web-crawler and why do you need one? All units in this course below: Unit 1: http://www.youtube.com/playlist?list=PLF6D042E98ED5C691 Unit 2: http://www.youtube.com/playlist?list=PL6A1005157875332F Unit 3: http://www.youtube.com/playlist?list=PL62AE4EA617CF97D7 Unit 4: http://www.youtube.com/playlist?list=PL886F98D98288A232& Unit 5: http://www.youtube.com/playlist?list=PLBA8DEB5640ECBBDD Unit 6: http://www.youtube.com/playlist?list=PL6B5C5EC17F3404D6 Unit 7: http://www.youtube.com/playlist?list=PL6511E7098EC577BE OfficeHours 1: http://www.youtube.com/playlist?list=PLDA5F9F71AFF4B69E Join the class at http://www.udacity.com to gain access to interactive quizzes, homework, programming assignments and a helpful community.
Views: 130139 Udacity
SPARQL Queries on a Web 3.0 Database
 
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The complex problems we face today involve knowledge and datasets growing in size at unprecedented rates. In government to identify threats, in finance to detect fraud, in e-commerce to provide contextual services according to individual profiles, in life sciences to discover new medicines from genome data, the problems we are trying to solve with computers are too complex to do with traditional database technology. Metadata and Semantic Technologies are one of the exciting new ways to represent and retrieve your knowledge. Franz has developed a new product line designed to tackle large scale business problems today. At the heart of this package is AllegroGraph, the premier commercial scalable RDF triple store in the market today with advanced query and reasoning capabilities. SPARQL is the W3C recommended query language for RDF. In our seminar we demonstrate how to load RDF and OWL knowledge bases into AllegroGraph and how queries are executed over the data with our optimized SPARQL engine. We'll also demonstrate geospatial, Prolog, and social network analysis queries on the same database. We will show how to interface with AllegroGraph through its Java API, and use TopBraid Composer as our ontology building tool and graphical interface to the triple store.
Views: 4789 AllegroGraph
What is RESOURCE DESCRIPTION FRAMEWORK? What does RESOURCE DESCRIPTION FRAMEWORK mean?
 
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What is RESOURCE DESCRIPTION FRAMEWORK? What does RESOURCE DESCRIPTION FRAMEWORK mean? RESOURCE DESCRIPTION FRAMEWORK meaning - RESOURCE DESCRIPTION FRAMEWORK definition - RESOURCE DESCRIPTION FRAMEWORK explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. The Resource Description Framework (RDF) is a family of World Wide Web Consortium (W3C) specifications originally designed as a metadata data model. It has come to be used as a general method for conceptual description or modeling of information that is implemented in web resources, using a variety of syntax notations and data serialization formats. It is also used in knowledge management applications. RDF was adopted as a W3C recommendation in 1999. The RDF 1.0 specification was published in 2004, the RDF 1.1 specification in 2014. The RDF data model is similar to classical conceptual modeling approaches (such as entity–relationship or class diagrams). It is based upon the idea of making statements about resources (in particular web resources) expressions, known as triples. Triples are so named because they follow a subject–predicate–object structure. The subject denotes the resource, and the predicate denotes traits or aspects of the resource, and expresses a relationship between the subject and the object. For example, one way to represent the notion "The sky has the color blue" in RDF is as the triple: a subject denoting "the sky", a predicate denoting "has the color", and an object denoting "blue". Therefore, RDF swaps object for subject in contrast to the typical approach of an entity–attribute–value model in object-oriented design: entity (sky), attribute (color), and value (blue). RDF is an abstract model with several serialization formats (i.e. file formats), so the particular encoding for resources or triples varies from format to format. This mechanism for describing resources is a major component in the W3C's Semantic Web activity: an evolutionary stage of the World Wide Web in which automated software can store, exchange, and use machine-readable information distributed throughout the Web, in turn enabling users to deal with the information with greater efficiency and certainty. RDF's simple data model and ability to model disparate, abstract concepts has also led to its increasing use in knowledge management applications unrelated to Semantic Web activity. A collection of RDF statements intrinsically represents a labeled, directed multi-graph. This theoretically makes an RDF data model better suited to certain kinds of knowledge representation than other relational or ontological models. However, in practice, RDF data is often persisted in relational database or native representations (also called Triplestores—or Quad stores, if context (i.e. the named graph) is also persisted for each RDF triple). ShEX, or Shape Expressions, is a language for expressing constraints on RDF graphs. It includes the cardinality constraints from OSLC Resource Shapes and Dublin Core Description Set Profiles, as well as logical connectives for disjunction and polymorphism. As RDFS and OWL demonstrate, one can build additional ontology languages upon RDF.
Views: 2014 The Audiopedia
Represention Of Map Information ll Sensorial, Geometric, Local Relational, Topology, Semantic
 
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📚📚📚📚📚📚📚📚 GOOD NEWS FOR COMPUTER ENGINEERS INTRODUCING 5 MINUTES ENGINEERING 🎓🎓🎓🎓🎓🎓🎓🎓 SUBJECT :- Discrete Mathematics (DM) Theory Of Computation (TOC) Artificial Intelligence(AI) Database Management System(DBMS) Software Modeling and Designing(SMD) Software Engineering and Project Planning(SEPM) Data mining and Warehouse(DMW) Data analytics(DA) Mobile Communication(MC) Computer networks(CN) High performance Computing(HPC) Operating system System programming (SPOS) Web technology(WT) Internet of things(IOT) Design and analysis of algorithm(DAA) 💡💡💡💡💡💡💡💡 EACH AND EVERY TOPIC OF EACH AND EVERY SUBJECT (MENTIONED ABOVE) IN COMPUTER ENGINEERING LIFE IS EXPLAINED IN JUST 5 MINUTES. 💡💡💡💡💡💡💡💡 THE EASIEST EXPLANATION EVER ON EVERY ENGINEERING SUBJECT IN JUST 5 MINUTES. 🙏🙏🙏🙏🙏🙏🙏🙏 YOU JUST NEED TO DO 3 MAGICAL THINGS LIKE SHARE & SUBSCRIBE TO MY YOUTUBE CHANNEL 5 MINUTES ENGINEERING 📚📚📚📚📚📚📚📚
Views: 8052 5 Minutes Engineering
Business Information Semantics & Rules
 
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Is your enterprise defining a business transformation led by value chain analysis or a balanced scorecard? Are you having trouble finding data to serve as measures? Are you missing business data from your warehouse? These are symptoms of missing business information architecture. Business information architecture is an aspect of a business blueprint. It defines and relates the terms that business people use to talk about business information, and it ties those terms to the logical data architectures defined by IT people. Business information architecture provides a common vocabulary of business information terms within the business architecture, across multiple lines of business and between business and IT. It allows a direct analysis of the information transformations needed to support a business transformation initiative. The linkages between terms in the vocabulary provide support for information impact analysis. The vocabulary can also be linked to other aspects of the business architecture such as business processes, business capabilities, organization, value chains, and so forth. Such linkages support a more thorough analysis of transformation impact. Finally, the vocabulary can be linked to IT information models, allowing the business and IT organizations to jointly plan for change. In addition, this linkage can be used to identify information needed for business but not provided, and information provided but not needed. The course will introduce the primary components of business information architecture: the formal definition of the vocabulary and the linkages to other aspects of the business architecture. Examples are used to illustrate the methods for the creation of the business information architecture and for keeping it synchronized with the evolution of the business.
Views: 354 BPMInstitute
Webcast - Navigating Time and Probability in Knowledge Graphs
 
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The market for knowledge graphs is rapidly developing and evolving to solve widely acknowledged deficiencies with data warehouse approaches. Graph databases are providing the foundation for these knowledge graphs and in our enterprise customer base we see two approaches forming: static knowledge graphs and dynamic event driven knowledge graphs. Static knowledge graphs focus mostly on metadata about entities and the relationships between these entities but they donot capture ongoing business processes. DBPedia, Geonames and Census or Pubmed are great examples of static knowledge. Dynamic knowledge graphs are used in the enterprise to facilitate internal processes, facilitate the improvement of products or services or gather dynamic knowledge about customers. Dr. Aasman recently authored an IEEE article describing this evolution of knowledge graphs in the Enterprise and during this presentation we will describe two critical success factors for dynamic knowledge graphs, a uniform way to model, query and interactively navigate time and the power of incorporating probabilities into the graph. The presentation will cover three use cases and live demos showing the confluence of knowledge via machine learning, visual querying, distributed graph databases, and big data not only displays links between objects, but also quantifies the probability of their occurrence. IEEE Paper link - https://allegrograph.com/the-enterprise-knowledge-graph/
Views: 3179 AllegroGraph
Goal Stack Planning Implementation Explained With Example In Artificial Intelligence (HINDI)
 
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Goal Stack Planning ll Pickup, Putdown,Stack,Unstack,Precondition And Actions Explained With Example https://youtu.be/WlG0H0u8aCg 📚📚📚📚📚📚📚📚 GOOD NEWS FOR COMPUTER ENGINEERS INTRODUCING 5 MINUTES ENGINEERING 🎓🎓🎓🎓🎓🎓🎓🎓 SUBJECT :- Artificial Intelligence(AI) Database Management System(DBMS) Software Modeling and Designing(SMD) Software Engineering and Project Planning(SEPM) Data mining and Warehouse(DMW) Data analytics(DA) Mobile Communication(MC) Computer networks(CN) High performance Computing(HPC) Operating system System programming (SPOS) Web technology(WT) Internet of things(IOT) Design and analysis of algorithm(DAA) 💡💡💡💡💡💡💡💡 EACH AND EVERY TOPIC OF EACH AND EVERY SUBJECT (MENTIONED ABOVE) IN COMPUTER ENGINEERING LIFE IS EXPLAINED IN JUST 5 MINUTES. 💡💡💡💡💡💡💡💡 THE EASIEST EXPLANATION EVER ON EVERY ENGINEERING SUBJECT IN JUST 5 MINUTES. 🙏🙏🙏🙏🙏🙏🙏🙏 YOU JUST NEED TO DO 3 MAGICAL THINGS LIKE SHARE & SUBSCRIBE TO MY YOUTUBE CHANNEL 5 MINUTES ENGINEERING 📚📚📚📚📚📚📚📚
Views: 6309 5 Minutes Engineering
Identifiers.org: Practical Integration Tool for Heterogeneous Datasets
 
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http://togotv.dbcls.jp/20130703.html http://identifiers.org/media/2013-BioHackathon.pdf NBDC / DBCLS BioHackathon 2013 was held in Tokyo, Japan. Main focus of this BioHackathon is semantic interoperability and standardization of bioinformatics data and Web services. The participants discussed, explored and developed web applications and interoperability (DDBJ/UniProt, SADI, TogoGenome, Schema.org etc.), generation and standardization of RDF data (Open Bio* tools, SIO, FALDO, Identifiers.org etc.), text-mining, NLP and ontology mapping (LODQA, BioPortal, NanoPublication etc.), quality assessment of SPARQL endpoints (availability, contents, CORS etc.) and standardization of RDF data in specific domains. On the first day of the BioHackathon (Jun. 23), public symposium of the BioHackathon 2013 was held at Tokyo Skytree Space 634. In this talk, Nick Juty makes a presentation entitled "Identifiers.org: Practical Integration Tool for Heterogeneous Datasets". (13:33)
Views: 105 togotv
Self Adaptive Semantic Focused Crawler for Mining Services Information Discovery
 
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ChennaiSunday Systems Pvt.Ltd We are ready to provide guidance to successfully complete your projects and also download the abstract, base paper from our website IEEE 2014 Java Projects: http://www.chennaisunday.com/projectsNew.php?id=1&catName=IEEE_2014-2015_Java_Projects IEEE 2014 Dotnet Projects: http://www.chennaisunday.com/projectsNew.php?id=20&catName=IEEE_2014-2015_DotNet_Projects Output Videos: https://www.youtube.com/channel/UCCpF34pmRlZbAsbkareU8_g/videos IEEE 2013 Java Projects: http://www.chennaisunday.com/projectsNew.php?id=2&catName=IEEE_2013-2014_Java_Projects IEEE 2013 Dotnet Projects: http://www.chennaisunday.com/projectsNew.php?id=3&catName=IEEE_2013-2014_Dotnet_Projects Output Videos: https://www.youtube.com/channel/UCpo4sL0gR8MFTOwGBCDqeFQ/videos IEEE 2012 Java Projects: http://www.chennaisunday.com/projectsNew.php?id=26&catName=IEEE_2012-2013_Java_Projects Output Videos: https://www.youtube.com/user/siva6351/videos IEEE 2012 Dotnet Projects: http://www.chennaisunday.com/projectsNew.php?id=28&catName=IEEE_2012-2013_Dotnet_Projects Output Videos: https://www.youtube.com/channel/UC4nV8PIFppB4r2wF5N4ipqA/videos IEEE 2011 Java Projects: http://chennaisunday.com/projectsNew.php?id=29&catName=IEEE_2011-2012_Java_Project IEEE 2011 Dotnet Projects: http://chennaisunday.com/projectsNew.php?id=33&catName=IEEE_2011-2012_Dotnet_Projects Output Videos: https://www.youtube.com/channel/UCtmBGO0q5XZ5UsMW0oDhZ-A/videos IEEE PHP Projects: http://www.chennaisunday.com/projectsNew.php?id=41&catName=IEEE_PHP_Projects Output Videos: https://www.youtube.com/user/siva6351/videos Java Application Projects: http://www.chennaisunday.com/projectsNew.php?id=34&catName=Java_Application_Projects Output Videos: https://www.youtube.com/channel/UCPqHN-x10SazValUi9Konlg/videos Dotnet Application Projects: http://www.chennaisunday.com/projectsNew.php?id=35&catName=Dotnet_Application_Projects Output Videos: https://www.youtube.com/channel/UCMTKwKCCJvpErttDqCuG1jA/videos Android Application Projects: http://www.chennaisunday.com/projectsNew.php?id=36&catName=Android_Application_Projects PHP Application Projects: http://www.chennaisunday.com/projectsNew.php?id=37&catName=PHP_Application_Projects Struts Application Projects: http://www.chennaisunday.com/projectsNew.php?id=38&catName=Struts_Application_Projects Java Mini Projects: http://www.chennaisunday.com/projectsNew.php?id=39&catName=Java_Mini_Projects Dotnet Mini Projects: http://www.chennaisunday.com/projectsNew.php?id=40&catName=Dotnet_Mini_Projects -- *Contact * * P.Sivakumar MCA Director Chennai Sunday Systems Pvt Ltd Phone No: 09566137117 No: 1,15th Street Vel Flats Ashok Nagar Chennai-83 Landmark R3 Police Station Signal (Via 19th Street) URL: www.chennaisunday.com Map View: http://chennaisunday.com/locationmap.php
Views: 381 siva kumar
SHELDON
 
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SHELDON is the first true hybridization of NLP machine reading and Semantic Web. It is a framework that builds upon a ma- chine reader for extracting RDF graphs from text so that the output is compliant to Semantic Web and Linked Data patterns. It extends the current human-readable web by using Semantic Web practices and technologies in a machine-processable form. Given a sentence in any language, it provides different semantic functionalities (frame detection, topic extraction, named entity recognition, resolution and coreference, terminology extraction, sense tagging and disambiguation, taxonomy induction, semantic role labeling, type induction, sentiment analysis, citation inference, relation and event extraction) as well as nice visualization tools which make use of the JavaScript infoVis Toolkit and RelFinder, as well as a knowledge enrichment component that extends machine reading to Semantic Web data. The system can be freely used at http://wit.istc.cnr.it/stlab-tools/sheldon.
011 Render Html in django | django ecommerce | django tutorials
 
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Python is at number one in top technologies in the world, with top rate python rest framework django. now a days it's bein use in almost every technology like Data Mining, Machine Learning, Internet Of THings (IOT), Data Sacience, Big Data, Data Analysis....etc If you are willing to learn Python Django framework for building your ECommerce Web Application or Learning Django Framework then this course is specially for you. You can build your Own ECommerce Application at free of Cost. Now the Django Comlete Course for Building Ecommerce Applicaion is Availabel for free Now Major Topics Covered in in this complete udemy course are listed Below. Topics: 1. Getting Started 2. Hello World 3. Products Component 4. Templates 5. Bootstrap Framework 6. Search Component 7. Cart Component 8. Checkout Process 9. Fast Track to jQuery 10. Products & Async 11. Custom User Model 12. Custom Analytics 13. Stripe Integration 14. Mailchimp Integration 15. Go Live 16. Account & Settings 17. Selling Digital Items 18. Graphs and Sales 19. Thank you Don't forget to subscribe my channel for more premium content related to technology, bussiness class study and other kind of premium courses for free of cost. Youtube Channel Link: https://www.youtube.com/channel/UCUGY8RiGqnWW9qBSnQ2c8TA Complete fiverr seo course link: https://www.youtube.com/watch?v=yBDOb80oeFo&list=PLV2_Iivd4jxYDgPAtossMcZrvnTY8EBVy Check out for other courses like: Internet marketing: https://www.youtube.com/watch?v=Kik2yfe2Oog&list=PLV2_Iivd4jxbsUwA9cOEaM804euT0OGbp Python django ecommerce: https://www.youtube.com/watch?v=5bTvseLFkAo&list=PLV2_Iivd4jxYVDWCcxmccusNaUx2kWCg1 Have a Nice Day. how to learn python? how ecommerce website design. how to do ecommerce website development? what is ecommerce website? ecommerce website templates? what is python ecommerce? best django tutorial. how to do ecommerce business? ecommerce website. django server. python tutorial for beginners. python tutorials. python projects. python web development. python language. python projects for begineers.
Views: 947 ePayMinds
Bridging the gap between 2.0, 3.0 and Cloud APIs
 
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Presenter: Chris Scott Date: 12/1/2009 Company: Nstein Technologies Venue: Online Info - Olympia, London Getting creative with user generated content. The current evolution of the web is largely being driven by three key components: the Interactive Web, the Semantic Web and the commoditization of functionality available through Cloud APIs. We see features of each of these components in more and more Web sites but rarely in an integrated way. The value of the Semantic Web is understood by many online publishers, striving to add understanding to their internally generated content. Unfortunately, most users are not well equipped to provide semantically relevant metadata to any large degree. Certainly, a community will not consistently supply metadata about the content they have contributed. The field of automated semantic enrichment provides an opportunity to overcome this limitation and to manage the increasing volume of content on the Web. In this talk we explore the benefits of applying semantic analysis in connecting content and people to resources available on the web, such as Google Maps, Salesforce.com, etc. How by extracting known quantities, like geographic locations, from a users contributions we can enrich that content with services that require specific inputs, like a mapping tool. By semantically enriching interactive, user generated content site designers can confidently take advantage of the huge spectrum of Cloud functionalities available on the web. Chris bio (added by Lorna): A Computational Mathematics graduate of University Of Sussex, Chris has quickly become of one Nstein most influential technologists. A specialist in online distribution and digital asset management, he has repeatedly illustrated the power of Text Mining in everything from Web Content Management to Twitter. His technological prowess has helped Nstein's customers including Independant News & media and DC Thomson, maximise their online potential.
Views: 158 NsteinTechnologies

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