Probabilistic Models for the Semantic Web

2010 ◽  
pp. 74-105 ◽  
Author(s):  
Livia Predoiu
Keyword(s):  
Semantic Web ◽  
Knowledge Representation ◽  
Classical Logic ◽  
Probabilistic Models ◽  
Expressive Power ◽  
Knowledge Representation And Reasoning ◽  
Uncertain Information ◽  
New Approaches ◽  
The Web

Recently, there has been an increasing interest in formalisms for representing uncertain information on the Semantic Web. This interest is triggered by the observation that knowledge on the web is not always crisp and we have to be able to deal with incomplete, inconsistent and vague information. The treatment of this kind of information requires new approaches for knowledge representation and reasoning on the web as existing Semantic Web languages are based on classical logic which is known to be inadequate for representing uncertainty in many cases. While different general approaches for extending Semantic Web languages with the ability to represent uncertainty are explored, we focus our attention on probabilistic approaches. We survey existing proposals for extending semantic web languages or formalisms underlying Semantic Web languages in terms of their expressive power, reasoning capabilities as well as their suitability for supporting typical tasks associated with the Semantic Web.

scholarly journals LSTM Neural Network for Textual Ngrams

2018 ◽  
Author(s):  
Shaun C. D'Souza
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Human Brain ◽  
Cognitive Neuroscience ◽  
Probabilistic Models ◽  
Prediction Models ◽  
Machine Learning Algorithms ◽  
Language Models ◽  
Brain Functions ◽  
The Web

Cognitive neuroscience is the study of how the human brain functions on tasks like decision making, language, perception and reasoning. Deep learning is a class of machine learning algorithms that use neural networks. They are designed to model the responses of neurons in the human brain. Learning can be supervised or unsupervised. Ngram token models are used extensively in language prediction. Ngrams are probabilistic models that are used in predicting the next word or token. They are a statistical model of word sequences or tokens and are called Language Models or Lms. Ngrams are essential in creating language prediction models. We are exploring a broader sandbox ecosystems enabling for AI. Specifically, around Deep learning applications on unstructured content form on the web.

scholarly journals LSTM neural network for textual ngrams

2018 ◽  
Author(s):  
Shaun C. D'Souza
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Human Brain ◽  
Cognitive Neuroscience ◽  
Probabilistic Models ◽  
Prediction Models ◽  
Machine Learning Algorithms ◽  
Language Models ◽  
Brain Functions ◽  
The Web

Cognitive neuroscience is the study of how the human brain functions on tasks like decision making, language, perception and reasoning. Deep learning is a class of machine learning algorithms that use neural networks. They are designed to model the responses of neurons in the human brain. Learning can be supervised or unsupervised. Ngram token models are used extensively in language prediction. Ngrams are probabilistic models that are used in predicting the next word or token. They are a statistical model of word sequences or tokens and are called Language Models or Lms. Ngrams are essential in creating language prediction models. We are exploring a broader sandbox ecosystems enabling for AI. Specifically, around Deep learning applications on unstructured content form on the web.

scholarly journals Probabilistic Models for Ad Viewability Prediction on the Web

2017 ◽  
Vol 29 (9) ◽  
pp. 2012-2025 ◽  
Author(s):  
Chong Wang ◽  
Achir Kalra ◽  
Li Zhou ◽  
Cristian Borcea ◽  
Yi Chen
Keyword(s):  
Probabilistic Models ◽  
The Web

scholarly journals AAAI-08 and IAAI-08 Conferences Provide Focal Point for AI

AI Magazine ◽  
2009 ◽  
Vol 30 (1) ◽  
pp. 87
Author(s):  
Sara Reese Hedberg
Keyword(s):  
Artificial Intelligence ◽  
Poster Session ◽  
Intelligent System ◽  
Focal Point ◽  
Game Playing ◽  
General Game Playing ◽  
General Game ◽  
The Web ◽  
Applications Of Ai

This summer's AAAI Conference on Artificial Intelligence (AAAI-08) and its sister Conference on Innovative Applications of AI (IAAI-08) continued their long tradition of being a focal point of AI. This year's conferences were held in Chicago at the Hyatt Regency McCormick Place, July 13-17, 2008. The multidimensional conference offerings included nine invited talks, 251 technical papers, 22 innovative applications of AI papers, three competitions (poker, AI video, and general game playing), three special tracks (AI and the web, integrated intelligence, and physically grounded AI), 15 tutorials, 15 workshops, and 11 intelligent system demonstrations, as well as a number of awards, a doctoral consortium, student poster session and programs, and a vendor exhibit. This translated into a plethora of choices for the 921 conference attendees. An additional 175 people exclusively attended the tutorials, workshops, or exhibit.

Web Service Evaluation Using Probabilistic Models

2014 ◽  
pp. 288-294
Author(s):  
S. Zimeras
Keyword(s):  
Information System ◽  
Web Services ◽  
Web Service ◽  
Software Metrics ◽  
Probabilistic Models ◽  
Web Applications ◽  
Evaluation Criteria ◽  
Service Evaluation ◽  
The Web

Information system users, administrators, and designers are all interested in performance evaluation since their goal is to obtain or provide the highest performance at the lowest cost. This goal has resulted in continuing evolution of higher performance and lower cost systems leading to today’s proliferation of workstations and personal computers, many of which have better performance than earlier supercomputers. As the variety of Web services applications (Websites) increases, it gets more important to have a set of evaluation criteria that should evaluate the performance of their effectiveness. Based on those criteria, the quality of the services that the Web applications are providing could be analysed. This work represents software metrics that could (or need) be used to quantify the quality of the information that the Web services are providing. These measures could be useful to understand problematic frameworks during the implementation of the Websites and could lead to solutions preventing those problems.

Web Service Evaluation Using Probabilistic Models

2018 ◽  
pp. 1275-1283
Author(s):  
S. Zimeras
Keyword(s):  
Information System ◽  
Web Services ◽  
Web Service ◽  
Software Metrics ◽  
Probabilistic Models ◽  
Web Applications ◽  
Evaluation Criteria ◽  
Service Evaluation ◽  
The Web

Information system users, administrators, and designers are all interested in performance evaluation since their goal is to obtain or provide the highest performance at the lowest cost. This goal has resulted in continuing evolution of higher performance and lower cost systems leading to today's proliferation of workstations and personal computers, many of which have better performance than earlier supercomputers. As the variety of Web services applications (Websites) increases, it gets more important to have a set of evaluation criteria that should evaluate the performance of their effectiveness. Based on those criteria, the quality of the services that the Web applications are providing could be analysed. This work represents software metrics that could (or need) be used to quantify the quality of the information that the Web services are providing. These measures could be useful to understand problematic frameworks during the implementation of the Websites and could lead to solutions preventing those problems.

Probabilistic Models for the Semantic Web

2011 ◽  
pp. 1896-1928 ◽  
Author(s):  
Livia Predoiu ◽  
Heiner Stuckenschmidt
Keyword(s):  
Semantic Web ◽  
Knowledge Representation ◽  
Classical Logic ◽  
Probabilistic Models ◽  
Expressive Power ◽  
Knowledge Representation And Reasoning ◽  
Uncertain Information ◽  
New Approaches ◽  
The Web

Recently, there has been an increasing interest in formalisms for representing uncertain information on the Semantic Web. This interest is triggered by the observation that knowledge on the web is not always crisp and we have to be able to deal with incomplete, inconsistent and vague information. The treatment of this kind of information requires new approaches for knowledge representation and reasoning on the web as existing Semantic Web languages are based on classical logic which is known to be inadequate for representing uncertainty in many cases. While different general approaches for extending Semantic Web languages with the ability to represent uncertainty are explored, we focus our attention on probabilistic approaches. We survey existing proposals for extending semantic web languages or formalisms underlying Semantic Web languages in terms of their expressive power, reasoning capabilities as well as their suitability for supporting typical tasks associated with the Semantic Web

scholarly journals LSTM neural network for textual ngrams

2018 ◽  
Author(s):  
Shaun C. D'Souza
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Human Brain ◽  
Cognitive Neuroscience ◽  
Probabilistic Models ◽  
Prediction Models ◽  
Machine Learning Algorithms ◽  
Language Models ◽  
Brain Functions ◽  
The Web

Cognitive neuroscience is the study of how the human brain functions on tasks like decision making, language, perception and reasoning. Deep learning is a class of machine learning algorithms that use neural networks. They are designed to model the responses of neurons in the human brain. Learning can be supervised or unsupervised. Ngram token models are used extensively in language prediction. Ngrams are probabilistic models that are used in predicting the next word or token. They are a statistical model of word sequences or tokens and are called Language Models or Lms. Ngrams are essential in creating language prediction models. We are exploring a broader sandbox ecosystems enabling for AI. Specifically, around Deep learning applications on unstructured content form on the web.

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