To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
The Application Programming Interface restricts
the types of queries that the Web service can answer. For
instance, a Web service might provide a method that returns the
books of a given author in fast manner, but it might not provide a
method that returns the authors of a given book. If the user asks
for the author of some specific book, then the Web service cannot
be called – even though the underlying database might have the
preferred piece of information, this scenario is called asymmetry.
This asymmetry is particularly problematic if the service is used
in a Web service orchestration system. In this survey, we propose
to use on-the-fly information extraction (IE).IE used to collect
values, and then the value can be used as parameter Bindings for
the Web service. This survey shows how the information
extraction can be integrated into a Web service orchestration
system. The proposed approach is fully implemented in a
prototype called Search Using Services and Information
Extraction (SUSIE). Real-life data and services are used to
demonstrate the practical viability and good performance of our
approach
Computing semantic similarity measure between words using web search enginecsandit
Semantic Similarity measures between words plays an important role in information retrieval,
natural language processing and in various tasks on the web. In this paper, we have proposed a
Modified Pattern Extraction Algorithm to compute the supervised semantic similarity measure
between the words by combining both page count method and web snippets method. Four
association measures are used to find semantic similarity between words in page count method
using web search engines. We use a Sequential Minimal Optimization (SMO) support vector
machines (SVM) to find the optimal combination of page counts-based similarity scores and
top-ranking patterns from the web snippets method. The SVM is trained to classify synonymous
word-pairs and non-synonymous word-pairs. The proposed Modified Pattern Extraction
Algorithm outperforms by 89.8 percent of correlation value.
A generalized flow based method for analysis of implicit relationships on wik...JPINFOTECH JAYAPRAKASH
This document proposes a new method for measuring implicit relationships between objects on Wikipedia. It uses a generalized maximum flow algorithm that reflects three important factors: distance, connectivity, and co-citation. Existing methods are inadequate for implicit relationships because they use only one or two factors. The proposed method ranks destination objects by their strength of relationship to the source object. Experiments show it more appropriately measures relationships than previous methods. It also identifies "elucidatory objects" that help explain relationships.
WEB SEARCH ENGINE BASED SEMANTIC SIMILARITY MEASURE BETWEEN WORDS USING PATTE...cscpconf
Semantic Similarity measures plays an important role in information retrieval, natural language processing and various tasks on web such as relation extraction, community mining, document clustering, and automatic meta-data extraction. In this paper, we have proposed a Pattern Retrieval Algorithm [PRA] to compute the semantic similarity measure between the words by
combining both page count method and web snippets method. Four association measures are used to find semantic similarity between words in page count method using web search engines. We use a Sequential Minimal Optimization (SMO) support vector machines (SVM) to find the optimal combination of page counts-based similarity scores and top-ranking patterns from the web snippets method. The SVM is trained to classify synonymous word-pairs and nonsynonymous word-pairs. The proposed approach aims to improve the Correlation values,
Precision, Recall, and F-measures, compared to the existing methods. The proposed algorithm outperforms by 89.8 % of correlation value.
This document discusses keyword query routing to identify relevant data sources for keyword searches over multiple structured and linked data sources. It proposes using a multilevel inter-relationship graph and scoring mechanism to compute relevance and generate routing plans that route keywords only to pertinent sources. This improves keyword search performance without compromising result quality. An algorithm is developed based on modeling the search space and developing a summary model to incorporate relevance at different levels and dimensions. Experiments showed the summary model preserves relevant information compactly.
The document proposes a novel method for routing keyword queries to only relevant data sources to reduce the high cost of processing queries over all sources. It employs a compact keyword-element relationship summary to represent relationships between keywords and data elements. A multilevel scoring mechanism is used to compute the relevance of routing plans based on scores at different levels. Experiments on 150 publicly available sources showed the method can compute valid, highly relevant plans in 1 second on average and routing improves keyword search performance without compromising result quality.
Android aml efficient approximate membership localization within a web-based...ecway
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This document discusses methods for measuring semantic similarity between words. It begins by discussing how traditional lexical similarity measurements do not consider semantics. It then discusses several existing approaches that measure semantic similarity using web search engines and text snippets. These approaches calculate word co-occurrence statistics from page counts and analyze lexical patterns extracted from snippets. Pattern clustering is used to group semantically similar patterns. The approaches are evaluated using datasets and metrics like precision and recall. Finally, the document proposes a new method that combines page count statistics, lexical pattern extraction and clustering, and support vector machines to measure semantic similarity.
The Application Programming Interface restricts
the types of queries that the Web service can answer. For
instance, a Web service might provide a method that returns the
books of a given author in fast manner, but it might not provide a
method that returns the authors of a given book. If the user asks
for the author of some specific book, then the Web service cannot
be called – even though the underlying database might have the
preferred piece of information, this scenario is called asymmetry.
This asymmetry is particularly problematic if the service is used
in a Web service orchestration system. In this survey, we propose
to use on-the-fly information extraction (IE).IE used to collect
values, and then the value can be used as parameter Bindings for
the Web service. This survey shows how the information
extraction can be integrated into a Web service orchestration
system. The proposed approach is fully implemented in a
prototype called Search Using Services and Information
Extraction (SUSIE). Real-life data and services are used to
demonstrate the practical viability and good performance of our
approach
Computing semantic similarity measure between words using web search enginecsandit
Semantic Similarity measures between words plays an important role in information retrieval,
natural language processing and in various tasks on the web. In this paper, we have proposed a
Modified Pattern Extraction Algorithm to compute the supervised semantic similarity measure
between the words by combining both page count method and web snippets method. Four
association measures are used to find semantic similarity between words in page count method
using web search engines. We use a Sequential Minimal Optimization (SMO) support vector
machines (SVM) to find the optimal combination of page counts-based similarity scores and
top-ranking patterns from the web snippets method. The SVM is trained to classify synonymous
word-pairs and non-synonymous word-pairs. The proposed Modified Pattern Extraction
Algorithm outperforms by 89.8 percent of correlation value.
A generalized flow based method for analysis of implicit relationships on wik...JPINFOTECH JAYAPRAKASH
This document proposes a new method for measuring implicit relationships between objects on Wikipedia. It uses a generalized maximum flow algorithm that reflects three important factors: distance, connectivity, and co-citation. Existing methods are inadequate for implicit relationships because they use only one or two factors. The proposed method ranks destination objects by their strength of relationship to the source object. Experiments show it more appropriately measures relationships than previous methods. It also identifies "elucidatory objects" that help explain relationships.
WEB SEARCH ENGINE BASED SEMANTIC SIMILARITY MEASURE BETWEEN WORDS USING PATTE...cscpconf
Semantic Similarity measures plays an important role in information retrieval, natural language processing and various tasks on web such as relation extraction, community mining, document clustering, and automatic meta-data extraction. In this paper, we have proposed a Pattern Retrieval Algorithm [PRA] to compute the semantic similarity measure between the words by
combining both page count method and web snippets method. Four association measures are used to find semantic similarity between words in page count method using web search engines. We use a Sequential Minimal Optimization (SMO) support vector machines (SVM) to find the optimal combination of page counts-based similarity scores and top-ranking patterns from the web snippets method. The SVM is trained to classify synonymous word-pairs and nonsynonymous word-pairs. The proposed approach aims to improve the Correlation values,
Precision, Recall, and F-measures, compared to the existing methods. The proposed algorithm outperforms by 89.8 % of correlation value.
This document discusses keyword query routing to identify relevant data sources for keyword searches over multiple structured and linked data sources. It proposes using a multilevel inter-relationship graph and scoring mechanism to compute relevance and generate routing plans that route keywords only to pertinent sources. This improves keyword search performance without compromising result quality. An algorithm is developed based on modeling the search space and developing a summary model to incorporate relevance at different levels and dimensions. Experiments showed the summary model preserves relevant information compactly.
The document proposes a novel method for routing keyword queries to only relevant data sources to reduce the high cost of processing queries over all sources. It employs a compact keyword-element relationship summary to represent relationships between keywords and data elements. A multilevel scoring mechanism is used to compute the relevance of routing plans based on scores at different levels. Experiments on 150 publicly available sources showed the method can compute valid, highly relevant plans in 1 second on average and routing improves keyword search performance without compromising result quality.
Android aml efficient approximate membership localization within a web-based...ecway
Final Year IEEE Projects, Final Year Projects, Academic Final Year Projects, Academic Final Year IEEE Projects, Academic Final Year IEEE Projects 2013, Academic Final Year IEEE Projects 2014, IEEE JAVA, .NET Projects, 2013 IEEE JAVA, .NET Projects, 2013 IEEE JAVA, .NET Projects in Chennai, 2013 IEEE JAVA, .NET Projects in Trichy, 2013 IEEE JAVA, .NET Projects in Karur, 2013 IEEE JAVA, .NET Projects in Erode, 2013 IEEE JAVA, .NET Projects in Madurai, 2013 IEEE JAVA, .NET Projects in Salem, 2013 IEEE JAVA, .NET Projects in Coimbatore, 2013 IEEE JAVA, .NET Projects in Tirupur, 2013 IEEE JAVA, .NET Projects in Bangalore, 2013 IEEE JAVA, .NET Projects in Hydrabad, 2013 IEEE JAVA, .NET Projects in Kerala, 2013 IEEE JAVA, .NET Projects in Namakkal, IEEE JAVA, .NET Image Processing, IEEE JAVA, .NET Face Recognition, IEEE JAVA, .NET Face Detection, IEEE JAVA, .NET Brain Tumour, IEEE JAVA, .NET Iris Recognition, IEEE JAVA, .NET Image Segmentation, Final Year JAVA, .NET Projects in Pondichery, Final Year JAVA, .NET Projects in Tamilnadu, Final Year JAVA, .NET Projects in Chennai, Final Year JAVA, .NET Projects in Trichy, Final Year JAVA, .NET Projects in Erode, Final Year JAVA, .NET Projects in Karur, Final Year JAVA, .NET Projects in Coimbatore, Final Year JAVA, .NET Projects in Tirunelveli, Final Year JAVA, .NET Projects in Madurai, Final Year JAVA, .NET Projects in Salem, Final Year JAVA, .NET Projects in Tirupur, Final Year JAVA, .NET Projects in Namakkal, Final Year JAVA, .NET Projects in Tanjore, Final Year JAVA, .NET Projects in Coimbatore, Final Year JAVA, .NET Projects in Bangalore, Final Year JAVA, .NET Projects in Hydrabad, Final Year JAVA, .NET Projects in Kerala, Final Year JAVA, .NET IEEE Projects in Pondichery, Final Year JAVA, .NET IEEE Projects in Tamilnadu, Final Year JAVA, .NET IEEE Projects in Chennai, Final Year JAVA, .NET IEEE Projects in Trichy, Final Year JAVA, .NET IEEE Projects in Erode, Final Year JAVA, .NET IEEE Projects in Karur, Final Year JAVA, .NET IEEE Projects in Coimbatore, Final Year JAVA, .NET IEEE Projects in Tirunelveli, Final Year JAVA, .NET IEEE Projects in Madurai, Final Year JAVA, .NET IEEE Projects in Salem, Final Year JAVA, .NET IEEE Projects in Tirupur, Final Year JAVA, .NET IEEE Projects in Namakkal, Final Year JAVA, .NET IEEE Projects in Tanjore, Final Year JAVA, .NET IEEE Projects in Coimbatore, Final Year JAVA, .NET IEEE Projects in Bangalore, Final Year JAVA, .NET IEEE Projects in Hydrabad, Final Year JAVA, .NET IEEE Projects in Kerala, Final Year IEEE MATLAB Projects, Final Year Projects, Academic Final Year Projects, Academic Final Year IEEE MATLAB Projects, Academic Final Year IEEE MATLAB Projects 2013, Academic Final Year IEEE MATLAB Projects 2014, IEEE MATLAB Projects, 2013 IEEE MATLAB Projects, 2013 IEEE MATLAB Projects in Chennai, 2013 IEEE MATLAB Projects in Trichy, 2013 IEEE MATLAB Projects in Karur, 2013 IEEE MATLAB Projects in Erode, 2013 IEEE MATLAB Projects in Madurai, 2013 IEEE MATLAB
This document discusses methods for measuring semantic similarity between words. It begins by discussing how traditional lexical similarity measurements do not consider semantics. It then discusses several existing approaches that measure semantic similarity using web search engines and text snippets. These approaches calculate word co-occurrence statistics from page counts and analyze lexical patterns extracted from snippets. Pattern clustering is used to group semantically similar patterns. The approaches are evaluated using datasets and metrics like precision and recall. Finally, the document proposes a new method that combines page count statistics, lexical pattern extraction and clustering, and support vector machines to measure semantic similarity.
Error tolerant resource allocation and payment minimization for cloud systemIEEEFINALYEARPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
Participatory privacy enabling privacy in participatory sensingIEEEFINALYEARPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
Automatic semantic content extraction in videos using a fuzzy ontology and ru...IEEEFINALYEARPROJECTS
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On the real time hardware implementation feasibility of joint radio resource ...IEEEFINALYEARPROJECTS
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Discovery and verification of neighbor positions in mobile ad hoc networksIEEEFINALYEARPROJECTS
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On quality of monitoring for multi channel wireless infrastructure networksIEEEFINALYEARPROJECTS
The document discusses maximizing the quality of monitoring (QoM) in multi-channel wireless infrastructure networks using distributed wireless sniffers. It defines QoM as the expected number of active users monitored and investigates assigning sniffers to channels to maximize this. Two capture models are considered - user-centric which distinguishes users, and sniffer-centric using only channel activity data. Maximizing QoM is NP-hard for user-centric but a constant approximation is possible. For sniffer-centric, stochastic inference transforms it to user-centric to apply approximation algorithms. Evaluation uses synthetic and real-world traces showing the effectiveness of proposed schemes.
Distributed processing of probabilistic top k queries in wireless sensor netw...IEEEFINALYEARPROJECTS
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Dcim distributed cache invalidation method for maintaining cache consistency ...IEEEFINALYEARPROJECTS
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Cloud computing for mobile users can offloading computation save energyIEEEFINALYEARPROJECTS
The document discusses using cloud computing to offload computation from mobile devices to save energy. It describes how cloud computing works and how offloading computation can reduce the energy usage of mobile apps. The proposed system would determine whether to offload parts of an app's computation to the cloud based on factors like bandwidth, amount of computation, and data transfer size. Challenges include privacy, security since data is stored remotely, and reliability depending on the network and cloud service.
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
Protecting sensitive labels in social network data anonymizationIEEEFINALYEARPROJECTS
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A fast clustering based feature subset selection algorithm for high-dimension...IEEEFINALYEARPROJECTS
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Noise reduction based on partial reference, dual-tree complex wavelet transfo...IEEEFINALYEARPROJECTS
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The document proposes a new intrusion detection system called EAACK that is designed specifically for mobile ad hoc networks (MANETs). Existing intrusion detection systems for MANETs have weaknesses including being vulnerable to false misbehavior reports and forged acknowledgment packets. EAACK addresses these weaknesses by using digital signatures to authenticate acknowledgment packets and thereby guarantee their validity. This helps EAACK more accurately detect malicious nodes and behaviors. The system is also designed to reduce network overhead compared to previous acknowledgment-based approaches.
Object surface segmentation, Image segmentation, Region growing, X-Y-Z image,...cscpconf
Semantic Similarity measures between words plays an important role in information retrieval, natural language processing and in various tasks on the web. In this paper, we have proposed a Modified Pattern Extraction Algorithm to compute the supervised semantic similarity measure
between the words by combining both page count method and web snippets method. Four association measures are used to find semantic similarity between words in page count method
using web search engines. We use a Sequential Minimal Optimization (SMO) support vector machines (SVM) to find the optimal combination of page counts-based similarity scores and
top-ranking patterns from the web snippets method. The SVM is trained to classify synonymous word-pairs and non-synonymous word-pairs. The proposed Modified Pattern Extraction
Algorithm outperforms by 89.8 percent of correlation value.
The document proposes an approach for enhancing information retrieval from invisible web data sources by integrating them. It presents algorithms for effectiveness calculation, estimation of queries and workload, centralized sample database and duplicate detection, and resource selection and integration. The proposed approach is evaluated experimentally and shown to achieve higher accuracy than existing algorithms based on an F-measure calculation on real datasets. Future work could involve building a more complete directory of invisible web resources and improving classification of sources into subject hierarchies.
Implemenation of Enhancing Information Retrieval Using Integration of Invisib...iosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
professional fuzzy type-ahead rummage around in xml type-ahead search techni...Kumar Goud
Abstract – It is a research venture on the new information-access standard called type-ahead search, in which systems discover responds to a keyword query on-the-fly as users type in the uncertainty. In this paper we learn how to support fuzzy type-ahead search in XML. Underneath fuzzy search is important when users have limited knowledge about the exact representation of the entities they are looking for, such as people records in an online directory. We have developed and deployed several such systems, some of which have been used by many people on a daily basis. The systems received overwhelmingly positive feedbacks from users due to their friendly interfaces with the fuzzy-search feature. We describe the design and implementation of the systems, and demonstrate several such systems. We show that our efficient techniques can indeed allow this search paradigm to scale on large amounts of data.
Index Terms - type-ahead, large data set, server side, online directory, search technique.
Automatically Constructing Semantic Web Services From Online SourcesAsia Smith
The document describes an end-to-end system called Deimos that automatically constructs semantic web services from online sources. Deimos takes as input an example source and its semantic description, then discovers similar sources, extracts data from them, builds semantic descriptions of their inputs and outputs, models their functionality, and exposes them as semantic web services. The system integrates prior work on source discovery, data extraction, semantic typing, and source modeling into a unified approach evaluated across five domains.
Error tolerant resource allocation and payment minimization for cloud systemIEEEFINALYEARPROJECTS
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Participatory privacy enabling privacy in participatory sensingIEEEFINALYEARPROJECTS
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To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
Automatic semantic content extraction in videos using a fuzzy ontology and ru...IEEEFINALYEARPROJECTS
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On the real time hardware implementation feasibility of joint radio resource ...IEEEFINALYEARPROJECTS
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Discovery and verification of neighbor positions in mobile ad hoc networksIEEEFINALYEARPROJECTS
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On quality of monitoring for multi channel wireless infrastructure networksIEEEFINALYEARPROJECTS
The document discusses maximizing the quality of monitoring (QoM) in multi-channel wireless infrastructure networks using distributed wireless sniffers. It defines QoM as the expected number of active users monitored and investigates assigning sniffers to channels to maximize this. Two capture models are considered - user-centric which distinguishes users, and sniffer-centric using only channel activity data. Maximizing QoM is NP-hard for user-centric but a constant approximation is possible. For sniffer-centric, stochastic inference transforms it to user-centric to apply approximation algorithms. Evaluation uses synthetic and real-world traces showing the effectiveness of proposed schemes.
Distributed processing of probabilistic top k queries in wireless sensor netw...IEEEFINALYEARPROJECTS
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Dcim distributed cache invalidation method for maintaining cache consistency ...IEEEFINALYEARPROJECTS
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Cloud computing for mobile users can offloading computation save energyIEEEFINALYEARPROJECTS
The document discusses using cloud computing to offload computation from mobile devices to save energy. It describes how cloud computing works and how offloading computation can reduce the energy usage of mobile apps. The proposed system would determine whether to offload parts of an app's computation to the cloud based on factors like bandwidth, amount of computation, and data transfer size. Challenges include privacy, security since data is stored remotely, and reliability depending on the network and cloud service.
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
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Protecting sensitive labels in social network data anonymizationIEEEFINALYEARPROJECTS
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To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
A fast clustering based feature subset selection algorithm for high-dimension...IEEEFINALYEARPROJECTS
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Noise reduction based on partial reference, dual-tree complex wavelet transfo...IEEEFINALYEARPROJECTS
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The document proposes a new intrusion detection system called EAACK that is designed specifically for mobile ad hoc networks (MANETs). Existing intrusion detection systems for MANETs have weaknesses including being vulnerable to false misbehavior reports and forged acknowledgment packets. EAACK addresses these weaknesses by using digital signatures to authenticate acknowledgment packets and thereby guarantee their validity. This helps EAACK more accurately detect malicious nodes and behaviors. The system is also designed to reduce network overhead compared to previous acknowledgment-based approaches.
Object surface segmentation, Image segmentation, Region growing, X-Y-Z image,...cscpconf
Semantic Similarity measures between words plays an important role in information retrieval, natural language processing and in various tasks on the web. In this paper, we have proposed a Modified Pattern Extraction Algorithm to compute the supervised semantic similarity measure
between the words by combining both page count method and web snippets method. Four association measures are used to find semantic similarity between words in page count method
using web search engines. We use a Sequential Minimal Optimization (SMO) support vector machines (SVM) to find the optimal combination of page counts-based similarity scores and
top-ranking patterns from the web snippets method. The SVM is trained to classify synonymous word-pairs and non-synonymous word-pairs. The proposed Modified Pattern Extraction
Algorithm outperforms by 89.8 percent of correlation value.
The document proposes an approach for enhancing information retrieval from invisible web data sources by integrating them. It presents algorithms for effectiveness calculation, estimation of queries and workload, centralized sample database and duplicate detection, and resource selection and integration. The proposed approach is evaluated experimentally and shown to achieve higher accuracy than existing algorithms based on an F-measure calculation on real datasets. Future work could involve building a more complete directory of invisible web resources and improving classification of sources into subject hierarchies.
Implemenation of Enhancing Information Retrieval Using Integration of Invisib...iosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
professional fuzzy type-ahead rummage around in xml type-ahead search techni...Kumar Goud
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Ontological approach for improving semantic web search resultseSAT Journals
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IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
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IRJET- A Literature Review and Classification of Semantic Web Approaches for ...IRJET Journal
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The document discusses keyword query routing for keyword search over multiple structured data sources. It proposes computing top-k routing plans based on their potential to contain results for a given keyword query. A keyword-element relationship summary compactly represents keyword and data element relationships. A multilevel scoring mechanism computes routing plan relevance based on scores at different levels, from keywords to subgraphs. Experiments on 150 public sources showed relevant plans can be computed in 1 second on average desktop computer. Routing helps improve keyword search performance without compromising result quality.
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IRJET- Automatic Text Summarization using Text RankIRJET Journal
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Hybrid approach for generating non overlapped substring using genetic algorithmeSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
SEMANTIC INFORMATION EXTRACTION IN UNIVERSITY DOMAINcscpconf
Today’s conventional search engines hardly do provide the essential content relevant to the
user’s search query. This is because the context and semantics of the request made by the user
is not analyzed to the full extent. So here the need for a semantic web search arises. SWS is
upcoming in the area of web search which combines Natural Language Processing and
Artificial Intelligence.
The objective of the work done here is to design, develop and implement a semantic search
engine- SIEU(Semantic Information Extraction in University Domain) confined to the
university domain. SIEU uses ontology as a knowledge base for the information retrieval
process. It is not just a mere keyword search. It is one layer above what Google or any other
search engines retrieve by analyzing just the keywords. Here the query is analyzed both
syntactically and semantically.
The developed system retrieves the web results more relevant to the user query through keyword
expansion. The results obtained here will be accurate enough to satisfy the request made by the
user. The level of accuracy will be enhanced since the query is analyzed semantically. The
system will be of great use to the developers and researchers who work on web. The Google results are re-ranked and optimized for providing the relevant links. For ranking an algorithm has been applied which fetches more apt results for the user query
This document discusses methods for measuring semantic similarity between words. It begins by discussing how traditional lexical similarity measurements do not consider semantics. It then discusses several existing approaches that measure semantic similarity using web search engines and text snippets. These approaches calculate word co-occurrence statistics from page counts and analyze lexical patterns extracted from snippets. Pattern clustering is used to group semantically similar patterns. The approaches are evaluated and combined using support vector machines. Finally, the document proposes a new method that applies lexical pattern extraction and clustering to page count data and text snippets, before using an SVM to measure semantic similarity.
Cluster Based Web Search Using Support Vector MachineCSCJournals
Now days, searches for the web pages of a person with a given name constitute a notable fraction of queries to Web search engines. This method exploits a variety of semantic information extracted from web pages. The rapid growth of the Internet has made the Web a popular place for collecting information. Today, Internet user access billions of web pages online using search engines. Information in the Web comes from many sources, including websites of companies, organizations, communications and personal homepages, etc. Effective representation of Web search results remains an open problem in the Information Retrieval community. For ambiguous queries, a traditional approach is to organize search results into groups (clusters), one for each meaning of the query. These groups are usually constructed according to the topical similarity of the retrieved documents, but it is possible for documents to be totally dissimilar and still correspond to the same meaning of the query. To overcome this problem, the relevant Web pages are often located close to each other in the Web graph of hyperlinks. It presents a graphical approach for entity resolution & complements the traditional methodology with the analysis of the entity-relationship (ER) graph constructed for the dataset being analyzed. It also demonstrates a technique that measures the degree of interconnectedness between various pairs of nodes in the graph. It can significantly improve the quality of entity resolution. Using Support vector machines (SVMs) which are a set of related Supervised learning methods used for classification of load of user queries to the sever machine to different client machines so that system will be stable. clusters web pages based on their capacities stores whole database on server machine. Keywords: SVM, cluster; ER.
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Susie search using services and information extraction
1. SUSIE: Search Using Services and Information
Extraction
ABSTRACT
The API of a Web service restricts the types of queries that the service can answer. For example, a
Web service might provide a method that returns the songs of a given singer, but it might not
provide a method that returns the singers of a given song. If the user asks for the singer of some
specific song, then the Web service cannot be called – even though the underlying database might
have the desired piece of information. This asymmetry is particularly problematic if the service is
used in a Web service orchestration system. In this paper, we propose to use on-the-fly information
extraction to collect values that can be used as parameter bindings for the Web service. We show
how this idea can be integrated into a Web service orchestration system. Our approach is fully
implemented in a prototype called SUSIE. We present experiments with real-life data and services to
demonstrate the practical viability and good performance of our approach.
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2. Architecture
Existing System:
Existing methods exists a conjunctive query plan over the views that is equivalent to
the original query is NP-hard in the size of the query. This rewriting strategy assumes that the views
are complete (i.e., contain all the tuples in their definition). This assumption is unrealistic in our
setting with Web services, where sources may overlap or complement each other but are usually
incomplete. When sources are incomplete, one aims to find maximal contained rewritings of the
initial query, in order to provide the maximal number of answers to compose existing functions to
compute answers, which often consumes the entire budget before any answer is returned
Proposed System:
In this paper, we propose to use on-the-fly information extraction to collect values that can be used
as parameter bindings for the Web service. We show how this idea can be integrated into a Web
service orchestration system. Our approach is fully implemented in a prototype called SUSIE.
3. We propose to use Web-based information extraction (IE) on the fly to determine the right input
values for the asymmetric Web services. Solutions for reducing the number of accesses. Notions of
minimal rewritings have been proposed however, the goal remains the computation of maximal
results.
We have proposed to use information extraction to guess bindings for the input variables and then
validate these bindings by the Web service. Through this approach, a whole new class of queries
has become tractable.
Modules:
1. QUERY ANSWERING:
2. INFORMATION EXTRACTION:
3. Web services:
4. Motivation:
5. EXTRACTING CANDIDATES:
QUERY ANSWERING:
Most related to our setting is the problem of answering queries using views with limited access
patterns [3]. The approach of [3] rewrites the initial query into a set of queries to be executed over
the given views. The authors show that for a conjunctive query over a global schema and a set of
views over the same schema.
INFORMATION EXTRACTION:
Information extraction (IE) is concerned with extracting structured data from documents. IE
methods suffer from the inherent imprecision of the extraction process. Usually, the extracted data
is way too noisy to allow direct querying. SUSIE overcomes this limitation, by using IE solely for
finding candidate entities of interest and feeding these as inputs into Web service calls. Named
Entity Recognition (NER) approaches [29–31] aim to detect interesting entities in text documents.
They can be used to generate candidates for SUSIE. The first approach discussed in this paper
matches noun phrases against the names of entities that are registered in a knowledge base – a
simple but effective technique that circumvents the noise in learning-based NER techniques.
4. Web services:
We have shown that a considerable number of real world Web services allow asking for only one
argument of a relationship, but not for the other. We have proposed to use information extraction to
guess bindings for the input variables and then validate these bindings by the Web service. Through
this approach, a whole new class of queries has become tractable. We have shown that providing
inverse functions alone is not enough. They also have to be prioritized accordingly. We have
implemented our system, SUSIE, and showed the validity of our approach on real data sets. We
believe that the beauty of our approach lies in the fruitful symbiosis of information extraction and
Web services.
Motivation:
There is a growing number of Web services that provide a wealth of information. There are Web
services about books (isbndb.org, librarything. com, Amazon, AbeBooks), about movies
(api.internetvideoarchive.com), about music (musicbrainz.org, lastfm.com), and about a large
variety of other topics. Usually, a Web service is an interface that provides access to an
encapsulated back-end database. For example, the site musicbrainz.org offers a Web service for
accessing its database.
EXTRACTING CANDIDATES:
Once the Web pages have been retrieved, it remains to extract the candidate entities. Information
extraction is a challenging endeavor, because it often requires near-human understanding of the
input documents. Our scenario is somewhat simpler, because we are only interested in extracting
the entities of a certain type from a set of Web pages.
SYSTEM SPECIFICATION
Hardware Requirements:
• System : Pentium IV 2.4 GHz.
• Hard Disk : 40 GB.
• Floppy Drive : 1.44 Mb.
5. • Monitor : 14’ Colour Monitor.
• Mouse : Optical Mouse.
• Ram : 512 Mb.
• Keyboard : 101 Keyboard.
Software Requirements:
• Operating system : Windows 7 and IIS
• Coding Language : ASP.Net 4.0 with C#
• Data Base : SQL Server 2008.