semi-structured text sources

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A Framework for Extracting Information from Semi-Structured Web Data Sources

A Framework for Extracting Information from Semi-Structured Web Data Sources

Interface (QI) which is used for accepting user's queries and searching web pages based on the user's queries through search engine, Information Extraction (IE) which is used for extracting and classifying the web pages obtained from QI and converting the extracted and classified information into text form, and Relevant Information Analyzer (RIA) which is used for determining the relevant information extracted from Information Extraction (IE). The rest of the chapter is organized as follows. In section 2, we explain the concepts related to a typical Information Extraction (IE). In section 3, the previous works related to this research are reported. In section 4, we present the proposed framework. Conclusion is presented in the final section 5.
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A Survey on Unsupervised Extraction of Product
          Information from Semi-Structured Sources

A Survey on Unsupervised Extraction of Product Information from Semi-Structured Sources

The pages collected by the form crawler are output to the wrapper generator to induce the regular expression wrapper based on the pages' HTML-tag structures. Since pre- defined templates generate the web pages, the HTML tag- structure enclosing data objects may appear repeatedly if the page contains more than one instance of a data object. Therefore, the wrapper generator first considers the web page as a token sequence composed of HTML tags and a special token "text" representing any text string enclosed by pairs of HTML-tags, then extracts repeated HTML tag substrings from the token sequence and induces a regular expression wrapper from the repeated substrings according to some hierarchical relationships among them.
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A Comparative Review on Data Mining With Text Mining

A Comparative Review on Data Mining With Text Mining

Text mining is also called text data mining and it is defined as finding previously unknown and potentially useful from textual data, textual data may be either semi structured or unstructured. Text mining is used to extract interesting information or knowledge or pattern from the unstructured texts that are from different sources. It converts the words and phrases in unstructured information into numerical values which may be linked with structured information in database and analyzed with ancient data mining techniques. There are many techniques used in text mining such as information extraction, information retrieval, natural language processing (NLP), query processing, and categorization and clustering.
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Analysis and Implementation of Text Mining for Different Documents

Analysis and Implementation of Text Mining for Different Documents

Abhilasha [6], analysed retrieval of text data to select right method for text mining is an important task. The author also focused on automatic text mining to find effective and easy to use method. Michele et al., [7] the authors developed the text mining tool for linguistic, analysed the pros and cons of the text mining tool and compared with conventional pattern classification. Zhou et al., [8], suggested a new improved KNN algorithm for text classification to avoid the complexity. The results are shown with greater accuracy. Songbo Tan [9] dealt uneven text data and NWKNN algorithm was proposed. The experimental result achieves performance improvement.
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Do parent and child outcome expectations align when attending a weight management programme?

Do parent and child outcome expectations align when attending a weight management programme?

AdultsIntervention: and Weigh to Go YP – Semi-structured Early Factors associated with earlyinterviews Young people – Participatory Research Methods and semi-structured interviews engage[r]

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Model fidelity and team members’ experiences of assertive community treatment in Norway: a sequential mixed-methods study

Model fidelity and team members’ experiences of assertive community treatment in Norway: a sequential mixed-methods study

The fidelity assessments were based on the following data sources obtained from each team: semi-structured interviews with all team members (seven interviews, one with each group of t[r]

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The weakest link: competence and prestige as constraints to referral by isolated nurses in rural Niger

The weakest link: competence and prestige as constraints to referral by isolated nurses in rural Niger

In order to understand the nature of the constraints to referral that relate to the interaction between nurses and patients, information was gathered and triangulated from three sources in two rural districts in Niger: first, semi- structured interviews with 46 nurses; second, 42 focus group discussions with an average of 12 participants – patients, relatives of patients and others; third, 231 inter- views with referred patients of which 215 (93%) had com- plied with a referral and 16 (7%) had not. A social scientist and a last year medical student, familiar with the local language and culture and specifically trained for this work, conducted these interviews. The focus groups were conducted in 2 different cultural zones and villages were selected according to distance to the health centre and to the district hospital, explaining the relatively high number of focus groups. Culture or distance did not significantly influence the content of the dialogues though.
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Novel Framework for Distributed Data Stream Mining in Big data Analytics Using Time Sensitive Sliding Window

Novel Framework for Distributed Data Stream Mining in Big data Analytics Using Time Sensitive Sliding Window

In the digital world, large volume of data has been continuously generating from sensors, social media sites, videos, purchase transaction records, and cell phone GPS signals. These types of data are called big data. Big data is used to describe collection of data sets so large and complex, structured and unstructured types and it becomes difficult to process using on-hand data management tools or traditional data processing applications. Due to complex types and volumes of big data, the traditional static database and mining procedure is not suitable for Big Data Analytics. Predicting patterns in such dynamic environment is a challenging task in big data analytics. This paper proposes a novel frame work for mining frequent patterns in real time dynamic environment based on time sensitive sliding window. Our framework proposes a distributed mining which predicts frequent patterns from continuous data streams over various tilted time window. The framework suggests the distributed file system to store the continuous data stream and tilted time window model to hold the part of stream of data. The system recommends data distribution model, so that data window are distributed to different processing commodity node and the frequent pattern mining procedure is applied on separate node simultaneously. In this paper we have proposed framework which will use the power of Hadoop for mining the frequent Itemset in a Distributed environment.
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SYNTHESIS LECTURES ON DATA MINING AND KNOWLEDGE DISCOVERY. Elena Zheleva Evimaria Terzi Lise Getoor. Privacy in Social Networks

SYNTHESIS LECTURES ON DATA MINING AND KNOWLEDGE DISCOVERY. Elena Zheleva Evimaria Terzi Lise Getoor. Privacy in Social Networks

Synthesis Lectures on Data Mining and Knowledge Discovery is edited by Jiawei Han, Lise Getoor, Wei Wang, and Johannes Gehrke. The series publishes 50- to 150-page publications on topics pertaining to data mining, web mining, text mining, and knowledge discovery, including tutorials and case studies. The scope will largely follow the purview of premier computer science conferences, such as KDD. Potential topics include, but not limited to, data mining algorithms, innovative data mining applications, data mining systems, mining text, web and semi-structured data, high performance and parallel/distributed data mining, data mining standards, data mining and knowledge discovery framework and process, data mining foundations, mining data streams and sensor data, mining multi-media data, mining social networks and graph data, mining spatial and temporal data, pre-processing and post-processing in data mining, robust and scalable statistical methods, security, privacy, and adversarial data mining, visual data mining, visual analytics, and data visualization.
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Attribute Relation Extraction from Template inconsistent Semi structured Text by Leveraging Site level Knowledge

Attribute Relation Extraction from Template inconsistent Semi structured Text by Leveraging Site level Knowledge

To identify weak semi-block, we take the advan- tage of site-level knowledge to make several em- pirical rules based on the alignment of site-level templates and text of each block. The strings ex- tracted by the templates are attribute candidates (AttCandi for short). We think only AttCandies extracted by authentic templates are correct at- tributes. A template is regarded as authentic once an AttCandi extracted by it exists in the site-level attributes. In the extraction of attribute’s values, we follow the method in (Yoshinaga and Torisawa, 2007) with the hypothesis that an attribute imme- diately precedes its value, and another AV pair im- mediately follows those values.
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An Analysis and Implication of Data Mining Techniques using Semi structured Data towards clinical data sets

An Analysis and Implication of Data Mining Techniques using Semi structured Data towards clinical data sets

4. Ahmad Basheer Hassanat , Mohammed Ali Abbadi and Ghada Awad Altarawneh .Solving the problem of the K Parameter in the KNN Classifier using an Ensemble Learning Approach. International Journal of Computer Science and Information Security. 2014; 12(8). 5. Suneetha Manne, Sita Kumari Kotha and Dr. S. Sameen Fatima. A query based text

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Fusion Architecture of Database for Large and Diverse Dataset

Fusion Architecture of Database for Large and Diverse Dataset

In past decades the data model used for a standard storage system is a relational data model. Predominantly it uses table and record structure to store data. Recent advancements in the nature and behavior of data in terms of volume, velocity and variety have led to the necessity of a new database model. Non-relational data models are one solution to it but at the cost of basic ACID properties. This large dataset consists of structured, semi-structured and unstructured data. All of them have to be treated differently. Both relational and non-relational data models have their respective advantages and disadvantages in data handling. This paper proposes a fusion architecture of database which combines the advantages of both and at the same time minimizing the disadvantages. The proposed architecture will be capable of handling diverse and voluminous data. Proposed approach intends to bridge a gap between relational and non-relational approaches and to provide an efficient data handling method for large and diverse datasets.
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Methods and tools for temporal knowledge harvesting

Methods and tools for temporal knowledge harvesting

There is no 100% perfect extraction methodology. Incorrect extractions obviously affect the precision of the generated summary. Furthermore, extraction recall also influences the short summary since we report a statistical number. For example, if we only extract one award of David Beckham, the short summary “David Beckham won about one honor” is incorrect. This error may only happen for extractive summaries when the source data contains mistakes. To reduce such errors, we might consider including also vague statements like “more than”. Opinosis compresses the text by considering the sentence redundancy, so the new generated sentences may change the semantics of the original sentences. This holds for semi-structured contents which are presented as natural-language sentences, such as “Rui Costa has won Toulon Tournament in 1992. Rui Costa has won FIFA U-20 World Cup in 1991.” Opinosis is able to compress them into one meaningful sentence “Rui Costa has won Toulon Tournament in 1992 and FIFA U-20 World Cup in 1991.” While for other sentences in the Wikipedia article, most generated sentences are meaningless and even incorrect. See the running examples of David Beckham in the last subsection. Opinosis generates one sentence “Beckham’s marriage in 2007- -/:.” However Beckham actually got married with Victoria on 1999-July-04. Also, the sentence “David Beckham enjoyed tremendous following” is meaningless. Such a sentence is given a score of 3. So the overall precision of Opinosis is about 3. Noisy text, such as URL links which are recognized as sentences, is accounted as an error for precision during the evaluation. This is also the reason why the precision of NIST-Wiki is not 100% correct.
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Searching without SQL: Re-engineering a database-centric web application with open-source information retrieval software.

Searching without SQL: Re-engineering a database-centric web application with open-source information retrieval software.

As I get ready to deploy the redesigned version of the WXYC card catalog application, I have already begun thinking of future enhancements and extensions to the current system. Cross-references (“See also….”) that once existed in the old paper-based system could potentially be implemented by including the referring artist amidst the indexed fields of a referred-to document. User-generated tags might allow for additional facets on which to filter. And ultimately, song data could be added to the index, greatly increasing the amount of searchable text included in release-based documents. These changes would have been difficult to implement efficiently in the old MySQL-based system, but the search functionality of the new Lucene-based system is a lot more extensible.
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Using ConceptGrid as an easy authoring technique to check natural language responses

Using ConceptGrid as an easy authoring technique to check natural language responses

ConceptGrid tutors on short natural language responses, the typical length being a sentence or two. It is designed to evaluate semi-structured natural language, meaning text in which y[r]

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An analysis and Implication of Data Mining Techniques using Semi structured Data towards clinical data sets

An analysis and Implication of Data Mining Techniques using Semi structured Data towards clinical data sets

Data mining plays a dynamic role in today’s world. In this era, technological inventions, innovations and also development of algorithms has increasing day by day. One of the prevalent applications of data mining is web mining. Web Mining is an active area. Web structure mining, web content mining and web usage mining are the classifications of Web Mining. This paper stipulates the semi structured data and how data mining techniques are used in the semi-structured data and comparison learning has done to showcase the better technique for supporting semi structured data. KEYWORDS: Web Mining, Data mining, Support Vector Machine, Polynomial ker
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A Semi-Structured Structured Tailoring-Driven Approach for ERP Selection

A Semi-Structured Structured Tailoring-Driven Approach for ERP Selection

It has been widely reported that selecting an inappropriate system is a major reason for ERP implementation failures. The selection of an ERP system is therefore critical. While the number of papers related to ERP implementation is substantial, ERP evaluation and selection approaches have received few attention. Motivated by the adaptation concept of the ERP systems, we propose in this paper a semi-structured approach for ERP system selection that has a more holistic focus by simultaneously evaluating candidate products according to both functional and non-functional requirements and considering the anticipated fitness of ERP solutions after the optimal resolution, within limited resources, of a set of the identified mismatches. The approach consists of an iterative selection process and an evaluation methodology that combines a 0-1 linear programming model to determine functional measurement metrics with MACBETH cardinal scales to elaborate multi-criteria performance expressions.
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Relation Extraction with Matrix Factorization and Universal Schemas

Relation Extraction with Matrix Factorization and Universal Schemas

Note that by operating simultaneously on rela- tions observed in text and in pre-existing structured databases such as Freebase, we are able to reason about unstructured and structured data in mutually- supporting ways. For example, we can predict sur- face pattern relations that effectively serve as addi- tional features when predicting Freebase relations, hence improving generalization. Also notice that users of our system will not have to study and un- derstand the complexities of a particular schema in order to issue queries; they can ask in whatever form naturally occurs to them, and our system will likely already have that relation in our universal schema.
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Space characters in Chinese semi structured texts

Space characters in Chinese semi structured texts

Space characters are natural delimiters in some languages. In English and many other Latin-based languages for example, spaces are used for separat- ing words and certain punctuation marks (e.g. period and colon). However, in formal Chinese typesetting, spaces are not used to delimit words or characters. Hence the need for automatic word segmentation sys- tems (Zhang et al., 2003). The current segmenta- tion systems mainly focus on resolving ambiguities and detecting new words in segmenting text with no spaces (Gao et al., 2005). However, ambiguities can be caused not only by characters themselves, but also the spaces and layout around them. The paper will later demonstrate this in terms of recognising entities, but the same should apply to segmentation.
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Semi structured Interview of Moral cognitionS (SIMS)

Semi structured Interview of Moral cognitionS (SIMS)

This ‘Semi-structured Interview of Moral cognitionS’ (SIMS) is a synthesis of our experience and research in the fields of clinical/forensic psychology and forensic psychiatry. As an interview the SIMS aims to make the non-understandable understandable and to demystify serious acts of violence. We would like to take this opportunity to thank all of the patients who have generously given their time to participate in this and associated projects. We are grateful for what they have had to teach us about human nature and the psyche. It is our sincere hope that the SIMS will lead to improved outcomes for patients and their families. We believe our instrument will be of interest to others in the field of forensic psychology and psychiatry.
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