This document proposes an ontology and text mining technique to select research papers. It involves 3 phases: 1) constructing a research ontology using keywords and frequencies from past papers, 2) classifying new papers based on ontology keywords, and 3) clustering papers in each domain using text mining and the K-means algorithm. The technique aims to better group papers and assign them to relevant reviewers by addressing limitations of keyword-based methods. It constructs a research ontology, classifies papers, clusters them based on textual similarities, and systematically assigns papers to reviewers.
ESTIMATION OF REGRESSION COEFFICIENTS USING GEOMETRIC MEAN OF SQUARED ERROR F...ijaia
Regression models and their statistical analyses is one of the most important tool used by scientists and practitioners. The aim of a regression model is to fit parametric functions to data. It is known that the true regression is unknown and specific methods are created and used strictly pertaining to the roblem. For the pioneering work to develop procedures for fitting functions, we refer to the work on the methods of least
absolute deviations, least squares deviations and minimax absolute deviations. Today’s widely celebrated
procedure of the method of least squares for function fitting is credited to the published works of Legendre and Gauss. However, the least squares based models in practice may fail to provide optimal results in nonGaussian situations especially when the errors follow distributions with the fat tails. In this paper an unorthodox method of estimating linear regression coefficients by minimising GMSE(geometric mean of squared errors) is explored. Though GMSE(geometric mean of squared errors) is used to compare models it is rarely used to obtain the coefficients. Such a method is tedious to handle due to the large number of roots obtained by minimisation of the loss function. This paper offers a way to tackle that problem.
Application is illustrated with the ‘Advertising’ dataset from ISLR and the obtained results are compared
with the results of the method of least squares for single index linear regression model.
Semantics-based clustering approach for similar research area detectionTELKOMNIKA JOURNAL
The manual process of searching out individuals in an already existing
research field is cumbersome and time-consuming. Prominent and rookie
researchers alike are predisposed to seek existing research publications in
a research field of interest before coming up with a thesis. From
extant literature, automated similar research area detection systems have
been developed to solve this problem. However, most of them use
keyword-matching techniques, which do not sufficiently capture the implicit
semantics of keywords thereby leaving out some research articles. In this
study, we propose the use of ontology-based pre-processing, Latent Semantic
Indexing and K-Means Clustering to develop a prototype similar research area
detection system, that can be used to determine similar research domain
publications. Our proposed system solves the challenge of high dimensionality
and data sparsity faced by the traditional document clustering technique. Our
system is evaluated with randomly selected publications from faculties
in Nigerian universities and results show that the integration of ontologies
in preprocessing provides more accurate clustering results.
Faster Case Retrieval Using Hash Indexing TechniqueWaqas Tariq
The main objective of case retrieval is to scan and to map the most similar old cases in case base with a new problem. Beside accurateness, the time taken to retrieve case is also important. With the increasing number of cases in case base, the retrieval task is becoming more challenging where faster retrieval time and good accuracy are the main aim. Traditionally, sequential indexing method has been applied to search for possible cases in case base. This technique worked fast when the number of cases is small but requires more time to retrieve when the number of data in case base grows. As an alternative, this paper presents the integration of hashing indexing technique in case retrieval to mine large cases and speed up the retrieval time. Hashing indexing searches a record by determining the index using only an entry’s search key without traversing all records. To test the proposed method, real data namely Timah Tasoh Dam operational dataset, which is temporal in nature that represents the historical hydrological data of daily Timah Tasoh dam operation in Perlis, Malaysia ranging from year 1997-2005, was chosen as experiment. Then, the hashing indexing performance is compared with sequential method in term of retrieval time and accuracy. The finding indicates that hashing indexing is more accurate and faster than sequential approach in retrieving cases. Besides that, the combination of hashing search key x produces better result compared to single search key.
ESTIMATION OF REGRESSION COEFFICIENTS USING GEOMETRIC MEAN OF SQUARED ERROR F...ijaia
Regression models and their statistical analyses is one of the most important tool used by scientists and practitioners. The aim of a regression model is to fit parametric functions to data. It is known that the true regression is unknown and specific methods are created and used strictly pertaining to the roblem. For the pioneering work to develop procedures for fitting functions, we refer to the work on the methods of least
absolute deviations, least squares deviations and minimax absolute deviations. Today’s widely celebrated
procedure of the method of least squares for function fitting is credited to the published works of Legendre and Gauss. However, the least squares based models in practice may fail to provide optimal results in nonGaussian situations especially when the errors follow distributions with the fat tails. In this paper an unorthodox method of estimating linear regression coefficients by minimising GMSE(geometric mean of squared errors) is explored. Though GMSE(geometric mean of squared errors) is used to compare models it is rarely used to obtain the coefficients. Such a method is tedious to handle due to the large number of roots obtained by minimisation of the loss function. This paper offers a way to tackle that problem.
Application is illustrated with the ‘Advertising’ dataset from ISLR and the obtained results are compared
with the results of the method of least squares for single index linear regression model.
Semantics-based clustering approach for similar research area detectionTELKOMNIKA JOURNAL
The manual process of searching out individuals in an already existing
research field is cumbersome and time-consuming. Prominent and rookie
researchers alike are predisposed to seek existing research publications in
a research field of interest before coming up with a thesis. From
extant literature, automated similar research area detection systems have
been developed to solve this problem. However, most of them use
keyword-matching techniques, which do not sufficiently capture the implicit
semantics of keywords thereby leaving out some research articles. In this
study, we propose the use of ontology-based pre-processing, Latent Semantic
Indexing and K-Means Clustering to develop a prototype similar research area
detection system, that can be used to determine similar research domain
publications. Our proposed system solves the challenge of high dimensionality
and data sparsity faced by the traditional document clustering technique. Our
system is evaluated with randomly selected publications from faculties
in Nigerian universities and results show that the integration of ontologies
in preprocessing provides more accurate clustering results.
Faster Case Retrieval Using Hash Indexing TechniqueWaqas Tariq
The main objective of case retrieval is to scan and to map the most similar old cases in case base with a new problem. Beside accurateness, the time taken to retrieve case is also important. With the increasing number of cases in case base, the retrieval task is becoming more challenging where faster retrieval time and good accuracy are the main aim. Traditionally, sequential indexing method has been applied to search for possible cases in case base. This technique worked fast when the number of cases is small but requires more time to retrieve when the number of data in case base grows. As an alternative, this paper presents the integration of hashing indexing technique in case retrieval to mine large cases and speed up the retrieval time. Hashing indexing searches a record by determining the index using only an entry’s search key without traversing all records. To test the proposed method, real data namely Timah Tasoh Dam operational dataset, which is temporal in nature that represents the historical hydrological data of daily Timah Tasoh dam operation in Perlis, Malaysia ranging from year 1997-2005, was chosen as experiment. Then, the hashing indexing performance is compared with sequential method in term of retrieval time and accuracy. The finding indicates that hashing indexing is more accurate and faster than sequential approach in retrieving cases. Besides that, the combination of hashing search key x produces better result compared to single search key.
Novelty detection via topic modeling in research articlescsandit
In today’s world redundancy is the most vital problem faced in almost all domains. Novelty
detection is the identification of new or unknown data or signal that a machine learning system
is not aware of during training. The problem becomes more intense when it comes to “Research
Articles”. A method of identifying novelty at each sections of the article is highly required for
determining the novel idea proposed in the research paper. Since research articles are semistructured,
detecting novelty of information from them requires more accurate systems. Topic
model provides a useful means to process them and provides a simple way to analyze them. This
work compares the most predominantly used topic model- Latent Dirichlet Allocation with the
hierarchical Pachinko Allocation Model. The results obtained are promising towards
hierarchical Pachinko Allocation Model when used for document retrieval.
Data mining is the knowledge discovery in databases and the gaol is to extract patterns and knowledge from
large amounts of data. The important term in data mining is text mining. Text mining extracts the quality
information highly from text. Statistical pattern learning is used to high quality information. High –quality in
text mining defines the combinations of relevance, novelty and interestingness. Tasks in text mining are text
categorization, text clustering, entity extraction and sentiment analysis. Applications of natural language
processing and analytical methods are highly preferred to turn
Nowadays much is written about how to manage projects, but too little on what really happens in project
actuality. Project Actuality came out in the Rethinking Project Management (RPM) agenda in 2006 and it
aims at understanding what really happens at project context. To be able to understand project actuality
phenomenon, we first need to get a better comprehension on its definition and discover how to observe it
and analyse it. This paper presents the results of the systematic review conducted to collect evidence on
Project Actuality. The research focused on four search engines, in publications from 1994 to 2013. Among
others, the study concludes that project actuality has been analysed by several methods and techniques,
mostly on large organization and public sectors, in Northern Europe. The most common definitions,
techniques, and tips were identified as well as the intent of transforming the results in knowledge.
Semantic tagging for documents using 'short text' informationcsandit
Tagging documents with relevant and comprehensive k
eywords offer invaluable assistance to
the readers to quickly overview any document. With
the ever increasing volume and variety of
the documents published on the internet, the intere
st in developing newer and successful
techniques for annotating (tagging) documents is al
so increasing. However, an interesting
challenge in document tagging occurs when the full
content of the document is not readily
accessible. In such a scenario, techniques which us
e “short text”, e.g., a document title, a news
article headline, to annotate the entire article ar
e particularly useful. In this paper, we pro-
pose a novel approach to automatically tag document
s with relevant tags or key-phrases using
only “short text” information from the documents. W
e employ crowd-sourced knowledge from
Wikipedia, Dbpedia, Freebase, Yago and similar open
source knowledge bases to generate
semantically relevant tags for the document. Using
the intelligence from the open web, we prune
out tags that create ambiguity in or “topic drift”
from the main topic of our query document.
We have used real world dataset from a corpus of re
search articles to annotate 50 research
articles. As a baseline, we used the full text info
rmation from the document to generate tags. The
proposed and the baseline approach were compared us
ing the author assigned keywords for the
documents as the ground truth information. We found
that the tags generated using proposed
approach are better than using the baseline in term
s of overlap with the ground truth tags
measured via Jaccard index (0.058 vs. 0.044). In te
rms of computational efficiency, the
proposed approach is at least 3 times faster than t
he baseline approach. Finally, we
qualitatively analyse the quality of the predicted
tags for a few samples in the test corpus. The
evaluation shows the effectiveness of the proposed
approach both in terms of quality of tags
generated and the computational time.
Text preprocessing is a vital stage in text classification (TC) particularly and text mining generally. Text preprocessing tools is to reduce multiple forms of the word to one form. In addition, text preprocessing techniques are provided a lot of significance and widely studied in machine learning. The basic phase in text classification involves preprocessing features, extracting relevant features against the features in a database. However, they have a great impact on reducing the time requirement and speed resources needed. The effect of the preprocessing tools on English text classification is an area of research. This paper provides an evaluation study of several preprocessing tools for English text classification. The study includes using the raw text, the tokenization, the stop words, and the stemmed. Two different methods chi-square and TF-IDF with cosine similarity score for feature extraction are used based on BBC English dataset. The Experimental results show that the text preprocessing effect on the feature extraction methods that enhances the performance of English text classification especially for small threshold values.
A Semantic Retrieval System for Extracting Relationships from Biological Corpusijcsit
The World Wide Web holds a large size of different information. Sometimes while searching the World Wide Web, users always do not gain the type of information they expect. In the subject of information extraction, extracting semantic relationships between terms from documents become a challenge. This
paper proposes a system helps in retrieving documents based on the query expansion and tackles the extracting of semantic relationships from biological documents. This system retrieved documents that are relevant to the input terms then it extracts the existence of a relationship. In this system, we use Boolean
model and the pattern recognition which helps in determining the relevant documents and determining the place of the relationship in the biological document. The system constructs a term-relation table that accelerates the relation extracting part. The proposed method offers another usage of the system so the
researchers can use it to figure out the relationship between two biological terms through the available information in the biological documents. Also for the retrieved documents, the system measures the percentage of the precision and recall.
Survey on Existing Text Mining Frameworks and A Proposed Idealistic Framework...ijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Ontology Based PMSE with Manifold PreferenceIJCERT
International journal from http://www.ijcert.org
IJCERT Standard on-line Journal
ISSN(Online):2349-7084,(An ISO 9001:2008 Certified Journal)
iso nicir csir
IJCERT (ISSN 2349–7084 (Online)) is approved by National Science Library (NSL), National Institute of Science Communication And Information Resources (NISCAIR), Council of Scientific and Industrial Research, New Delhi, India.
A Comprehensive Survey on Comparisons across Contextual Pre-Filtering, Contex...TELKOMNIKA JOURNAL
Recently, there has been growing interest in recommender systems (RS) and particularly in context-aware RS. Methods for generating context-aware recommendations are classified into pre-filtering, post-filtering and contextual modelling approaches. In this paper, we present the several novel approaches of the different variant of each of these three contextualization paradigms and present a complete survey on the state-of-the-art comparisons across them. We then identify the significant challenges that require being addressed by the current RS researchers, which will help academicians and practitioners in comparing these three approaches to select the best alternative according to their strategies.
A BOOTSTRAPPING METHOD FOR AUTOMATIC CONSTRUCTING OF THE WEB ONTOLOGY INSTANC...IJwest
With the phenomenal growth of the Web resources, to construct ontologies by using existing resources structured in the Web has gotten more and more attention. Previous studies for constructing ontologies from the Web have not carefully considered all the semantic features of the Web documents. Hereby it is difficult to correctly construct ontology elements from the Web documents that are increasing daily. The machine learning methods play an important role in automatic constructing of the Web ontology. Bootstrapping technique is a semi-supervised learning method that can automatically generate many terms from the few seed terms entered by human. This paper proposes bootstrapping method that can automatically construct instances and data type properties of the Web ontology, taking proper noun as semantic core element of the Web table. Experimental result shows that proposed method can rapidly and effectually construct instances and its properties of the Web ontology
Multiple Equilibria and Chemical Distribution of Some Bio Metals With β-Amide...IOSR Journals
Abstract: Solution Chemistry of some bivalent metal ions (viz. CoII , NiII ,CuII ,ZnII ) with β-amide α-aminosuccinate (Asparagine)/ α-aminoisoverate( Valine ) (A) and 5-methyl 2,4- dioxopyrimidine ( Thymine ) (B)ligands have been analyzed. Formation constant of quaternary metal complexes and complexation equilibria at 30±1ºC and at constant ionic strength (I=0.1M NaNO3 ) have been explored potentiometrically. Formation of quaternary species in addition to hydroxyl, protonated, binary and ternary species have been reported. Overall formation constant have been evaluated using SCOGS computer program.Species distribution curves of complexes have been plotted as a function of pH to visualize the equlibria system and was refined using ORIGIN program.The metal ligand formation constant of MA,MB,MAB and M1M2AB type of complexes follow Irving William order. The order of stability constants of quaternary systems have been observed as: Cu – Ni > Cu –Zn > Cu–Co > Ni – Zn > Ni – Co > Co –Zn. Solution structures of metal complexes with said ligands have been compared and discussed.
Novelty detection via topic modeling in research articlescsandit
In today’s world redundancy is the most vital problem faced in almost all domains. Novelty
detection is the identification of new or unknown data or signal that a machine learning system
is not aware of during training. The problem becomes more intense when it comes to “Research
Articles”. A method of identifying novelty at each sections of the article is highly required for
determining the novel idea proposed in the research paper. Since research articles are semistructured,
detecting novelty of information from them requires more accurate systems. Topic
model provides a useful means to process them and provides a simple way to analyze them. This
work compares the most predominantly used topic model- Latent Dirichlet Allocation with the
hierarchical Pachinko Allocation Model. The results obtained are promising towards
hierarchical Pachinko Allocation Model when used for document retrieval.
Data mining is the knowledge discovery in databases and the gaol is to extract patterns and knowledge from
large amounts of data. The important term in data mining is text mining. Text mining extracts the quality
information highly from text. Statistical pattern learning is used to high quality information. High –quality in
text mining defines the combinations of relevance, novelty and interestingness. Tasks in text mining are text
categorization, text clustering, entity extraction and sentiment analysis. Applications of natural language
processing and analytical methods are highly preferred to turn
Nowadays much is written about how to manage projects, but too little on what really happens in project
actuality. Project Actuality came out in the Rethinking Project Management (RPM) agenda in 2006 and it
aims at understanding what really happens at project context. To be able to understand project actuality
phenomenon, we first need to get a better comprehension on its definition and discover how to observe it
and analyse it. This paper presents the results of the systematic review conducted to collect evidence on
Project Actuality. The research focused on four search engines, in publications from 1994 to 2013. Among
others, the study concludes that project actuality has been analysed by several methods and techniques,
mostly on large organization and public sectors, in Northern Europe. The most common definitions,
techniques, and tips were identified as well as the intent of transforming the results in knowledge.
Semantic tagging for documents using 'short text' informationcsandit
Tagging documents with relevant and comprehensive k
eywords offer invaluable assistance to
the readers to quickly overview any document. With
the ever increasing volume and variety of
the documents published on the internet, the intere
st in developing newer and successful
techniques for annotating (tagging) documents is al
so increasing. However, an interesting
challenge in document tagging occurs when the full
content of the document is not readily
accessible. In such a scenario, techniques which us
e “short text”, e.g., a document title, a news
article headline, to annotate the entire article ar
e particularly useful. In this paper, we pro-
pose a novel approach to automatically tag document
s with relevant tags or key-phrases using
only “short text” information from the documents. W
e employ crowd-sourced knowledge from
Wikipedia, Dbpedia, Freebase, Yago and similar open
source knowledge bases to generate
semantically relevant tags for the document. Using
the intelligence from the open web, we prune
out tags that create ambiguity in or “topic drift”
from the main topic of our query document.
We have used real world dataset from a corpus of re
search articles to annotate 50 research
articles. As a baseline, we used the full text info
rmation from the document to generate tags. The
proposed and the baseline approach were compared us
ing the author assigned keywords for the
documents as the ground truth information. We found
that the tags generated using proposed
approach are better than using the baseline in term
s of overlap with the ground truth tags
measured via Jaccard index (0.058 vs. 0.044). In te
rms of computational efficiency, the
proposed approach is at least 3 times faster than t
he baseline approach. Finally, we
qualitatively analyse the quality of the predicted
tags for a few samples in the test corpus. The
evaluation shows the effectiveness of the proposed
approach both in terms of quality of tags
generated and the computational time.
Text preprocessing is a vital stage in text classification (TC) particularly and text mining generally. Text preprocessing tools is to reduce multiple forms of the word to one form. In addition, text preprocessing techniques are provided a lot of significance and widely studied in machine learning. The basic phase in text classification involves preprocessing features, extracting relevant features against the features in a database. However, they have a great impact on reducing the time requirement and speed resources needed. The effect of the preprocessing tools on English text classification is an area of research. This paper provides an evaluation study of several preprocessing tools for English text classification. The study includes using the raw text, the tokenization, the stop words, and the stemmed. Two different methods chi-square and TF-IDF with cosine similarity score for feature extraction are used based on BBC English dataset. The Experimental results show that the text preprocessing effect on the feature extraction methods that enhances the performance of English text classification especially for small threshold values.
A Semantic Retrieval System for Extracting Relationships from Biological Corpusijcsit
The World Wide Web holds a large size of different information. Sometimes while searching the World Wide Web, users always do not gain the type of information they expect. In the subject of information extraction, extracting semantic relationships between terms from documents become a challenge. This
paper proposes a system helps in retrieving documents based on the query expansion and tackles the extracting of semantic relationships from biological documents. This system retrieved documents that are relevant to the input terms then it extracts the existence of a relationship. In this system, we use Boolean
model and the pattern recognition which helps in determining the relevant documents and determining the place of the relationship in the biological document. The system constructs a term-relation table that accelerates the relation extracting part. The proposed method offers another usage of the system so the
researchers can use it to figure out the relationship between two biological terms through the available information in the biological documents. Also for the retrieved documents, the system measures the percentage of the precision and recall.
Survey on Existing Text Mining Frameworks and A Proposed Idealistic Framework...ijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Ontology Based PMSE with Manifold PreferenceIJCERT
International journal from http://www.ijcert.org
IJCERT Standard on-line Journal
ISSN(Online):2349-7084,(An ISO 9001:2008 Certified Journal)
iso nicir csir
IJCERT (ISSN 2349–7084 (Online)) is approved by National Science Library (NSL), National Institute of Science Communication And Information Resources (NISCAIR), Council of Scientific and Industrial Research, New Delhi, India.
A Comprehensive Survey on Comparisons across Contextual Pre-Filtering, Contex...TELKOMNIKA JOURNAL
Recently, there has been growing interest in recommender systems (RS) and particularly in context-aware RS. Methods for generating context-aware recommendations are classified into pre-filtering, post-filtering and contextual modelling approaches. In this paper, we present the several novel approaches of the different variant of each of these three contextualization paradigms and present a complete survey on the state-of-the-art comparisons across them. We then identify the significant challenges that require being addressed by the current RS researchers, which will help academicians and practitioners in comparing these three approaches to select the best alternative according to their strategies.
A BOOTSTRAPPING METHOD FOR AUTOMATIC CONSTRUCTING OF THE WEB ONTOLOGY INSTANC...IJwest
With the phenomenal growth of the Web resources, to construct ontologies by using existing resources structured in the Web has gotten more and more attention. Previous studies for constructing ontologies from the Web have not carefully considered all the semantic features of the Web documents. Hereby it is difficult to correctly construct ontology elements from the Web documents that are increasing daily. The machine learning methods play an important role in automatic constructing of the Web ontology. Bootstrapping technique is a semi-supervised learning method that can automatically generate many terms from the few seed terms entered by human. This paper proposes bootstrapping method that can automatically construct instances and data type properties of the Web ontology, taking proper noun as semantic core element of the Web table. Experimental result shows that proposed method can rapidly and effectually construct instances and its properties of the Web ontology
Multiple Equilibria and Chemical Distribution of Some Bio Metals With β-Amide...IOSR Journals
Abstract: Solution Chemistry of some bivalent metal ions (viz. CoII , NiII ,CuII ,ZnII ) with β-amide α-aminosuccinate (Asparagine)/ α-aminoisoverate( Valine ) (A) and 5-methyl 2,4- dioxopyrimidine ( Thymine ) (B)ligands have been analyzed. Formation constant of quaternary metal complexes and complexation equilibria at 30±1ºC and at constant ionic strength (I=0.1M NaNO3 ) have been explored potentiometrically. Formation of quaternary species in addition to hydroxyl, protonated, binary and ternary species have been reported. Overall formation constant have been evaluated using SCOGS computer program.Species distribution curves of complexes have been plotted as a function of pH to visualize the equlibria system and was refined using ORIGIN program.The metal ligand formation constant of MA,MB,MAB and M1M2AB type of complexes follow Irving William order. The order of stability constants of quaternary systems have been observed as: Cu – Ni > Cu –Zn > Cu–Co > Ni – Zn > Ni – Co > Co –Zn. Solution structures of metal complexes with said ligands have been compared and discussed.
IOSR Journal of Mathematics(IOSR-JM) is an open access international journal that provides rapid publication (within a month) of articles in all areas of mathemetics and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in mathematics. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Navigation through citation network based on content similarity using cosine ...Salam Shah
The rate of scientific literature has been increased in the past few decades; new topics and information is added in the form of articles, papers, text documents, web logs, and patents. The growth of information at rapid rate caused a tremendous amount of additions in the current and past knowledge, during this process, new topics emerged, some topics split into many other sub-topics, on the other hand, many topics merge to formed single topic. The selection and search of a topic manually in such a huge amount of information have been found as an expensive and workforce-intensive task. For the emerging need of an automatic process to locate, organize, connect, and make associations among these sources the researchers have proposed different techniques that automatically extract components of the information presented in various formats and organize or structure them. The targeted data which is going to be processed for component extraction might be in the form of text, video or audio. The addition of different algorithms has structured information and grouped similar information into clusters and on the basis of their importance, weighted them. The organized, structured and weighted data is then compared with other structures to find similarity with the use of various algorithms. The semantic patterns can be found by employing visualization techniques that show similarity or relation between topics over time or related to a specific event. In this paper, we have proposed a model based on Cosine Similarity Algorithm for citation network which will answer the questions like, how to connect documents with the help of citation and content similarity and how to visualize and navigate through the document.
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
09 9241 it co-citation network investigation (edit ari)IAESIJEECS
This paper reports a co-citation network investigation (bibliometric investigation) of 10 journals in the management of technology (MOT) field. And also presenting different bibliometric thoughts, organize investigation devices recognize and investigate the ideas secured by the field and their between connections. Particular outcomes from various levels of investigation demonstrate the diverse measurements of technology administration: Co-word terms recognize subjects, Journal co-reference arrange: connecting to different controls, Co-citation network indicate groupings of topics. The examination demonstrates that MOT has a connecting part in coordinating thoughts from a few particular orders. This recommends administration and technique are vital to MOT which basically identifies with the firm as opposed to arrangement. Additionally we have a double concentrate on abilities, however can see inconspicuous contrasts by the way we see these thoughts, either through an inwards looking focal point to perceive how associations capacity, or all the more outward to comprehend setting and change in landscapes.
Assignment 2 Case StudyRead the following articleAgostinhodesteinbrook
Assignment 2: Case Study
Read the following article:
Agostinho, S. (2004). Naturalistic inquiry in e-learning research.
International Journal of Qualitative Methods, 4
(1), Article 2. Retrieved Dec. 16, 2005, from
http://www.ualberta.ca/~iiqm/backissues/4_1/pdf/agostinho.pdf
Read the article and respond to the following:
Critique the process of data analysis that was conducted in the original research, as described in the journal article.
In the original research upon which this article is based, the researcher indicated that dominant themes were identified, and the themes were clustered into categories. Critique the underlying inductive logic utilized to analyze the data.
Discuss the researcher’s rationale, as described in the journal article, of adopting a naturalistic inquiry paradigm with regard to the process of data collection and analysis.
All written assignments and responses should follow APA rules for attributing sources.
Assignment 2 Grading Criteria
Maximum Points
Critique the process of data analysis.10Identified the dominant themes as highlighted in the article.10Critique the underlying inductive logic used to analyze the data.10Discuss the researcher’s rationale of adopting a naturalistic inquiry.10Justified ideas and responses by using appropriate examples, material and references from texts, library, and online resource database (see LIBRARY link on left.).10Wrote in a clear, concise, and organized manner; demonstrated ethical scholarship in accurate representation and attribution of sources, displayed accurate spelling, grammar, and punctuation.15
Total:65
*****M6A2 SAMPLE
Case Study Critique
*********M6A2 SAMPLE
Methods and Analysis of Qualitative Research
Introduction
In 2004 Ms. Shirley Agostinho published an article which was titled “Naturalistic inquiry in e-learning research” In this paper the researcher Agostinho (2004) addresses how the naturalistic inquiry paradigm was used in an e-learning research study that looked at the use of the World Wide Web technology in higher education. The purpose of the paper is to review and critique the data analysis process, dominant themes presented, underlying logic and researcher’s rationale for adopting a naturalistic inquiry methodology.
Critiquing the data analysis process
There are numerous approaches for analyzing qualitative data. According to Shilling (2006) content analysis is generally defined as a method of analyzing written, verbal or visual communication messages. Wildemuth and Zhang (2009) isolated eight steps which comprise the process of qualitative content analysis. The following table applies the eight step process to the data reflected in the researchers’ article.
Strengths
Weaknesses and limitations
1). Preparing the data
The researcher collected data through participant observations, interviews, questionnaire and reflexive journal.
Creswell (2009) identifies triangulation as the use of multiple sourc ...
Tutors India offers assistance in quantitative studies and data analyses and explains the steps to conduct a bibliometric analysis.
https://www.tutorsindia.com/blog/how-to-conduct-bibliometric-analyses/
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Research Paper Selection Based On an Ontology and Text Mining Technique Using Clustering
1. IOSR Journal of Computer Engineering (IOSR-JCE)
e-ISSN: 2278-0661,p-ISSN: 2278-8727, Volume 17, Issue 1, Ver. I (Jan – Feb. 2015), PP 65-71
www.iosrjournals.org
DOI: 10.9790/0661-17116571 www.iosrjournals.org 65 | Page
Research Paper Selection Based On an Ontology and Text Mining
Technique Using Clustering
1
Snehal Shivaji Patil 2
S.A.Uddin
Alhabeeb college of Engg. & Tech. Hyderabad, India.
Alhabeeb college of Engg. & Tech. Hyderabad, India.
Abstract: Research Paper Selection is important decision making task for the Government funding Agency,
Universities, research Institutes. Ontology is Knowledge Repository in which concepts and terms defined as well
as relationship between these concepts. In this paper Ontology is old research papers repository of keywords
and frequencies of that keywords of the research papers of funding agencies. Ontology makes the tasks of
searching similar patterns of text that is to be more effective, efficient and interactive. The current method of
grouping of papers for research paper selection based on similarities of Keywords and Frequencies of research
papers of ontology. Text mining is method for extracting unknown information from the large documents. The
Research Papers in each domain are clustered using Text mining Technique. Grouped Research papers are
assigned to appropriate reviewer or domain experts for peer review systematically. The Reviewer results are
collected and papers are get ranked based on experts review results.
Keywords: Ontology, Text Mining, Classification, Document Preprocessing, Clustering.
I. Introduction
Research Paper Selection is important decision making task for many Organizations such as
Government funding Agency, Universities, research Institutes.
Fig.1. Research Paper Selection Process.
Fig. 1 shows the processes of research project selection i.e. Call for papers (CFP), paper submission,
paper grouping, paper assignment to experts, peer review, aggregation of review results, panel evaluation, and
final awarding decision. These processes are very similar in other funding agencies, except that there are a very
large number of papers that need to be grouped for peer review. Four to five reviewers are assigned to review
each paper so as to assure accurate and reliable opinions on papers. To deal with the large volume, it is
necessary to group papers according to their similarities in research disciplines and then to assign the paper
groups to relevant reviewers.
In the first section we Call for Research Paper means uploading Research paper and submitting the
details of that paper. Classification of research papers is based on keywords of papers similar with ontology
keywords and frequencies of those keywords. Department members are classified into six groups according to
their decision making in research paper selection. Decision making cooperate with each other to accomplish
overall goal of selecting research paper as shown above in figure the output of one block is input to the next
block.
Department is responsible for selection of research papers for classification. Department members
classify research papers and assign them to external reviewer for evaluation and commentary. If department
member may not have knowledge about research paper in all research domain and contents of many papers were
not fully understood when papers were grouped and while assigning grouped papers to external reviewers.
Therefore, there was an effective approach to group the submitted research Papers and assign the papers to
external reviewers with computer supports. So we use ontology based text- mining approach is proposed to
solve the problem.
2. Research Paper Selection Based On an Ontology and Text Mining Technique Using Clustering
DOI: 10.9790/0661-17116571 www.iosrjournals.org 66 | Page
Section II reviews the literature on research paper selection, grouping of papers and assigning the
grouped papers to external reviewer systematically. The proposed method on an ontology based text mining
framework for Research Paper Selection is described in Section III. Section IV validates and evaluates the
method, and then discusses the potential application.
II. Literature Review
Selection of research projects is an important research topic in research and development (R&D)
project management. Previous re- search deals with specific topics, and several formal methods and models are
available for this purpose.
For example, Chen and Gorla [2] proposed a fuzzy-logic-based model as a decision tool for project
selection. Henriksen and Traynor [3] presented a scoring tool for project evaluation and selection. Ghasemzadeh
and Archer [4] offered a decision support approach to project portfolio selection. Machacha and Bhattacharya
[5] proposed a fuzzy logic approach to project selection. Butler et al. [6] used a multiple attribute utility theory
for project ranking and selection. Loch and Kavadias [7] established a dynamic programming model for project
selection, while Meade and Presley [8] developed an analytic network process model. Greiner et al. [9] proposed
a hybrid AHP and integer programming approach to support project selection, and Tian et al. [10] suggested an
organizational decision support approach for selecting R&D projects. Cook et al. [11] presented a method of
optimal allocation of papers to reviewers in order to facilitate the selection process. Arya and Mittendorf [12]
proposed a rotation program method for project assignment. For example Choi and Park [13] used text-mining
approach for R&D paper screening. Girotra et al. [14] offered an empirical study to value projects in a portfolio.
Sun et al. [15] developed a decision support system to evaluate reviewers for research project selection. Finally,
Sun et al. [16] proposed a hybrid knowledge-based and modeling approach to assign reviewers to papers for
research project selection.
Cheng and Wei [2008] proposed clustering-based category-hierarchy integration (CHI) technique,
which is an extension of the clustering-based category integration (CCI) technique. This method was improving
the effectiveness of category-hierarchy integration compared with that attained by nonhierarchical category-
integration techniques particularly homogeneous [16].
Methods have been developed to group papers for peer review tasks. For example, Hettich and Pazzani
[2006] proposed a text-mining approach to group papers, identify reviewers, and assign reviewers to papers.
Current methods group papers according to keywords. Unfortunately, papers with similar research areas might
be placed in wrong groups due to the following reasons: first, keywords are incomplete information about the
full content of the papers. Second, keywords are provided by applicants who may have subjective views and
misconceptions, and keywords are only a partial representation of the research papers. Third, manual grouping
is usually conducted by division managers or program directors in funding agencies. They may have different
understanding about the research disciplines and may not have adequate knowledge to assign papers into the
right groups. [17]
III. Existing System
The existing system is an Ontology-Based Text-Mining Method to cluster research papers based on
their similarities in research areas. It consists of three phases. Ontology is a knowledge repository in which
concepts and terms are defined as well as relationships between these concepts.
It consists of axioms, relationships and set of concepts that describe a domain of interests and
represents an agreed-upon conceptualization of the domain’s ―real-world‖ setting. Implicit knowledge for
humans is made explicit for computers by ontology. Thus, ontology can automate information processing and
can facilitate text mining in a specific domain (such as research project selection). An ontology based text
mining framework has been built for clustering the research papers according to their discipline areas.
Text mining refers generally to the process of extracting interesting information and knowledge from
unstructured text. The main difference between regular data mining and text mining is that text mining patterns
are extracted from natural language text rather than from structured databases of facts.
3. Research Paper Selection Based On an Ontology and Text Mining Technique Using Clustering
DOI: 10.9790/0661-17116571 www.iosrjournals.org 67 | Page
Fig.2 Main Framework of Proposed system.
1. Constructing Research Ontology: A research ontology containing the projects funded in latest five years
is constructed according to keywords, and it is updated annually. As domain ontology research ontology is a
public concept set of the research project management domain. The research topics of different disciplines
can be clearly expressed by research ontology.
2. Classifying New Research Papers: New research papers are classified according to the keyword stored in
ontology with the topic identified using Topic Identification Algorithm.
3. Clustering: Research Papers Based on Similarities Using Text Mining: After the research papers are
classified by the discipline areas, the papers in each discipline are clustered using the text- mining
technique. The main clustering process consists of five steps, text document collection, text document
preprocessing, text document encoding.
A. Graphs:
In the existing system we display the graph accoprding to domain, keywords and institute wise.
1. Domain wise graph:
Fig.3 Domain wise graph
4. Research Paper Selection Based On an Ontology and Text Mining Technique Using Clustering
DOI: 10.9790/0661-17116571 www.iosrjournals.org 68 | Page
This bar chart shows the domain wise graph. This graph contains the domain and the frequency. AS per
we submit the domain wise paper in the existing systsem after classifying & reviewing paper the graph will
display the selected paper domain and domains frequency. This graph shows the cloud computing and data
mining domains and freqency is higher than biometrics.
2. Keyword wise graph:
Fig.4 Keyword wise graph
This bar chart shows the keyword wise graph of selected papers.This graph contains the domain wise
paper and papers keyword and the frequency. AS per we submit the domain wise paper in the existing systsem
after classifying & reviewing paper the graph will display the selected paper domain and keywords of that
papers and domain frequency. This graph shows the keywords of cloud computing and data mining and
freqency is higher than biometrics.
3. Institute wise graph:
Fig.5 Institute wise graph
This bar chart shows the institute name wise graph. The graph shows the college name and value means
how many papers get submitted. In this graph MIT and PICT College’s student submitted lot of papers so value
increased as compared other colleges.
IV. Ontology Based Text Mining Framewok
In the R&D, after papers are submitted, the next important task is to group papers and assign them to
reviewers. The papers in each group should have similar research characteristics. For instance, if the papers in a
group fall into the same primary research discipline (e.g., supply chain management) and the number of papers
is small, manual grouping based on keywords listed in papers can be used and assign them to reviewer
manually. However, if the number of papers is large, it is very difficult to group papers and assign them to
reviewer manually. So the papers are classified using ontology and topic identification algorithm and then
papers are clustered using text mining and last it is submitted to reviewer systematically.
5. Research Paper Selection Based On an Ontology and Text Mining Technique Using Clustering
DOI: 10.9790/0661-17116571 www.iosrjournals.org 69 | Page
Fig.6 Structure of Research Ontology
As shown in Above Fig. It is Tree like Structure Containing Category, Sector, Domain and Topic
Name of Research Papers. Considering the example Scientific Research is the Category, under that there are
different sectors like Computer Science, E&TC. Sectors containing the Different Domains of the research area
.Each domain contain the various topic names of the Research papers. Each topic name contains keywords of
more than two domains then that topic name gets classified under domain which comes first. By using this
method we solve the problem of grouping of research papers under the proper domain area.
Module1: Research Ontology Building:
Step1) creating the research topics: The keywords of the supported research projects each year are collected,
and their frequencies are counted. The keyword frequency is the sum of the same keywords that appeared in the
discipline during the most recent five years. Creating the research topics of the discipline Ak, (k =1 ,2,...,K). The
keywords and their frequencies are denoted by the feature set (Nok,IDk,year,{ (keyword1,frequency1),
(keyword2, frequency2),..., (keywordk,frequencyk)}, where Nok is the sequence number of the kth record and
IDk is the corresponding discipline code. For instance, if discipline Ak has two keywords in 2007 (i.e., ―data
mining‖ and ―business intelligence‖) and the total number of counts for them are 30 and 50, respectively, the
discipline can be denoted by (Nok, IDk, 2007, {(data mining, 30), (business intelligence, 50)}). In this way, a
feature set of each discipline can be created. The keyword frequency in the feature set is the sum of the same
keywords that appeared in this discipline during the most recent five years (shown in Fig. 4), and then, the
feature set of Ak is denoted by (Nok,IDk,{(keyword1,frequency1)(keyword2, frequency2) (keywordk,
frequencyk)}).
Step2) Constructing the research ontology: First, the research ontology is categorized according to scientific
research areas introduced in the background. It is then developed on the basis of several specific research areas.
Sep3) Updating the research ontology: Once the project funding is completed each year, the research ontology
is updated according to agency’s policy and the change of the feature set.
Module 2: Classifying New Research Papers:
Research papers are classified by the domain area based on ontology keywords. This is done using the research
ontology as follows. Suppose that there are K discipline areas, and Ak denotes area k(k =1 ,2,...,K). Pi denotes
papers i(i =1 ,2,...,I), and Sk represents the set of papers which belongs to area k.
Module 3: Clustering Research Papers Based on Similarities Using Text Mining:
After the research papers are classified by the domain areas, the papers in each domain are clustered using the
text-mining technique. The main clustering process consists of four steps, as shown in Fig. text document
collection, text document preprocessing, text document encoding, and text vector clustering. The details of each
step are as follows.
Fig.7 Process of text mining
6. Research Paper Selection Based On an Ontology and Text Mining Technique Using Clustering
DOI: 10.9790/0661-17116571 www.iosrjournals.org 70 | Page
Step 1) Text document collection: After the research papers are classified according to the discipline areas, the
paper documents in each discipline Ak(k =1 ,2,...,K) are collected for text document preprocessing.
Step 2) Text document preprocessing: The contents of papers are usually nano structured. Because the texts
of the papers consist of characters which are difficult to segment, the research ontology is used to analyze,
extract, and identify the keywords in the full text of the papers.. Finally, a further reduction in the vocabulary
size can be achieved through the removal of all stop words that appeared only a few times in all paper
documents.
Step 3) Text document encoding: After text documents are segmented, they are converted into a feature vector
representation: V =( v1,v2,...,vM), where M is the number of features selected and vi(i =1 ,2,...,M) is the TF-
IDF encoding [18] of the keyword wi. TF-IDF encoding describes a weighted method based on inverse
document frequency (IDF) combined with the term frequency (TF) to produce the feature v, such that vi = tfi
∗log(N/dfi), where N is the total number of papers in the discipline, tfi is the term frequency of the feature word
wi, anddf i is the number of papers containing the word wi. Thus, research papers can be represented by
corresponding feature vectors.
Step 4) Text vector clustering: This step uses K-MEANS clustering algorithm to cluster the feature vectors
based on similarities of re- search areas. The K-MEANS clustering algorithm is a typical unsupervised learning
neural network model that clusters input data with similarities. Details of the K-MEANS algorithm.
V. Clustering Using K-Means Algorithm
After the construction of the document vector, the process of clustering is carried out. The K-MEANS clustering
algorithm is used to meet the purpose of this project. The basic algorithm of K-MEANS used for the project is
as following:
K-Means Algorithm Use:
For partitioning where each cluster’s center is represented by the mean value of the objects in the cluster.
Input:
k: the number of clusters,
Output:
A set of k clusters.
Method:
Step 1: Choose k numbers of clusters to be determined.
Step 2: Choose Ck centroids randomly as the initial centers of the clusters.
Step 3: Repeat
3.1: Assign each object to their closest cluster center using Euclidean distance.
3.2: Compute new cluster center by calculating mean points.
Step 4: Until
4.1: No change in cluster center OR
4.2: No object changes its clusters.
VI. Conclusion And Future Work
This paper has presented a framework on ontology based text mining for grouping research papers and
assigning the grouped paper to reviewers systematically. Research ontology is constructed to categorize the
concept terms in different discipline areas and to form relationships among them. It facilitates text-mining and
optimization techniques to cluster research papers based on their similarities and then to assign them to reviewer
according to their concerned research area. The papers are assigned to reviewer with the help of knowledge
based agent. Future work is needed to replace the work of reviewer by system. Also, there is a need to
empirically compare the results of manual classification to text-mining classification. Also there is need to
sending message on user’s mobile number and also further profile schedule.
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