Probabilistic Models for Ad Viewability Prediction on 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.

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Learning probabilistic models of the Web (poster session)

Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR '00 ◽

10.1145/345508.345660 ◽

2000 ◽

Cited By ~ 1

Author(s):

Thomas Hofmann

Keyword(s):

Poster Session ◽

Probabilistic Models ◽

The Web

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LSTM Neural Network for Textual Ngrams

10.20944/preprints201811.0579.v1 ◽

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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LSTM neural network for textual ngrams

10.7287/peerj.preprints.27377 ◽

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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Web Service Evaluation Using Probabilistic Models

Evaluating Websites and Web Services - Advances in Web Technologies and Engineering ◽

10.4018/978-1-4666-5129-6.ch015 ◽

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.

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Web Service Evaluation Using Probabilistic Models

Application Development and Design ◽

10.4018/978-1-5225-3422-8.ch053 ◽

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.

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Probabilistic Models for the Semantic Web

Web Technologies ◽

10.4018/978-1-60566-982-3.ch102 ◽

2011 ◽

pp. 1896-1928 ◽

Cited By ~ 3

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

Download Full-text

LSTM neural network for textual ngrams

10.7287/peerj.preprints.27377v1 ◽

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.

Download Full-text