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

    published: 22 Feb 2016
  • Knowledge Extraction

    published: 06 Jun 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 from Games Workshop Afternoon Session

    https://sites.google.com/view/kegworkshop/schedule?authuser=0 1:00-3:30 Invited Speakers § Emily Short § Raph Koster § Ben Samuel -3:30-4:00 Coffee Break- 4:00-5:30 Gameplay Session § Concept-Aware Feature Extraction for Knowledge Transfer in Reinforcement Learning § Extraction of Interaction Events for Learning Reasonable Behavior in an Open-World Survival Game § Towards Explainable NPCs

    published: 03 Feb 2018
  • 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
  • Extraction of Iron

    This video explains the steps behind using a blast furnace to extract iron from hematite. Order Special Notes from http://buukbook.com/ Online 1 to 1 tuition lessons at http://www.mega.edu.my/ Education Tips & News http://mrsaimun.com/

    published: 19 Jun 2017
  • Knowledge Extraction Process

    Knowledge extraction process employed by Triumph India Software Services.

    published: 18 Sep 2012
  • Open and Exploratory Extraction of Relations and Common Sense from Large Text Corpora - Alan Akbik

    Alan Akbik November 10, 2014 Title: Open and Exploratory Extraction of Relations (and Common Sense) from Large Text Corpora Abstract: The use of deep syntactic information such as typed dependencies has been shown to be very effective in Information Extraction (IE). Despite this potential, the process of manually creating rule-based information extractors that operate on dependency trees is not intuitive for persons without an extensive NLP background. In this talk, I present an approach and a graphical tool that allows even novice users to quickly and easily define extraction patterns over dependency trees and directly execute them on a very large text corpus. This enables users to explore a corpus for structured information of interest in a highly interactive and data-guided fashion, an...

    published: 26 Nov 2014
  • Knowledge Extraction from Games Morning Session

    https://sites.google.com/view/kegworkshop/ -8:30-9:00 Opening Comments- 9:00-10:30 Speculative Session § Combinatorial Creativity for Procedural Content Generation via Machine Learning § Roles that Plan, Activity, and Intent Recognition with Planning Can Play in Games § Towards Inductive Logic Programming for Game Analysis: Leda -10:30-11:00 Coffee Break- 11:00-12:00 State-extraction Session § Retrieving Game States with Moment Vectors § Threat, Explore, Barter, Puzzle: A Semantically-Informed Algorithm for Extracting Interaction Modes -12:00-1:00 Lunch- 1:00-3:30 Invited Speakers § Emily Short § Raph Koster § Ben Samuel -3:30-4:00 Coffee Break- 4:00-5:30 Gameplay Session § Concept-Aware Feature Extraction for Knowledge Transfer in Reinforcement L...

    published: 02 Feb 2018
  • KESSI - Knowledge Extraction System for Scientific Interviews

    A Definition Extraction System for Scientific Interviews. - Skip between 2:00 and 7:40, as that's where the training takes place.

    published: 14 Jun 2013
  • CHEM117 04 Liquid Liquid Extraction Fundamentals

    published: 02 Oct 2015
  • Extraction vs Microextraction

    Sample extraction is the most popular sample preparation method in an analytical method. Microextraction is relatively newer area which provides some unique features. Here, I have discussed the main differences between extraction and microextraction. For other videos please check: http://www.sampleextraction.com/

    published: 26 Sep 2016
  • Model of human knowledge extraction for a KBS using reasoning cases

    published: 06 Jan 2017
  • MCAT Extraction

    Check out more MCAT lectures and prep materials on our website: https://premedhqdime.com Analytical Techniques Part 4 : Extraction Explanation of what extraction is and an example running through the process.

    published: 26 Nov 2015
  • 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
  • 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
  • Protein Extraction from Cells Part 1

    In this video we discuss the extraction of protein from cells.

    published: 14 Jan 2015
  • How Data Minng works or The KDD Process

    This video explains about the process of knowledge discovery in databases.

    published: 04 Jun 2016
  • Sarasi Lalithsena : Domain-specific Knowledge Extraction from Web of Data

    Committee: Dr. Amit Sheth, Advisor Dr. TK Prasad Dr. Derek Doran Dr. Saeedeh Shekarpour (University of Dayton) Slides : https://www.slideshare.net/knoesis/domainspecific-knowledge-extraction-from-the-web-of-data ABSTRACT: Structured data on the Web frequently referred to as knowledge graphs consists of large number of datasets representing diverse domains. Widely used commercial applications such as entity recommendation, search, question answering and knowledge discovery use these knowledge graphs as their knowledge source. Majority of these applications have a particular domain of interest, hence require only the segment of the Web of data representing that domain (e.g., movie, biomedical, sports). In fact, leveraging the entire Web of data for a domain-specific application is not o...

    published: 28 Mar 2018
developed with YouTube
Extracting Knowledge from Text - Pedro Domingos
1:04:37

Extracting Knowledge from Text - Pedro Domingos

  • Order:
  • Duration: 1:04:37
  • Updated: 26 Aug 2014
  • views: 6628
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
3:21

Machine Learning Knowledge Extraction MAKE it short

  • Order:
  • Duration: 3:21
  • Updated: 13 May 2017
  • views: 538
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
59:59

Extracting Knowledge from Informal Text

  • Order:
  • Duration: 59:59
  • Updated: 08 Aug 2016
  • views: 1918
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
11:51

Machine Learning, Knowledge Extraction And Blockchain: The Knowledge Engine

  • Order:
  • Duration: 11:51
  • Updated: 03 Nov 2017
  • views: 437
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
3:56

Artificial Intelligence | Tutorial #29 | Knowledge Extraction

  • Order:
  • Duration: 3:56
  • Updated: 02 Oct 2017
  • views: 871
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
Relationship Extraction from Unstructured Text Based on Stanford NLP with Spark
27:06

Relationship Extraction from Unstructured Text Based on Stanford NLP with Spark

  • Order:
  • Duration: 27:06
  • Updated: 22 Feb 2016
  • views: 11278
videos
https://wn.com/Relationship_Extraction_From_Unstructured_Text_Based_On_Stanford_Nlp_With_Spark
Knowledge Extraction
13:03

Knowledge Extraction

  • Order:
  • Duration: 13:03
  • Updated: 06 Jun 2017
  • views: 27
videos
https://wn.com/Knowledge_Extraction
Analyzing the Web from Start to Finish - Knowledge Extraction using KNIME - Bernd Wiswedel - #1
33:59

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

  • Order:
  • Duration: 33:59
  • Updated: 11 Mar 2014
  • views: 10489
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 from Games Workshop Afternoon Session
4:12:44

Knowledge Extraction from Games Workshop Afternoon Session

  • Order:
  • Duration: 4:12:44
  • Updated: 03 Feb 2018
  • views: 222
videos
https://sites.google.com/view/kegworkshop/schedule?authuser=0 1:00-3:30 Invited Speakers § Emily Short § Raph Koster § Ben Samuel -3:30-4:00 Coffee Break- 4:00-5:30 Gameplay Session § Concept-Aware Feature Extraction for Knowledge Transfer in Reinforcement Learning § Extraction of Interaction Events for Learning Reasonable Behavior in an Open-World Survival Game § Towards Explainable NPCs
https://wn.com/Knowledge_Extraction_From_Games_Workshop_Afternoon_Session
Knowledge Extraction for Retail
36:00

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
Extraction of Iron
2:19

Extraction of Iron

  • Order:
  • Duration: 2:19
  • Updated: 19 Jun 2017
  • views: 9206
videos
This video explains the steps behind using a blast furnace to extract iron from hematite. Order Special Notes from http://buukbook.com/ Online 1 to 1 tuition lessons at http://www.mega.edu.my/ Education Tips & News http://mrsaimun.com/
https://wn.com/Extraction_Of_Iron
Knowledge Extraction Process
0:10

Knowledge Extraction Process

  • Order:
  • Duration: 0:10
  • Updated: 18 Sep 2012
  • views: 84
videos
Knowledge extraction process employed by Triumph India Software Services.
https://wn.com/Knowledge_Extraction_Process
Open and Exploratory Extraction of Relations and Common Sense from Large Text Corpora - Alan Akbik
51:08

Open and Exploratory Extraction of Relations and Common Sense from Large Text Corpora - Alan Akbik

  • Order:
  • Duration: 51:08
  • Updated: 26 Nov 2014
  • views: 1081
videos
Alan Akbik November 10, 2014 Title: Open and Exploratory Extraction of Relations (and Common Sense) from Large Text Corpora Abstract: The use of deep syntactic information such as typed dependencies has been shown to be very effective in Information Extraction (IE). Despite this potential, the process of manually creating rule-based information extractors that operate on dependency trees is not intuitive for persons without an extensive NLP background. In this talk, I present an approach and a graphical tool that allows even novice users to quickly and easily define extraction patterns over dependency trees and directly execute them on a very large text corpus. This enables users to explore a corpus for structured information of interest in a highly interactive and data-guided fashion, and allows them to create extractors for those semantic relations they find interesting. I then present a project in which we use Information Extraction to automatically construct a very large common sense knowledge base. This knowledge base - dubbed "The Weltmodell" - contains common sense facts that pertain to proper noun concepts; an example of this is the concept "coffee", for which we know that it is typically drunk by a person or brought by a waiter. I show how we mine such information from very large amounts of text, how we quantify notions such as typicality and similarity, and discuss some ideas how such world knowledge can be used to address reasoning tasks.
https://wn.com/Open_And_Exploratory_Extraction_Of_Relations_And_Common_Sense_From_Large_Text_Corpora_Alan_Akbik
Knowledge Extraction from Games Morning Session
3:07:41

Knowledge Extraction from Games Morning Session

  • Order:
  • Duration: 3:07:41
  • Updated: 02 Feb 2018
  • views: 131
videos
https://sites.google.com/view/kegworkshop/ -8:30-9:00 Opening Comments- 9:00-10:30 Speculative Session § Combinatorial Creativity for Procedural Content Generation via Machine Learning § Roles that Plan, Activity, and Intent Recognition with Planning Can Play in Games § Towards Inductive Logic Programming for Game Analysis: Leda -10:30-11:00 Coffee Break- 11:00-12:00 State-extraction Session § Retrieving Game States with Moment Vectors § Threat, Explore, Barter, Puzzle: A Semantically-Informed Algorithm for Extracting Interaction Modes -12:00-1:00 Lunch- 1:00-3:30 Invited Speakers § Emily Short § Raph Koster § Ben Samuel -3:30-4:00 Coffee Break- 4:00-5:30 Gameplay Session § Concept-Aware Feature Extraction for Knowledge Transfer in Reinforcement Learning § Extraction of Interaction Events for Learning Reasonable Behavior in an Open-World Survival Game § Towards Explainable NPCs
https://wn.com/Knowledge_Extraction_From_Games_Morning_Session
KESSI - Knowledge Extraction System for Scientific Interviews
9:25

KESSI - Knowledge Extraction System for Scientific Interviews

  • Order:
  • Duration: 9:25
  • Updated: 14 Jun 2013
  • views: 74
videos
A Definition Extraction System for Scientific Interviews. - Skip between 2:00 and 7:40, as that's where the training takes place.
https://wn.com/Kessi_Knowledge_Extraction_System_For_Scientific_Interviews
CHEM117 04   Liquid Liquid Extraction Fundamentals
26:46

CHEM117 04 Liquid Liquid Extraction Fundamentals

  • Order:
  • Duration: 26:46
  • Updated: 02 Oct 2015
  • views: 58674
videos
https://wn.com/Chem117_04_Liquid_Liquid_Extraction_Fundamentals
Extraction vs Microextraction
4:21

Extraction vs Microextraction

  • Order:
  • Duration: 4:21
  • Updated: 26 Sep 2016
  • views: 4294
videos
Sample extraction is the most popular sample preparation method in an analytical method. Microextraction is relatively newer area which provides some unique features. Here, I have discussed the main differences between extraction and microextraction. For other videos please check: http://www.sampleextraction.com/
https://wn.com/Extraction_Vs_Microextraction
Model of human knowledge extraction for a KBS using reasoning cases
4:22

Model of human knowledge extraction for a KBS using reasoning cases

  • Order:
  • Duration: 4:22
  • Updated: 06 Jan 2017
  • views: 4
videos
https://wn.com/Model_Of_Human_Knowledge_Extraction_For_A_Kbs_Using_Reasoning_Cases
MCAT Extraction
17:30

MCAT Extraction

  • Order:
  • Duration: 17:30
  • Updated: 26 Nov 2015
  • views: 5053
videos
Check out more MCAT lectures and prep materials on our website: https://premedhqdime.com Analytical Techniques Part 4 : Extraction Explanation of what extraction is and an example running through the process.
https://wn.com/Mcat_Extraction
Knowledge extraction from the wastewater treatment plant operation - Y. Kim
20:17

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

  • Order:
  • Duration: 20:17
  • Updated: 26 Nov 2012
  • views: 690
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
Strawberry DNA Extraction
6:15

Strawberry DNA Extraction

  • Order:
  • Duration: 6:15
  • Updated: 06 Jan 2016
  • views: 39690
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
Protein Extraction from Cells Part 1
20:39

Protein Extraction from Cells Part 1

  • Order:
  • Duration: 20:39
  • Updated: 14 Jan 2015
  • views: 22750
videos
In this video we discuss the extraction of protein from cells.
https://wn.com/Protein_Extraction_From_Cells_Part_1
How Data Minng works or The KDD Process
12:13

How Data Minng works or The KDD Process

  • Order:
  • Duration: 12:13
  • Updated: 04 Jun 2016
  • views: 6280
videos
This video explains about the process of knowledge discovery in databases.
https://wn.com/How_Data_Minng_Works_Or_The_Kdd_Process
Sarasi Lalithsena : Domain-specific Knowledge Extraction from Web of Data
1:26:04

Sarasi Lalithsena : Domain-specific Knowledge Extraction from Web of Data

  • Order:
  • Duration: 1:26:04
  • Updated: 28 Mar 2018
  • views: 29
videos
Committee: Dr. Amit Sheth, Advisor Dr. TK Prasad Dr. Derek Doran Dr. Saeedeh Shekarpour (University of Dayton) Slides : https://www.slideshare.net/knoesis/domainspecific-knowledge-extraction-from-the-web-of-data ABSTRACT: Structured data on the Web frequently referred to as knowledge graphs consists of large number of datasets representing diverse domains. Widely used commercial applications such as entity recommendation, search, question answering and knowledge discovery use these knowledge graphs as their knowledge source. Majority of these applications have a particular domain of interest, hence require only the segment of the Web of data representing that domain (e.g., movie, biomedical, sports). In fact, leveraging the entire Web of data for a domain-specific application is not only computationally intensive, but also the irrelevant portion negatively impact the accuracy of the application. Hence, finding the relevant portion of the Web of data for domain-specific applications has become a paramount issue. Identifying the relevant portion of the Web of data consists of two sub-tasks; 1) find the relevant datasets that contain knowledge on the domain of interest, and 2) extract the subgraph representing domain of interest from the knowledge graphs that represent multiple domains (e.g., DBpedia, YAGO, Freebase). In this talk, I will discuss both data-driven and knowledge-driven approaches to solve these two sub-tasks. The domain-specific subgraphs extracted by our approach were 80% less in size in terms of the number of paths compared to original KG and resulted in more than tenfold reduction of required computational time for domain-specific tasks, yet produced better accuracy on domain-specific applications. We believe that this work can significantly contribute for utilizing knowledge graphs for domain-specific applications, specially with the explosive growth in the creation of knowledge graphs.
https://wn.com/Sarasi_Lalithsena_Domain_Specific_Knowledge_Extraction_From_Web_Of_Data
×