SUBGRAPH MATCHING WITH SET SIMILARITY IN A LARGE GRAPH DATABASE - IEEE PROJE...Nexgen Technology
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NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
Learning Collaborative Agents with Rule Guidance for Knowledge Graph ReasoningDeren Lei
Walk-based models have shown their advantages in knowledge graph (KG) reasoning by achieving decent performance while providing interpretable decisions. However, the sparse reward signals offered by the KG during traversal are often insufficient to guide a sophisticated walk-based reinforcement learning (RL) model. An alternate approach is to use traditional symbolic methods (e.g., rule induction), which achieve good performance but can be hard to generalize due to the limitation of symbolic representation. In this paper, we propose RuleGuider, which leverages high-quality rules generated by symbolic-based methods to provide reward supervision for walk-based agents. Experiments on benchmark datasets show that RuleGuider improves the performance of walk-based models without losing interpretability.
SUBGRAPH MATCHING WITH SET SIMILARITY IN A LARGE GRAPH DATABASE - IEEE PROJE...Nexgen Technology
Nexgen Technology Address:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com.
www.nexgenproject.com
Mobile: 9751442511,9791938249
Telephone: 0413-2211159.
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
Learning Collaborative Agents with Rule Guidance for Knowledge Graph ReasoningDeren Lei
Walk-based models have shown their advantages in knowledge graph (KG) reasoning by achieving decent performance while providing interpretable decisions. However, the sparse reward signals offered by the KG during traversal are often insufficient to guide a sophisticated walk-based reinforcement learning (RL) model. An alternate approach is to use traditional symbolic methods (e.g., rule induction), which achieve good performance but can be hard to generalize due to the limitation of symbolic representation. In this paper, we propose RuleGuider, which leverages high-quality rules generated by symbolic-based methods to provide reward supervision for walk-based agents. Experiments on benchmark datasets show that RuleGuider improves the performance of walk-based models without losing interpretability.
‘Erules’ [3] is an integrated algorithm that is used to mine any data warehouse to extract useful and reliable rule sets effectively. It is used to generate positive &negative; conjunctive & disjunctive rules with the help of genetic algorithm and modified FP growth & Apriori Algorithms accordingly. It is an integrated algorithm for useful and effective association rule mining to capture even useful rare items; Lift Factor is also used to analyze the strength of derived rules. However redundant rules were one of the major challenges which were not addressed. This paper concisely deals the elimination of rule sets with the appropriate modification with the existing algorithm so that it can generate positive and negative rule sets for the non redundant rules with less cost. Besides a voluminous Pharmacy data set has been taken and the effectiveness /performance of ‘Erules’ got measured on it.
Today’s market evolution and high volatility of business requirements put an increasing emphasis on the
ability for systems to accommodate the changes required by new organizational needs while maintaining
security objectives satisfiability. This is all the more true in case of collaboration and interoperability
between different organizations and thus between their information systems. Ontology mapping has been
used for interoperability and several mapping systems have evolved to support the same. Usual solutions
do not take care of security. That is almost all systems do a mapping of ontologies which are unsecured.
We have developed a system for mapping secured ontologies using graph similarity concept.
Proposed Method for String Transformation using Probablistic ApproachEditor IJMTER
For this system the string is given as an input to the system generates the k most likely output strings corresponding to the input string. This system proposes both accurate and efficient feature by using a novel and probabilistic approach to string transformation, which is. The approach is includes the use of a log linear model, a method for training the model, and an algorithm for generating the top k candidates, whether there is or is not a predefined dictionary. The log linear model is defined as a conditional probability distribution of an output string and a rule set for the transformation conditioned on an input string. The learning method employs maximum likelihood estimation for parameter estimation. The string generation algorithm based on pruning is guaranteed to generate the optimal top k candidates. The proposed method will apply to correction of spelling errors in queries as well are formulation of queries in web search.
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.
A Survey of String Matching AlgorithmsIJERA Editor
The concept of string matching algorithms are playing an important role of string algorithms in finding a place where one or several strings (patterns) are found in a large body of text (e.g., data streaming, a sentence, a paragraph, a book, etc.). Its application covers a wide range, including intrusion detection Systems (IDS) in computer networks, applications in bioinformatics, detecting plagiarism, information security, pattern recognition, document matching and text mining. In this paper we present a short survey for well-known and recent updated and hybrid string matching algorithms. These algorithms can be divided into two major categories, known as exact string matching and approximate string matching. The string matching classification criteria was selected to highlight important features of matching strategies, in order to identify challenges and vulnerabilities.
Parallel Materialisation of Datalog Programs in Centralised, Main-Memory RDF ...DBOnto
Abstract:
We present a novel approach to parallel materialisation (i.e.,
fixpoint computation) of datalog programs in centralised,
main-memory, multi-core RDF systems. Our approach comprises an algorithm that evenly distributes the workload to cores, and an RDF indexing data structure that supports efficient, ‘mostly’ lock-free parallel updates. Our empirical evaluation shows that our approach parallelises computation very well: with 16 physical cores, materialisation can be up to 13.9 times faster than with just one core.
A FILM SYNOPSIS GENRE CLASSIFIER BASED ON MAJORITY VOTEkevig
We propose an automatic classification system of movie genres based on different features from their textual
synopsis. Our system is first trained on thousands of movie synopsis from online open databases, by learning relationships between textual signatures and movie genres. Then it is tested on other movie synopsis,
and its results are compared to the true genres obtained from the Wikipedia and the Open Movie Database
(OMDB) databases. The results show that our algorithm achieves a classification accuracy exceeding 75%.
A FILM SYNOPSIS GENRE CLASSIFIER BASED ON MAJORITY VOTEijnlc
We propose an automatic classification system of movie genres based on different features from their textual synopsis. Our system is first trained on thousands of movie synopsis from online open databases, by learning relationships between textual signatures and movie genres. Then it is tested on other movie synopsis, and its results are compared to the true genres obtained from the Wikipedia and the Open Movie Database
(OMDB) databases. The results show that our algorithm achieves a classification accuracy exceeding 75%.
MECHANICAL PROJECTS IN PONDICHERRY, 2020-21 MECHANICAL PROJECTS IN CHE...Nexgen Technology
Nexgen Technology Address:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: mailtonexgentech@gmail.com.
www.nexgenproject.com
Mobile: 9791938249,9025656779
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
MECHANICAL PROJECTS IN PONDICHERRY, 2020-21 MECHANICAL PROJECTS IN CHE...Nexgen Technology
Nexgen Technology Address:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: mailtonexgentech@gmail.com.
www.nexgenproject.com
Mobile: 9791938249,9025656779
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
More Related Content
Similar to Rule based method for entity resolution
‘Erules’ [3] is an integrated algorithm that is used to mine any data warehouse to extract useful and reliable rule sets effectively. It is used to generate positive &negative; conjunctive & disjunctive rules with the help of genetic algorithm and modified FP growth & Apriori Algorithms accordingly. It is an integrated algorithm for useful and effective association rule mining to capture even useful rare items; Lift Factor is also used to analyze the strength of derived rules. However redundant rules were one of the major challenges which were not addressed. This paper concisely deals the elimination of rule sets with the appropriate modification with the existing algorithm so that it can generate positive and negative rule sets for the non redundant rules with less cost. Besides a voluminous Pharmacy data set has been taken and the effectiveness /performance of ‘Erules’ got measured on it.
Today’s market evolution and high volatility of business requirements put an increasing emphasis on the
ability for systems to accommodate the changes required by new organizational needs while maintaining
security objectives satisfiability. This is all the more true in case of collaboration and interoperability
between different organizations and thus between their information systems. Ontology mapping has been
used for interoperability and several mapping systems have evolved to support the same. Usual solutions
do not take care of security. That is almost all systems do a mapping of ontologies which are unsecured.
We have developed a system for mapping secured ontologies using graph similarity concept.
Proposed Method for String Transformation using Probablistic ApproachEditor IJMTER
For this system the string is given as an input to the system generates the k most likely output strings corresponding to the input string. This system proposes both accurate and efficient feature by using a novel and probabilistic approach to string transformation, which is. The approach is includes the use of a log linear model, a method for training the model, and an algorithm for generating the top k candidates, whether there is or is not a predefined dictionary. The log linear model is defined as a conditional probability distribution of an output string and a rule set for the transformation conditioned on an input string. The learning method employs maximum likelihood estimation for parameter estimation. The string generation algorithm based on pruning is guaranteed to generate the optimal top k candidates. The proposed method will apply to correction of spelling errors in queries as well are formulation of queries in web search.
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.
A Survey of String Matching AlgorithmsIJERA Editor
The concept of string matching algorithms are playing an important role of string algorithms in finding a place where one or several strings (patterns) are found in a large body of text (e.g., data streaming, a sentence, a paragraph, a book, etc.). Its application covers a wide range, including intrusion detection Systems (IDS) in computer networks, applications in bioinformatics, detecting plagiarism, information security, pattern recognition, document matching and text mining. In this paper we present a short survey for well-known and recent updated and hybrid string matching algorithms. These algorithms can be divided into two major categories, known as exact string matching and approximate string matching. The string matching classification criteria was selected to highlight important features of matching strategies, in order to identify challenges and vulnerabilities.
Parallel Materialisation of Datalog Programs in Centralised, Main-Memory RDF ...DBOnto
Abstract:
We present a novel approach to parallel materialisation (i.e.,
fixpoint computation) of datalog programs in centralised,
main-memory, multi-core RDF systems. Our approach comprises an algorithm that evenly distributes the workload to cores, and an RDF indexing data structure that supports efficient, ‘mostly’ lock-free parallel updates. Our empirical evaluation shows that our approach parallelises computation very well: with 16 physical cores, materialisation can be up to 13.9 times faster than with just one core.
A FILM SYNOPSIS GENRE CLASSIFIER BASED ON MAJORITY VOTEkevig
We propose an automatic classification system of movie genres based on different features from their textual
synopsis. Our system is first trained on thousands of movie synopsis from online open databases, by learning relationships between textual signatures and movie genres. Then it is tested on other movie synopsis,
and its results are compared to the true genres obtained from the Wikipedia and the Open Movie Database
(OMDB) databases. The results show that our algorithm achieves a classification accuracy exceeding 75%.
A FILM SYNOPSIS GENRE CLASSIFIER BASED ON MAJORITY VOTEijnlc
We propose an automatic classification system of movie genres based on different features from their textual synopsis. Our system is first trained on thousands of movie synopsis from online open databases, by learning relationships between textual signatures and movie genres. Then it is tested on other movie synopsis, and its results are compared to the true genres obtained from the Wikipedia and the Open Movie Database
(OMDB) databases. The results show that our algorithm achieves a classification accuracy exceeding 75%.
MECHANICAL PROJECTS IN PONDICHERRY, 2020-21 MECHANICAL PROJECTS IN CHE...Nexgen Technology
Nexgen Technology Address:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: mailtonexgentech@gmail.com.
www.nexgenproject.com
Mobile: 9791938249,9025656779
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
MECHANICAL PROJECTS IN PONDICHERRY, 2020-21 MECHANICAL PROJECTS IN CHE...Nexgen Technology
Nexgen Technology Address:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: mailtonexgentech@gmail.com.
www.nexgenproject.com
Mobile: 9791938249,9025656779
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
MECHANICAL PROJECTS IN PONDICHERRY, 2020-21 MECHANICAL PROJECTS IN CHE...Nexgen Technology
Nexgen Technology Address:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: mailtonexgentech@gmail.com.
www.nexgenproject.com
Mobile: 9791938249,9025656779
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
MECHANICAL PROJECTS IN PONDICHERRY, 2020-21 MECHANICAL PROJECTS IN CHE...Nexgen Technology
Nexgen Technology Address:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: mailtonexgentech@gmail.com.
www.nexgenproject.com
Mobile: 9791938249,9025656779
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
MECHANICAL PROJECTS IN PONDICHERRY, 2020-21 MECHANICAL PROJECTS IN CHE...Nexgen Technology
Nexgen Technology Address:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: mailtonexgentech@gmail.com.
www.nexgenproject.com
Mobile: 9791938249,9025656779
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
MECHANICAL PROJECTS IN PONDICHERRY, 2020-21 MECHANICAL PROJECTS IN CHE...Nexgen Technology
Nexgen Technology Address:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: mailtonexgentech@gmail.com.
www.nexgenproject.com
Mobile: 9791938249,9025656779
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
MECHANICAL PROJECTS IN PONDICHERRY, 2020-21 MECHANICAL PROJECTS IN CH...Nexgen Technology
Nexgen Technology Address:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: mailtonexgentech@gmail.com.
www.nexgenproject.com
Mobile: 9791938249,9025656779
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
MECHANICAL PROJECTS IN PONDICHERRY, 2020-21 MECHANICAL PROJECTS IN CHENN...Nexgen Technology
Nexgen Technology Address:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: mailtonexgentech@gmail.com.
www.nexgenproject.com
Mobile: 9791938249,9025656779
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
MECHANICAL PROJECTS IN PONDICHERRY, 2020-21 MECHANICAL PROJECTS IN CHE...Nexgen Technology
Nexgen Technology Address:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: mailtonexgentech@gmail.com.
www.nexgenproject.com
Mobile: 9791938249,9025656779
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
MECHANICAL PROJECTS IN PONDICHERRY, 2020-21 MECHANICAL PROJECTS IN CHE...Nexgen Technology
Nexgen Technology Address:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: mailtonexgentech@gmail.com.
www.nexgenproject.com
Mobile: 9791938249,9025656779
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
MECHANICAL PROJECTS IN PONDICHERRY, 2020-21 MECHANICAL PROJECTS IN CHENNA...Nexgen Technology
Nexgen Technology Address:
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www.nexgenproject.com
Mobile: 9791938249,9025656779
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
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NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
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Mobile: 9791938249,9025656779
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
Nexgen Technology Address:
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Email Id: mailtonexgentech@gmail.com.
www.nexgenproject.com
Mobile: 9791938249,9025656779
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
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Mobile: 9791938249,9025656779
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
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NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
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NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
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Mobile: 9791938249,9025656779
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
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Rule based method for entity resolution
1. RULE-BASED METHOD FOR ENTITY RESOLUTION
Abstract—The objective of entity resolution (ER) is to identify records referring
to the same real-world entity. Traditional ER approaches identify records based on
pairwise similarity comparisons, which assumes that records referring to the same
entity are more similar to each other than otherwise. However, this assumption
does not always hold in practice and similarity comparisons do not work well when
such assumption breaks. We propose a new class of rules which could describe the
complex matching conditions between records and entities. Based on this class of
rules, we present the rule-based entity resolution problem and develop an on-line
approach for ER. In this framework, by applying rules to each record, we identify
which entity the record refers to. Additionally, we propose an effective and
efficient rule discovery algorithm. We experimentally evaluated our rule-based ER
algorithm on real data sets. The experimental results show that both our rule
discovery algorithm and rule-based ER algorithm can achieve high performance.
EXISTING SYSTEM:
The work on entity resolution can be broadly divided into three categories.
Pairwise ER. Most works on ER focus on record matching, which involves
2. comparing record pairs and identifying whether they match. A major part of work
on record matching focuses on similarity functions. To capture string variations,
proposed a transformation-based framework for record matching. Some machine-
learningbased approaches can identify matching strings which are syntactically far
apart. Similarity based on record relationships are also proposed to solve the
people identification problem. Since in our work, records are not compared with
each other, our work is orthogonal to record matching. However, string similarity
functions can be applied to fuzzy match operator (denoted by _) in ER-rules. For
example, given a string s, we say s _ “wei wang” if the edit distance between s and
“wei wang” is smaller than a given threshold. Decision trees are employed to teach
record matching rules. However, decision trees cannot be used to discover ER-
rules. This is because the domain of the righthand side of record matching rules is
{yes, no} (two records are mapped or not mapped), while the domain of the
righthand side of ER-rules is an entity set. Non-pairwise ER. The research on non-
pairwise ER includes clustering strategies and classifiers. Most strategies solve ER
based on the relationship graph among records, by modeling the records as nodes
and the relationships as edges. Machine learning approaches are also proposed by
using global information to solve ER effectively. However, these methods are not
suitable for massive data because of efficiency issues. We choose a representative
work for comparison.
3. PROPOSED SYSTEM:
This paper aims at the aforementioned problems, and the main contributions of the
paper are as following.
1) The syntax and semantics of the rules for ER are designed, and the
independence, consistency, completeness and validity of the rules are defined and
analyzed.
2) An efficient rule discovery algorithm based on training data is proposed and
analyzed.
3) An efficient rule-based algorithm for solving entity resolution problem is
proposedand analyzed.
4) A rule maintaining method is proposedwhen entity information is changed.
5) Experiments are performed on real data to verify the effectiveness and
efficiency of the proposed algorithms.
In fact, our method and traditional ER approaches can be considered as the
complementary to each other and be applied together. This is because our rule-
based method can identify records which cannot be resolved by traditional ER
methods and traditional ER methods can identify most of the records effectively
4. and do not require the availability of correct entity set. In this way, the limitations
of both methods can be overcome.
Module 1
Rules for entity resolution
In this section, a rule system for entity resolution, called ERrule, is defined.
(1)The If clause includes constraints on attributes of records, such as “including
zhang in coauthors”, and
(2)Then clause indicates the real world entity referred by the records that
satisfy the first clause of the rule, such as “refers to entity e1”. Thus, we use
A ) B to express the rules “8o, If o satisfies A Then o refers to B” for ER.
We denote the left-hand side and the right-hand side of a rule r as LHSðrÞ
and RHSðrÞ respectively.
Syntax
An ER-rule is where clause with the form of (Ai opi vi), (vi opi Ai), :(Aiopi vi) or
:(vi opi Ai), where Ai is an attribute, vi is a constant in the domain of Ai and opi
can be any domaindependent operator defined by users, such as exact match
operator ¼, fuzzy match operator _ [16] for string value, for numeric value, or 2
for set value. The clausewith form (Ai opi vi) or (vi opi Ai) is calledpositive
clause, and the clause with form :(Ai opi vi) or :(vi opiAi) is callednegative clause.
5. Semantics
In the following definitions, we let o be a record, S be a data set, r be an ER-rule
and R be an ER-rule set. For the convenience of discussion, we assume the
mapping from each record in S to its actual entity is given. Since an ER-rule does
not include disjoint clauses, we define the condition of matching the left-hand and
righthand sides of rule as follows.
Definition 1. 0 matches the LHS of r if o satisfies all the clauses in LHSðrÞ. o
matches the RHS of r if o refers to entity RHSðrÞ.
Module 2
Properties of ER-Rule Set
Given an ER-rule set R and a data set S, to ensure R performs well on S, we
require that (1) there is no false matches
between record and entity (validity); (2) there is no conflicting decisions by R
(consistency); (3) each record in S can be mapped to an entity by R (completeness)
and (4) there is no redundant rules in R (independence). Now we present the
formal definitions of these properties. Definition 4 (Validity). R is valid for S if
each ER-rule in R is
valid for S.
Module 3
6. RULE DISCOVERY
Since it might be too expensive to construct ER-rules manually, we discuss how to
discover useful rules from a training data set for efficient and effective entity
resolution in this section. We assume that the operator on each attribute can be any
domain-dependent operator defined by users. First, we discuss the requirements of
the discovered rule sets and present our framework of rule discovery. Then we
describe the algorithms in the rule discovery framework and study the correctness
and complexity of our algorithm. For exposition, proofs have been deferred to the
Appendix. For the convenience of discussion, some concepts are introduced first.
We classify ER-rules into two categories according to whether negative clauses are
included. Definition 8 (PR). PR is an ER-rule which only includes positive clauses.
Module 4
Requirements for rule Discovery
Even though these properties are satisfied on the training data set, it cannot be
ensured that the generated rule set can
also perform well on the other data sets. To make the rule set suitable for ER for
many data sets other than only suitable for the training data set, we require the
7. discovered ERrule set, denoted by R, should also satisfy two requirements
described as follows.
_ Length Requirement: Given a threshold l, each rule r in R satisfies that jrj To
determine whether record o matches the LHS of ERrule r, we should check
whether o satisfies each clause in LHSðrÞ. Thus to guarantee the efficiency of
rule-based ER (R-ER) and avoid overfitting, the length of each rule (the number of
clauses) should be no more than a threshold.
_ PR Requirement: each rule r in R is a PR. The reasonwhywe give priority toPRs
is that,positiveliterals lead to bounded spaceswhile negative literals lead to
unbounded spaces. Therefore the discovered PRs are more possible to identify
other data sets effectively than the discoveredNRs.
Module 5
Rule-basedentity resolution
In this section, we discuss the algorithm of entity resolution by leveraging ER-
rules. We first define the rule-based ER
problem. Next we develop an online algorithm for rulebased ER problem. Finally,
we describe how to incorporate this algorithm into a generalized ER framework.
Problem 1 (Rule-based ER). Rule-based ER takes U and RE as input, and outputs
8. U. U is a data set, RE is an ER-rule set of entity set E ¼ fe1; . . . ; emg, U ¼ fU1; .
. . ; Umg is a partition of records where each group Uj(1 _ j _ m) is a subset of U
which are determined to refer to the entity ej and [1_j_mUj is a subset of U. Our
rule-based ER algorithm R-ER scans records one by one and determines the entity
for each record. The determination process can be divided into the following steps.
First, we find all the rules satisfied by o (FINDRULES). Second, for each entity e
to which o might refer, we compute the confidence that o refers to e according to
the rules of e that are satisfied by o (COMPCONF). Third, we select the entity e
with the largest confidence to which o might refer, and if this confidence is larger
than a confidence threshold, it is determined that o refers to e (SELENTITY).
These procedures are described as follows. It takes a record to and find all the rules
that are satisfied by it. The intuitive idea is to compare with each rule. In practice,
it does not match the LHS of most of the rules. In order to find the rules whose
LHS are matched by oi efficiently, we construct an inverted index and a B-tree for
rules, denoted by LR and TR respectively. LR is for rules including clauses with ¼
and 2 operators and TR is for rules including clauses with range operators.
CONCLUSION
This paper developed a class of ER-rules which are capable to describe the
complex matching conditions between records and entities. Based on these rules,
9. we developed an ER algorithm R-ER. We experimentally evaluated our algorithms
on real data sets. The experimental results show that our algorithm can achieve a
good performance both on efficiency and accuracy. For future work, we would like
to extend our techniques to more general cases. For instance, how to discover ER-
rules when the operator for each attribute is not given? We would also like to
consider how to incorporate human resources, such as Crowd, into our rule-
discovery framework to improve the quality of rules.
REFERENCES
[1] N. Koudas, S. Sarawagi, and D. Srivastava, “Record linkage: Similarity
measures and algorithms,” in Proc. ACM SIGMOD Int. Conf. Manage. Data,
2006, pp. 802–803.
[2] M. Bilenko and R. J. Mooney, “Adaptive duplicate detection using learnable
string similarity measures,” in Proc. ACM SIGKDD Int. Conf. Knowl. Discovery
Data Mining, 2003, pp. 39–48.
[3] W. W. Cohen, “Integration of heterogeneous databases without common
domains using queries based on textual similarity,” ACM SIGMOD Rec., vol. 27,
no. 2, pp. 201–212, 1998.
10. [4] L. Gravano, P. G. Ipeirotis, N. Koudas, and D. Srivastava, “Text joins in an
RDBMS for web data integration,” in Proc. 12th Int. Conf. World Wide Web,
2003, pp. 90–101.
[5] M. A. Jaro, “Advances in record-linkage methodology as applied to matching
the 1985 census of Tampa, Florida,” J. Amer. Statist. Assoc., vol. 84, no. 406, pp.
414–420, 1989.
[6] A. Arasu, S. Chaudhuri, and R. Kaushik, “Transformation-based framework for
record matching,” in Proc. 24th Int. Conf. Data Eng., 2008, pp. 40–49.
[7] S. Chaudhuri, B. C. Chen, V. Ganti, and R. Kaushik, “Exampledriven design of
efficient record matching queries,” in Proc. 33rd Int. Conf. Very Large Databases,
2007, pp. 327–338.
[8] A. Arasu, S. Chaudhuri, and R. Kaushik, “Learning string transformations from
examples,” Proc. VLDB Endowment, vol. 2, no. 1, pp. 514–525, 2009.
[9] R. Bekkerman and A. McCallum, “Disambiguating web appearances of people
in a social network,” in Proc. 14th Int. Conf. World Wide Web, 2005, pp. 463–470.
[10] S. Tejada, C. Knoblock, and S. Minton, “Learning object identification rules
for information integration,” Inf. Syst., vol. 26, no. 8, pp. 607–633, 2001.
[11] X. Fan, J. Wang, X. Pu, L. Zhou, and B. Lv, “On graph-based name
disambiguation,” J. Data Inf. Quality, vol. 2, no. 2, p. 10, 2011.
11. [12] L. Shu, B. Long, and W. Meng, “A latent topic model for complete entity
resolution,” in Proc. 25th Int. Conf. Data Eng., 2009, pp. 880– 891.