This paper presents the similarity measurement algorithm for domain specific terms collected in the
ontology based data integration system. This similarity measurement algorithm can be used in ontology
mapping and query service of ontology based data integration system. In this paper, we focus on the web
query service to apply this proposed algorithm. Concepts similarity is important for web query service
because the words in user input query are not same wholly with the concepts in ontology. So, we need to
extract the possible concepts that are match or related to the input words with the help of machine readable
dictionary WordNet. Sometimes, we use the generated mapping rules in query generation procedure for
some words that cannot be confirmed the similarity of these words by WordNet. We prove the effect of this
algorithm with two degree semantic result of web mining by generating the concepts results obtained form
the input query.
Semantic similarity and semantic relatedness
measure in particular is very important in the current scenario
due to the huge demand for natural language processing based
applications such as chatbots and information retrieval systems
such as knowledge base based FAQ systems. Current approaches
generally use similarity measures which does not use the context
sensitive relationships between the words. This leads to erroneous
similarity predictions and is not of much use in real life
applications. This work proposes a novel approach that gives an
accurate relatedness measure of any two words in a sentence by
taking their context into consideration. This context correction
results in a more accurate similarity prediction which results in
higher accuracy of information retrieval systems.
Computing semantic similarity between two words comes with variety of approaches. This is mainly
essential for the applications such as text analysis, text understanding. In traditional system search engines are used to compute the similarity between words. In that search engines are keyword based. There is one drawback that user should know what exactly they are looking for. There are mainly two main approaches for computation namely
knowledge based and corpus based approaches. But there is one drawback that these two approaches are not suitable for computing similarity between multi-word expressions. This system provides efficient and effective approach for computing term similarity using semantic network approach. A clustering approach is used in order to improve the
accuracy of the semantic similarity. This approach is more efficient than other computing algorithms. technique
can also apply to large scale dataset to compute term similarity.
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.
A COMPARISON OF DOCUMENT SIMILARITY ALGORITHMSgerogepatton
Document similarity is an important part of Natural Language Processing and is most commonly used for
plagiarism-detection and text summarization. Thus, finding the overall most effective document similarity
algorithm could have a major positive impact on the field of Natural Language Processing. This report sets
out to examine the numerous document similarity algorithms, and determine which ones are the most
useful. It addresses the most effective document similarity algorithm by categorizing them into 3 types of
document similarity algorithms: statistical algorithms, neural networks, and corpus/knowledge-based
algorithms. The most effective algorithms in each category are also compared in our work using a series of
benchmark datasets and evaluations that test every possible area that each algorithm could be used in.
Semantic similarity and semantic relatedness
measure in particular is very important in the current scenario
due to the huge demand for natural language processing based
applications such as chatbots and information retrieval systems
such as knowledge base based FAQ systems. Current approaches
generally use similarity measures which does not use the context
sensitive relationships between the words. This leads to erroneous
similarity predictions and is not of much use in real life
applications. This work proposes a novel approach that gives an
accurate relatedness measure of any two words in a sentence by
taking their context into consideration. This context correction
results in a more accurate similarity prediction which results in
higher accuracy of information retrieval systems.
Computing semantic similarity between two words comes with variety of approaches. This is mainly
essential for the applications such as text analysis, text understanding. In traditional system search engines are used to compute the similarity between words. In that search engines are keyword based. There is one drawback that user should know what exactly they are looking for. There are mainly two main approaches for computation namely
knowledge based and corpus based approaches. But there is one drawback that these two approaches are not suitable for computing similarity between multi-word expressions. This system provides efficient and effective approach for computing term similarity using semantic network approach. A clustering approach is used in order to improve the
accuracy of the semantic similarity. This approach is more efficient than other computing algorithms. technique
can also apply to large scale dataset to compute term similarity.
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.
A COMPARISON OF DOCUMENT SIMILARITY ALGORITHMSgerogepatton
Document similarity is an important part of Natural Language Processing and is most commonly used for
plagiarism-detection and text summarization. Thus, finding the overall most effective document similarity
algorithm could have a major positive impact on the field of Natural Language Processing. This report sets
out to examine the numerous document similarity algorithms, and determine which ones are the most
useful. It addresses the most effective document similarity algorithm by categorizing them into 3 types of
document similarity algorithms: statistical algorithms, neural networks, and corpus/knowledge-based
algorithms. The most effective algorithms in each category are also compared in our work using a series of
benchmark datasets and evaluations that test every possible area that each algorithm could be used in.
A COMPARISON OF DOCUMENT SIMILARITY ALGORITHMSgerogepatton
Document similarity is an important part of Natural Language Processing and is most commonly used for
plagiarism-detection and text summarization. Thus, finding the overall most effective document similarity
algorithm could have a major positive impact on the field of Natural Language Processing. This report sets
out to examine the numerous document similarity algorithms, and determine which ones are the most
useful. It addresses the most effective document similarity algorithm by categorizing them into 3 types of
document similarity algorithms: statistical algorithms, neural networks, and corpus/knowledge-based
algorithms. The most effective algorithms in each category are also compared in our work using a series of
benchmark datasets and evaluations that test every possible area that each algorithm could be used in.
In this paper, we present three techniques for incorporating syntactic metadata in a textual retrieval system. The first technique involves just a syntactic analysis of the query and it generates a different weight for each term of the query, depending on its grammar category in the query phrase. These weights will be used for each term in the retrieval process. The second technique involves a storage optimization of the system's inverted index that is the inverse index will store only terms that are subjects or predicates in the document they appear in. Finally, the third technique builds a full syntactic index, meaning that for each term in the term collection, the inverse index stores besides the term-frequency and the inverse-document-frequency, also the grammar category of the term for each of its occurrences in a document.
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.
This paper presents a natural language processing based automated system called DrawPlus for generating UML diagrams, user scenarios and test cases after analyzing the given business requirement specification which is written in natural language. The DrawPlus is presented for analyzing the natural languages and extracting the relative and required information from the given business requirement Specification by the user. Basically user writes the requirements specifications in simple English and the designed system has conspicuous ability to analyze the given requirement specification by using some of the core natural language processing techniques with our own well defined algorithms. After compound analysis and extraction of associated information, the DrawPlus system draws use case diagram, User scenarios and system level high level test case description. The DrawPlus provides the more convenient and reliable way of generating use case, user scenarios and test cases in a way reducing the time and cost of software development process while accelerating the 70 of works in Software design and Testing phase Janani Tharmaseelan ""Cohesive Software Design"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd22900.pdf
Paper URL: https://www.ijtsrd.com/computer-science/other/22900/cohesive-software-design/janani-tharmaseelan
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.
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.
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.
SEMANTIC INTEGRATION FOR AUTOMATIC ONTOLOGY MAPPING cscpconf
In the last decade, ontologies have played a key technology role for information sharing and agents interoperability in different application domains. In semantic web domain, ontologies are efficiently used toface the great challenge of representing the semantics of data, in order to bring the actual web to its full
power and hence, achieve its objective. However, using ontologies as common and shared vocabularies requires a certain degree of interoperability between them. To confront this requirement, mapping ontologies is a solution that is not to be avoided. In deed, ontology mapping build a meta layer that allows different applications and information systems to access and share their informations, of course, after resolving the different forms of syntactic, semantic and lexical mismatches. In the contribution presented in this paper, we have integrated the semantic aspect based on an external lexical resource, wordNet, to design a new algorithm for fully automatic ontology mapping. This fully automatic character features the
main difference of our contribution with regards to the most of the existing semi-automatic algorithms of ontology mapping, such as Chimaera, Prompt, Onion, Glue, etc. To better enhance the performances of our algorithm, the mapping discovery stage is based on the combination of two sub-modules. The former
analysis the concept’s names and the later analysis their properties. Each one of these two sub-modules is
it self based on the combination of lexical and semantic similarity measures.
Tracing Requirements as a Problem of Machine Learning ijseajournal
Software requirement engineering and evolution essential to software development process, which defines and elaborates what is to be built in a project. Requirements are mostly written in text and will later evolve to fine-grained and actionable artifacts with details about system configurations, technology stacks, etc. Tracing the evolution of requirements enables stakeholders to determine the origin of each requirement and
understand how well the software’s design reflects to its requirements. Reckoning requirements traceability
is not a trivial task, a machine learning approach is used to classify traceability between various associated requirements. In particular, a 2-learner, ontology-based, pseudo-instances-enhanced approach, where two classifiers are trained to separately exploit two types of features, lexical features and features derived from a hand-built ontology, is investigated for such task. The hand-built ontology is also leveraged to generate
pseudo training instances to improve machine learning results. In comparison to a supervised baseline system that uses only lexical features, our approach yields a relative error reduction of 56.0%. Most interestingly, results do not deteriorate when the hand-built ontology is replaced with its automatically
constructed counterpart.
Building a recommendation system based on the job offers extracted from the w...IJECEIAES
Recruitment, or job search, is increasingly used throughout the world by a large population of users through various channels, such as websites, platforms, and professional networks. Given the large volume of information related to job descriptions and user profiles, it is complicated to appropriately match a user's profile with a job description, and vice versa. The job search approach has drawbacks since the job seeker needs to search a job offers in each recruitment platform, manage their accounts, and apply for the relevant job vacancies, which wastes considerable time and effort. The contribution of this research work is the construction of a recommendation system based on the job offers extracted from the web and on the e-portfolios of job seekers. After the extraction of the data, natural language processing is applied to structured data and is ready for filtering and analysis. The proposed system is a content-based system, it measures the degree of correspondence between the attributes of the e-portfolio with those of each job offer of the same list of competence specialties using the Euclidean distance, the result is classified with a decreasing way to display the most relevant to the least relevant job offers
IDENTIFYING THE SEMANTIC RELATIONS ON UNSTRUCTURED DATAijistjournal
Ontologisms have been applied to many applications in recent years, especially on Sematic Web, Information Retrieval, Information Extraction, and Question and Answer. The purpose of domain-specific ontology is to get rid of conceptual and terminological confusion. It accomplishes this by specifying a set of generic concepts that characterizes the domain as well as their definitions and interrelationships. This paper will describe some algorithms for identifying semantic relations and constructing an Information Technology Ontology, while extracting the concepts and objects from different sources. The Ontology is constructed based on three main resources: ACM, Wikipedia and unstructured files from ACM Digital Library. Our algorithms are combined of Natural Language Processing and Machine Learning. We use Natural Language Processing tools, such as OpenNLP, Stanford Lexical Dependency Parser in order to explore sentences. We then extract these sentences based on English pattern in order to build training set. We use a random sample among 245 categories of ACM to evaluate our results. Results generated show that our system yields superior performance.
Ontologisms have been applied to many applications in recent years, especially on Sematic Web, Information
Retrieval, Information Extraction, and Question and Answer. The purpose of domain-specific ontology
is to get rid of conceptual and terminological confusion. It accomplishes this by specifying a set of generic
concepts that characterizes the domain as well as their definitions and interrelationships. This paper will
describe some algorithms for identifying semantic relations and constructing an Information Technology
Ontology, while extracting the concepts and objects from different sources. The Ontology is constructed
based on three main resources: ACM, Wikipedia and unstructured files from ACM Digital Library. Our
algorithms are combined of Natural Language Processing and Machine Learning. We use Natural Language
Processing tools, such as OpenNLP, Stanford Lexical Dependency Parser in order to explore sentences.
We then extract these sentences based on English pattern in order to build training set. We use a
random sample among 245 categories of ACM to evaluate our results. Results generated show that our
system yields superior performance.
Query expansion using novel use case scenario relationship for finding featur...IJECEIAES
Feature location is a technique for determining source code that implements specific features in software. It developed to help minimize effort on program comprehension. The main challenge of feature location research is how to bridge the gap between abstract keywords in use cases and detail in source code. The use case scenarios are software requirements artifacts that state the input, logic, rules, actor, and output of a function in the software. The sentence on use case scenario is sometimes described another sentence in other use case scenario. This study contributes to creating expansion queries in feature locations by finding the relationship between use case scenarios. The relationships include inner association, outer association and intratoken association. The research employs latent Dirichlet allocation (LDA) to create model topics on source code. Query expansion using inner, outer and intratoken was tested for finding feature locations on a Java-based open-source project. The best precision rate was 50%. The best recall was 100%, which was found in several use case scenarios implemented in a few files. The best average precision rate was 16.7%, which was found in inner association experiments. The best average recall rate was 68.3%, which was found in all compound association experiments.
A hybrid composite features based sentence level sentiment analyzerIAESIJAI
Current lexica and machine learning based sentiment analysis approaches
still suffer from a two-fold limitation. First, manual lexicon construction and
machine training is time consuming and error-prone. Second, the
prediction’s accuracy entails sentences and their corresponding training text
should fall under the same domain. In this article, we experimentally
evaluate four sentiment classifiers, namely support vector machines (SVMs),
Naive Bayes (NB), logistic regression (LR) and random forest (RF). We
quantify the quality of each of these models using three real-world datasets
that comprise 50,000 movie reviews, 10,662 sentences, and 300 generic
movie reviews. Specifically, we study the impact of a variety of natural
language processing (NLP) pipelines on the quality of the predicted
sentiment orientations. Additionally, we measure the impact of incorporating
lexical semantic knowledge captured by WordNet on expanding original
words in sentences. Findings demonstrate that the utilizing different NLP
pipelines and semantic relationships impacts the quality of the sentiment
analyzers. In particular, results indicate that coupling lemmatization and
knowledge-based n-gram features proved to produce higher accuracy results.
With this coupling, the accuracy of the SVM classifier has improved to
90.43%, while it was 86.83%, 90.11%, 86.20%, respectively using the three
other classifiers.
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.
A COMPARISON OF DOCUMENT SIMILARITY ALGORITHMSgerogepatton
Document similarity is an important part of Natural Language Processing and is most commonly used for
plagiarism-detection and text summarization. Thus, finding the overall most effective document similarity
algorithm could have a major positive impact on the field of Natural Language Processing. This report sets
out to examine the numerous document similarity algorithms, and determine which ones are the most
useful. It addresses the most effective document similarity algorithm by categorizing them into 3 types of
document similarity algorithms: statistical algorithms, neural networks, and corpus/knowledge-based
algorithms. The most effective algorithms in each category are also compared in our work using a series of
benchmark datasets and evaluations that test every possible area that each algorithm could be used in.
In this paper, we present three techniques for incorporating syntactic metadata in a textual retrieval system. The first technique involves just a syntactic analysis of the query and it generates a different weight for each term of the query, depending on its grammar category in the query phrase. These weights will be used for each term in the retrieval process. The second technique involves a storage optimization of the system's inverted index that is the inverse index will store only terms that are subjects or predicates in the document they appear in. Finally, the third technique builds a full syntactic index, meaning that for each term in the term collection, the inverse index stores besides the term-frequency and the inverse-document-frequency, also the grammar category of the term for each of its occurrences in a document.
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.
This paper presents a natural language processing based automated system called DrawPlus for generating UML diagrams, user scenarios and test cases after analyzing the given business requirement specification which is written in natural language. The DrawPlus is presented for analyzing the natural languages and extracting the relative and required information from the given business requirement Specification by the user. Basically user writes the requirements specifications in simple English and the designed system has conspicuous ability to analyze the given requirement specification by using some of the core natural language processing techniques with our own well defined algorithms. After compound analysis and extraction of associated information, the DrawPlus system draws use case diagram, User scenarios and system level high level test case description. The DrawPlus provides the more convenient and reliable way of generating use case, user scenarios and test cases in a way reducing the time and cost of software development process while accelerating the 70 of works in Software design and Testing phase Janani Tharmaseelan ""Cohesive Software Design"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd22900.pdf
Paper URL: https://www.ijtsrd.com/computer-science/other/22900/cohesive-software-design/janani-tharmaseelan
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.
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.
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.
SEMANTIC INTEGRATION FOR AUTOMATIC ONTOLOGY MAPPING cscpconf
In the last decade, ontologies have played a key technology role for information sharing and agents interoperability in different application domains. In semantic web domain, ontologies are efficiently used toface the great challenge of representing the semantics of data, in order to bring the actual web to its full
power and hence, achieve its objective. However, using ontologies as common and shared vocabularies requires a certain degree of interoperability between them. To confront this requirement, mapping ontologies is a solution that is not to be avoided. In deed, ontology mapping build a meta layer that allows different applications and information systems to access and share their informations, of course, after resolving the different forms of syntactic, semantic and lexical mismatches. In the contribution presented in this paper, we have integrated the semantic aspect based on an external lexical resource, wordNet, to design a new algorithm for fully automatic ontology mapping. This fully automatic character features the
main difference of our contribution with regards to the most of the existing semi-automatic algorithms of ontology mapping, such as Chimaera, Prompt, Onion, Glue, etc. To better enhance the performances of our algorithm, the mapping discovery stage is based on the combination of two sub-modules. The former
analysis the concept’s names and the later analysis their properties. Each one of these two sub-modules is
it self based on the combination of lexical and semantic similarity measures.
Tracing Requirements as a Problem of Machine Learning ijseajournal
Software requirement engineering and evolution essential to software development process, which defines and elaborates what is to be built in a project. Requirements are mostly written in text and will later evolve to fine-grained and actionable artifacts with details about system configurations, technology stacks, etc. Tracing the evolution of requirements enables stakeholders to determine the origin of each requirement and
understand how well the software’s design reflects to its requirements. Reckoning requirements traceability
is not a trivial task, a machine learning approach is used to classify traceability between various associated requirements. In particular, a 2-learner, ontology-based, pseudo-instances-enhanced approach, where two classifiers are trained to separately exploit two types of features, lexical features and features derived from a hand-built ontology, is investigated for such task. The hand-built ontology is also leveraged to generate
pseudo training instances to improve machine learning results. In comparison to a supervised baseline system that uses only lexical features, our approach yields a relative error reduction of 56.0%. Most interestingly, results do not deteriorate when the hand-built ontology is replaced with its automatically
constructed counterpart.
Building a recommendation system based on the job offers extracted from the w...IJECEIAES
Recruitment, or job search, is increasingly used throughout the world by a large population of users through various channels, such as websites, platforms, and professional networks. Given the large volume of information related to job descriptions and user profiles, it is complicated to appropriately match a user's profile with a job description, and vice versa. The job search approach has drawbacks since the job seeker needs to search a job offers in each recruitment platform, manage their accounts, and apply for the relevant job vacancies, which wastes considerable time and effort. The contribution of this research work is the construction of a recommendation system based on the job offers extracted from the web and on the e-portfolios of job seekers. After the extraction of the data, natural language processing is applied to structured data and is ready for filtering and analysis. The proposed system is a content-based system, it measures the degree of correspondence between the attributes of the e-portfolio with those of each job offer of the same list of competence specialties using the Euclidean distance, the result is classified with a decreasing way to display the most relevant to the least relevant job offers
IDENTIFYING THE SEMANTIC RELATIONS ON UNSTRUCTURED DATAijistjournal
Ontologisms have been applied to many applications in recent years, especially on Sematic Web, Information Retrieval, Information Extraction, and Question and Answer. The purpose of domain-specific ontology is to get rid of conceptual and terminological confusion. It accomplishes this by specifying a set of generic concepts that characterizes the domain as well as their definitions and interrelationships. This paper will describe some algorithms for identifying semantic relations and constructing an Information Technology Ontology, while extracting the concepts and objects from different sources. The Ontology is constructed based on three main resources: ACM, Wikipedia and unstructured files from ACM Digital Library. Our algorithms are combined of Natural Language Processing and Machine Learning. We use Natural Language Processing tools, such as OpenNLP, Stanford Lexical Dependency Parser in order to explore sentences. We then extract these sentences based on English pattern in order to build training set. We use a random sample among 245 categories of ACM to evaluate our results. Results generated show that our system yields superior performance.
Ontologisms have been applied to many applications in recent years, especially on Sematic Web, Information
Retrieval, Information Extraction, and Question and Answer. The purpose of domain-specific ontology
is to get rid of conceptual and terminological confusion. It accomplishes this by specifying a set of generic
concepts that characterizes the domain as well as their definitions and interrelationships. This paper will
describe some algorithms for identifying semantic relations and constructing an Information Technology
Ontology, while extracting the concepts and objects from different sources. The Ontology is constructed
based on three main resources: ACM, Wikipedia and unstructured files from ACM Digital Library. Our
algorithms are combined of Natural Language Processing and Machine Learning. We use Natural Language
Processing tools, such as OpenNLP, Stanford Lexical Dependency Parser in order to explore sentences.
We then extract these sentences based on English pattern in order to build training set. We use a
random sample among 245 categories of ACM to evaluate our results. Results generated show that our
system yields superior performance.
Query expansion using novel use case scenario relationship for finding featur...IJECEIAES
Feature location is a technique for determining source code that implements specific features in software. It developed to help minimize effort on program comprehension. The main challenge of feature location research is how to bridge the gap between abstract keywords in use cases and detail in source code. The use case scenarios are software requirements artifacts that state the input, logic, rules, actor, and output of a function in the software. The sentence on use case scenario is sometimes described another sentence in other use case scenario. This study contributes to creating expansion queries in feature locations by finding the relationship between use case scenarios. The relationships include inner association, outer association and intratoken association. The research employs latent Dirichlet allocation (LDA) to create model topics on source code. Query expansion using inner, outer and intratoken was tested for finding feature locations on a Java-based open-source project. The best precision rate was 50%. The best recall was 100%, which was found in several use case scenarios implemented in a few files. The best average precision rate was 16.7%, which was found in inner association experiments. The best average recall rate was 68.3%, which was found in all compound association experiments.
A hybrid composite features based sentence level sentiment analyzerIAESIJAI
Current lexica and machine learning based sentiment analysis approaches
still suffer from a two-fold limitation. First, manual lexicon construction and
machine training is time consuming and error-prone. Second, the
prediction’s accuracy entails sentences and their corresponding training text
should fall under the same domain. In this article, we experimentally
evaluate four sentiment classifiers, namely support vector machines (SVMs),
Naive Bayes (NB), logistic regression (LR) and random forest (RF). We
quantify the quality of each of these models using three real-world datasets
that comprise 50,000 movie reviews, 10,662 sentences, and 300 generic
movie reviews. Specifically, we study the impact of a variety of natural
language processing (NLP) pipelines on the quality of the predicted
sentiment orientations. Additionally, we measure the impact of incorporating
lexical semantic knowledge captured by WordNet on expanding original
words in sentences. Findings demonstrate that the utilizing different NLP
pipelines and semantic relationships impacts the quality of the sentiment
analyzers. In particular, results indicate that coupling lemmatization and
knowledge-based n-gram features proved to produce higher accuracy results.
With this coupling, the accuracy of the SVM classifier has improved to
90.43%, while it was 86.83%, 90.11%, 86.20%, respectively using the three
other classifiers.
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.
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Conceptual Similarity Measurement Algorithm For Domain Specific Ontology
1. International Journal of Information Technology, Modeling and Computing (IJITMC) Vol. 2, No.2, May 2014
DOI : 10.5121/ijitmc.2014.2204 29
CONCEPTUAL SIMILARITY MEASUREMENT ALGORITHM
FOR DOMAIN SPECIFIC ONTOLOGY
Zin Thu Thu Myint1
and Kay Khaing Win2
1
University of Technology, Yadanarpon Cyber City, Near Pyin Oo Lwin, Myanmar
2
Department of Advance Science and Technology, Nay Pyi Daw, Myanmar
ABSTRACT
This paper presents the similarity measurement algorithm for domain specific terms collected in the
ontology based data integration system. This similarity measurement algorithm can be used in ontology
mapping and query service of ontology based data integration system. In this paper, we focus on the web
query service to apply this proposed algorithm. Concepts similarity is important for web query service
because the words in user input query are not same wholly with the concepts in ontology. So, we need to
extract the possible concepts that are match or related to the input words with the help of machine readable
dictionary WordNet. Sometimes, we use the generated mapping rules in query generation procedure for
some words that cannot be confirmed the similarity of these words by WordNet. We prove the effect of this
algorithm with two degree semantic result of web mining by generating the concepts results obtained form
the input query.
KEYWORDS
Semantic Similarity, Ontology, Concepts, Triplets & Query Processing
1. INTRODUCTION
Ontology based data integration system can support the virtual web portal to the users’ view
because users can get the uniform access to the multiple data sources that are located in separated
locations. By considering the architecture, it has three main processing phases that are ontology
creation, ontology mapping and web query service. In ontology creation, the expert at each local
source builds the local ontology by using their domain concepts. So, the domain concepts at each
local source may be various and it causes the semantic conflicts when integrating these local
ontologies [1, 2]. It is a reason to create the mapping rules that help to solve the semantic
conflicts among multiple ontologies. Mapping rules mean to construct the semantic relations such
as equivalent, hyponym and homonym among multiple ontologies [3, 4]. These rules are applied
not only to assist the integrated ontology to handle the semantic conflicts but also to help the web
query service by the matching the words contained in the user input query with the domain
concepts in ontology. However, these mapping rules are not sufficient to extract the terms relating
the domain concepts from the user input query because user can submit the query without
knowing exactly the domain concepts in ontology. The words in the input query may not be
wholly equivalent to the terms contained in the mapping rules. For this reason, other similarity
measurement methods become to add in term matching process of query service.
2. International Journal of Information Technology, Modeling and Computing (IJITMC) Vol. 2, No.2, May 2014
30
Query service in ontology based data integration system contains the triplets’ extraction, query
generation and retrieval of knowledge from the ontology. Similarity measurement is mainly
suppose the triplet extraction process that can extract the specific triplets from the user input
query and can add the necessary information to build the ontology understanding query such as
SPARQL [5, 6]. There are many similarity measurement methods to match the keywords in the
input query and the domain concepts in the ontology. However, they return the many possible
concepts that are similar to the input keywords and so; their precision and recall degrees are low
at query processing in ontology based data integration system [7, 8, and 9]. The proposed
semantic similarity algorithm can reduce the problem of retrieving unnecessary information from
the ontology by finding the most closet concepts in ontology that are similar to the terms in user
input query with the help of machine readable dictionary WordNet. This also gives the specific
triplets to suppose the query generation procedure. So, the precision and recall degree of this
proposed algorithm is very high.
This paper is organised as follows. In Section II, this paper presents the overview of ontology
based data integration system. The detail of this proposed similarity measurement algorithm is
described in Section III. Section IV will fully explain the experimental results based on the
precision and recall rates by generating the SPARQL query and retrieving the require information
from ontology. Finally, we conclude the presentation of this paper with some remarks in Section
V.
2. ONTOLOGY BASED DATA INTEGRATION SYSTEM
Using ontology in data integration systems is an ideal solution to handle the semantic conflicts
between various data sources [10]. There are two trends to use the ontology in data integration
system: one use for translating query or their result and the other uses ontology for the generation
of global schema [11]. The system presented in this paper uses both of these two trends for data
integration and accessing data on integrated ontology. The system architecture is depicted in
figure 1.
Figure 1. Ontology based data integration system
Figure 1. Ontology based data integration system
Words
Concept
s
Global Ontology
Mapping table
Local n
Local 2
Local 1
Similarity measurement
WordNet
Detect Keywords/Names
Adjustabl
e
Similarity
Method
Edit
Distance
Method
Keyword
s
Names
Return concepts
Queries/Answers
End user
Queries/Answers
Send triplets
Triplet
Extraction
SPARQL
Generator
3. International Journal of Information Technology, Modeling and Computing (IJITMC) Vol. 2, No.2, May 2014
31
As architecture, the system follows the framework of Global As a View (GAV) approach [12].
Local ontology is firstly created to represent the relational structure of database at each local
source as the semantic model: table names are recognized as the ontology classes, column name
in each table are recognized as the data-type properties of each class that are defined for the
corresponding table and the between the classes are defined by the object-type properties. Global
ontology is built by using the data collected from the existing local ontology [13].
When users make queries and submit them to the system, the global ontology and mapping
schema are used to retrieve the information needed from the sources. Mapping rules mean to
construct equivalent, homonym and hyponym between the words in the input query and domain
ontology concepts [14, 15]. These mapping rules are constructed by referring to the semantic
similarity. Moreover, users’ input query may not be contained the terms that are wholly
equivalent to the concepts in ontology. So, it is needed to match the terms in input query with the
concepts in ontology by utilizing the proposed similarity measurement algorithm to suppose the
specific concepts in building the ontology understanding query such as SPARQL. This proposed
similarity measurement algorithm will be fully explained in the following section. SPARQL
query language has a graph-based structure and can be built by combining triple patterns
(subjects, predicates and objects). To fulfil the requirements in query generation procedure,
triplets extraction process can continually takes the necessary steps [16]. After achieving the
triplets from the input sentence, these are used to build the ontology understanding query
SPARQL and retrieve the required information from the ontology to reply to the user.
3. SEMANTIC SIMILARITY MEASUREMENT
For accessing the data on ontology, ontology understanding query such as SPARQL is needed.
According to the structure of SPARQL, it is needed to extract the specific triplets from the user
input query. Here, the terms contained in the user input query are not same exactly with the
concepts in ontology. So, similarity measurement algorithm is applied to match the terms
extracted from the user input query with the concepts in domain specific ontology. The proposed
algorithm is mainly based on the machine readable dictionary WordNet to define the forms of
each terms contained in the user input query. According to the senses such as words or names, it
uses the different similarity measurement methods to estimate the closest entities. This type
definition function in proposed algorithm is shown in the figure 2.
WordNet is the machine readable dictionary and it is widely used for confirming the semantic
relationships between overlapping domain concepts of ontology. In this system, WordNet is used
to discover the form of words (such as noun, adjective, name, etc.) that are contained as the
stream of words in the user input query. And then, the proposed algorithm finds the information
of the input words whether it is unknown or it has form. The rules for deciding on each word
according to its form are as follows.
• If the word has the unknown form, assumes these words as name stream and assigns null
in its form.
• If the word has the form, assigns this form type in its form.
After defining the form of words, it finds the similarity for these words with the concepts in
ontology by applying the GetSimilarity similarity measurement method and estimates the
similarity of naming streams by applying the EditDistance similarity measurement method. This
similarity estimation function is shown in figure 3.
4. International Journal of Information Technology, Modeling and Computing (IJITMC) Vol. 2, No.2, May 2014
32
Figure 2. Type definition function in proposed algorithm
As described in above, this proposed algorithm chooses the semantic similarity methods
according to the type of words return by the type definition function. So, it can estimate the closet
concepts in ontology by adjusting the threshold value of each similarity method respectively. The
following section (2.1 and 2.2) will explain the detail of these similarity measurement methods.
Figure 3. Similarity estimation function in proposed algorithm
3.1. Edit Distance Similarity Measurement Method
This method is used to estimate how many words are distant between the source and target
concepts. It can also estimate the distant degree for the stream of concepts containing space. This
method calculates how much distance based on the similarity matrix of two in put strings. Here,
it is used to compute the similarity between the words which have no meaning (e.g. the name of
the person. We fill each cell of first row in matrix with the word contained in a naming input
string and each cell of first column in matrix with the word contained in a naming concept stream.
5. International Journal of Information Technology, Modeling and Computing (IJITMC) Vol. 2, No.2, May 2014
33
The remaining cells are filled with the values obtained by applying the rule in equation 3. The
following recurrence relations define the edit distance, d(s1, s2), of two strings s1 and s2 [17].
d(ε, ε) = 0 // ε represents an empty string (1)
d(s, ε) = d(ε, s) = |s| // |s| is the length of string s (2)
d(s1–+c1, s2–+c2) = min( d(s1–, s2–) + p(c1, c2), d(s1–+c1, s2–) + 1, d(s1– , s2–+c2) + 1), (3)
where c1 and c2 are the last characters of s1 (= s1–+c1) and s2 (= s2–+c2) respectively, and
p(c1, c2) = 0 if c1 = c2; p(c1,c2) = 1, otherwise. The threshold value for Edit Distance similarity of
two concepts is defined as (distance < name-Length).
3.2. Get Similarity Measurement Method
This similarity measurement method estimates the similarity for each word that has the form
mainly dependent on the adjustable parameter . This addition of adjustable parameter in simple
similarity measurement equation can help to overcome the loss of information problem because
of the mismatch of one character in input word with the other one character in concept word (e.g.
organisation and organization). The similarity between the two concepts is calculated by using the
following equation.
( , ) = ( , ) (4)
where (1 ≤ ≤ ) is an adjustable parameter and is the number of characters in each
word. Moreover, = 1/ , which reflects the degree contributions to the overall semantic
similarity from to . ( , ) is respectively semantic similarity of each character
contained in a word. The threshold value for semantic similarity of two concepts is defined as 0.8.
We set this high threshold to obtain the closet elements from the ontology which are mostly
distant one character between the two words.
4. EXPERIMENTAL RESULT
In this section, we show the experimental results of proposed system that estimate the concepts’
similarity by applying the proposed semantic similarity algorithm. We build the local ontology
with the entities which contained at most twenty classes including main classes and subclasses
and twenty individual instances composed by more than 100 data-type properties. And then, we
create the global ontology by combining the two sample local ontologies. This data refers to staff
profile and history records of an organization. This proposed algorithm is applied to access the
data on global ontology. Here, we show the experimental results based on the two degree
semantic rates (precision and recall) by generating the concepts from user input query that will be
used for retrieving the required information from the domain specific ontology.
Applying the proposed semantic similarity measurement algorithm embedded in the triplets’
extraction approach on the sentences listed in below, it takes the process to extract the triplets
contained in the input string that are associated with the concept in ontology.
6. International Journal of Information Technology, Modeling and Computing (IJITMC) Vol. 2, No.2, May 2014
34
The testing queries are:
Query1: All about John. (n=3)
Query2: staff name at the software engineering department. (n=7)
Query3: Company’s names that are included in advance science and technology department.
(n=11)
Query4: name, age, NRC, father-name of staff who gets M.C.Sc degree and international paper
acceptance. (n=14)
Query5: Staff name, degree, position, start-year, department, compensation and bond-year who
get English exam marks > 50, Major exam marks >50 and had got Ph.D degree. (n=27)
We compare the two results of degree semantic rates (i.e. precision and recall) of the retrieving
concepts from the sentences listed in above by applying the proposed algorithm with the
retrieving concepts by applying the traditional semantic similarity measurement approach using
cosine similarity. This comparison is made by submitting the same queries which have the same
number of words count for each pair to access the data on the same global ontology. Here, the
precision and recall rates can be calculated by using the following equations [18].
=
ℎ
ℎ
(5)
=
ℎ
(6)
Figure 4. Prediction rates for different queries Figure 5. Recall rates for different queries
The illustration of figure 4 and figure 5 represents the obtaining precision and recall rates by
testing the five different queries listed in above. By seeing this appraisal, the proposed semantic
similarity measurement algorithm can take the preferable results.
The compared results of averaging precision and recall rates based on these five different queries
are shown in table 1.
8. International Journal of Information Technology, Modeling and Computing (IJITMC) Vol. 2, No.2, May 2014
36
[11] F. Hakimpour and A. Geppert, “Relsoving Semantic Heterogenity in Schema Integration: an
Ontology Based Approach,” in Proc. of Conference on Formal Ontology in Information Systems,
Ogunquit, Maine, USA, October 17-19, 2001.
[12] P. Haase and B. Motik, “A Mapping System for the Integration of OWL-DL Ontologies,” IHIS’05,
Bremen, Germany, November 4, 2005.
[13] M. Uschold, Creating, Integrating and Maintaining Local and Global Ontologies, 2002.
[14] H. Wache, T. Vogele, U. Visser, H. Stuckenschmidt, G. Schuster, H. Neumann, and S. Hubener,
“Ontology-based data integration – A Survey of Existing Approaches,” in Proc. of IJCIA-01
Workshop: Ontologies and Information Sharing, Seattle, WA, 2001.
[15] J. Lu, Y. Zhang, Z. Miao, and P. Zhou, The Semantic Web Principle and Technology, Science Press:
Beijing, 2007.
[16] Zin Thu Thu Myint, "Triple Patterns Extraction from Unstructured Sentence Using Domain Specific
Ontology", proceeding of 11th International Conference on Computer Application, 2013.
[17] Christopher D. Manning Prabhakar Raghavan Hinrich Schütze, "An Introduction to Informational
Retrieval", Cambridge UP, 2009, pp. 58-59.
[18] Till Plumbaum, Tino Stelter and Alexander Korth, “Semanticn Web Usage Mining: Using Semantics
to Understand User Intentions”, inG-J,LNCS 5535, Houben, Eds,Herdelberg: German, 2009, pp. 391-
396.