• Extracting Knowledge from Text - Pedro Domingos

    Title: Extracting Knowledge from Text w/ Tractable Markov Logic & Symmetry-Based Semantic Parsing Abstract: Building very large commonsense knowledge bases and reasoning with them is a long-standing dream of AI. Today that knowledge is available in text; all we have to do is extract it. Text, however, is extremely messy, noisy, ambiguous, incomplete, and variable. A formal representation of it needs to be both probabilistic and relational, either of which leads to intractable inference and therefore poor scalability. In the first part of this talk I will describe tractable Markov logic, a language that is restricted enough to be tractable yet expressive enough to represent much of the commonsense knowledge contained in text. Even then, transforming text into a formal representation of its ...

    published: 26 Aug 2014
  • Machine Learning Knowledge Extraction MAKE it short

    MAKE stands for MAchine Learning & Knowledge Extraction. Machine learning deals with understanding intelligence for the design and development of algorithms that can learn from data and improve over time. The original definition was “the artificial generation of knowledge from experience”. The challenge is to discover relevant structural patterns and/or temporal patterns (“knowledge”) in such data, which are often hidden and not accessible to a human. Today, machine learning is the fastest growing technical field, having many application domains, e.g. health, Industry 4.0, recommender systems, speech recognition, autonomous driving (Google car), etc. The grand challenge is in decision making under uncertainty, and probabilistic inference enormously influenced artificial intelligence and s...

    published: 13 May 2017
  • Relationship Extraction from Unstructured Text Based on Stanford NLP with Spark

    published: 22 Feb 2016
  • Extracting Knowledge from Informal Text

    The internet has revolutionized the way we communicate, leading to a constant flood of informal text available in electronic format, including: email, Twitter, SMS and also informal text produced in professional environments such as the clinical text found in electronic medical records. This presents a big opportunity for Natural Language Processing (NLP) and Information Extraction (IE) technology to enable new large scale data-analysis applications by extracting machine-processable information from unstructured text at scale. In this talk I will discuss several challenges and opportunities which arise when applying NLP and IE to informal text, focusing specifically on Twitter, which has recently rose to prominence, challenging the mainstream news media as the dominant source of real-tim...

    published: 08 Aug 2016
  • Machine Learning, Knowledge Extraction And Blockchain: The Knowledge Engine

    Part One of A Mathematical Theory of Knowledge: The Knowledge Engine - Machine Learning, Knowledge Extraction And Blockchain Live at The Humboldt Institut für Internet und Gesellschaft for the Blockchain for Science Hackathon. In cooperation with The Living Knowledge Network Foundation. Using mathematics such as algebras, information theory, graph theory, homomorphic encryption, algebraic information theory, domain theory, local computation, Markov Trees, linear algebra, many types of machine learning algorithms, fuzzy logic, many of which overlap, and even blockchain like structures, it can be shown that knowledge can be defined using three measures and extracted from datasets or other recorded observations, when a measure preserving mapping is found or an approximation thereof to form ...

    published: 03 Nov 2017
  • Artificial Intelligence | Tutorial #29 | Knowledge Extraction

    Watch this video to understand how the knowledge extraction is done in AI مشاهدة هذا الفيديو لفهم كيفية استخراج المعرفة يتم في منظمة العفو الدولية AIで知識抽出がどのように行われているかを理解するには、このビデオをご覧ください Regardez cette vidéo pour comprendre comment l'extraction de connaissances est effectuée en IA Sehen Sie sich dieses Video an, um zu verstehen, wie die Wissensgewinnung in AI erfolgt Посмотрите это видео, чтобы понять, как извлечение знаний выполняется в AI Vea este video para entender cómo se extrae el conocimiento en AI

    published: 02 Oct 2017
  • Analyzing the Web from Start to Finish - Knowledge Extraction using KNIME - Bernd Wiswedel - #1

    Slides: http://goo.gl/Gsyvxk züri ML meetup #1, february 25th 2014 Bernd Wiswedel, Co-Founder and CTO at KNIME Analyzing the Web from Start to Finish - Knowledge Extraction from a Web Forum using KNIME Abstract: Bernd will give us an introduction to KNIME, an open source graphical workbench for the entire analysis process from data access and preprocessing to analytics, visualization and reporting. As a practical example, he will show an application in which KNIME was used to analyze a public discussion forum. It includes the steps of web crawling, web analytics, topic detection and description of user interaction. http://www.knime.org/ http://en.wikipedia.org/wiki/KNIME 1st Züri Machine Learning Meetup 25th February 2014, ETH Zurich, Switzerland

    published: 11 Mar 2014
  • Knowledge Extraction for Retail

    Rafael Pous: Associate Professor - Ubiquitous Computing Applications Lab. María deMaeztu DTIC-UPF Workshop on Data Driven Knowledge Extraction, 28-29 June 2016, Barcelona To know more about the María de Maeztu Strategic Research Program and projects, visit http://www.upf.edu/mdm-dtic To know more about the department, visit http://www.upf.edu/mdm-dtic

    published: 13 Jul 2016
  • Caffeine extraction from coffee

    Welcome to what is undoubtedly by far the most laborious project I have ever done, which I think is the most large extraction of caffeine using this method on YT. It has been four weeks of extractions and editing advancing step by step each day. I hope you appreciate my work as well as enjoy it ;) Let me know what do you think about my yields compared with other people!!! Flute players, sorry for using that cleaner as a stirrer, dont take it into account. Anybody saw me at the end of the video? :D For better video experience, watch it in FullHD1080 Please rate and share if you like. Subscribe for more video uploadings. ¡¡¡THANKS FOR WATCHING!!!

    published: 03 Feb 2017
  • Multilingual Knowledge Extraction From Emails

    published: 07 May 2015
  • Knowledge Extraction

    published: 06 Jun 2017
  • CHEM117 04 Liquid Liquid Extraction Fundamentals

    published: 02 Oct 2015
  • The MdM Knowledge Extraction Strategic Research Program at DTIC-UPF

    Xavier Serra, Scientific Director of the DTIC-UPF María deMaeztu DTIC-UPF Workshop on Data Driven Knowledge Extraction, 28-29 June 2016, Barcelona To know more about the María de Maeztu Strategic Research Program and projects, visit http://www.upf.edu/mdm-dtic To know more about the department, visit http://www.upf.edu/mdm-dtic

    published: 11 Jul 2016
  • Liquid-Liquid Extraction

    published: 15 Jul 2014
  • Knowledge extraction from the wastewater treatment plant operation - Y. Kim

    knowledge extraction from the historical database of wastewater treatment plant operation using various data mining techniques. y. Kim, H. S. Kim, M. S. Kim, W. piao, D. Kang, C. Kim, Republic of Korea Chan Moon,Kim - Industrial Instrumentation & Control Professional Engineer Engineering Services Department, Korea Water Resources Corporation In this conference we will discuss the new developments in IT & Water. These developments are very important for the further evolution of the water sector. IT applications in the water sector cover a broad field of interest. On one hand, IT applications have the ability to integrate the water sector from a high strategic level with connections to security and energy services. on the other hand IT applications have the ability to improve the perform...

    published: 26 Nov 2012
  • Knowledge extraction in newspaper from sports domain

    Python

    published: 07 Nov 2017
  • Technique of Extraction and Washing

    Introduction on to the technique of liquid-liquid extraction and washing at the University of Manitoba. Undergraduate organic chemistry laboratory program under Dr. Horace Luong's supervision.

    published: 17 Dec 2016
  • NOVA | Extract Your DNA | PBS

    See the full episode of NOVA "Cracking the Genetic Code" here: http://video.pbs.org/video/2215641935/ Ever wish you could see the strands of genetic material that make you...you? You can, and there's no fancy lab equipment required. In this NOVA video short, learn how to extract your own DNA using just a few common household items. Watch NOVA's Cracking Your Genetic Code Wednesday, March 28th at 9PM/8c on PBS.

    published: 01 Mar 2012
  • Knowledge Extraction Process

    Knowledge extraction process employed by Triumph India Software Services.

    published: 18 Sep 2012
  • Presentation of paper "Diagnostic Knowledge Extraction ..."

    Presentation of the paper "Diagnostic Knowledge Extraction from MedlinePlus: An Application for Infectious Diseases" at PACBB Conference in Salamanca. June 4, 2015. Paper URL: https://link.springer.com/chapter/10.1007/978-3-319-19776-0_9

    published: 28 Feb 2017
  • Mining the Knowledge of Scientific Publications

    Horacio Saggion: Associate Professor - Natural Language Processing Research Group María deMaeztu DTIC-UPF Workshop on Data Driven Knowledge Extraction, 28-29 June 2016, Barcelona To know more about the María de Maeztu Strategic Research Program and projects, visit http://www.upf.edu/mdm-dtic To know more about the department, visit http://www.upf.edu/mdm-dtic

    published: 11 Jul 2016
  • Extraction of Knowledge from Email to Aid in Expertise Discovery - Dr Thomas Jackson

    Seminar given at by Dr Thomas Jackson at the Department Information Science Research Day at Loughborough University in July 2010. The talk describes the Extraction of Knowledge from Email to Aid in Expertise Discovery and how the research will move forward to classify and categorise email vaults.

    published: 11 Jan 2011
  • Doozer: Knowledge Extraction from Community-Generated Content

    Doozer is an on demand domain model creation tool, designed by Christopher Thomas at Knoesis Research Center. (http://knoesis.org) Project page: http://wiki.knoesis.org/index.php/Doozer

    published: 27 Oct 2011
  • Machine learning for autonomous systems and knowledge extraction from big data - Lyudmila Mihaylova

    CIS Seminar Series (http://cis.eecs.qmul.ac.uk/seminars.html) Machine learning for autonomous systems and knowledge extraction from big data Lyudmila S Mihaylova, University of Sheffield Where: Eng 209 Host: Andrea Cavallaro Abstract This talk focuses on methods for detection, tracking and decision making for autonomous or semi-autonomous surveillance systems. The data usually arrive from multiple heterogeneous sensors such as radar, LIDAR or cameras (optical and infrared). Methods for small and large groups comprised of hundreds or thousands of objects (normally referred to as cluster/crowd tracking) will be presented. Groups are structured objects characterised with particular motion patterns. The group can be comprised of a small number of interacting objects (e.g. pedestrians, sport...

    published: 27 Nov 2014
developed with YouTube
Extracting Knowledge from Text - Pedro Domingos

Extracting Knowledge from Text - Pedro Domingos

  • Order:
  • Duration: 1:04:37
  • Updated: 26 Aug 2014
  • views: 6132
videos
Title: Extracting Knowledge from Text w/ Tractable Markov Logic & Symmetry-Based Semantic Parsing Abstract: Building very large commonsense knowledge bases and reasoning with them is a long-standing dream of AI. Today that knowledge is available in text; all we have to do is extract it. Text, however, is extremely messy, noisy, ambiguous, incomplete, and variable. A formal representation of it needs to be both probabilistic and relational, either of which leads to intractable inference and therefore poor scalability. In the first part of this talk I will describe tractable Markov logic, a language that is restricted enough to be tractable yet expressive enough to represent much of the commonsense knowledge contained in text. Even then, transforming text into a formal representation of its meaning remains a difficult problem. There is no agreement on what the representation primitives should be, and labeled data in the form of sentence-meaning pairs for training a semantic parser is very hard to come by. In the second part of the talk I will propose a solution to both these problems, based on concepts from symmetry group theory. A symmetry of a sentence is a syntactic transformation that does not change its meaning. Learning a semantic parser for a language is discovering its symmetry group, and the meaning of a sentence is its orbit under the group (i.e., the set of all sentences it can be mapped to by composing symmetries). Preliminary experiments indicate that tractable Markov logic and symmetry-based semantic parsing can be powerful tools for scalably extracting knowledge from text.
https://wn.com/Extracting_Knowledge_From_Text_Pedro_Domingos
Machine Learning Knowledge Extraction MAKE it short

Machine Learning Knowledge Extraction MAKE it short

  • Order:
  • Duration: 3:21
  • Updated: 13 May 2017
  • views: 357
videos
MAKE stands for MAchine Learning & Knowledge Extraction. Machine learning deals with understanding intelligence for the design and development of algorithms that can learn from data and improve over time. The original definition was “the artificial generation of knowledge from experience”. The challenge is to discover relevant structural patterns and/or temporal patterns (“knowledge”) in such data, which are often hidden and not accessible to a human. Today, machine learning is the fastest growing technical field, having many application domains, e.g. health, Industry 4.0, recommender systems, speech recognition, autonomous driving (Google car), etc. The grand challenge is in decision making under uncertainty, and probabilistic inference enormously influenced artificial intelligence and statistical learning. The inverse probability allows to infer unknowns, learn from data and make predictions to support decision making. Whether in social networks, recommender systems, smart health or smart factory applications, the increasingly complex data sets require efficient, useful and useable intelligence for knowledge discovery and knowledge extraction. A synergistic combination of methodologies and approaches of two domains offer ideal conditions towards unraveling these challenges and to foster new, efficient and user-friendly machine learning algorithms and knowledge extraction tools: Human-Computer Interaction (HCI) and Knowledge Discovery/Data Mining (KDD), aiming at augmenting human intelligence with computational intelligence and vice versa. Successful Machine Learning & Knowledge extraction needs a concerted international effort without boundaries, supporting collaborative and integrative cross-disciplinary research between experts from 7 fields: in short: 1-data, 2-learning, 3-graphs, 4-topology, 5-entropy, 6-visualization, and 7-privacy; see http://hci-kdd.org/about-the-holzinger-group https://cd-make.net/about/ http://www.mdpi.com/journal/make/about Andreas Holzinger, 14.05.2017
https://wn.com/Machine_Learning_Knowledge_Extraction_Make_It_Short
Relationship Extraction from Unstructured Text Based on Stanford NLP with Spark

Relationship Extraction from Unstructured Text Based on Stanford NLP with Spark

  • Order:
  • Duration: 27:06
  • Updated: 22 Feb 2016
  • views: 8753
videos
https://wn.com/Relationship_Extraction_From_Unstructured_Text_Based_On_Stanford_Nlp_With_Spark
Extracting Knowledge from Informal Text

Extracting Knowledge from Informal Text

  • Order:
  • Duration: 59:59
  • Updated: 08 Aug 2016
  • views: 1092
videos
The internet has revolutionized the way we communicate, leading to a constant flood of informal text available in electronic format, including: email, Twitter, SMS and also informal text produced in professional environments such as the clinical text found in electronic medical records. This presents a big opportunity for Natural Language Processing (NLP) and Information Extraction (IE) technology to enable new large scale data-analysis applications by extracting machine-processable information from unstructured text at scale. In this talk I will discuss several challenges and opportunities which arise when applying NLP and IE to informal text, focusing specifically on Twitter, which has recently rose to prominence, challenging the mainstream news media as the dominant source of real-time information on current events. I will describe several NLP tools we have adapted to handle Twitter�s noisy style, and present a system which leverages these to automatically extract a calendar of popular events occurring in the near future (http://statuscalendar.cs.washington.edu). I will further discuss fundamental challenges which arise when extracting meaning from such massive open-domain text corpora. Several probabilistic latent variable models will be presented, which are applied to infer the semantics of large numbers of words and phrases and also enable a principled and modular approach to extracting knowledge from large open-domain text corpora.
https://wn.com/Extracting_Knowledge_From_Informal_Text
Machine Learning, Knowledge Extraction And Blockchain: The Knowledge Engine

Machine Learning, Knowledge Extraction And Blockchain: The Knowledge Engine

  • Order:
  • Duration: 11:51
  • Updated: 03 Nov 2017
  • views: 368
videos
Part One of A Mathematical Theory of Knowledge: The Knowledge Engine - Machine Learning, Knowledge Extraction And Blockchain Live at The Humboldt Institut für Internet und Gesellschaft for the Blockchain for Science Hackathon. In cooperation with The Living Knowledge Network Foundation. Using mathematics such as algebras, information theory, graph theory, homomorphic encryption, algebraic information theory, domain theory, local computation, Markov Trees, linear algebra, many types of machine learning algorithms, fuzzy logic, many of which overlap, and even blockchain like structures, it can be shown that knowledge can be defined using three measures and extracted from datasets or other recorded observations, when a measure preserving mapping is found or an approximation thereof to form a type of informational compression which can be defined as knowledge. The first lecture on this topic introduces the concept of a decentralized, trusted, public/private knowledge engine which makes use of the aforementioned methods to classify and extract knowledge and link the granules of knowledge together with inferred causality, creating a knowledge base that can be stored and processed on a blockchain like structure. Many previous and current machine learning algorithms can be improved upon and even shown to be equivalent using a mathematical theory of knowledge. Thus more computational expensive methods of machine learning can be avoided, especially once any possible local computation is factored in, and algorithms that still have to be run that are more computational expensive would only have to be run once before the knowledge could be extracted as a measure preserving mapping. This can apply to algorithms such as clustering algorithms, or neural networking such as support vector machines and even deep learning.
https://wn.com/Machine_Learning,_Knowledge_Extraction_And_Blockchain_The_Knowledge_Engine
Artificial Intelligence | Tutorial #29 | Knowledge Extraction

Artificial Intelligence | Tutorial #29 | Knowledge Extraction

  • Order:
  • Duration: 3:56
  • Updated: 02 Oct 2017
  • views: 535
videos
Watch this video to understand how the knowledge extraction is done in AI مشاهدة هذا الفيديو لفهم كيفية استخراج المعرفة يتم في منظمة العفو الدولية AIで知識抽出がどのように行われているかを理解するには、このビデオをご覧ください Regardez cette vidéo pour comprendre comment l'extraction de connaissances est effectuée en IA Sehen Sie sich dieses Video an, um zu verstehen, wie die Wissensgewinnung in AI erfolgt Посмотрите это видео, чтобы понять, как извлечение знаний выполняется в AI Vea este video para entender cómo se extrae el conocimiento en AI
https://wn.com/Artificial_Intelligence_|_Tutorial_29_|_Knowledge_Extraction
Analyzing the Web from Start to Finish - Knowledge Extraction using KNIME - Bernd Wiswedel - #1

Analyzing the Web from Start to Finish - Knowledge Extraction using KNIME - Bernd Wiswedel - #1

  • Order:
  • Duration: 33:59
  • Updated: 11 Mar 2014
  • views: 9499
videos
Slides: http://goo.gl/Gsyvxk züri ML meetup #1, february 25th 2014 Bernd Wiswedel, Co-Founder and CTO at KNIME Analyzing the Web from Start to Finish - Knowledge Extraction from a Web Forum using KNIME Abstract: Bernd will give us an introduction to KNIME, an open source graphical workbench for the entire analysis process from data access and preprocessing to analytics, visualization and reporting. As a practical example, he will show an application in which KNIME was used to analyze a public discussion forum. It includes the steps of web crawling, web analytics, topic detection and description of user interaction. http://www.knime.org/ http://en.wikipedia.org/wiki/KNIME 1st Züri Machine Learning Meetup 25th February 2014, ETH Zurich, Switzerland
https://wn.com/Analyzing_The_Web_From_Start_To_Finish_Knowledge_Extraction_Using_Knime_Bernd_Wiswedel_1
Knowledge Extraction for Retail

Knowledge Extraction for Retail

  • Order:
  • Duration: 36:00
  • Updated: 13 Jul 2016
  • views: 38
videos
Rafael Pous: Associate Professor - Ubiquitous Computing Applications Lab. María deMaeztu DTIC-UPF Workshop on Data Driven Knowledge Extraction, 28-29 June 2016, Barcelona To know more about the María de Maeztu Strategic Research Program and projects, visit http://www.upf.edu/mdm-dtic To know more about the department, visit http://www.upf.edu/mdm-dtic
https://wn.com/Knowledge_Extraction_For_Retail
Caffeine extraction from coffee

Caffeine extraction from coffee

  • Order:
  • Duration: 17:24
  • Updated: 03 Feb 2017
  • views: 4420
videos
Welcome to what is undoubtedly by far the most laborious project I have ever done, which I think is the most large extraction of caffeine using this method on YT. It has been four weeks of extractions and editing advancing step by step each day. I hope you appreciate my work as well as enjoy it ;) Let me know what do you think about my yields compared with other people!!! Flute players, sorry for using that cleaner as a stirrer, dont take it into account. Anybody saw me at the end of the video? :D For better video experience, watch it in FullHD1080 Please rate and share if you like. Subscribe for more video uploadings. ¡¡¡THANKS FOR WATCHING!!!
https://wn.com/Caffeine_Extraction_From_Coffee
Multilingual Knowledge Extraction From Emails

Multilingual Knowledge Extraction From Emails

  • Order:
  • Duration: 2:36
  • Updated: 07 May 2015
  • views: 26
videos
https://wn.com/Multilingual_Knowledge_Extraction_From_Emails
Knowledge Extraction

Knowledge Extraction

  • Order:
  • Duration: 13:03
  • Updated: 06 Jun 2017
  • views: 25
videos
https://wn.com/Knowledge_Extraction
CHEM117 04   Liquid Liquid Extraction Fundamentals

CHEM117 04 Liquid Liquid Extraction Fundamentals

  • Order:
  • Duration: 26:46
  • Updated: 02 Oct 2015
  • views: 47924
videos
https://wn.com/Chem117_04_Liquid_Liquid_Extraction_Fundamentals
The MdM Knowledge Extraction Strategic Research Program at DTIC-UPF

The MdM Knowledge Extraction Strategic Research Program at DTIC-UPF

  • Order:
  • Duration: 50:53
  • Updated: 11 Jul 2016
  • views: 74
videos
Xavier Serra, Scientific Director of the DTIC-UPF María deMaeztu DTIC-UPF Workshop on Data Driven Knowledge Extraction, 28-29 June 2016, Barcelona To know more about the María de Maeztu Strategic Research Program and projects, visit http://www.upf.edu/mdm-dtic To know more about the department, visit http://www.upf.edu/mdm-dtic
https://wn.com/The_Mdm_Knowledge_Extraction_Strategic_Research_Program_At_Dtic_Upf
Liquid-Liquid Extraction

Liquid-Liquid Extraction

  • Order:
  • Duration: 2:38
  • Updated: 15 Jul 2014
  • views: 26710
videos
https://wn.com/Liquid_Liquid_Extraction
Knowledge extraction from the wastewater treatment plant operation - Y. Kim

Knowledge extraction from the wastewater treatment plant operation - Y. Kim

  • Order:
  • Duration: 20:17
  • Updated: 26 Nov 2012
  • views: 663
videos
knowledge extraction from the historical database of wastewater treatment plant operation using various data mining techniques. y. Kim, H. S. Kim, M. S. Kim, W. piao, D. Kang, C. Kim, Republic of Korea Chan Moon,Kim - Industrial Instrumentation & Control Professional Engineer Engineering Services Department, Korea Water Resources Corporation In this conference we will discuss the new developments in IT & Water. These developments are very important for the further evolution of the water sector. IT applications in the water sector cover a broad field of interest. On one hand, IT applications have the ability to integrate the water sector from a high strategic level with connections to security and energy services. on the other hand IT applications have the ability to improve the performance of a single process, or part of a process, by improving the design or control with detailed models. IT applications should lead to better water quality, lower environmental impact and more efficient management, control, monitoring and maintenance of water systems, infrastructure and water treatment processes. Due to fast communication and new ways of personal interaction with stakeholders and customers, IT applications will not only support the water sector in carrying out its primary tasks but also in its communication with customers and stakeholders.
https://wn.com/Knowledge_Extraction_From_The_Wastewater_Treatment_Plant_Operation_Y._Kim
Knowledge extraction in newspaper from sports domain

Knowledge extraction in newspaper from sports domain

  • Order:
  • Duration: 7:03
  • Updated: 07 Nov 2017
  • views: 11
videos https://wn.com/Knowledge_Extraction_In_Newspaper_From_Sports_Domain
Technique of Extraction and Washing

Technique of Extraction and Washing

  • Order:
  • Duration: 11:41
  • Updated: 17 Dec 2016
  • views: 3501
videos
Introduction on to the technique of liquid-liquid extraction and washing at the University of Manitoba. Undergraduate organic chemistry laboratory program under Dr. Horace Luong's supervision.
https://wn.com/Technique_Of_Extraction_And_Washing
NOVA | Extract Your DNA | PBS

NOVA | Extract Your DNA | PBS

  • Order:
  • Duration: 2:46
  • Updated: 01 Mar 2012
  • views: 676430
videos
See the full episode of NOVA "Cracking the Genetic Code" here: http://video.pbs.org/video/2215641935/ Ever wish you could see the strands of genetic material that make you...you? You can, and there's no fancy lab equipment required. In this NOVA video short, learn how to extract your own DNA using just a few common household items. Watch NOVA's Cracking Your Genetic Code Wednesday, March 28th at 9PM/8c on PBS.
https://wn.com/Nova_|_Extract_Your_Dna_|_Pbs
Knowledge Extraction Process

Knowledge Extraction Process

  • Order:
  • Duration: 0:10
  • Updated: 18 Sep 2012
  • views: 82
videos
Knowledge extraction process employed by Triumph India Software Services.
https://wn.com/Knowledge_Extraction_Process
Presentation of paper "Diagnostic Knowledge Extraction ..."

Presentation of paper "Diagnostic Knowledge Extraction ..."

  • Order:
  • Duration: 16:02
  • Updated: 28 Feb 2017
  • views: 12
videos
Presentation of the paper "Diagnostic Knowledge Extraction from MedlinePlus: An Application for Infectious Diseases" at PACBB Conference in Salamanca. June 4, 2015. Paper URL: https://link.springer.com/chapter/10.1007/978-3-319-19776-0_9
https://wn.com/Presentation_Of_Paper_Diagnostic_Knowledge_Extraction_...
Mining the Knowledge of Scientific Publications

Mining the Knowledge of Scientific Publications

  • Order:
  • Duration: 36:37
  • Updated: 11 Jul 2016
  • views: 122
videos
Horacio Saggion: Associate Professor - Natural Language Processing Research Group María deMaeztu DTIC-UPF Workshop on Data Driven Knowledge Extraction, 28-29 June 2016, Barcelona To know more about the María de Maeztu Strategic Research Program and projects, visit http://www.upf.edu/mdm-dtic To know more about the department, visit http://www.upf.edu/mdm-dtic
https://wn.com/Mining_The_Knowledge_Of_Scientific_Publications
Extraction of Knowledge from Email to Aid in Expertise Discovery - Dr Thomas Jackson

Extraction of Knowledge from Email to Aid in Expertise Discovery - Dr Thomas Jackson

  • Order:
  • Duration: 13:01
  • Updated: 11 Jan 2011
  • views: 285
videos
Seminar given at by Dr Thomas Jackson at the Department Information Science Research Day at Loughborough University in July 2010. The talk describes the Extraction of Knowledge from Email to Aid in Expertise Discovery and how the research will move forward to classify and categorise email vaults.
https://wn.com/Extraction_Of_Knowledge_From_Email_To_Aid_In_Expertise_Discovery_Dr_Thomas_Jackson
Doozer: Knowledge Extraction from Community-Generated Content

Doozer: Knowledge Extraction from Community-Generated Content

  • Order:
  • Duration: 11:23
  • Updated: 27 Oct 2011
  • views: 192
videos
Doozer is an on demand domain model creation tool, designed by Christopher Thomas at Knoesis Research Center. (http://knoesis.org) Project page: http://wiki.knoesis.org/index.php/Doozer
https://wn.com/Doozer_Knowledge_Extraction_From_Community_Generated_Content
Machine learning for autonomous systems and knowledge extraction from big data - Lyudmila Mihaylova

Machine learning for autonomous systems and knowledge extraction from big data - Lyudmila Mihaylova

  • Order:
  • Duration: 49:59
  • Updated: 27 Nov 2014
  • views: 430
videos
CIS Seminar Series (http://cis.eecs.qmul.ac.uk/seminars.html) Machine learning for autonomous systems and knowledge extraction from big data Lyudmila S Mihaylova, University of Sheffield Where: Eng 209 Host: Andrea Cavallaro Abstract This talk focuses on methods for detection, tracking and decision making for autonomous or semi-autonomous surveillance systems. The data usually arrive from multiple heterogeneous sensors such as radar, LIDAR or cameras (optical and infrared). Methods for small and large groups comprised of hundreds or thousands of objects (normally referred to as cluster/crowd tracking) will be presented. Groups are structured objects characterised with particular motion patterns. The group can be comprised of a small number of interacting objects (e.g. pedestrians, sport players, convoy of cars) or of hundreds or thousands of interacting components such as crowds of people. Large groups pose different challenges compared with small group tracking due to the fact that sensors do not provide sufficient information for each individual target. Groups and extended objects generate multiple measurements which require advanced methods to cope with measurements origin uncertainty and large data sets. This talk will present recent results with Sequential Monte Carlo methods such as convolution particle filters and Box Particle filters. Challenges and future trends are also discussed.
https://wn.com/Machine_Learning_For_Autonomous_Systems_And_Knowledge_Extraction_From_Big_Data_Lyudmila_Mihaylova
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