The presentation discusses information extraction techniques for distilling structured data from unstructured text. It provides an example of building a website to find continuing education opportunities by extracting structured data from unstructured web pages. The presentation covers machine learning approaches to information extraction, such as using a wrapper to query unstructured sources as databases. It also discusses challenges such as verifying extracted data and automatically repairing wrappers when extractions change or fail.
Using Page Size for Controlling Duplicate Query Results in Semantic WebIJwest
Semantic web is a web of future. The Resource Description Framework (RDF) is a language
to represent resources in the World Wide Web. When these resources are queried the problem of duplicate
query results occurs. The present techniques used hash index comparison to remove duplicate query
results. The major drawback of using the hash index to remove duplicate query results is that, if there is a
slight change in formatting or word order, then hash index is changed and query results are no more
considered as duplicate even though they have same contents. We presented an algorithm for detection and
elimination of duplicate query results from semantic web using hash index and page size comparisons.
Experimental results showed that the proposed technique removed duplicate query results from semantic
web efficiently, solved the problems of using hash index for duplicate handling and could be embedded in
existing SQL-Based query system for semantic web. Research could be carried out for certain flexibilities
in existing SQL-Based query system of semantic web to accommodate other duplicate detection techniques
as well.
Document Classification Using Expectation Maximization with Semi Supervised L...ijsc
As the amount of online document increases, the demand for document classification to aid the analysis and management of document is increasing. Text is cheap, but information, in the form of knowing what classes a document belongs to, is expensive. The main purpose of this paper is to explain the expectation maximization technique of data mining to classify the document and to learn how to improve the accuracy while using semi-supervised approach. Expectation maximization algorithm is applied with both supervised and semi-supervised approach. It is found that semi-supervised approach is more accurate and effective. The main advantage of semi supervised approach is “DYNAMICALLY GENERATION OF NEW CLASS”. The algorithm first trains a classifier using the labeled document and probabilistically classifies the
unlabeled documents. The car dataset for the evaluation purpose is collected from UCI repository dataset in which some changes have been done from our side.
The premise of this paper is to discover frequent patterns by the use of data grids in WEKA 3.8 environment. Workload imbalance occurs due to the dynamic nature of the grid computing hence data grids are used for the creation and validation of data. Association rules are used to extract the useful information from the large database. In this paper the researcher generate the best rules by using WEKA 3.8 for better performance. WEKA 3.8 is used to accomplish best rules and implementation of various algorithms.
Multi Similarity Measure based Result Merging Strategies in Meta Search EngineIDES Editor
In Meta Search Engine result merging is the key
component. Meta Search Engines provide a uniform query
interface for Internet users to search for information.
Depending on users’ needs, they select relevant sources and
map user queries into the target search engines, subsequently
merging the results. The effectiveness of a Meta Search
Engine is closely related to the result merging algorithm it
employs. In this paper, we have proposed a Meta Search
Engine, which has two distinct steps (1) searching through
surface and deep search engine, and (2) Ranking the results
through the designed ranking algorithm. Initially, the query
given by the user is inputted to the deep and surface search
engine. The proposed method used two distinct algorithms
for ranking the search results, concept similarity based
method and cosine similarity based method. Once the results
from various search engines are ranked, the proposed Meta
Search Engine merges them into a single ranked list. Finally,
the experimentation will be done to prove the efficiency of
the proposed visible and invisible web-based Meta Search
Engine in merging the relevant pages. TSAP is used as the
evaluation criteria and the algorithms are evaluated based on
these criteria.
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Using Page Size for Controlling Duplicate Query Results in Semantic WebIJwest
Semantic web is a web of future. The Resource Description Framework (RDF) is a language
to represent resources in the World Wide Web. When these resources are queried the problem of duplicate
query results occurs. The present techniques used hash index comparison to remove duplicate query
results. The major drawback of using the hash index to remove duplicate query results is that, if there is a
slight change in formatting or word order, then hash index is changed and query results are no more
considered as duplicate even though they have same contents. We presented an algorithm for detection and
elimination of duplicate query results from semantic web using hash index and page size comparisons.
Experimental results showed that the proposed technique removed duplicate query results from semantic
web efficiently, solved the problems of using hash index for duplicate handling and could be embedded in
existing SQL-Based query system for semantic web. Research could be carried out for certain flexibilities
in existing SQL-Based query system of semantic web to accommodate other duplicate detection techniques
as well.
Document Classification Using Expectation Maximization with Semi Supervised L...ijsc
As the amount of online document increases, the demand for document classification to aid the analysis and management of document is increasing. Text is cheap, but information, in the form of knowing what classes a document belongs to, is expensive. The main purpose of this paper is to explain the expectation maximization technique of data mining to classify the document and to learn how to improve the accuracy while using semi-supervised approach. Expectation maximization algorithm is applied with both supervised and semi-supervised approach. It is found that semi-supervised approach is more accurate and effective. The main advantage of semi supervised approach is “DYNAMICALLY GENERATION OF NEW CLASS”. The algorithm first trains a classifier using the labeled document and probabilistically classifies the
unlabeled documents. The car dataset for the evaluation purpose is collected from UCI repository dataset in which some changes have been done from our side.
The premise of this paper is to discover frequent patterns by the use of data grids in WEKA 3.8 environment. Workload imbalance occurs due to the dynamic nature of the grid computing hence data grids are used for the creation and validation of data. Association rules are used to extract the useful information from the large database. In this paper the researcher generate the best rules by using WEKA 3.8 for better performance. WEKA 3.8 is used to accomplish best rules and implementation of various algorithms.
Multi Similarity Measure based Result Merging Strategies in Meta Search EngineIDES Editor
In Meta Search Engine result merging is the key
component. Meta Search Engines provide a uniform query
interface for Internet users to search for information.
Depending on users’ needs, they select relevant sources and
map user queries into the target search engines, subsequently
merging the results. The effectiveness of a Meta Search
Engine is closely related to the result merging algorithm it
employs. In this paper, we have proposed a Meta Search
Engine, which has two distinct steps (1) searching through
surface and deep search engine, and (2) Ranking the results
through the designed ranking algorithm. Initially, the query
given by the user is inputted to the deep and surface search
engine. The proposed method used two distinct algorithms
for ranking the search results, concept similarity based
method and cosine similarity based method. Once the results
from various search engines are ranked, the proposed Meta
Search Engine merges them into a single ranked list. Finally,
the experimentation will be done to prove the efficiency of
the proposed visible and invisible web-based Meta Search
Engine in merging the relevant pages. TSAP is used as the
evaluation criteria and the algorithms are evaluated based on
these criteria.
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Comparative analysis of relative and exact search for web information retrievaleSAT Journals
Abstract The volume of data on web repository is huge. To get specific and precise information for the web repository is a big challenge. Existing Information Retrieval (IR) techniques, given by contemporary researchers, are very useful in field of IR. Here, the authors have implemented and tested two of the techniques from the fields of IR. The authors dealt with Relative Search and Exact Search techniques one by one. Initially relative search tested on web repository data using web mining tool and then its results are analyzed. In the same manner, the exact search technique of IR tested on web repository data and the results are measured. The researchers have experienced the significant importance on exact search and relative search. The focused of the research paper is to retrieve relevant information from the web information repository. With the use of two searching criteria these can be done. With the use of the suggested methods the searchers may retrieve a relevant web data in a fewer time. Key Words: Web data Mining, Exact Search, Relative Search, PR, TM, CD, VSM and TASE
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Syntactic search relies on keywords contained in a query to find suitable documents. So, documents that do
not contain the keywords but contain information related to the query are not retrieved. Spreading
activation is an algorithm for finding latent information in a query by exploiting relations between nodes in
an associative network or semantic network. However, the classical spreading activation algorithm uses all
relations of a node in the network that will add unsuitable information into the query. In this paper, we
propose a novel approach for semantic text search, called query-oriented-constrained spreading activation
that only uses relations relating to the content of the query to find really related information. Experiments
on a benchmark dataset show that, in terms of the MAP measure, our search engine is 18.9% and 43.8%
respectively better than the syntactic search and the search using the classical constrained spreading
activation.
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International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Many data mining and knowledge discovery methodologies and process models have been developed, with varying degrees of success, there are three main methods used to discover patterns in data; KDD, SEMMA and CRISP-DM. They are presented in many of the publications of the area and are used in practice. To our knowledge, there is no clear methodology developed to support link mining. However, there is a well known methodology in knowledge discovery in databases, known as Cross Industry Standard Process for Data Mining (CRISPDM), developed by a consortium of several industrial companies which can be relevant to the study of link mining. In this study CRISP-DM has been adapted to the field of Link mining to detect anomalies. An important goal in link mining is the task of inferring links that are not yet known in a given network. This approach is implemented through the use of a case study of real world data (co-citation data). This case study aims to use mutual information to interpret the semantics of anomalies identified in co-citation, dataset that can provide valuable insights in determining the nature of a given link and potentially identifying important future link relationships
Meta documents and query extension to enhance information retrieval processeSAT Journals
Abstract In this paper, we present two facets indispensable for the efficiency of our information retrieval system: meta-documents and query extension. Meta-document represents a structure used to annotate our web documents collection and query extension represents an automatic process aimed to enhance user query expression by additional terms. The two facets are indirectly related since added terms to a given query are taken from an ontology based on meta-documents. The cooperation between meta-documents and query extension aims to have an enhanced information retrieval process. In this paper, we present our proposition particularity and its evaluation results which show its efficiency. Keywords: Information Retrieval, Meta-Document, Annotation, Query, Semantic Extension, OWL Ontology, Semantic Proximity.
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.
A Novel Data mining Technique to Discover Patterns from Huge Text CorpusIJMER
Today, we have far more information than we can handle: from business transactions and scientific
data, to satellite pictures, text reports and military intelligence. Information retrieval is simply not enough
anymore for decision-making. Confronted with huge collections of data, we have now created new needs to
help us make better managerial choices. These needs are automatic summarization of data, extraction of the
"essence" of information stored, and the discovery of patterns in raw data. With this, Data mining with
inventory pattern came into existence and got popularized. Data mining finds these patterns and relationships
using data analysis tools and techniques to build models.
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4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
Java Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
ECE IEEE Projects 2015
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4. Robotics project
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website:1croreprojects.com
Phone : +91 97518 00789 / +91 72999 51536
Comparative analysis of relative and exact search for web information retrievaleSAT Journals
Abstract The volume of data on web repository is huge. To get specific and precise information for the web repository is a big challenge. Existing Information Retrieval (IR) techniques, given by contemporary researchers, are very useful in field of IR. Here, the authors have implemented and tested two of the techniques from the fields of IR. The authors dealt with Relative Search and Exact Search techniques one by one. Initially relative search tested on web repository data using web mining tool and then its results are analyzed. In the same manner, the exact search technique of IR tested on web repository data and the results are measured. The researchers have experienced the significant importance on exact search and relative search. The focused of the research paper is to retrieve relevant information from the web information repository. With the use of two searching criteria these can be done. With the use of the suggested methods the searchers may retrieve a relevant web data in a fewer time. Key Words: Web data Mining, Exact Search, Relative Search, PR, TM, CD, VSM and TASE
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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
Syntactic search relies on keywords contained in a query to find suitable documents. So, documents that do
not contain the keywords but contain information related to the query are not retrieved. Spreading
activation is an algorithm for finding latent information in a query by exploiting relations between nodes in
an associative network or semantic network. However, the classical spreading activation algorithm uses all
relations of a node in the network that will add unsuitable information into the query. In this paper, we
propose a novel approach for semantic text search, called query-oriented-constrained spreading activation
that only uses relations relating to the content of the query to find really related information. Experiments
on a benchmark dataset show that, in terms of the MAP measure, our search engine is 18.9% and 43.8%
respectively better than the syntactic search and the search using the classical constrained spreading
activation.
We are the company providing Complete Solution for all Academic Final Year/Semester Student Projects. Our projects are
suitable for B.E (CSE,IT,ECE,EEE), B.Tech (CSE,IT,ECE,EEE),M.Tech (CSE,IT,ECE,EEE) B.sc (IT & CSE), M.sc (IT & CSE),
MCA, and many more..... We are specialized on Java,Dot Net ,PHP & Andirod technologies. Each Project listed comes with
the following deliverable: 1. Project Abstract 2. Complete functional code 3. Complete Project report with diagrams 4.
Database 5. Screen-shots 6. Video File
SERVICE AT CLOUDTECHNOLOGIES
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ME, M-TECH PAPER PUBLISHING
COLLEGE TRAINING
Thanks&Regards
cloudtechnologies
# 304, Siri Towers,Behind Prime Hospitals
Maitrivanam, Ameerpet.
Contact:-8121953811,8522991105.040-65511811
cloudtechnologiesprojects@gmail.com
http://cloudstechnologies.in/
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Many data mining and knowledge discovery methodologies and process models have been developed, with varying degrees of success, there are three main methods used to discover patterns in data; KDD, SEMMA and CRISP-DM. They are presented in many of the publications of the area and are used in practice. To our knowledge, there is no clear methodology developed to support link mining. However, there is a well known methodology in knowledge discovery in databases, known as Cross Industry Standard Process for Data Mining (CRISPDM), developed by a consortium of several industrial companies which can be relevant to the study of link mining. In this study CRISP-DM has been adapted to the field of Link mining to detect anomalies. An important goal in link mining is the task of inferring links that are not yet known in a given network. This approach is implemented through the use of a case study of real world data (co-citation data). This case study aims to use mutual information to interpret the semantics of anomalies identified in co-citation, dataset that can provide valuable insights in determining the nature of a given link and potentially identifying important future link relationships
Meta documents and query extension to enhance information retrieval processeSAT Journals
Abstract In this paper, we present two facets indispensable for the efficiency of our information retrieval system: meta-documents and query extension. Meta-document represents a structure used to annotate our web documents collection and query extension represents an automatic process aimed to enhance user query expression by additional terms. The two facets are indirectly related since added terms to a given query are taken from an ontology based on meta-documents. The cooperation between meta-documents and query extension aims to have an enhanced information retrieval process. In this paper, we present our proposition particularity and its evaluation results which show its efficiency. Keywords: Information Retrieval, Meta-Document, Annotation, Query, Semantic Extension, OWL Ontology, Semantic Proximity.
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.
A Novel Data mining Technique to Discover Patterns from Huge Text CorpusIJMER
Today, we have far more information than we can handle: from business transactions and scientific
data, to satellite pictures, text reports and military intelligence. Information retrieval is simply not enough
anymore for decision-making. Confronted with huge collections of data, we have now created new needs to
help us make better managerial choices. These needs are automatic summarization of data, extraction of the
"essence" of information stored, and the discovery of patterns in raw data. With this, Data mining with
inventory pattern came into existence and got popularized. Data mining finds these patterns and relationships
using data analysis tools and techniques to build models.
IEEE PROJECTS 2015
1 crore projects is a leading Guide for ieee Projects and real time projects Works Provider.
It has been provided Lot of Guidance for Thousands of Students & made them more beneficial in all Technology Training.
Dot Net
DOTNET Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
Java Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
ECE IEEE Projects 2015
1. Matlab project
2. Ns2 project
3. Embedded project
4. Robotics project
Eligibility
Final Year students of
1. BSc (C.S)
2. BCA/B.E(C.S)
3. B.Tech IT
4. BE (C.S)
5. MSc (C.S)
6. MSc (IT)
7. MCA
8. MS (IT)
9. ME(ALL)
10. BE(ECE)(EEE)(E&I)
TECHNOLOGY USED AND FOR TRAINING IN
1. DOT NET
2. C sharp
3. ASP
4. VB
5. SQL SERVER
6. JAVA
7. J2EE
8. STRINGS
9. ORACLE
10. VB dotNET
11. EMBEDDED
12. MAT LAB
13. LAB VIEW
14. Multi Sim
CONTACT US
1 CRORE PROJECTS
Door No: 214/215,2nd Floor,
No. 172, Raahat Plaza, (Shopping Mall) ,Arcot Road, Vadapalani, Chennai,
Tamin Nadu, INDIA - 600 026
Email id: 1croreprojects@gmail.com
website:1croreprojects.com
Phone : +91 97518 00789 / +91 72999 51536
Big Data & Text Mining: Finding Nuggets in Mountains of Textual Data
Big amount of information is available in textual form in databases or online sources, and for many enterprise functions (marketing, maintenance, finance, etc.) represents a huge opportunity to improve their business knowledge. For example, text mining is starting to be used in marketing, more specifically in analytical customer relationship management, in order to achieve the holy 360° view of the customer (integrating elements from inbound mails, web comments, surveys, internal notes, etc.).
Facing this new domain I have make a personal research, and realize a synthesis, which has help me to clarify some ideas. The below presentation does not intend to be exhaustive on the subject, but could perhaps bring you some useful insights.
Annotation for query result records based on domain specific ontologyijnlc
The World Wide Web is enriched with a large collection of data, scattered in deep web databases and web
pages in unstructured or semi structured formats. Recently evolving customer friendly web applications
need special data extraction mechanisms to draw out the required data from these deep web, according to
the end user query and populate to the output page dynamically at the fastest rate. In existing research
areas web data extraction methods are based on the supervised learning (wrapper induction) methods. In
the past few years researchers depicted on the automatic web data extraction methods based on similarity
measures. Among automatic data extraction methods our existing Combining Tag and Value similarity
method, lags to identify an attribute in the query result table. A novel approach for data extracting and
label assignment called Annotation for Query Result Records based on domain specific ontology. First, an
ontology domain is to be constructed using information from query interface and query result pages
obtained from the web. Next, using this domain ontology, a meaning label is assigned automatically to each
column of the extracted query result records.
The previous research has focused on quick and efficient generation of wrappers; the
development of tools for wrapper maintenance has received less attention. This is an important research
problem because Web sources often change in ways that prevent the wrappers from extracting data
correctly. Present an efficient algorithm that extract unstructured data to structural data from web. The
wrapper verification system detects when a wrapper is not extracting correct data, usually because the
Web source has changed its format. The Verification framework automatically recovers data using
Dimension Reduction Techniques from changes in the Web source by identifying data on Web pages.
After apply wrapped data to One Class Classification in Numerical features for avoid classification
problem. Finally, the result data apply in Top-K query for provide best rank based on probabilities
scores. Wrapper verification system relies on one-class classification techniques to beat previous
weaknesses to identify the problem by analysing both the signature and the classifier output. If there are
sufficient mislabelled slots, a technique to find a pattern could be explored.
A Novel Data Extraction and Alignment Method for Web DatabasesIJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
International Journal of Modern Engineering Research (IJMER) covers all the fields of engineering and science: Electrical Engineering, Mechanical Engineering, Civil Engineering, Chemical Engineering, Computer Engineering, Agricultural Engineering, Aerospace Engineering, Thermodynamics, Structural Engineering, Control Engineering, Robotics, Mechatronics, Fluid Mechanics, Nanotechnology, Simulators, Web-based Learning, Remote Laboratories, Engineering Design Methods, Education Research, Students' Satisfaction and Motivation, Global Projects, and Assessment…. And many more.
A Web Extraction Using Soft Algorithm for Trinity Structureiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Extraction of Data Using Comparable Entity Miningiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
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An effective search on web log from most popular downloaded contentijdpsjournal
A Web page recommender system effectively predicts the best related web page to search. While search
ing
a word from search engine it may display some unnecessary links and unrelated data’s to user so to a
void
this problem, the con
ceptual prediction model combines both the web usage and domain knowledge. The
proposed conceptual prediction model automatically generates a semantic network of the semantic Web
usage knowledge, which is the integration of domain knowledge and web usage i
nformation. Web usage
mining aims to discover interesting and frequent user access patterns from web browsing data. The
discovered knowledge can then be used for many practical web applications such as web
recommendations, adaptive web sites, and personali
zed web search and surfing
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
2. Referenced paper Andrew McCallum: Information Extraction: Distilling Structured Data from Unstructured Text. ACM Queue, volume 3, Number 9, November 2005. Craig A. Knoblock, Kristina Lerman, Steven Minton, Ion Muslea: Accurately and Reliably Extracting Data from the Web: A Machine Learning Approach. IEEE Data Eng. Bull. 23(4): 33-41 (2000)
3. Example Information Extraction: Distilling Structured Data from Unstructured Text Task: try to build a website to help people find continuing education opportunities at colleges, universities, and organization across the country, to support field searches over locations, dates, times etc. Problem: much of the data was not available in structured form. The only universally available public interfaces were web pages designed for human browsing.
5. Information extraction Information Extraction: Distilling Structured Data from Unstructured Text Information extraction is the process of filling the fields and records of a database from unstructured or loosely formatted text.
6.
7. Information extraction Information Extraction: Distilling Structured Data from Unstructured Text Information extraction involves five major subtasks
8. Technique in information extraction Information Extraction: Distilling Structured Data from Unstructured Text Some simple extraction tasks can be solved by writing regular expressions. Due to Frequently change of web pages, the previous method is not sufficient for the information extraction task. Over the past decade there has been a revolution in the use of statistical and machine-learning methods for information extraction.
9. A Machine Learning Approach Accurately and Reliably Extracting Data from the Web: A Machine Learning Approach A wrapper is a piece of software that enables a semi-structured Web source to be queried as if it were a database.
10. Contributions Accurately and Reliably Extracting Data from the Web: A Machine Learning Approach The ability to learn highly accurate extraction rules. To verify the wrapper to ensure that the correct data continues to be extracted. To automatically adapt to changes in the sites from which the data is being extracted.
11. Learning extraction rules Accurately and Reliably Extracting Data from the Web: A Machine Learning Approach One of the critical problems in building a wrapper is defining a set of extraction rules that precisely define how to locate the information on the page. For any given item to be extracted from a page, one needs an extraction rule to locate both the beginning and end of that item. A key idea underlying our work is that the extraction rules are based on “landmarks” (i.e., groups of consecutive tokens) that enable a wrapper to locate the start and end of the item within the page.
12. Samples Accurately and Reliably Extracting Data from the Web: A Machine Learning Approach
13. Rules Accurately and Reliably Extracting Data from the Web: A Machine Learning Approach Start rules: End rules are similar to start rules. Disjunctive rules:
14. STALKER to learn rules Accurately and Reliably Extracting Data from the Web: A Machine Learning Approach STALKER : a hierarchical wrapper induction algorithm that learns extraction rules based on examples labeled by the user. STALKER only requires no more than 10 examples because of the fixed web page format and the hierarchical structure. STALKER exploits the hierarchical structure of the source to constrain the learning problem.
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16. Then use another rule to break the list into tuples that correspond to individual restaurants;
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18. STALKER to learn rules Accurately and Reliably Extracting Data from the Web: A Machine Learning Approach Learning a start rule for address: First, it selects an example, say E4, to guide the search. Second, it generates a set of initial candidates, which are rules that consist of a single 1-token landmark; these landmarks are chosen so that they match the token that immediately precedes the beginning of the address in the guiding example.
19. STALKER to learn rules Accurately and Reliably Extracting Data from the Web: A Machine Learning Approach Learning a start rule for address: Because R6 has a better generalization potential, STALKER selects R6 for further refinements. While refining R6, STALKER creates, among others, the new candidates R7, R8, R9, and R10 shown below.
20. STALKER to learn rules Accurately and Reliably Extracting Data from the Web: A Machine Learning Approach Learning a start rule for address: As R10 works correctly on all four examples, STALKER stops the learning process and returns R10. Result of STALKER: In an empirical evaluation on 28 sources STALKER had to learn 206 extraction rules. They learned 182 perfect rules (100% accurate), and another 18 rules that had an accuracy of at least 90%. In other words, only 3% of the learned rules were less that 90% accurate.
21. Identifying highly informative examples Accurately and Reliably Extracting Data from the Web: A Machine Learning Approach The most informative examples illustrate exceptional cases. They have developed an active learning approach called co-testing that analyzes the set of unlabeled examples to automatically select examples for the user to label. Backward:
22. Identifying highly informative examples Accurately and Reliably Extracting Data from the Web: A Machine Learning Approach Basic idea: after the user labels one or two examples, the system learns both a forward and a backward rule. Then it runs both rules on a given set of unlabeled pages. Whenever the rules disagree on an example, the system considers that as an example for the user to label next. Co-testing makes it possible to generate accurate extraction rules with a very small number of labeled examples.
23. Identifying highly informative examples Accurately and Reliably Extracting Data from the Web: A Machine Learning Approach Assume that the initial training set consists of E1 and E2, while E3 and E4 are not labeled. Based on these examples, we learn the rules:
24. Identifying highly informative examples We applied co-testing on the 24 tasks on which STALKER fails to learn perfect rules. The results were excellent: the average accuracy over all tasks improved from 85.7% to 94.2%. Furthermore, 10 of the learned rules were 100% accurate, while another 11 rules were at least 90% accurate.
25. Verifying the extracted data Accurately and Reliably Extracting Data from the Web: A Machine Learning Approach Since the information for even a single field can vary considerably, the system learns the statistical distribution of the patterns for each field. Wrappers can be verified by comparing the patterns of data returned to the learned statistical distribution. When a significant difference is found, an operator can be notified or we can automatically launch the wrapper repair process.
26. Automatically repairing wrappers Accurately and Reliably Extracting Data from the Web: A Machine Learning Approach Locate correct examples of the data field on new pages. Re-label the new pages automatically. Labeled and re-labeled examples re-run through the STALKER to produce the correct rules for this site.
27. How to locate the correct example? Accurately and Reliably Extracting Data from the Web: A Machine Learning Approach Each new page is scanned to identify all text segments that begin with one of the starting patterns and end with one of the ending patterns. Those segments, which we call candidates. The candidates are then clustered to identify subgroups that share common features (relative position on the page, adjacent landmarks, and whether it is visible to the user). Each group is then given a score based on how similar it is to the training examples. We expect the highest ranked group to contain the correct examples of the data field.
29. Upcoming trends and capabilities Information Extraction: Distilling Structured Data from Unstructured Text Combine IE and data mining to perform text mining as well as improve the performance of the underlying extraction system. Rules mined from a database extracted from a corpus of texts are used to predict additional information to extract from future documents, thereby improving the recall of IE.
30. Upcoming trends and capabilities Information Extraction: Distilling Structured Data from Unstructured Text SQL --> Database
31. Information extraction, the Web and the future Information Extraction: Distilling Structured Data from Unstructured Text Second half internet revolution: machine access to this immense knowledge base
32. Information extraction, the Web and the future Information Extraction: Distilling Structured Data from Unstructured Text In web search there will be a transition from keyword search on documents to higher-level queries: queries where the search hits will be objects, such as people or companies instead of simply documents; queries that are structured and return information that has been integrated and synthesized from multiple pages; queries that are stated as natural language questions (“Who were the first three female U.S. Senators?”) and answered with succinct responses.