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Search results “Web knowledge mining taxonomy”
Data Mining Lecture - - Advance Topic | Web mining | Text mining (Eng-Hindi)
 
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Data mining Advance topics - Web mining - Text Mining -~-~~-~~~-~~-~- Please watch: "PL vs FOL | Artificial Intelligence | (Eng-Hindi) | #3" https://www.youtube.com/watch?v=GS3HKR6CV8E -~-~~-~~~-~~-~- Follow us on : Facebook : https://www.facebook.com/wellacademy/ Instagram : https://instagram.com/well_academy Twitter : https://twitter.com/well_academy
Views: 48319 Well Academy
Data Mining Classification and Prediction ( in Hindi)
 
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A tutorial about classification and prediction in Data Mining .
Views: 25691 Red Apple Tutorials
What is Web Mining
 
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Views: 13005 TechGig
Semantic Knowledge Graphs - Part 1
 
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What are Semantic Knowledge Graphs and why they make a difference in Enterprise Information Management. Learn more: https://www.poolparty.biz/
Taxonomies and Ontologies - The Yin and Yang of Knowledge Engineering
 
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Which kind of knowledge model fits well with my system requirements? How can our ontologies and taxonomies work together? When would SKOS be sufficient, when do I need OWL? In this webinar, we present different knowledge modelling approaches and exemplify how they differentiate. We provide an overview how different industries make use of taxonomies and ontologies and how these knowledge models fit with different kinds of applications. Find slides at https://www.slideshare.net/semwebcompany/taxonomies-and-ontologies-the-yin-and-yang-of-knowledge-modelling
PoolParty Semantic Classifier - Bringing Machine Learning, NLP and Knowledge Graphs together
 
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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.
What is an Ontology
 
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Description of an ontology and its benefits. Please contact [email protected] for more information.
Views: 140470 SpryKnowledge
Why Semantic Knowledge Graphs matter
 
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PoolParty 5 (http://www.poolparty.biz/) - Five more reasons to lean on a world-class semantic platform. For many organisations it has become obvious that knowledge graphs and taxonomies are key elements in order to solve several issues in information and data management. Nevertheless, various methods to develop and maintain those are being discussed and it often remains unclear which of them should be applied. The PoolParty approach (http://www.poolparty.biz/see-how-it-works/) envisages an initial simple taxonomy (based on SKOS) to become more and more extended over time, e.g. by the application of ontologies, rules, graph mappings and linked data harvesting. This gradual approach has turned out to be a more practicable way for most industries in contrast to start with sophisticated ontologies first. This slidedeck also provides a comprehensive overview over the new features of PoolParty version 5. Apart from minor improvements and fixes, users of PoolParty 5 benefit from the following new features and major improvements: . Highly precise entity extraction . Deep integration with Confluence, SharePoint & Drupal . Fully integrated web crawler . Refined look & feel, and . Full-blown ontology management & semantic reasoning. Find live demos of linked data applications based on PoolParty Semantic Platform. For example, a semantic search application, which is fully based on an RDF graph store and the standards-based query language SPARQL.
Views: 952 Helmut Blumauer
WDM 116: Various Classification Methods
 
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Various Classification Methods 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: 1047 Oresoft LWC
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: 34 The Audiopedia
Artificial Intelligence, Data, Knowledge, Social Networks towards Semantic Solutions
 
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More info at : http://www.semspirit.com ♦️ Sem Spirit ♦️ proposes several predefined use cases to show and explain the benefits of Artificial Intelligence for solving real world problems. - Content Analysis : Rumor Tracking & Opinion Analysis - Optimization : Legal & Fiscal Optimization, Best Agreement Matching & Automatic Scheduling - Content Validation : Legal Regulations Validation, Mandatory Prerequisites Validation (Health, Jobs Skills, etc) - Content Generation : Auto-promoting systems, Chatbots ♦️ Artificial Intelligence (AI) ♦️ is the computer science field aiming at automating high complexity problem resolution. Among the dozens of subtopics dealing with artificial intelligence, Sem Spirit provides expertise on the following ones : - Knowledge Representation & Management : OWL Ontologies & RDF/S, SPARQL and Triplestores - Symbolic Reasoning : OWL Profiles and Description Logics & Rules and Reasoning Engines - Constraint Checking : Constraint Solving Problems (CSPs) & Planning - Machine Learning : Data Mining, Neural networks & Deep Learning Sem Spirit also provides insights about : - Data processing : Data Mining, Text Mining, Natural Language Processing, Language Generation. - Networks and graphs analysis : Graph Theory, Spectral Theory of Graphs, Data & Structure extraction, Network Navigation & Visualization.
Views: 223 Sem Spirit
Computing Semantic Similarity of Concepts in Knowledge Graphs
 
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Computing Semantic Similarity of Concepts in Knowledge Graphs To get this project in ONLINE or through TRAINING Sessions, Contact: JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai -83.Landmark: Next to Kotak Mahendra Bank. Pondicherry Office: JP INFOTECH, #37, Kamaraj Salai,Thattanchavady, Puducherry -9.Landmark: Next to VVP Nagar Arch. Mobile: (0) 9952649690, Email: [email protected], web: http://www.jpinfotech.org This paper presents a method for measuring the semantic similarity between concepts in Knowledge Graphs (KGs) such as WordNet and DBpedia. Previous work on semantic similarity methods have focused on either the structure of the semantic network between concepts (e.g. path length and depth), or only on the Information Content (IC) of concepts. We propose a semantic similarity method, namely wpath, to combine these two approaches, using IC to weight the shortest path length between concepts. Conventional corpus-based IC is computed from the distributions of concepts over textual corpus, which is required to prepare a domain corpus containing annotated concepts and has high computational cost. As instances are already extracted from textual corpus and annotated by concepts in KGs, graph-based IC is proposed to compute IC based on the distributions of concepts over instances. Through experiments performed on well known word similarity datasets, we show that the wpath semantic similarity method has produced statistically significant improvement over other semantic similarity methods. Moreover, in a real category classification evaluation, the wpath method has shown the best performance in terms of accuracy and F score.
Views: 658 jpinfotechprojects
What is TEXT MINING? What does TEXT MINING mean? TEXT MINING meaning, definition & explanation
 
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What is TEXT MINING? What does TEXT MINING mean? TEXT MINING meaning - TEXT MINING definition - TEXT MINING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Text mining, also referred to as text data mining, roughly equivalent to text analytics, is the process of deriving high-quality information from text. High-quality information is typically derived through the devising of patterns and trends through means such as statistical pattern learning. Text mining usually involves the process of structuring the input text (usually parsing, along with the addition of some derived linguistic features and the removal of others, and subsequent insertion into a database), deriving patterns within the structured data, and finally evaluation and interpretation of the output. 'High quality' in text mining usually refers to some combination of relevance, novelty, and interestingness. Typical text mining tasks include text categorization, text clustering, concept/entity extraction, production of granular taxonomies, sentiment analysis, document summarization, and entity relation modeling (i.e., learning relations between named entities). Text analysis involves information retrieval, lexical analysis to study word frequency distributions, pattern recognition, tagging/annotation, information extraction, data mining techniques including link and association analysis, visualization, and predictive analytics. The overarching goal is, essentially, to turn text into data for analysis, via application of natural language processing (NLP) and analytical methods. A typical application is to scan a set of documents written in a natural language and either model the document set for predictive classification purposes or populate a database or search index with the information extracted. The term text analytics describes a set of linguistic, statistical, and machine learning techniques that model and structure the information content of textual sources for business intelligence, exploratory data analysis, research, or investigation. The term is roughly synonymous with text mining; indeed, Ronen Feldman modified a 2000 description of "text mining" in 2004 to describe "text analytics." The latter term is now used more frequently in business settings while "text mining" is used in some of the earliest application areas, dating to the 1980s, notably life-sciences research and government intelligence. The term text analytics also describes that application of text analytics to respond to business problems, whether independently or in conjunction with query and analysis of fielded, numerical data. It is a truism that 80 percent of business-relevant information originates in unstructured form, primarily text. These techniques and processes discover and present knowledge – facts, business rules, and relationships – that is otherwise locked in textual form, impenetrable to automated processing.
Views: 1984 The Audiopedia
Keyword Knowledge Graphs: Using AI to Create Outside-In Taxonomies
 
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We're changing up our webinars with live expert interviews. Mike talked with James Mathewsonk, the program director of content marketing platforms at IBM. James writes frequently about keyword ontology -- the powerful tool for understanding keywords you should be building content around to attract prospects, engage with them, and convert them into loyal clients. Your audience doesn't always arrive at content that sells your product through a search. The vast majority of searches are for answers to questions or solutions to problems that are not obviously product related. If you provide the answers to the questions your target audiences ask, and if you help to solve their problems, many of them will trust you enough to try or buy your products when they're ready. But how do you provide the content that helps clients and prospects journey from generic questions about product categories to buying your products? The answer is keyword ontology. A keyword ontology is a set of relationships between the keywords potential buyers use in search and the products or services they are ultimately looking for. It helps you understand which words tend to go with which products, and how you can out think your competition to deliver the content your prospects need, in each stage of the buyer's journey. James will discuss why and how you should be creating a keyword ontology. James Mathewson is IBM's Distinguished Technical Marketer for search. He has 20 years of experience in web editorial, content strategy, and SEO for large and small companies. A frequent speaker, lecturer and blogger, James has published more than 1600 articles and two books on how web technology and user experience change the nature of effective content. James has two advanced degrees on related subjects from the University of Minnesota. Thanks to our sponsors Gerris and SoloSegment!
Views: 114 BiznologyChannel
Discovering Latent Semantics in Web Documents using Fuzzy Clustering
 
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Discovering Latent Semantics in Web Documents using Fuzzy Clustering TO GET THIS PROJECT IN ONLINE OR THROUGH TRAINING SESSIONS CONTACT: Chennai Office: JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai – 83. Landmark: Next to Kotak Mahendra Bank / Bharath Scans. Landline: (044) - 43012642 / Mobile: (0)9952649690 Pondicherry Office: JP INFOTECH, #45, Kamaraj Salai, Thattanchavady, Puducherry – 9. Landmark: Opp. To Thattanchavady Industrial Estate & Next to VVP Nagar Arch. Landline: (0413) - 4300535 / Mobile: (0)8608600246 / (0)9952649690 Email: [email protected], Website: www.jpinfotech.org, Blog: www.jpinfotech.blogspot.com Web documents are heterogeneous and complex. There exists complicated associations within one web document and linking to the others. The high interactions between terms in documents demonstrate vague and ambiguous meanings. Efficient and effective clustering methods to discover latent and coherent meanings in context are necessary. This paper presents a fuzzy linguistic topological space along with a fuzzy clustering algorithm to discover the contextual meaning in the web documents. The proposed algorithm extracts features from the web documents using conditional random field methods and builds a fuzzy linguistic topological space based on the associations of features. The associations of co-occurring features organize a hierarchy of connected semantic complexes called ‘CONCEPTS,’ wherein a fuzzy linguistic measure is applied on each complex to evaluate (1) the relevance of a document belonging to a topic, and (2) the difference between the other topics. Web contents are able to be clustered into topics in the hierarchy depending on their fuzzy linguistic measures; web users can further explore the CONCEPTS of web contents accordingly. Besides the algorithm applicability in web text domains, it can be extended to other applications, such as data mining, bioinformatics, content-based or collaborative information filtering, and so forth.
Views: 283 jpinfotechprojects
SharePoint Content Classification: Layer2 Auto Tagger
 
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Download: https://www.layer2solutions.com/registration-layer2-knowledge-management-suite Features: • Increased productivity and precision while bulk-tagging SharePoint items and documents automatically. • Content classification rules supported to increase the precision of classification. • Installed IFilters are used for content analysis, e.g. Word, Excel, PowerPoint, PDF and many more. • Fully integrated with SharePoint 2010 / 2013 default tagging. • Helps to manage libraries that host more than 5.000 items (list view threshold). • Flexible background operation settings. • External data sources fully supported. The Layer2 Auto Tagger for Microsoft SharePoint Server 2010 and 2013 automatically categorizes SharePoint items and documents in background using taxonomy-based managed metadata and classification rules organized in the SharePoint Term Store. Content classification rules, item and document properties and metadata, information store context and textual document contents are considered with the auto-classification. By default Microsoft SharePoint Server 2010 and 2013 offers a manual content classification feature only.
"Semantic Similarity & Taxonomic Distance:.." Andrew Clegg
 
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Semantic Similarity & Taxonomic Distance: Using Structured Metadata in Data Science Models, Andrew Clegg, Data Scientist at Etsy Slides can be found here: http://bit.ly/2g7iTCB Watch more from Data Natives Berlin 2016 here: http://bit.ly/2fE1sEo Visit the conference website to learn more: www.datanatives.io Follow Data Natives: https://www.facebook.com/DataNatives https://twitter.com/DataNativesConf Stay Connected to Data Natives by Email: Subscribe to our newsletter to get the news first about Data Natives 2017: http://bit.ly/1WMJAqS About the Author: Andrew joined Etsy in 2014, and lives in London, making him their first data scientist outside the USA. Since then he has worked on a variety of challenges including localized recommendations, image similarity search and anomaly detection. Prior to Etsy he spent almost 15 years designing machine learning workflows, and building search and analytics services, in academia, startups and enterprises, and in an ever-growing list of research areas including biomedical informatics, computational linguistics, social media analytics, and educational gaming. These days he’s interested in probabilistic algorithms and data structures, online learning, deep learning, data visualization, and the convergence of search and recommender systems. He can count to over 1000 on his fingers but doesn’t know how to drive a car.
Views: 428 Data Natives
sOnr Web Mining for Confluence - PoolParty Tutorial #23
 
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SONR IS A TOOL FOR MARKET OBSERVERS AND TREND SCOUTS (http://www.sonr-webmining.com/). With sOnr, you will keep track of everything that happens in a domain or industry of your interest. SONR IS BASED ON SEMANTIC TECHNOLOGIES. It is embedded in Atlassian Confluence, a highly useful collaboration platform. This architectural approach supports teams of market observers to extract relevant information from news services, blogs, and short messages automatically. SONR HELPS TO EXCHANGE IDEAS AND TO STRUCTURE KNOWLEDGE. A built-in semantic search engine is one of its core elements. Automatic agents crawl the web and the intranet. Collaborative features leverage the value of your findings! USERS WILL BENEFIT FROM - enterprise-readiness, - highly precise search results, - collaborative knowledge management, - a coffee break while sOnr is mining the web
Mining linked data - Petko Valtchev
 
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Summer School in cognitive Science: Web Science and the Mind Institut des sciences cognitives, UQAM, Montréal, Canada http://www.summer14.isc.uqam.ca/ http://www.isc.uqam.ca/ PETKO VALTCHEV UQÀM Mining Patterns from Linked Data OVERVIEW: The Web of Data (WoD) can be seen as global database made of multiple datasets. These datasets are published separately — by using new or reusing existing schemas on the Web — yet get interlinked through either direct references between data items or indirect ones, i.e., identity links between items representing the same entity. The technology underlying the WoD, called Linked Data (LD) allows for the construction of a global data graph in which data items are vertices related by edges of different nature. Entities, aka resources, as well as their links, aka properties, are globally identified through URLs. Beside this inherent graph structure, parts of the WoD can behave as a traditional, i.e., relational, database. After substantial efforts on the standards for publishing and querying of LD on the Web, and lately the interlinking and cleansing of sets of LD, the next big issue is properly extracting new knowledge from the WoD. Data Mining (DM) discipline is about finding chunks of useful knowledge hidden in the data. DM methods are roughly divided into predictive ones, where past experience is analyzed in order to guess what the outcome of an unfolding situation, and descriptive ones whose aim is to provide insights into the regularities in the data without a specific goal. Mining LD is both useful and challenging for many reasons, not the least among them being the rich and complex graph structure induced by a large variety of link types, the availability of domain knowledge expressed as schemas, and even fully-blown ontologies, the heterogeneity in the modelling goals behind individual datasets, etc. In this talk we discuss the implications of LD for a specific branch of descriptive DM, called pattern mining. We present two different mining methods for that are complementary in many respects. The first one targets usage regularities: It analyses the consumption of resources from the WoD by the users of a specific semantic application and summarizes it as behavioural patterns. The second one mines purely descriptive patterns from a dataset of multiple resource types, which are expressed in a WoD-compliant language and therefore supports ontology design.
Taxonomy Visualizer, MasdarDNA
 
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Taxonomy Visualizer is a novel web-based application for taxonomy based technology forecasting and interactive visualization. Main goal of this framework is to allow rapidly growing areas of research to be easily detected and grasped using various data and text mining techniques. This work is supported by the Data & Network Analytics Research Group at Masdar Institute of Science and Technology in Abu Dhabi, UAE. Find it out more: http://www.dnagroup.org/
INFORMATION RETRIEVAL TECHNIQUES IN HINDI
 
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Find the notes of INFORMATION RETRIEVAL on this link - https://viden.io/knowledge/information-retrieval?utm_campaign=creator_campaign&utm_medium=referral&utm_source=youtube&utm_term=ajaze-khan-1
Views: 7308 LearnEveryone
Semi-unsupervised learning of taxonomic and non-taxonomic relationships from the web
 
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Due to the size of the World Wide Web, it is necessary to develop tools for automatic or semi-automatic analyses of web data, such as finding patterns and implicit information in the web, a task usually known as Web Mining. In particular, web content mining consists of automatically mining data from textual web documents that can be represented with machine-readable semantic formalisms. While more traditional approaches to Information Extraction from text, such as those applied to the Message Understanding Conferences during the nineties, relied on small collections of documents with many semantic annotations, the characteristics of the web (its size, redundancy and the lack of semantic annotations in most texts) favor efficient algorithms able to learn from unannotated data. Furthermore, new types of web content such as web forums, blogs and wikis, are also a source of textual information that contain an underlying structure from which specialist systems can benefit. This talk will describe an ongoing project for automatically acquiring ontological knowledge (both taxonomic and non-taxonomic relationships) from the web in a partially unsupervised way. The proposed approach combines distributional semantics techniques with rote extractors. A particular focus will be set on an automatic addition of semantic tags to the Wikipedia with the aim of transforming it, with small effort, into a Semantic Wikipedia.
Views: 25 Microsoft Research
INTRODUCTION TO CLASSIFICATION - DATA MINING
 
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Classification consists of predicting a certain outcome based on a given input. In order to predict the outcome, the algorithm processes a training set containing a set of attributes and the respective outcome, usually called goal or prediction attribute. The algorithm tries to discover relationships between the attributes that would make it possible to predict the outcome. Next the algorithm is given a data set not seen before, called prediction set, which contains the same set of attributes, except for the prediction attribute – not yet known. The algorithm analyses the input and produces a prediction.
Views: 31961 Nina Canares
Biological taxonomy and chemistry search: SciBite and ChemAxon partnering
 
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We have partnered with SciBite to provide even more sophisticated text analytics functionality. Access the chemical content in your biological literature - the video shows how SciBite's bio-based taxonomy works together with ChemAxon's small molecule search.
Views: 75 ChemAxon
Semantic SharePoint
 
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This video was recorded by http://www.semantic-sharepoint.com/ Slides: http://www.slideshare.net/semwebcompany/semantic-sharepoint-36297561 Semantic technologies build the basis for smart content management systems. Functionalities of such technologies range from automatic tagging / text mining to taxonomy / ontology management. From a user perspective, improved search, contextualisation of information, e.g. automatic content recommendation, and means for a better understanding of interlinked information are key for professional information management. SharePoint is a frequently used carrier-system of enterprise content which offers some basic functionalities for semantic information management out-of-the-box. In this video, you will see how these features are usually used, e.g. SharePoint's Term Store, and how those components can be extended by a set of additional functionalities provided by Semantic SP. We demonstrate and discuss the benefit of use cases based on the following components of the Semantic SP product family: - PowerTagging for SharePoint: Automatic tagging and semantic indexing of documents by use of text mining based on enterprise vocabularies. Semantic search based on SharePoint's standard search component. - Semantic Knowledge Base for SharePoint: See how to publish and navigate enterprise vocabularies, complex semantic networks and/or ontologies within a SharePoint server. - Taxonomy Creator for SharePoint: See how to create and maintain very large and complex taxonomies by use of PoolParty Thesaurus Server, to import into SP Term Store or to enable PowerTagging for SharePoint.
PoolParty 6.0 - The Most Complete Semantic Middleware on the Market
 
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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.
Waking up to data mining
 
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Subscribe to France 24 now: http://f24.my/youtubeEN FRANCE 24 live news stream: all the latest news 24/7 http://f24.my/YTliveEN With Facebook back in the news, public awareness of the realities of data mining is on the increase. In this week's show, we take a look at how the digital generaton is waking up to the troubling facts about the use and abuse of private data. Also, coding is a key skill for those working in the tech sector. We take a look at two leading schools, one in San Francisco and another in Paris. http://www.france24.com/en/taxonomy/emission/19928 Visit our website: http://www.france24.com Subscribe to our YouTube channel: http://f24.my/youtubeEN Like us on Facebook: https://www.facebook.com/FRANCE24.English Follow us on Twitter: https://twitter.com/France24_en
Views: 813 FRANCE 24 English
Why SKOS Should Be a Focal Point of Your Linked Data Strategy
 
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Slide deck for this webinar at http://goo.gl/3Gtk4j The Simple Knowledge Organization System (SKOS) has become one of the 'sweet spots' in the linked data ecosystem in recent years. Especially when semantic web technologies are being adapted for the requirements of enterprises or public administration, SKOS has played a most central role to create knowledge graphs. In this webinar, key people from the Semantic Web Company will describe why controlled vocabularies based on SKOS play a central role in a linked data strategy, and how SKOS can be enriched by ontologies and linked data to further improve semantic information management. SKOS unfolds its potential at the intersection of three disciplines and their methods: *) library sciences: taxonomy and thesaurus management *) information sciences: knowledge engineering and ontology management *) computational linguistics: text mining and entity extraction Linked Data based IT-architectures cover all three aspects and provide means for agile data, information, and knowledge management. In this webinar, you will learn about the following questions and topics: *) How SKOS builds the foundation of enterprise knowledge graphs to be enriched by additional vocabularies and ontologies? *) How can knowledge graphs be used build the backbone of metadata services in organisations? *) How text mining can be used to create high-quality taxonomies and thesauri? *) How can knowledge graphs be used for enterprise information integration? Based on PoolParty Semantic Suite, you will see several live demos of end-user applications based on linked data and of PoolParty's latest release which provides outstanding facilities for professional linked data management, including taxonomy, thesaurus and ontology management.
Thesaurus based Text Mining - PoolParty Tutorial #18
 
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This video demonstrates how Thesaurus based Text Mining with PoolParty Extractor provides high-precision text analytics (See: http://www.poolparty.biz). It shows how any kind of content can be transformed into SKOS/RDF and how it later can be used for semantic mashups. Entity extraction based on knowledge graphs in contrast to simple term extraction offers an approach for information integration based on semantic knowledge models.
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
PoolParty Semantic Suite - Release 6.0 Technical Webinar
 
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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.
International Journal of Web & Semantic Technology (IJWesT)
 
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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 meaningful ways. 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] Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal. Important Dates: Submission Deadline : August 05, 2017 Acceptance Notification : September 05, 2017 Final Manuscript Due : September 13, 2017 Publication Date : Determined by the Editor-in-Chief For other details please visit http://www.airccse.org/journal/ijwest/ijwest.html
Views: 18 IJWEST JOURNAL
Batch Linking Between Taxonomies - PoolParty Tutorial #27
 
06:54
PoolParty Semantic Platform (http://www.poolparty.biz) offers standards-based tools for the professional information professional and taxonomy manager. Creating links between concepts of various taxonomies and knowledge graphs is a powerful feature of linked vocabularies based on SKOS. It helps to create metadata infrastructure in distributed organizations. Linking & mapping of taxonomies becomes a cost-effective task when it's supported by automatic algorithms. In this video we show how PoolParty Thesaurus Server supports this task based on a comfortable GUI.
WorkScript Knowledge Management Database
 
06:26
WorkScript is a Knowledge Management database that links process maps to policies, procedures, work instructions, reference documents, training material, and applications. It then uses these maps to generate interactive wizards for operational tasks.
Views: 3321 Greg Collette
Embeddings for Everything: Search in the Neural Network Era
 
01:18:23
Dean's lecture, with Dan Gillick — Retrieval systems like internet search still use the same underlying keyword-based index they used back in the 1990s. Dan Gillick will describe his research on building a new kind of retrieval system based, somewhat unsurprisingly, on neural networks. He’ll try to explain the key pieces of technology and discuss how this may change the way we look for and find things. Dan Gillick is a research scientist at Google and teaches machine learning and natural language processing in the MIDS program.
The Semantic Web: The Inside Story - Jim Hendler
 
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Summer School in cognitive Science: Web Science and the Mind Institut des sciences cognitives, UQAM, Montréal, Canada http://www.summer14.isc.uqam.ca/ http://www.isc.uqam.ca/ JIM HENDLER, Rensselaer Polytechnic Institute, Department of Computer Science The Semantic Web: The Inside Story OVERVIEW: In this talk I look at the Semantic Web idea of adding knowledge to the Web in ways compatible with machine processing. Emerging in the late 90s, and growing since then,the languages , usage and uptake of semantic technologies has been increasing. I'll discuss the genesis of this idea, some key steps in its history, and current usage. I also proposes challenges: Having far surpassed the original vision, how do we continue to use and grow the semantic web?
Building Ontologies: An Introduction for Engineers (Part 1)
 
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Begins with some historical background on the growth of ontology as a discipline on the borderlines of computer science, data science and philosophy. Sketches the development of the Semantic Web and the use of ontologies in the biomedical domain. Concludes with some reflections on the problems associated with the idea of 'linked open data'. Lecture presented at the Swiss Federal Institute of Technology (EPFL), Lausanne, January 30, 2017
Views: 8750 Barry Smith
Uploading and Tagging Documents - PoolParty Tutorial #11
 
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This video displays an advanced feature of PoolParty Thesaurus Server (See: http://www.poolparty.biz) which allows to do text mining in order to extend a thesaurus by new candidate terms. PoolParty is based on SKOS and offers some comfortable features to manage even the largest taxonomies. This video is part of a series of screencasts which help users to understand how to create linked data knowledge graphs by means of PoolParty software.
Big Data Meetup 23. Presentation by Krish on Data Mining - Part 1
 
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Data Mining - Techniques, Issues and Challenges - by Krish https://www.meetup.com/Mississauga-Big-Data-Analytics-Meetup https://kumarvn.wordpress.com/
Views: 30 Kumar VN
Import Excel - PoolParty Tutorial #19
 
02:49
See how easily Excel sheets can be imported into PoolParty Thesaurus Server (http://www.poolparty.biz/). Converting Excel taxonomies to SKOS to further extend them is the next logical step towards a professional taxonomy management system. With PoolParty, thesauri and taxonomies can be integrated with all kinds of applications like CMS, web shops, CRM or search engines.
A New Ontology of Unwanted Web Automation - Colin Watson - AppSecUSA 2015
 
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Recorded at AppSecUSA 2015 in San Francisco https://2015.appsecusa.org/ A New Ontology of Unwanted Web Automation Web applications are subjected to unwanted automated usage – day in, day out. Often these events relate to misuse of inherent valid functionality, rather than the attempted exploitation of unmitigated vulnerabilities. Also, excessive misuse is commonly mistakenly reported as application denial-of-service (DoS) like HTTP-flooding, when in fact the DoS is a side-effect instead of the primary intent. Some examples commonly referred to are: * Account enumeration * Click fraud * Comment spam * Content scraping * Data aggregation * Email address harvesting * Fake account creation * Password cracking * Payment card testing * Site crawling * Transaction automation Frequently these have sector-specific names. Most of these problems seen regularly by web application owners are not listed in any OWASP Top Ten or other top issue list. Furthermore, they are not enumerated or defined adequately in existing dictionaries. These factors have contributed to inadequate visibility, and an inconsistency in naming such threats, with a consequent lack of clarity in attempts to address the issues. Without sharing a common language between devops, architects, business owners, security engineers, purchasers and suppliers/vendors, everyone has to make extra effort to communicate clearly. Misunderstandings can be costly. The adverse impacts affect the privacy and security of individuals as well as the security of the applications and related system components. This presentation for the first time describes the work undertaken earlier this year and the concrete outputs completed including a new ontology of web application automation threats. Additionally the talk describes the primary and secondary symptoms, and current efforts to document and map relevant mitigations and protections. Attendees who own or operate production web sites, web APIs and other web applications will gain knowledge gathered from research and their peers about these threats, attack vectors, detection methods and protections against the unwanted automations. To develop the ontology, research was undertaken to identify prior work and existing information about the types of automated threats to web applications using academic papers, breach reports, security incidents, and existing attack and vulnerability taxonomies. This has been refined using insider knowledge from application security experts and using interviews with web application owners. The initial objective was to assess and define a shared vocabulary about these sorts of "attacks", so that the problem can be defined and addressed further. The analysis focused on real-world external threats and attack vectors, although the impacts on individuals, intermediaries, partners and third party organisations are also being considered. Common Misuse Scoring System (CMSS) has been used in the analysis. The generated web application-specific ontology has also been mapped to other relevant sources including Security Content Automation Protocol (SCAP) components and the relevant parts of Mitre's Common Weakness Enumeration and Common Attack Pattern Enumeration and Classification (CAPEC). The ontology has been published by the "OWASP Automation Threats to Web Applications Project" and is free to download and use. This OWASP project is intended to be an information hub for web application owners, providing practical resources to help them to protect their systems against these automated processes. The project is also seeking input in the form of event data that can be used to rank the threats for sectors such as financial services, ecommerce, hotel, travel, government, social media, gaming and gambling. Colin Watson Technical Director, Watson Hall Ltd Colin Watson is founder of Watson Hall Ltd, based in London, where his work involves the management of application risk, designing defensive measures, building security & privacy in to systems development and keeping abreast of relevant international legislation and standards. He holds a BSc in Chemical Engineering from Heriot-Watt University in Edinburgh, and an MSc in Computation from the University of Oxford. - Managed by the official OWASP Media Project https://www.owasp.org/index.php/OWASP_Media_Project
Views: 427 OWASP
Lecture 37 — Text Categorization  Methods | UIUC
 
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. Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "FAIR USE" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use. .
International journal of Web & Semantic Technology (IJWesT)
 
00:07
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 meaningful ways. 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] Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal.
Views: 6 IJWEST JOURNAL
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: 99 IJWEST JOURNAL

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