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AN EFFICIENT APPROACH FOR SEMANTICALLYENHANCED DOCUMENT CLUSTERING BY USING W...ijaia
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Traditional techniques of document clustering do not consider the semantic relationships between words
when assigning documents to clusters. For instance, if two documents talking about the same topic do that
using different words (which may be synonyms or semantically associated), these techniques may assign
documents to different clusters. Previous research has approached this problem by enriching the document
representation with the background knowledge in an ontology. This paper presents a new approach to
enhance document clustering by exploiting the semantic knowledge contained in Wikipedia. We first map
terms within documents to their corresponding Wikipedia concepts. Then, similarity between each pair of
terms is calculated by using the Wikipedia's link structure. The documentâs vector representation is then
adjusted so that terms that are semantically related gain more weight. Our approach differs from related
efforts in two aspects: first, unlink others who built their own methods of measuring similarity through the
Wikipedia categories; our approach uses a similarity measure that is modelled after the Normalized
Google Distance which is a well-known and low-cost method of measuring term similarity. Second, it is
more time efficient as it applies an algorithm for phrase extraction from documents prior to matching terms
with Wikipedia. Our approach was evaluated by being compared with different methods from the state of
the art on two different datasets. Empirical results showed that our approach improved the clustering
results as compared to other approaches.
An efficient approach for semantically enhanced document clustering by using ...ijaia
Â
Traditional techniques of document clustering do not consider the semantic relationships between words
when assigning documents to clusters. For instance, if two documents talking about the same topic do that
using different words (which may be synonyms or semantically associated), these techniques may assign
documents to different clusters. Previous research has approached this problem by enriching the document
representation with the background knowledge in an ontology. This paper presents a new approach to
enhance document clustering by exploiting the semantic knowledge contained in Wikipedia. We first map
terms within documents to their corresponding Wikipedia concepts. Then, similarity between each pair of
terms is calculated by using the Wikipedia's link structure. The documentâs vector representation is then
adjusted so that terms that are semantically related gain more weight. Our approach differs from related
efforts in two aspects: first, unlink others who built their own methods of measuring similarity through the
Wikipedia categories; our approach uses a similarity measure that is modelled after the Normalized
Google Distance which is a well-known and low-cost method of measuring term similarity. Second, it is
more time efficient as it applies an algorithm for phrase extraction from documents prior to matching terms
with Wikipedia. Our approach was evaluated by being compared with different methods from the state of
the art on two different datasets. Empirical results showed that our approach improved the clustering
results as compared to other approaches.
Based on the Influence Factors in the Heterogeneous Network t-path Similarity...IJRESJOURNAL
Â
ABSTRACTďźIn view of the existing heterogeneous network largely depends on the meta-path similarity calculation method of direct link, ignores the influence factors of different entities and the time difference of different paths. The hetero-geneous network is proposed in this paper, based on the factors affecting the time path of similarity algorithm Hete-DS. In HeteRecom algorithm on the basis of the algorithm, make up for the path of only consider different types of weights, different object relations by in the path to the weight computation of time factor, strengthen the effectiveness of the path to weight. At the same time, through the influence factors of different entities, object relational matrix was established considering the difference between different nodes under the same path type links. In multiple data sets on the experimental results show that, compared with HeteRecom algorithm, Hete-DS algorithm has higher accuracy.
Statistical Analysis based Hypothesis Testing Method in Biological Knowledge ...ijcsa
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The correlation and interactions among different biological entities comprise the biological system. Although already revealed interactions contribute to the understanding of different existing systems, researchers face many questions everyday regarding inter-relationships among entities. Their queries have potential role in exploring new relations which may open up a new area of investigation. In this paper, we introduce a text mining based method for answering the biological queries in terms of statistical computation such that researchers can come up with new knowledge discovery. It facilitates user to submit their query in natural linguistic form which can be treated as hypothesis. Our proposed approach analyzes the hypothesis and measures the p-value of the hypothesis with respect to the existing literature. Based on the measured value, the system either accepts or rejects the hypothesis from statistical point of view. Moreover, even it does not find any direct relationship among the entities of the hypothesis, it presents a network to give an integral overview of all the entities through which the entities might be related. This is also congenial for the researchers to widen their view and thus think of new hypothesis for further investigation. It assists researcher to get a quantitative evaluation of their assumptions such that they can reach a logical conclusion and thus aids in relevant re-searches of biological knowledge discovery. The system also provides the researchers a graphical interactive interface to submit their hypothesis for assessment in a more convenient way.
We all have good and bad thoughts from time to time and situation to situation. We are bombarded daily with spiraling thoughts(both negative and positive) creating all-consuming feel , making us difficult to manage with associated suffering. Good thoughts are like our Mob Signal (Positive thought) amidst noise(negative thought) in the atmosphere. Negative thoughts like noise outweigh positive thoughts. These thoughts often create unwanted confusion, trouble, stress and frustration in our mind as well as chaos in our physical world. Negative thoughts are also known as âdistorted thinkingâ.
AN EFFICIENT APPROACH FOR SEMANTICALLYENHANCED DOCUMENT CLUSTERING BY USING W...ijaia
Â
Traditional techniques of document clustering do not consider the semantic relationships between words
when assigning documents to clusters. For instance, if two documents talking about the same topic do that
using different words (which may be synonyms or semantically associated), these techniques may assign
documents to different clusters. Previous research has approached this problem by enriching the document
representation with the background knowledge in an ontology. This paper presents a new approach to
enhance document clustering by exploiting the semantic knowledge contained in Wikipedia. We first map
terms within documents to their corresponding Wikipedia concepts. Then, similarity between each pair of
terms is calculated by using the Wikipedia's link structure. The documentâs vector representation is then
adjusted so that terms that are semantically related gain more weight. Our approach differs from related
efforts in two aspects: first, unlink others who built their own methods of measuring similarity through the
Wikipedia categories; our approach uses a similarity measure that is modelled after the Normalized
Google Distance which is a well-known and low-cost method of measuring term similarity. Second, it is
more time efficient as it applies an algorithm for phrase extraction from documents prior to matching terms
with Wikipedia. Our approach was evaluated by being compared with different methods from the state of
the art on two different datasets. Empirical results showed that our approach improved the clustering
results as compared to other approaches.
An efficient approach for semantically enhanced document clustering by using ...ijaia
Â
Traditional techniques of document clustering do not consider the semantic relationships between words
when assigning documents to clusters. For instance, if two documents talking about the same topic do that
using different words (which may be synonyms or semantically associated), these techniques may assign
documents to different clusters. Previous research has approached this problem by enriching the document
representation with the background knowledge in an ontology. This paper presents a new approach to
enhance document clustering by exploiting the semantic knowledge contained in Wikipedia. We first map
terms within documents to their corresponding Wikipedia concepts. Then, similarity between each pair of
terms is calculated by using the Wikipedia's link structure. The documentâs vector representation is then
adjusted so that terms that are semantically related gain more weight. Our approach differs from related
efforts in two aspects: first, unlink others who built their own methods of measuring similarity through the
Wikipedia categories; our approach uses a similarity measure that is modelled after the Normalized
Google Distance which is a well-known and low-cost method of measuring term similarity. Second, it is
more time efficient as it applies an algorithm for phrase extraction from documents prior to matching terms
with Wikipedia. Our approach was evaluated by being compared with different methods from the state of
the art on two different datasets. Empirical results showed that our approach improved the clustering
results as compared to other approaches.
Based on the Influence Factors in the Heterogeneous Network t-path Similarity...IJRESJOURNAL
Â
ABSTRACTďźIn view of the existing heterogeneous network largely depends on the meta-path similarity calculation method of direct link, ignores the influence factors of different entities and the time difference of different paths. The hetero-geneous network is proposed in this paper, based on the factors affecting the time path of similarity algorithm Hete-DS. In HeteRecom algorithm on the basis of the algorithm, make up for the path of only consider different types of weights, different object relations by in the path to the weight computation of time factor, strengthen the effectiveness of the path to weight. At the same time, through the influence factors of different entities, object relational matrix was established considering the difference between different nodes under the same path type links. In multiple data sets on the experimental results show that, compared with HeteRecom algorithm, Hete-DS algorithm has higher accuracy.
Statistical Analysis based Hypothesis Testing Method in Biological Knowledge ...ijcsa
Â
The correlation and interactions among different biological entities comprise the biological system. Although already revealed interactions contribute to the understanding of different existing systems, researchers face many questions everyday regarding inter-relationships among entities. Their queries have potential role in exploring new relations which may open up a new area of investigation. In this paper, we introduce a text mining based method for answering the biological queries in terms of statistical computation such that researchers can come up with new knowledge discovery. It facilitates user to submit their query in natural linguistic form which can be treated as hypothesis. Our proposed approach analyzes the hypothesis and measures the p-value of the hypothesis with respect to the existing literature. Based on the measured value, the system either accepts or rejects the hypothesis from statistical point of view. Moreover, even it does not find any direct relationship among the entities of the hypothesis, it presents a network to give an integral overview of all the entities through which the entities might be related. This is also congenial for the researchers to widen their view and thus think of new hypothesis for further investigation. It assists researcher to get a quantitative evaluation of their assumptions such that they can reach a logical conclusion and thus aids in relevant re-searches of biological knowledge discovery. The system also provides the researchers a graphical interactive interface to submit their hypothesis for assessment in a more convenient way.
We all have good and bad thoughts from time to time and situation to situation. We are bombarded daily with spiraling thoughts(both negative and positive) creating all-consuming feel , making us difficult to manage with associated suffering. Good thoughts are like our Mob Signal (Positive thought) amidst noise(negative thought) in the atmosphere. Negative thoughts like noise outweigh positive thoughts. These thoughts often create unwanted confusion, trouble, stress and frustration in our mind as well as chaos in our physical world. Negative thoughts are also known as âdistorted thinkingâ.
How to Make a Field invisible in Odoo 17Celine George
Â
It is possible to hide or invisible some fields in odoo. Commonly using âinvisibleâ attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
Ethnobotany and Ethnopharmacology:
Ethnobotany in herbal drug evaluation,
Impact of Ethnobotany in traditional medicine,
New development in herbals,
Bio-prospecting tools for drug discovery,
Role of Ethnopharmacology in drug evaluation,
Reverse Pharmacology.
The Art Pastor's Guide to Sabbath | Steve ThomasonSteve Thomason
Â
What is the purpose of the Sabbath Law in the Torah. It is interesting to compare how the context of the law shifts from Exodus to Deuteronomy. Who gets to rest, and why?
Model Attribute Check Company Auto PropertyCeline George
Â
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
Operation âBlue Starâ is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdfTechSoup
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In this webinar you will learn how your organization can access TechSoup's wide variety of product discount and donation programs. From hardware to software, we'll give you a tour of the tools available to help your nonprofit with productivity, collaboration, financial management, donor tracking, security, and more.
This is a presentation by Dada Robert in a Your Skill Boost masterclass organised by the Excellence Foundation for South Sudan (EFSS) on Saturday, the 25th and Sunday, the 26th of May 2024.
He discussed the concept of quality improvement, emphasizing its applicability to various aspects of life, including personal, project, and program improvements. He defined quality as doing the right thing at the right time in the right way to achieve the best possible results and discussed the concept of the "gap" between what we know and what we do, and how this gap represents the areas we need to improve. He explained the scientific approach to quality improvement, which involves systematic performance analysis, testing and learning, and implementing change ideas. He also highlighted the importance of client focus and a team approach to quality improvement.
Instructions for Submissions thorugh G- Classroom.pptxJheel Barad
Â
This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
How to Create Map Views in the Odoo 17 ERPCeline George
Â
The map views are useful for providing a geographical representation of data. They allow users to visualize and analyze the data in a more intuitive manner.
Students, digital devices and success - Andreas Schleicher - 27 May 2024..pptxEduSkills OECD
Â
Andreas Schleicher presents at the OECD webinar âDigital devices in schools: detrimental distraction or secret to success?â on 27 May 2024. The presentation was based on findings from PISA 2022 results and the webinar helped launch the PISA in Focus âManaging screen time: How to protect and equip students against distractionâ https://www.oecd-ilibrary.org/education/managing-screen-time_7c225af4-en and the OECD Education Policy Perspective âStudents, digital devices and successâ can be found here - https://oe.cd/il/5yV
Students, digital devices and success - Andreas Schleicher - 27 May 2024..pptx
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A generalized flow based method for analysis of implicit relationships on wikipedia
1. A GENERALIZED FLOW-BASED METHOD FOR ANALYSIS OF IMPLICIT
RELATIONSHIPS ON WIKIPEDIA
ABSTRACT:
We focus on measuring relationships between pairs of objects in Wikipedia whose pages can be
regarded as individual objects. Two kinds of relationships between two objects exist: in
Wikipedia, an explicit relationship is represented by a single link between the two pages for the
objects, and an implicit relationship is represented by a link structure containing the two pages.
Some of the previously proposed methods for measuring relationships are cohesion-based
methods, which underestimate objects having high degrees, although such objects could be
important in constituting relationships in Wikipedia.
The other methods are inadequate for measuring implicit relationships because they use only one
or two of the following three important factors: distance, connectivity, and cocitation. We
propose a new method using a generalized maximum flow which reflects all the three factors and
does not underestimate objects having high degree. We confirm through experiments that our
method can measure the strength of a relationship more appropriately than these previously
proposed methods do. Another remarkable aspect of our method is mining elucidatory objects,
that is, objects constituting a relationship. We explain that mining elucidatory objects would
open a novel way to deeply understand a relationship.
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