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
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.
NATURE: A TOOL RESULTING FROM THE UNION OF ARTIFICIAL INTELLIGENCE AND NATURA...ijaia
This paper presents the final results of the research project that aimed for the construction of a tool which
is aided by Artificial Intelligence through an Ontology with a model trained with Machine Learning, and is
aided by Natural Language Processing to support the semantic search of research projects of the Research
System of the University of Nariño. For the construction of NATURE, as this tool is called, a methodology
was used that includes the following stages: appropriation of knowledge, installation and configuration of
tools, libraries and technologies, collection, extraction and preparation of research projects, design and
development of the tool. The main results of the work were three: a) the complete construction of the
Ontology with classes, object properties (predicates), data properties (attributes) and individuals
(instances) in Protegé, SPARQL queries with Apache Jena Fuseki and the respective coding with
Owlready2 using Jupyter Notebook with Python within the virtual environment of anaconda; b) the
successful training of the model for which Machine Learning algorithms were used and specifically
Natural Language Processing algorithms such as: SpaCy, NLTK, Word2vec and Doc2vec, this was also
performed in Jupyter Notebook with Python within the virtual environment of anaconda and with
Elasticsearch; and c) the creation of NATURE by managing and unifying the queries for the Ontology and
for the Machine Learning model. The tests showed that NATURE was successful in all the searches that
were performed as its results were satisfactory
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
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.
`A Survey on approaches of Web Mining in Varied Areasinventionjournals
There has been lot of research in recent years for efficient web searching. Several papers have proposed algorithm for user feedback sessions, to evaluate the performance of inferring user search goals. When the information is retrieved, user clicks on a particular URL. Based on the click rate, ranking will be done automatically, clustering the feedback sessions. Web search engines have made enormous contributions to the web and society. They make finding information on the web quick and easy. However, they are far from optimal. A major deficiency of generic search engines is that they follow the ‘‘one size fits all’’ model and are not adaptable to individual users.
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.
NATURE: A TOOL RESULTING FROM THE UNION OF ARTIFICIAL INTELLIGENCE AND NATURA...ijaia
This paper presents the final results of the research project that aimed for the construction of a tool which
is aided by Artificial Intelligence through an Ontology with a model trained with Machine Learning, and is
aided by Natural Language Processing to support the semantic search of research projects of the Research
System of the University of Nariño. For the construction of NATURE, as this tool is called, a methodology
was used that includes the following stages: appropriation of knowledge, installation and configuration of
tools, libraries and technologies, collection, extraction and preparation of research projects, design and
development of the tool. The main results of the work were three: a) the complete construction of the
Ontology with classes, object properties (predicates), data properties (attributes) and individuals
(instances) in Protegé, SPARQL queries with Apache Jena Fuseki and the respective coding with
Owlready2 using Jupyter Notebook with Python within the virtual environment of anaconda; b) the
successful training of the model for which Machine Learning algorithms were used and specifically
Natural Language Processing algorithms such as: SpaCy, NLTK, Word2vec and Doc2vec, this was also
performed in Jupyter Notebook with Python within the virtual environment of anaconda and with
Elasticsearch; and c) the creation of NATURE by managing and unifying the queries for the Ontology and
for the Machine Learning model. The tests showed that NATURE was successful in all the searches that
were performed as its results were satisfactory
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
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.
`A Survey on approaches of Web Mining in Varied Areasinventionjournals
There has been lot of research in recent years for efficient web searching. Several papers have proposed algorithm for user feedback sessions, to evaluate the performance of inferring user search goals. When the information is retrieved, user clicks on a particular URL. Based on the click rate, ranking will be done automatically, clustering the feedback sessions. Web search engines have made enormous contributions to the web and society. They make finding information on the web quick and easy. However, they are far from optimal. A major deficiency of generic search engines is that they follow the ‘‘one size fits all’’ model and are not adaptable to individual users.
Application of hidden markov model in question answering systemsijcsa
By the increase of the volume of the saved information on web, Question Answering (QA) systems have been very important for Information Retrieval (IR). QA systems are a specialized form of information retrieval. Given a collection of documents, a Question Answering system attempts to retrieve correct answers to questions posed in natural language. Web QA system is a sample of QA systems that in this system answers retrieval from web environment doing. In contrast to the databases, the saved information on web does not follow a distinct structure and are not generally defined. Web QA systems is the task of automatically answering a question posed in Natural Language Processing (NLP). NLP techniques are used in applications that make queries to databases, extract information from text, retrieve relevant documents from a collection, translate from one language to another, generate text responses, or recognize spoken words converting them into text. To find the needed information on a mass of the non-structured information we have to use techniques in which the accuracy and retrieval factors are implemented well. In this paper in order to well IR in web environment, The QA system in designed and also implemented based on the Hidden Markov Model (HMM)
UML MODELING AND SYSTEM ARCHITECTURE FOR AGENT BASED INFORMATION RETRIEVALijcsit
In this current technological era, there is an enormous increase in the information available on web and
also in the online databases. This information abundance increases the complexity of finding relevant
information. To solve such challenges, there is a need for improved and intelligent systems for efficient
search and retrieval. Intelligent Agents can be used for better search and information retrieval in a
document collection. The information required by a user is scattered in a large number of databases. In this
paper, the object oriented modeling for agent based information retrieval system is presented. The paper
also discusses the framework of agent architecture for obtaining the best combination terms that serve as
an input query to the information retrieval system. The communication and cooperation among the agents
are also explained. Each agent has a task to perform in information retrieval.
Voice Based Search Engine for Visually Impairment PeoplesIJASRD Journal
World Wide Web (WWW) is unexpectedly emerging because the accepted records supply for our society. The WWW is normally reachable the usage of an internet-browsing package from a networked pc. The layout of facts on the net is visually orientated. The reliance on visible presentation locations excessive cognitive demands on a person to function this sort of system. The interaction might also sometimes require the whole attention of a consumer. The design of information presentation at the web is predominately visible-oriented. This presentation technique requires most, if no longer all, of the person’s attention and imposes significant cognitive load on a user. This technique isn't always sensible, in particular for the visually impaired persons. The awareness of this challenge is to develop a prototype which supports net browsing the use of a speech-based interface, e.g. A telephone, and to degree its effectiveness. The command input and the delivery of web contents are totally in voice. Audio icons are constructed into the prototype so that users will have higher knowledge of the original shape/purpose of a web page. Navigation and manage commands are available to decorate the net browsing enjoy. The effectiveness of this prototype is evaluated in a consumer take a look at involving both generally sighted and visually impaired humans. Voice browsers allow human beings to get right of entry to the Web the usage of speech synthesis, pre-recorded audio, and speech reputation. This may be supplemented via keypads and small presentations. Voice may also be supplied as an accessory to standard computing device browsers with high resolution graphical presentations, presenting an on hand alternative to the use of the keyboard or screen, as an instance in cars in which palms/eyes unfastened operation is crucial. Voice interplay can get away the bodily obstacles on keypads and shows as cell devices turn out to be ever smaller. The browser will have an integrated textual content extraction engine that inspects the content of the page to construct a structured illustration. The inner nodes of the structure constitute diverse tiers of abstraction of the content. This enables in easy and bendy navigation of the page so that it will hastily home into gadgets of interest.
Context Sensitive Search String Composition Algorithm using User Intention to...IJECEIAES
Finding the required URL among the first few result pages of a search engine is still a challenging task. This may require number of reformulations of the search string thus adversely affecting user's search time. Query ambiguity and polysemy are major reasons for not obtaining relevant results in the top few result pages. Efficient query composition and data organization are necessary for getting effective results. Context of the information need and the user intent may improve the autocomplete feature of existing search engines. This research proposes a Funnel Mesh-5 algorithm (FM5) to construct a search string taking into account context of information need and user intention with three main steps 1) Predict user intention with user profiles and the past searches via weighted mesh structure 2) Resolve ambiguity and polysemy of search strings with context and user intention 3) Generate a personalized disambiguated search string by query expansion encompassing user intention and predicted query. Experimental results for the proposed approach and a comparison with direct use of search engine are presented. A comparison of FM5 algorithm with K Nearest Neighbor algorithm for user intention identification is also presented. The proposed system provides better precision for search results for ambiguous search strings with improved identification of the user intention. Results are presented for English language dataset as well as Marathi (an Indian language) dataset of ambiguous search strings.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Classification-based Retrieval Methods to Enhance Information Discovery on th...IJMIT JOURNAL
The widespread adoption of the World-Wide Web (the Web) has created challenges both for society as a whole and for the technology used to build and maintain the Web. The ongoing struggle of information retrieval systems is to wade through this vast pile of data and satisfy users by presenting them with information that most adequately it’s their needs. On a societal level, the Web is expanding faster than we can comprehend its implications or develop rules for its use. The ubiquitous use of the Web has raised important social concerns in the areas of privacy, censorship, and access to information. On a technical level, the novelty of the Web and the pace of its growth have created challenges not only in the development of new applications that realize the power of the Web, but also in the technology needed to scale applications to accommodate the resulting large data sets and heavy loads. This thesis presents searching algorithms and hierarchical classification techniques for increasing a search service's understanding of web queries. Existing search services rely solely on a query's occurrence in the document collection to locate relevant documents. They typically do not perform any task or topic-based analysis of queries using other available resources, and do not leverage changes in user query patterns over time. Provided within are a set of techniques and metrics for performing temporal analysis on query logs. Our log analyses are shown to be reasonable and informative, and can be used to detect changing trends and patterns in the query stream, thus providing valuable data to a search service.
Performance Evaluation of Query Processing Techniques in Information Retrievalidescitation
The first element of the search process is the query.
The user query being on an average restricted to two or three
keywords makes the query ambiguous to the search engine.
Given the user query, the goal of an Information Retrieval
[IR] system is to retrieve information which might be useful
or relevant to the information need of the user. Hence, the
query processing plays an important role in IR system.
The query processing can be divided into four categories
i.e. query expansion, query optimization, query classification and
query parsing. In this paper an attempt is made to evaluate the
performance of query processing algorithms in each of the
category. The evaluation was based on dataset as specified by
Forum for Information Retrieval [FIRE15]. The criteria used
for evaluation are precision and relative recall. The analysis is
based on the importance of each step in query processing. The
experimental results show that the significance of each step
in query processing and also the relevance of web semantics
and spelling correction in the user query.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
Context Driven Technique for Document ClassificationIDES Editor
In this paper we present an innovative hybrid Text
Classification (TC) system that bridges the gap between
statistical and context based techniques. Our algorithm
harnesses contextual information at two stages. First it extracts
a cohesive set of keywords for each category by using lexical
references, implicit context as derived from LSA and wordvicinity
driven semantics. And secondly, each document is
represented by a set of context rich features whose values are
derived by considering both lexical cohesion as well as the extent
of coverage of salient concepts via lexical chaining. After
keywords are extracted, a subset of the input documents is
apportioned as training set. Its members are assigned categories
based on their keyword representation. These labeled
documents are used to train binary SVM classifiers, one for
each category. The remaining documents are supplied to the
trained classifiers in the form of their context-enhanced feature
vectors. Each document is finally ascribed its appropriate
category by an SVM classifier.
Scaling Down Dimensions and Feature Extraction in Document Repository Classif...ijdmtaiir
-In this study a comprehensive evaluation of two
supervised feature selection methods for dimensionality
reduction is performed - Latent Semantic Indexing (LSI) and
Principal Component Analysis (PCA). This is gauged against
unsupervised techniques like fuzzy feature clustering using
hard fuzzy C-means (FCM) . The main objective of the study is
to estimate the relative efficiency of two supervised techniques
against unsupervised fuzzy techniques while reducing the
feature space. It is found that clustering using FCM leads to
better accuracy in classifying documents in the face of
evolutionary algorithms like LSI and PCA. Results show that
the clustering of features improves the accuracy of document
classification
The size of the Internet enlarging as per to grow the users of search providers continually demand search
results that are accurate to their wishes. Personalized Search is one of the options available to users in
order to sculpt search results based on their personal data returned to them provided to the search
provider. This brings up fears of privacy issues however, as users are typically anxious to revealing
personal info to an often faceless service provider along the Internet. This work proposes to administer
with the privacy issues surrounding personalized search and discusses ways that privacy can be improved
so that users can get easier with the dismissal of their personal information in order to obtain more precise
search results.
Semantic Information Retrieval Using Ontology in University Domain dannyijwest
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.
Semantic Search of E-Learning Documents Using Ontology Based Systemijcnes
The keyword searching mechanism is traditionally used for information retrieval from Web based systems. However, this system fails to meet the requirements in Web searching of the expert knowledge base based on the popular semantic systems. Semantic search of E-learning documents based on ontology is increasingly adopted in information retrieval systems. Ontology based system simplifies the task of finding correct information on the Web by building a search system based on the meaning of keyword instead of the keyword itself. The major function of the ontology based system is the development of specification of conceptualization which enhances the connection between the information present in the Web pages with that of the background knowledge.The semantic gap existing between the keyword found in documents and those in query can be matched suitably using Ontology based system. This paper provides a detailed account of the semantic search of E-learning documents using ontology based system by making comparison between various ontology systems. Based on this comparison, this survey attempts to identify the possible directions for future research.
Ontology Based Approach for Semantic Information Retrieval SystemIJTET Journal
Abstract—The Information retrieval system is taking an important role in current search engine which performs searching operation based on keywords which results in an enormous amount of data available to the user, from which user cannot figure out the essential and most important information. This limitation may be overcome by a new web architecture known as the semantic web which overcome the limitation of the keyword based search technique called the conceptual or the semantic search technique. Natural language processing technique is mostly implemented in a QA system for asking user’s questions and several steps are also followed for conversion of questions to the query form for retrieving an exact answer. In conceptual search, search engine interprets the meaning of the user’s query and the relation among the concepts that document contains with respect to a particular domain that produces specific answers instead of showing lists of answers. In this paper, we proposed the ontology based semantic information retrieval system and the Jena semantic web framework in which, the user enters an input query which is parsed by Standford Parser then the triplet extraction algorithm is used. For all input queries, the SPARQL query is formed and further, it is fired on the knowledge base (Ontology) which finds appropriate RDF triples in knowledge base and retrieve the relevant information using the Jena framework.
Application of hidden markov model in question answering systemsijcsa
By the increase of the volume of the saved information on web, Question Answering (QA) systems have been very important for Information Retrieval (IR). QA systems are a specialized form of information retrieval. Given a collection of documents, a Question Answering system attempts to retrieve correct answers to questions posed in natural language. Web QA system is a sample of QA systems that in this system answers retrieval from web environment doing. In contrast to the databases, the saved information on web does not follow a distinct structure and are not generally defined. Web QA systems is the task of automatically answering a question posed in Natural Language Processing (NLP). NLP techniques are used in applications that make queries to databases, extract information from text, retrieve relevant documents from a collection, translate from one language to another, generate text responses, or recognize spoken words converting them into text. To find the needed information on a mass of the non-structured information we have to use techniques in which the accuracy and retrieval factors are implemented well. In this paper in order to well IR in web environment, The QA system in designed and also implemented based on the Hidden Markov Model (HMM)
UML MODELING AND SYSTEM ARCHITECTURE FOR AGENT BASED INFORMATION RETRIEVALijcsit
In this current technological era, there is an enormous increase in the information available on web and
also in the online databases. This information abundance increases the complexity of finding relevant
information. To solve such challenges, there is a need for improved and intelligent systems for efficient
search and retrieval. Intelligent Agents can be used for better search and information retrieval in a
document collection. The information required by a user is scattered in a large number of databases. In this
paper, the object oriented modeling for agent based information retrieval system is presented. The paper
also discusses the framework of agent architecture for obtaining the best combination terms that serve as
an input query to the information retrieval system. The communication and cooperation among the agents
are also explained. Each agent has a task to perform in information retrieval.
Voice Based Search Engine for Visually Impairment PeoplesIJASRD Journal
World Wide Web (WWW) is unexpectedly emerging because the accepted records supply for our society. The WWW is normally reachable the usage of an internet-browsing package from a networked pc. The layout of facts on the net is visually orientated. The reliance on visible presentation locations excessive cognitive demands on a person to function this sort of system. The interaction might also sometimes require the whole attention of a consumer. The design of information presentation at the web is predominately visible-oriented. This presentation technique requires most, if no longer all, of the person’s attention and imposes significant cognitive load on a user. This technique isn't always sensible, in particular for the visually impaired persons. The awareness of this challenge is to develop a prototype which supports net browsing the use of a speech-based interface, e.g. A telephone, and to degree its effectiveness. The command input and the delivery of web contents are totally in voice. Audio icons are constructed into the prototype so that users will have higher knowledge of the original shape/purpose of a web page. Navigation and manage commands are available to decorate the net browsing enjoy. The effectiveness of this prototype is evaluated in a consumer take a look at involving both generally sighted and visually impaired humans. Voice browsers allow human beings to get right of entry to the Web the usage of speech synthesis, pre-recorded audio, and speech reputation. This may be supplemented via keypads and small presentations. Voice may also be supplied as an accessory to standard computing device browsers with high resolution graphical presentations, presenting an on hand alternative to the use of the keyboard or screen, as an instance in cars in which palms/eyes unfastened operation is crucial. Voice interplay can get away the bodily obstacles on keypads and shows as cell devices turn out to be ever smaller. The browser will have an integrated textual content extraction engine that inspects the content of the page to construct a structured illustration. The inner nodes of the structure constitute diverse tiers of abstraction of the content. This enables in easy and bendy navigation of the page so that it will hastily home into gadgets of interest.
Context Sensitive Search String Composition Algorithm using User Intention to...IJECEIAES
Finding the required URL among the first few result pages of a search engine is still a challenging task. This may require number of reformulations of the search string thus adversely affecting user's search time. Query ambiguity and polysemy are major reasons for not obtaining relevant results in the top few result pages. Efficient query composition and data organization are necessary for getting effective results. Context of the information need and the user intent may improve the autocomplete feature of existing search engines. This research proposes a Funnel Mesh-5 algorithm (FM5) to construct a search string taking into account context of information need and user intention with three main steps 1) Predict user intention with user profiles and the past searches via weighted mesh structure 2) Resolve ambiguity and polysemy of search strings with context and user intention 3) Generate a personalized disambiguated search string by query expansion encompassing user intention and predicted query. Experimental results for the proposed approach and a comparison with direct use of search engine are presented. A comparison of FM5 algorithm with K Nearest Neighbor algorithm for user intention identification is also presented. The proposed system provides better precision for search results for ambiguous search strings with improved identification of the user intention. Results are presented for English language dataset as well as Marathi (an Indian language) dataset of ambiguous search strings.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Classification-based Retrieval Methods to Enhance Information Discovery on th...IJMIT JOURNAL
The widespread adoption of the World-Wide Web (the Web) has created challenges both for society as a whole and for the technology used to build and maintain the Web. The ongoing struggle of information retrieval systems is to wade through this vast pile of data and satisfy users by presenting them with information that most adequately it’s their needs. On a societal level, the Web is expanding faster than we can comprehend its implications or develop rules for its use. The ubiquitous use of the Web has raised important social concerns in the areas of privacy, censorship, and access to information. On a technical level, the novelty of the Web and the pace of its growth have created challenges not only in the development of new applications that realize the power of the Web, but also in the technology needed to scale applications to accommodate the resulting large data sets and heavy loads. This thesis presents searching algorithms and hierarchical classification techniques for increasing a search service's understanding of web queries. Existing search services rely solely on a query's occurrence in the document collection to locate relevant documents. They typically do not perform any task or topic-based analysis of queries using other available resources, and do not leverage changes in user query patterns over time. Provided within are a set of techniques and metrics for performing temporal analysis on query logs. Our log analyses are shown to be reasonable and informative, and can be used to detect changing trends and patterns in the query stream, thus providing valuable data to a search service.
Performance Evaluation of Query Processing Techniques in Information Retrievalidescitation
The first element of the search process is the query.
The user query being on an average restricted to two or three
keywords makes the query ambiguous to the search engine.
Given the user query, the goal of an Information Retrieval
[IR] system is to retrieve information which might be useful
or relevant to the information need of the user. Hence, the
query processing plays an important role in IR system.
The query processing can be divided into four categories
i.e. query expansion, query optimization, query classification and
query parsing. In this paper an attempt is made to evaluate the
performance of query processing algorithms in each of the
category. The evaluation was based on dataset as specified by
Forum for Information Retrieval [FIRE15]. The criteria used
for evaluation are precision and relative recall. The analysis is
based on the importance of each step in query processing. The
experimental results show that the significance of each step
in query processing and also the relevance of web semantics
and spelling correction in the user query.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
Context Driven Technique for Document ClassificationIDES Editor
In this paper we present an innovative hybrid Text
Classification (TC) system that bridges the gap between
statistical and context based techniques. Our algorithm
harnesses contextual information at two stages. First it extracts
a cohesive set of keywords for each category by using lexical
references, implicit context as derived from LSA and wordvicinity
driven semantics. And secondly, each document is
represented by a set of context rich features whose values are
derived by considering both lexical cohesion as well as the extent
of coverage of salient concepts via lexical chaining. After
keywords are extracted, a subset of the input documents is
apportioned as training set. Its members are assigned categories
based on their keyword representation. These labeled
documents are used to train binary SVM classifiers, one for
each category. The remaining documents are supplied to the
trained classifiers in the form of their context-enhanced feature
vectors. Each document is finally ascribed its appropriate
category by an SVM classifier.
Scaling Down Dimensions and Feature Extraction in Document Repository Classif...ijdmtaiir
-In this study a comprehensive evaluation of two
supervised feature selection methods for dimensionality
reduction is performed - Latent Semantic Indexing (LSI) and
Principal Component Analysis (PCA). This is gauged against
unsupervised techniques like fuzzy feature clustering using
hard fuzzy C-means (FCM) . The main objective of the study is
to estimate the relative efficiency of two supervised techniques
against unsupervised fuzzy techniques while reducing the
feature space. It is found that clustering using FCM leads to
better accuracy in classifying documents in the face of
evolutionary algorithms like LSI and PCA. Results show that
the clustering of features improves the accuracy of document
classification
The size of the Internet enlarging as per to grow the users of search providers continually demand search
results that are accurate to their wishes. Personalized Search is one of the options available to users in
order to sculpt search results based on their personal data returned to them provided to the search
provider. This brings up fears of privacy issues however, as users are typically anxious to revealing
personal info to an often faceless service provider along the Internet. This work proposes to administer
with the privacy issues surrounding personalized search and discusses ways that privacy can be improved
so that users can get easier with the dismissal of their personal information in order to obtain more precise
search results.
Semantic Information Retrieval Using Ontology in University Domain dannyijwest
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.
Semantic Search of E-Learning Documents Using Ontology Based Systemijcnes
The keyword searching mechanism is traditionally used for information retrieval from Web based systems. However, this system fails to meet the requirements in Web searching of the expert knowledge base based on the popular semantic systems. Semantic search of E-learning documents based on ontology is increasingly adopted in information retrieval systems. Ontology based system simplifies the task of finding correct information on the Web by building a search system based on the meaning of keyword instead of the keyword itself. The major function of the ontology based system is the development of specification of conceptualization which enhances the connection between the information present in the Web pages with that of the background knowledge.The semantic gap existing between the keyword found in documents and those in query can be matched suitably using Ontology based system. This paper provides a detailed account of the semantic search of E-learning documents using ontology based system by making comparison between various ontology systems. Based on this comparison, this survey attempts to identify the possible directions for future research.
Ontology Based Approach for Semantic Information Retrieval SystemIJTET Journal
Abstract—The Information retrieval system is taking an important role in current search engine which performs searching operation based on keywords which results in an enormous amount of data available to the user, from which user cannot figure out the essential and most important information. This limitation may be overcome by a new web architecture known as the semantic web which overcome the limitation of the keyword based search technique called the conceptual or the semantic search technique. Natural language processing technique is mostly implemented in a QA system for asking user’s questions and several steps are also followed for conversion of questions to the query form for retrieving an exact answer. In conceptual search, search engine interprets the meaning of the user’s query and the relation among the concepts that document contains with respect to a particular domain that produces specific answers instead of showing lists of answers. In this paper, we proposed the ontology based semantic information retrieval system and the Jena semantic web framework in which, the user enters an input query which is parsed by Standford Parser then the triplet extraction algorithm is used. For all input queries, the SPARQL query is formed and further, it is fired on the knowledge base (Ontology) which finds appropriate RDF triples in knowledge base and retrieve the relevant information using the Jena framework.
Intelligent Semantic Web Search Engines: A Brief Survey dannyijwest
The World Wide Web (WWW) allows the people to share the information (data) from the large database repositories globally. The amount of information grows billions of databases. We need to search the information will specialize tools known generically search engine. There are many of search engines available today, retrieving meaningful information is difficult. However to overcome this problem in search engines to retrieve meaningful information intelligently, semantic web technologies are playing a major role. In this paper we present survey on the search engine generations and the role of search engines in intelligent web and semantic search technologies.
Semantic Search Engine using OntologiesIJRES Journal
Nowadays the volume of the information on the Web is increasing dramatically. Facilitating users to get useful information has become more and more important to information retrieval systems. While information retrieval technologies have been improved to some extent, users are not satisfied with the low precision and recall. With the emergence of the Semantic Web, this situation can be remarkably improved if machines could “understand” the content of web pages. The existing information retrieval technologies can be classified mainly into three classes.The traditional information retrieval technologies mostly based on the occurrence of words in documents. It is only limited to string matching. However, these technologies are of no use when a search is based on the meaning of words, rather than onwards themselves.Search engines limited to string matching and link analysis. The most widely used algorithms are the PageRank algorithm and the HITS algorithm. The PageRank algorithm is based on the number of other pages pointing to the Web page and the value of the pages pointing to it. Search engines like Google combine information retrieval techniques with PageRank. In contrast to the PageRank algorithm, the HITS algorithm employs a query dependent ranking technique. In addition to this, the HITS algorithm produces the authority and the hub score. The widespread availability of machine understandable information on the Semantic Web offers which some opportunities to improve traditional search. If machines could “understand” the content of web pages, searches with high precision and recall would be possible.
Intelligent Semantic Web Search Engines: A Brief Survey dannyijwest
The World Wide Web (WWW) allows the people to share the information (data) from the large database repositories globally. The amount of information grows billions of databases. We need to search the information will specialize tools known generically search engine. There are many of search engines available today, retrieving meaningful information is difficult. However to overcome this problem in search engines to retrieve meaningful information intelligently, semantic web technologies are playing a major role. In this paper we present survey on the search engine generations and the role of search engines in intelligent web and semantic search technologies.
Ontological approach for improving semantic web search resultseSAT Journals
Abstract we propose personalized information system to provide more user-oriented information considering context information such as a personal profile based, location based and click based. Our system can provide associated search results from relations between the objects using context ontologies modeling created by the categorized layers data. Based on the similarities between item descriptions and user profiles, and the semantic relations between concepts, content based and collaborative recommendation models are supported by the system. User defined rules based on the ontology description of the service and interoperates within any service domain that has an ontology description. Keywords- Ontology; Semantic Web; RDF;OWL
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.
To provide relevant data to users form massive data available on web the Semantic Web technique is used. This presentation gives introduction of semantic web and how NLP can be used in it.
Enhanced Web Usage Mining Using Fuzzy Clustering and Collaborative Filtering ...inventionjournals
Information is overloaded in the Internet due to the unstable growth of information and it makes information search as complicate process. Recommendation System (RS) is the tool and largely used nowadays in many areas to generate interest items to users. With the development of e-commerce and information access, recommender systems have become a popular technique to prune large information spaces so that users are directed toward those items that best meet their needs and preferences. As the exponential explosion of various contents generated on the Web, Recommendation techniques have become increasingly indispensable. Web recommendation systems assist the users to get the exact information and facilitate the information search easier. Web recommendation is one of the techniques of web personalization, which recommends web pages or items to the user based on the previous browsing history. But the tremendous growth in the amount of the available information and the number of visitors to web sites in recent years places some key challenges for recommender system. The recent recommender systems stuck with producing high quality recommendation with large information, resulting unwanted item instead of targeted item or product, and performing many recommendations per second for millions of user and items. To avoid these challenges a new recommender system technologies are needed that can quickly produce high quality recommendation, even for a very large scale problems. To address these issues we use two recommender system process using fuzzy clustering and collaborative filtering algorithms. Fuzzy clustering is used to predict the items or product that will be accessed in the future based on the previous action of user browsers behavior. Collaborative filtering recommendation process is used to produce the user expects result from the result of fuzzy clustering and collection of Web Database data items. Using this new recommendation system, it results the user expected product or item with minimum time. This system reduces the result of unrelated and unwanted item to user and provides the results with user interested domain.
The web has become a resourceful tool for almost all domains today. Search engines prominently use
inverted indexing technique to locate the web pages having the users query. The performance of inverted
index fundamentally depends upon the searching of keyword in the list maintained by search engine. Text
matching is done with the help of string matching algorithm. It is important to any string matching
algorithm to locate quickly the occurrences of the user specified pattern in large text. In this paper a new
string matching algorithm for keyword searching is proposed. The proposed algorithm relies on new
technique based on pattern length and FML (First-Middle-Last) character match. This proposed
algorithm is analysed and implemented. The extensive testing and comparisons are done with BoyerMoore, Naïve, Improved Naïve, Horspool and Zhu Takaoka. The result shows that the proposed
algorithm takes less time than other existing algorithm.
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To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
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2. 514 Computer Science & Information Technology ( CS & IT )
query is a location dependent query the apt results relevant to the location may not be retrieved
appropriately.
1.1 SEMANTIC WEB
The Semantic Web is a Web with a meaning. It describes things in a way that computers can
understand. It is an extension to the normal Web and is not about links -relationships between
things and its properties. Conventional Web consists of human operator and uses computer
systems for tasks like finding, searching and aggregating whereas Semantic Web is the one
understood by computers, does the searching, aggregating and combining information without a
human operator. It is easily processable by machines, on a global scale. It is the efficient way of
representing data on the World Wide Web.
1.1.1 LIMITATIONS IN CONVENTIONAL WEB SEARCH
Conventional web search engines are the most widely used system nowadays for searching and
retrieving the results. But the problem is that, the documents and contents are retrieved only
based on keywords. This may not provide the most relevant and useful content related to the user
query. Here semantics of the query is not considered. It is a mere keyword based search.
A user may also require the web services associated with the retrieved content. But the generic
search engines do not provide the web services associated with the request automatically. If the
query is a location dependent query the apt results relevant to the location may not be retrieved
appropriately.
1.1.2 NEED FOR SEMANTIC WEB:
The above limitations present in the conventional web search is overcome by building a semantic
search engine, thereby analyzing the meaning of the query and providing more appropriate results
to the users through keyword expansion.
1.2 OBJECTIVE
The aim of the project is to design and implement a semantic search that retrieves the search
results analyzing the context and semantics of the query. The semantic search retrieves the most
relevant results for the queries under university domain. This search is made possible by
construction of a strong ontology which forms the knowledge base. The system eliminates the
irrelevant results by forming refined queries and ranking the retrieved links.
1.3 PROBLEM DEFINITION
In this information age, it is a deplorable state that despite the overload of information, we
regularly fail to locate relevant information. Particularly, in the field of education, several
terabytes of content related to various educational institutions such as universities, colleges are
uploaded on the internet every week, and the demand for such resources is always on the rise. But
access to this information using a generic search engine is not satisfactory in terms of the
relevance of links and the overtime on bad links. This can be attributed to several factors, the
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most important being the absence of identification of context and semantics of the user query in
fetching the required results.
In order to overcome these critical issues the proposed system Semantic Information Extraction
in University Domain(SIEU) is designed. SIEU retrieves the semantically relevant results for the
user query by considering the semantics and context of the query. The Semantics of the query is
analyzed by means of the following procedures:
• The user query is initially analyzed grammatically and syntactically by parsing.
• The related synsets for the keywords in the query are retrieved.
• The domain related keywords in the ontology are retrieved to form the refined
query.
The results obtained in SIEU are more relevant by adopting the following procedure
• The refined queries that serve as the input for the search engine are formed based
on the semantic analysis of the user query.
• The web links retrieved for all the newly formed refined queries are re-ranked
based on the domain specific information.
In this way SIEU provides a semantic search that retrieves the appropriate results for the user
query.
1.4 SCOPE
• Provides an exclusive search service for university related information on the web
• People belonging to different domain can retrieve university related information in
an easier way.
• The web results are ranked and more appropriate to the user query.
• The users of this system are provided with the satisfactory results.
This system can also be implemented in a mobile device
2. LITERATURE SURVEY
Semantic Web Searches are an upcoming trend in WWW search. They let knowledge workers
concert their efforts and provide a high degree of relevancy and accuracy. Semantic information
extraction can be achieved through a multitude of approaches. In this section, we present a survey
of some of the existing systems and highlight their unique features.
2.1 EXISTING SYSTEMS
Semantic Information Retrieval has become the core part of any search engine. Many papers deal
with SWS that uses the OWL language for constructing ontology. DySE System (Dynamic
Semantic Engine) [1] implements a context-driven approach in which the keywords are processed
in the context of the information in which they are retrieved, in order to solve semantic ambiguity
and to give a more accurate retrieval based on user interests. DySE splits the user query into
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subject keywords and the domain specific keywords. It uses a dynamic system that constructs
ontology dynamically and uses that as a knowledge base. The DSN (Dynamic Semantic Network)
is created by the DSN Builder, which generates it from Word Net by means of the domain
keyword submitted by the user during query submission. In this way a relevance assessment is
made in order to compare the results. Ontology Construction in Education Domain [2] deals with
the construction of Ontology for specific University constructing instances specifically. Here the
usage of Protégé tool for constructing the ontology is illustrated. It states the various issues which
play a key role in realizing the vision of semantic web such as XML (Extensible Markup
Language) and XML Schema, RDF(Resource Description Framework and RDF Schema,
URI(Uniform Resource Identifier), Unicode and SPARQL(Standard Protocol for RDF Query
language), Search Engines and Agents, and Ontology etc. Ontology development is the objective
of the above system and it has provided the guidelines to work in it with education domain as
example. Query sentences as semantic networks [3] paper describes procedure for representing
the queries in natural language as semantic networks. Here a syntactic analysis of the query is
done by parsing the query using Stanford parser to tag each and every word with their
corresponding parts of speech. Candidate set generation is an important method used here. A
plain text based and word net based comparisons are done to match the related concepts in the
ontology. There are several ways to information from ontology. Semantic Information Retrieval
System [4] is mainly concerned with retrieving information from a sports ontology using the
SPARQL query language. Here specific information is retrieved from the ontology. The sports
related information is queried from the ontology and it is done using SPARQL language. It
provides a basement for any further research to achieve intelligent fuzzy retrieval of sport
information through fuzzy ontology. The pages retrieved from web search needs to be ranked for
getting more relevant links. A Relation-Based Page Rank Algorithm for Semantic Web Search
Engines [5] proves that relations among concepts embedded into semantic annotations can be
effectively exploited to define a ranking strategy for Semantic Web search engines. This sort of
ranking behaves at an inner level (that is, it exploits more precise information that can be made
available within a Web page) and can be used in conjunction with other established ranking
strategies to further improve the accuracy of query results. With respect to other ranking
strategies for the Semantic Web, this approach only relies on the knowledge of the user query, the
web pages to be ranked, and the underlying ontology. Thus, it allows one to effectively manage
the search space and to reduce the complexity associated with the ranking task.
The overview of the existing systems gives multitude approaches for semantic information
extraction. Though these above systems perform a semantic analysis, it has been implemented in
a more generic way. Hence in order to further enrich this process to retrieve more promising
results a system has been proposed for queries relating to university domain (SIEU).In this
proposed system, in combination with some of the above said methodologies, some more
procedures have also been added to perform semantic information extraction in a better way.
2.2 CONTRIBUTIONS
SIEU has been designed for retrieving promising results for the queries under university domain.
Here after performing a syntactic and semantic analysis of the user query, with the keywords
extracted from ontology the refined queries are formed and sent to the search engine. Once the
web links are obtained after sending it to a conventional search engine, a re-ranking algorithm has
been proposed which justifies that the most relevant web links are filtered and ranked with higher
importance and then the less relevant links are produced. Our system also classifies that if it is a
5. Computer Science & Information Technology ( CS & IT ) 517
location based user query, an analysis is done based on certain keywords in the input query and is
separately processed for fetching the most apt results for what the user has asked.
3. INITIAL PROCEEDINGS
Ontology is “a formal explicit specification of a shared conceptualization”. Ontology provides a
common understanding of a term and also its relationship with other terms. Thus a hierarchy can
be formed with the related terms. Thus considering our domain, each University will express their
purposes and functionalities in different terms. The user query should be parsed so that the stop
words that are not needed can be removed from the query. By parsing the query the nouns, verbs
and other parts of speech can be used separately if needed. Also Word Net can be used to get
synsets of the verbs so that the query can be refined more. The nouns can be used to get their
related terms and properties from the constructed ontology.
4. FLOW DIAGRAM
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5. DETAILED DESCRIPTION
5.1 ONTOLOGY CONSTRUCTION: Knowledge Base
ONTOLOGY, a formal representation of knowledge as a set of concepts within a domain forms
the knowledge base for our project that is constructed based on the concepts related to the
university domain. By referring through various university websites a handful of information is
gathered and based on that a strong ontology is constructed taking into consideration, the various
important areas under university domain.
TOOL USED: Protégé 4.1
5.2 USER INPUT:
The user of the system enters a query related to university domain in natural language. The
expected output of this query is the semantically relevant web links. The irrelevant links are
filtered out.
5.3 PARSING OF INPUT QUERY:
The input query given by the user is initially parsed by means of the parser. The parsing is done
to analyze the query syntactically which determines the part of speech of each and every word in
the query. In this way the given query is analyzed grammatically.
TOOL USED: Stanford Parser
5.4 WORDNET:
The output obtained from the parser is sent to the wordnet to get the related synsets of various
words contained in the query. So here semantically related words are obtained from the output of
the wordnet.
TOOL USED: Wordnet API
5.5 EXTRACTION FROM ONTOLOGY:
This process involves of more importance where the information related to the given user query is
extracted from the built ontology. The initially given query after passing through the Stanford
parser and wordnet a set of classified and semantically analyzed words are obtained. These words
are matched with the concepts contained in the ontology to get a set of more related key words.
At the end of this process we get a collection of words which are semantically related and domain
specific key words.
TOOL USED: Jena API
5.6 FORMATION OF REFINED QUERY:
Next process involves of query formation with these collection of words. Permutations and
combinations are needed to form various refined queries from the words obtained. The queries
formed will be more refined and will fetch more semantically related web links on passing these
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queries as input to the search engines. The refined queries are sent to search API which fetches
the web links related to the user query.
TOOL USED: Google Search API
5.7 RANKING OF WEB PAGES:
The web links obtained after passing the refined queries to the search API are now filtered and
ranked to make it more refined. If any of the web links are not relevant to the given query they
are filtered out. Ranking is applied to all web links obtained from all possible queries formed
under permutation. On applying ranking the web links are re-ranked in the appropriate order of
semantic relatedness.
TOOL USED: Ranking Algorithm
6. RESULTS OBTAINED IN EVERY STAGE:
6.1 QUERY ENTRY BY THE USER:
The user enters the query in the interface provided by our system.
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6.2 PARSING OF THE INPUT QUERY:
The query given by the user is parsed by means of Stanford parser and the output is:
6.3 RETRIEVAL OF SYNSETS FROM WORDNET:
Now the related synsets for the words present in the query are retrieved from the wordnet.
6.4 EXTRACTION OF DOMAIN KEYWORDS FROM ONTOLOGY:
The domain keywords that are semantically related to the words in the query are extracted from
ontology.
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6.5 WEB LINKS RETRIEVED:
6.5.1 User Query:
6.5.2 With Refined Query:
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7. PERFORMANCE STUDIES
7.1 MEASURES USED
Recall:
Measure of how much relevant information the system has extracted (coverage of
system).
The Recall calculated here is the relative recall in which performance is compared in
relative to the google search engine.
Precision:
Measure of how much of the information the system returns is correct
(accuracy).
7.2 SIEU VS GOOGLE
When our proposed system SIEU was tested, a marked improvement in performance was
observed for most of the queries as a result of semantic analysis, although a small fraction of
them had negative and similar performance with a generic search engine.
The table 5.11 shows the samples of the performance levels of our system comparing it with
Google.
Table 1. Precision and Recall values of sample queries
SAMPLE QUERIES GOOGLE SIEU
PRECISION RECALL PRECISION RECALL
colleges for doing M.B.A 0.68 0.44 0.87 0.5
teaching staff in computer
science department in Anna
university
0.62 0.41 0.86 0.6
RECALL = # of relevant links given by the system
Total # of relevant links in GOOGLE and SIEU
PRECISION = # of relevant links given by the system
Total # of links retrieved
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professors with more
number of publications in
IIT in department IT
0.68 0.5 0.78 0.54
last date to apply for M.S in
Stanford university
0.56 0.43 0.77 0.57
financial aid offered for
summer internships in UK
0.75 0.46 0.87 0.56
Deadline for payment of fees
for M.B.A course in sastra
university
0.53 0.31 0.77 0.56
Associations formed for
students in California
university
0.7 0.45 0.88 0.55
Provide me the details of the
chairman of board of
committee members
0.66 0.52 0.73 0.6
Research areas in IIT where
foreign collaborations exists
0.56 0.5 0.68 0.54
Details about the facilities
available in research
institutions of delhi
university
0.6 0.55 0.57 0.55
Provide me the information
about the correspondence
students of MIT
0.7 0.45 0.7 0.56
Road maps to visit the
campus of Stanford
0.65 0.55 0.78 0.61
12. 524 Computer Science & Information Technology ( CS & IT )
university
Information regarding the
universities in abroad which
provides internship in
accounting
0.68 0.46 0.60 0.45
Procedure to apply online
for M.S in U.S university
0.74 0.45 0.79 0.65
How far is tagore university
located from anna nagar
0.76 0.58 0.81 0.59
What are colleges located
near by tambaram for doing
regular M.E course
0.67 0.45 0.83 0.55
The above table depicts that the precision value of our system SIEU is higher than the values
obtained in google search engines. The relative recall values estimates the retrieval effectiveness
between Google and our system. The more relative recall values of out system shows that SIEU is
more effective in retrieval than Google search engine.
7.3 Precision Recall curve
Figure 5.1 shows the precision Vs recall graph for SIEU and GOOGLE system .The graph is
drawn taking the first 5 queries into consideration, their corresponding precision and recall values
are plotted for both Google and SIEU systems. The precision recall curve in the graph clearly
depicts that SIEU system retrieves the accurate links for the user query based on semantic
relatedness. The values of precision and recall depicts the performance of SIEU system.
13. Computer Science & Information Technology ( CS & IT ) 525
Figure 1. Precision Vs Recall graph for SIEU Vs GOOGLE
Table 2. Average precision and recall of SIEU
SIEU GOOGLE
AVERAGE
PRECISION
0.79 0.64
AVERAGE RECALL 0.55 0.48
Table 5.12 shows the average precision and recall of SIEU and Google. From this we can infer
that the higher value of average precision and recall for our proposed system SIEU when
compared to Google depicts that our system has a better performance and accuracy in retrieving
the results than the generic search engines. The recall values depict the coverage of the system.
The average relative recall value of SIEU system is also higher compared to that of the Google.
The higher value denotes the best coverage of our system compared to the generic search engines.
8. CONCLUSION:
Semantic relevant information has been retrieved as a result of this system. To add on to it many
more services are to be added. Location based information retrieval is an additional feature
where we have planned to use google maps for this purpose by means of which our system gets
enhanced with this location independent feature. And invocation of web services may be provided
14. 526 Computer Science & Information Technology ( CS & IT )
to the users if incase there exists a web service related to their query which could be made
possible with the help of RSS feeds. Thus we believe an information system with these enhanced
features will be developed under university domain.
9. REFERENCES:
[1] Antonio M. Rinaldi, “An Ontology-Driven Approach for Semantic Information Retrieval on the Web”
,2009, ACM Transactions on Internet Technologies, Vol .9, no.3, pp:10:1-10:24.
[2] Sanjay Kumar Malik ET. al. ,“Developing a University Ontology in Education Domain using Protégé
for Semantic Web”, 2010, International Journal of Engineering Science and Technology, Vol.2, no.9,
pp:4673-4681.
[3] Joel Booth, Barbara Di Eugenio, Isabel F. Cruz, Ouri Wolfson,” Query Sentences as Semantic (Sub)
Networks”,2009, IEEE International Conference on Semantic Computing,Chicago,USA,pp:89-92
[4] Jun Zhai, Kaitao Zhou,” Semantic Retrieval for Sports Information Based on Ontology and SPARQL”,
2010 , International Conference of Information Science and Management Engineering (ICME),Xian,Vol.19
no.2 pp: 315-323.
[5] Fabrizio Lamberti,Andrea Sanna, and Claudio Demartini, ” A Relation-Based Page Rank Algorithm for
Semantic Web Search Engines”,2009 ,IEEE Transactions on Knowledge and Data Engineering, Vol.21,
no.1,pp:123-136.
[6] Stijn Vandamme, Johannes Deleu,” CROEQS: Contemporaneous Role Ontology-based Expanded
Query Search --Implementation and Evaluation”,2009, International Conference on Communication
Software and Networks, Ghent, Belgium,Vol.22,pp:448-452.
[7] Chen-Yu Lee, Von-Wun Soo, ”Ontology based Information Retrieval and Extraction”, 2005, IEEE
Transactions on Knowledge and Data Engineering,pp:265-269.
[8] Haibo Yu, Tsunenori Mine and Makoto Amamiya,” An Architecture for Personal Semantic Web
Information Retrieval System– Integrating Web services and Web contents”, 2005 ,Proceedings of the
IEEE International Conference on Web Services, Orlando, Florida,vol.5 ,No.3,pp.329-336
[9] Regina M.M.Braga, Claudia M. L. Werner ,Marta Mattoso,” Using Ontologies for Domain Information
Retrieval”,2000, IEEE International Conference on Database and Expert Systems
Applications,Brazil,pp:836-840.
15. Computer Science & Information Technology ( CS & IT ) 527
APPENDIX A
• Stanford parser
Stanford parser is a natural language parser that works out the grammatical structure of
sentences, for instance, which groups of words go together (as "phrases") and which words
are the subject or object of a verb. Tagging process is done by the parser where the words are
tagged by their parts of speech.
• Word net
Word Net is a large lexical database of English. Nouns, verbs, adjectives and adverbs are
grouped into sets of cognitive synonyms called as synsets, each expressing a distinct concept.
Java API for Word Net Searching (JAWS) is an API that provides Java applications with the
ability to retrieve data from the Word Net database
• Jena Framework Api
Jena is a Java framework for building Semantic Web applications. It provides a programmatic
environment for RDF, RDFS and OWL and includes a rule-based inference engine. Jena is
open source.
• Google search Api
Google Search Api coordinates a search across a collection of search services. It provides all
kinds of search such as local search, web search, News search, Video search etc. Google Api
loader loads this search Api which provides the search results from the generic search
engines.
• HTML Parser
HTML Parser is a Java library used to parse HTML. Primarily it is used for transformation or
extraction. It features filters, visitors, custom tags. It is a fast, robust.It is used to extract the
meta tags from the web pages.
