Domain ontology generation in web search

Ontologies can be used to enhance information retrieval and interpretation greatly. They also enhance the applications readability and understandability of web documents. Ontology learning is a field in information extraction where we are concerned with semi-automatic extraction of concepts and rela...

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Main Author: Lau, Steven Jia Lim.
Other Authors: Shi Daming
Format: Final Year Project
Language:English
Published: 2009
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Online Access:http://hdl.handle.net/10356/16999
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-169992023-03-03T20:39:48Z Domain ontology generation in web search Lau, Steven Jia Lim. Shi Daming School of Computer Engineering Centre for Computational Intelligence DRNTU::Engineering::Computer science and engineering::Information systems::Information systems applications Ontologies can be used to enhance information retrieval and interpretation greatly. They also enhance the applications readability and understandability of web documents. Ontology learning is a field in information extraction where we are concerned with semi-automatic extraction of concepts and relations from a corpus or raw data to create an ontology model. There are many methods which can achieve these aims as there are disciplines. We often require data for varied applications, many structure data differently thus the ontology model doesn’t try to model for any one application. Rather it structures the data into relevant concepts and relations, making it more meaningful and useful in other ways. The method thru this is achieved has always involved the field of natural language processing where we are concerned with the semantics and the latent relationships between each word and sentence. Algorithms such as the Hidden Markov Model (HMM) allow for learning and inference. Not only will we be able to disambiguate the latent semantics of each word but also topics within a corpus. In this report, an experiment involving a food and beverage recommendation agent mechanism is proposed where the application combines the ontology with intelligent ontology construction agent and the intelligent search mechanism agent. We shall see how the training corpus can create an HMM which can predict approximately the correct states and hence improve ontology modeling thru the 4-layer object orientated structure. An ontology model will also generate a significantly improved result based on user queries and keywords. Much more than compared to the general web search engine that we all so commonly use. Bachelor of Engineering (Computer Engineering) 2009-05-29T03:32:11Z 2009-05-29T03:32:11Z 2009 2009 Final Year Project (FYP) http://hdl.handle.net/10356/16999 en Nanyang Technological University 68 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering::Computer science and engineering::Information systems::Information systems applications
spellingShingle DRNTU::Engineering::Computer science and engineering::Information systems::Information systems applications
Lau, Steven Jia Lim.
Domain ontology generation in web search
description Ontologies can be used to enhance information retrieval and interpretation greatly. They also enhance the applications readability and understandability of web documents. Ontology learning is a field in information extraction where we are concerned with semi-automatic extraction of concepts and relations from a corpus or raw data to create an ontology model. There are many methods which can achieve these aims as there are disciplines. We often require data for varied applications, many structure data differently thus the ontology model doesn’t try to model for any one application. Rather it structures the data into relevant concepts and relations, making it more meaningful and useful in other ways. The method thru this is achieved has always involved the field of natural language processing where we are concerned with the semantics and the latent relationships between each word and sentence. Algorithms such as the Hidden Markov Model (HMM) allow for learning and inference. Not only will we be able to disambiguate the latent semantics of each word but also topics within a corpus. In this report, an experiment involving a food and beverage recommendation agent mechanism is proposed where the application combines the ontology with intelligent ontology construction agent and the intelligent search mechanism agent. We shall see how the training corpus can create an HMM which can predict approximately the correct states and hence improve ontology modeling thru the 4-layer object orientated structure. An ontology model will also generate a significantly improved result based on user queries and keywords. Much more than compared to the general web search engine that we all so commonly use.
author2 Shi Daming
author_facet Shi Daming
Lau, Steven Jia Lim.
format Final Year Project
author Lau, Steven Jia Lim.
author_sort Lau, Steven Jia Lim.
title Domain ontology generation in web search
title_short Domain ontology generation in web search
title_full Domain ontology generation in web search
title_fullStr Domain ontology generation in web search
title_full_unstemmed Domain ontology generation in web search
title_sort domain ontology generation in web search
publishDate 2009
url http://hdl.handle.net/10356/16999
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