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

    published: 22 Feb 2016
  • 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
  • Liquid-Liquid Extraction

    published: 15 Jul 2014
  • 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
  • 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
  • Strawberry DNA Extraction

    Check out our free educational resources tools at http://ncbionetwork.org. Strawberry DNA Extraction is a fun experiment that you can perform using everyday materials. The purpose of this experiment is to develop the interest, knowledge and skills of our students in the areas of biotechnology and life sciences. It engages students with a hands-on activity that is fun, interesting and informative and that can be used with a variety of levels from grade school to high school by tailoring the complexity of accompanying material. Watch the most exciting lab safety video ever! "Zombie College: The 5 Rules of Lab Safety" http://youtu.be/S6WARqVdWrE

    published: 06 Jan 2016
  • Extraction with Mark Niemczyk, Ph.D.

    Extraction with Mark Niemczyk, Ph.D.

    published: 20 Sep 2013
  • SOXHLET EXTRACTION with Dr. Mark Niemczyk, Ph.D.

    published: 23 Jan 2015
  • CHEM117 04 Liquid Liquid Extraction Fundamentals

    published: 02 Oct 2015
  • Knowledge Extraction

    published: 06 Jun 2017
  • 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
Extracting Knowledge from Text - Pedro Domingos

Extracting Knowledge from Text - Pedro Domingos

  • Order:
  • Duration: 1:04:37
  • Updated: 26 Aug 2014
  • views: 4490
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
Extracting Knowledge from Informal Text

Extracting Knowledge from Informal Text

  • Order:
  • Duration: 59:59
  • Updated: 08 Aug 2016
  • views: 124
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 MAKE it short

Machine Learning Knowledge Extraction MAKE it short

  • Order:
  • Duration: 3:21
  • Updated: 13 May 2017
  • views: 70
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
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: 6669
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 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: 97
videos https://wn.com/Knowledge_Extraction_And_Semantic_Linking_In_The_Encyclopedia_Of_Life
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: 4910
videos
https://wn.com/Relationship_Extraction_From_Unstructured_Text_Based_On_Stanford_Nlp_With_Spark
Mining the Knowledge of Scientific Publications

Mining the Knowledge of Scientific Publications

  • Order:
  • Duration: 36:37
  • Updated: 11 Jul 2016
  • views: 75
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
Liquid-Liquid Extraction

Liquid-Liquid Extraction

  • Order:
  • Duration: 2:38
  • Updated: 15 Jul 2014
  • views: 16539
videos
https://wn.com/Liquid_Liquid_Extraction
Doozer: Knowledge Extraction from Community-Generated Content

Doozer: Knowledge Extraction from Community-Generated Content

  • Order:
  • Duration: 11:23
  • Updated: 27 Oct 2011
  • views: 160
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
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: 56
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
Strawberry DNA Extraction

Strawberry DNA Extraction

  • Order:
  • Duration: 6:15
  • Updated: 06 Jan 2016
  • views: 23605
videos
Check out our free educational resources tools at http://ncbionetwork.org. Strawberry DNA Extraction is a fun experiment that you can perform using everyday materials. The purpose of this experiment is to develop the interest, knowledge and skills of our students in the areas of biotechnology and life sciences. It engages students with a hands-on activity that is fun, interesting and informative and that can be used with a variety of levels from grade school to high school by tailoring the complexity of accompanying material. Watch the most exciting lab safety video ever! "Zombie College: The 5 Rules of Lab Safety" http://youtu.be/S6WARqVdWrE
https://wn.com/Strawberry_Dna_Extraction
Extraction with Mark Niemczyk, Ph.D.

Extraction with Mark Niemczyk, Ph.D.

  • Order:
  • Duration: 29:16
  • Updated: 20 Sep 2013
  • views: 1923
videos
Extraction with Mark Niemczyk, Ph.D.
https://wn.com/Extraction_With_Mark_Niemczyk,_Ph.D.
SOXHLET EXTRACTION with Dr. Mark Niemczyk, Ph.D.

SOXHLET EXTRACTION with Dr. Mark Niemczyk, Ph.D.

  • Order:
  • Duration: 11:31
  • Updated: 23 Jan 2015
  • views: 113943
videos
https://wn.com/Soxhlet_Extraction_With_Dr._Mark_Niemczyk,_Ph.D.
CHEM117 04   Liquid Liquid Extraction Fundamentals

CHEM117 04 Liquid Liquid Extraction Fundamentals

  • Order:
  • Duration: 26:46
  • Updated: 02 Oct 2015
  • views: 35252
videos
https://wn.com/Chem117_04_Liquid_Liquid_Extraction_Fundamentals
Knowledge Extraction

Knowledge Extraction

  • Order:
  • Duration: 13:03
  • Updated: 06 Jun 2017
  • views: 18
videos
https://wn.com/Knowledge_Extraction
Knowledge Extraction for Retail

Knowledge Extraction for Retail

  • Order:
  • Duration: 36:00
  • Updated: 13 Jul 2016
  • views: 25
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
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