• 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
  • 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
  • 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
  • Relationship Extraction from Unstructured Text Based on Stanford NLP with Spark

    published: 22 Feb 2016
  • Liquid-Liquid Extraction

    published: 15 Jul 2014
  • Ellyn Mulcahy - DNA Extraction

    Professor Ellyn Mulcahy demonstrates DNA extraction at Johnson County Community College. http://www.jccc.edu/

    published: 13 Jun 2012
  • Knowledge extraction and semantic linking in the Encyclopedia of Life

    Anne Thessen Boston Python - Parsing with PLY and Lightning Talks 04/25/2013 Microsoft NERD, Cambridge, MA

    published: 27 Apr 2013
  • 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
  • Multilingual Knowledge Extraction From Emails

    published: 07 May 2015
  • Extraction and Fluorescence of Chlorophyll

    Please ask any questions in the comments! This is a very easy and fun experiment to do, so I encourage you to try it yourself. LINK TO PAUL PYRO: https://www.youtube.com/user/ThePaulPyro

    published: 14 Dec 2014
  • CHEM117 04 Liquid Liquid Extraction Fundamentals

    published: 02 Oct 2015
  • Extraction with Mark Niemczyk, Ph.D.

    Extraction with Mark Niemczyk, Ph.D.

    published: 20 Sep 2013
  • 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
  • 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
  • Knowledge Extraction Process

    Knowledge extraction process employed by Triumph India Software Services.

    published: 18 Sep 2012
Extracting Knowledge from Text - Pedro Domingos

Extracting Knowledge from Text - Pedro Domingos

  • Order:
  • Duration: 1:04:37
  • Updated: 26 Aug 2014
  • views: 5541
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: 163
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
Extracting Knowledge from Informal Text

Extracting Knowledge from Informal Text

  • Order:
  • Duration: 59:59
  • Updated: 08 Aug 2016
  • views: 544
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
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: 8383
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
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: 6406
videos
https://wn.com/Relationship_Extraction_From_Unstructured_Text_Based_On_Stanford_Nlp_With_Spark
Liquid-Liquid Extraction

Liquid-Liquid Extraction

  • Order:
  • Duration: 2:38
  • Updated: 15 Jul 2014
  • views: 18826
videos
https://wn.com/Liquid_Liquid_Extraction
Ellyn Mulcahy - DNA Extraction

Ellyn Mulcahy - DNA Extraction

  • Order:
  • Duration: 5:36
  • Updated: 13 Jun 2012
  • views: 20341
videos https://wn.com/Ellyn_Mulcahy_Dna_Extraction
Knowledge extraction and semantic linking in the Encyclopedia of Life

Knowledge extraction and semantic linking in the Encyclopedia of Life

  • Order:
  • Duration: 9:42
  • Updated: 27 Apr 2013
  • views: 108
videos https://wn.com/Knowledge_Extraction_And_Semantic_Linking_In_The_Encyclopedia_Of_Life
Mining the Knowledge of Scientific Publications

Mining the Knowledge of Scientific Publications

  • Order:
  • Duration: 36:37
  • Updated: 11 Jul 2016
  • views: 92
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
Multilingual Knowledge Extraction From Emails

Multilingual Knowledge Extraction From Emails

  • Order:
  • Duration: 2:36
  • Updated: 07 May 2015
  • views: 25
videos
https://wn.com/Multilingual_Knowledge_Extraction_From_Emails
Extraction and Fluorescence of Chlorophyll

Extraction and Fluorescence of Chlorophyll

  • Order:
  • Duration: 1:37
  • Updated: 14 Dec 2014
  • views: 12958
videos
Please ask any questions in the comments! This is a very easy and fun experiment to do, so I encourage you to try it yourself. LINK TO PAUL PYRO: https://www.youtube.com/user/ThePaulPyro
https://wn.com/Extraction_And_Fluorescence_Of_Chlorophyll
CHEM117 04   Liquid Liquid Extraction Fundamentals

CHEM117 04 Liquid Liquid Extraction Fundamentals

  • Order:
  • Duration: 26:46
  • Updated: 02 Oct 2015
  • views: 37166
videos
https://wn.com/Chem117_04_Liquid_Liquid_Extraction_Fundamentals
Extraction with Mark Niemczyk, Ph.D.

Extraction with Mark Niemczyk, Ph.D.

  • Order:
  • Duration: 29:16
  • Updated: 20 Sep 2013
  • views: 2002
videos
Extraction with Mark Niemczyk, Ph.D.
https://wn.com/Extraction_With_Mark_Niemczyk,_Ph.D.
Knowledge Extraction for Retail

Knowledge Extraction for Retail

  • Order:
  • Duration: 36:00
  • Updated: 13 Jul 2016
  • views: 35
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
Doozer: Knowledge Extraction from Community-Generated Content

Doozer: Knowledge Extraction from Community-Generated Content

  • Order:
  • Duration: 11:23
  • Updated: 27 Oct 2011
  • views: 185
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
Knowledge Extraction Process

Knowledge Extraction Process

  • Order:
  • Duration: 0:10
  • Updated: 18 Sep 2012
  • views: 79
videos
Knowledge extraction process employed by Triumph India Software Services.
https://wn.com/Knowledge_Extraction_Process
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