Information Extraction, Data Integration, and Uncertain Data Management: The State of The Art

Information Extraction, data Integration, and uncertain data management are different areas of research that got vast focus in the last two decades. Many researches tackled those areas of research individually. However, information extraction systems should have integrated with data integration methods to make use of the extracted information. Handling uncertainty in extraction and integration process is an important issue to enhance the quality of the data in such integrated systems. This article presents the state of the art of the mentioned areas of research and shows the common grounds and how to integrate information extraction and data integration under uncertainty management cover

Social Network Threat Detection

Various government agencies have a stake in knowing when bad actors cross the United States\u27 borders, or how bad actors may be involved in the ow of people across borders. Interviews conducted at border checkpoints with individuals who intend to cross the border can contain valuable information. The quantity of interviews is such that intelligence analysts could benefit greatly from an automation system that extracts the information they are looking for from within the interviews. This would allow them to focus more of their time on analyzing what is extracted as opposed to inspecting all interviews themselves. The information extracted can be written to an SQL database, allowing the information to then be easily and efficiently queried for valuable insight and analysis

An Improving Genetic Programming Approach Based Deduplication Using KFINDMR

Abstract-The record deduplication is the task of identifying, in a data repository, records that refer to the same real world entity or object in spite of misspelling words, types, different writing styles or even different schema representations or data types. In existing system aims at providing Unsupervised Duplication Detection (UDD) method which can be used to identify and remove the duplicate records from different data sources. Starting from the non duplicate set, the two cooperating classifiers, a Weighted Component Similarity Summing Classifier (WCSS) and Support Vector Machine (SVM) are used to iteratively identify the duplicate records from the non duplicate record and present a genetic programming (GP) approach to record deduplication. Their GP-based approach is also able to automatically find effective deduplication functions. The genetic programming approach is time consuming task so we propose new algorithm KFINDMR (KFIND using Most Represented data samples) to find the most represented data samples to improve the accuracy of the classifier. The proposed system calculates the mean value of the most represented data samples in centroid of the record members; it selects the first most represented data sample that closest to the mean value calculates the minimum distance. The system Remove the duplicate dataset samples in the system and find the optimization solution to deduplication of records or data samples

Understanding, Estimating, and Incorporating Output Quality Into Join Algorithms For Information Extraction

Information extraction (IE) systems are trained to extract specific relations from text databases. Real-world applications often require that the output of multiple IE systems be joined to produce the data of interest. To optimize the execution of a join of multiple extracted relations, it is not sufficient to consider only execution time. In fact, the quality of the join output is of critical importance: unlike in the relational world, different join execution plans can produce join results of widely different quality whenever IE systems are involved. In this paper, we develop a principled approach to understand, estimate, and incorporate output quality into the join optimization process over extracted relations. We argue that the output quality is affected by (a) the configuration of the IE systems used to process the documents, (b) the document retrieval strategies used to retrieve documents, and (c) the actual join algorithm used. Our analysis considers a variety of join algorithms from relational query optimization, and predicts the output quality –and, of course, the execution time– of the alternate execution plans. We establish the accuracy of our analytical models, as well as study the effectiveness of a quality-aware join optimizer, with a large-scale experimental evaluation over real-world text collections and state-of-the-art IE systems

Data Profiling to Reveal Meaningful Structures for Standardization

Today many organisations and enterprises are using data from several sources either for strategic decision making or other business goals such as data integration. Data quality problems are always a hindrance to effective and efficient utilization of such data. Tools have been built to clean and standardize data, however, there is a need to pre-process this data by applying techniques and processes from statistical semantics, NLP, and lexical analysis. Data profiling employed these techniques to discover, reveal commonalties and differences in the inherent data structures, present ideas for creation of unified data model, and provide metrics for data standardization and verification. The IBM WebSphere tool was used to pre-process dataset/records by design and implementation of rule sets which were developed in QualityStage and tasks which were created in DataStage. Data profiling process generated set of statistics (frequencies), token/phrase relationships (RFDs, GRFDs), and other findings in the dataset that provided an overall view of the data source's inherent properties and structures. The examination of data ( identifying violations of the normal forms and other data commonalities) from a dataset and collecting the desired information provided useful statistics for data standardization and verification by enable disambiguation and classification of data.Master i Informatikk - programutviklingMAMN-INFPRINFP

Semi-automatic matching of semi-structured data updates

Includes bibliographical references.Data matching, also referred to as data linkage or field matching, is a technique used to combine multiple data sources into one data set. Data matching is used for data integration in a number of sectors and industries; from politics and health care to scientific applications. The motivation for this study was the observation of the day-to-day struggles of a large non-governmental organisation (NGO) in managing their membership database. With a membership base of close to 2.4 million, the challenges they face with regard to the capturing and processing of the semi-structured membership updates are monumental. Updates arrive from the field in a multitude of formats, often incomplete and unstructured, and expert knowledge is geographically localised. These issues are compounded by an extremely complex organisational hierarchy and a general lack of data validation processes. An online system was proposed for pre-processing input and then matching it against the membership database. Termed the Data Pre-Processing and Matching System (DPPMS), it allows for single or bulk updates. Based on the success of the DPPMS with the NGO’s membership database, it was subsequently used for pre-processing and data matching of semi-structured patient and financial customer data. Using the semi-automated DPPMS rather than a clerical data matching system, true positive matches increased by 21% while false negative matches decreased by 20%. The Recall, Precision and F-Measure values all improved and the risk of false positives diminished. The DPPMS was unable to match approximately 8% of provided records; this was largely due to human error during initial data capture. While the DPPMS greatly diminished the reliance on experts, their role remained pivotal during the final stage of the process

Understanding, Estimating, and Incorporating Output Quality Into Join Algorithms For Information Extraction

Information extraction (IE) systems are trained to extract specific relations from text databases. Real-world applications often require that the output of multiple IE systems be joined to produce the data of interest. To optimize the execution of a join of multiple extracted relations, it is not sufficient to consider only execution time. In fact, the quality of the join output is of critical importance: unlike in the relational world, different join execution plans can produce join results of widely different quality whenever IE systems are involved. In this paper, we develop a principled approach to understand, estimate, and incorporate output quality into the join optimization process over extracted relations. We argue that the output quality is affected by (a) the configuration of the IE systems used to process the documents, (b) the document retrieval strategies used to retrieve documents, and (c) the actual join algorithm used. Our analysis considers a variety of join algorithms from relational query optimization, and predicts the output quality –and, of course, the execution time– of the alternate execution plans. We establish the accuracy of our analytical models, as well as study the effectiveness of a quality-aware join optimizer, with a large-scale experimental evaluation over real-world text collections and state-of-the-art IE systems

Data Profiling to Reveal Meaningful Structures for Standardization

Today many organisations and enterprises are using data from several sources either for strategic decision making or other business goals such as data integration. Data quality problems are always a hindrance to effective and efficient utilization of such data. Tools have been built to clean and standardize data, however, there is a need to pre-process this data by applying techniques and processes from statistical semantics, NLP, and lexical analysis. Data profiling employed these techniques to discover, reveal commonalties and differences in the inherent data structures, present ideas for creation of unified data model, and provide metrics for data standardization and verification. The IBM WebSphere tool was used to pre-process dataset/records by design and implementation of rule sets which were developed in QualityStage and tasks which were created in DataStage. Data profiling process generated set of statistics (frequencies), token/phrase relationships (RFDs, GRFDs), and other findings in the dataset that provided an overall view of the data source's inherent properties and structures. The examination of data ( identifying violations of the normal forms and other data commonalities) from a dataset and collecting the desired information provided useful statistics for data standardization and verification by enable disambiguation and classification of data

Entity Extraction from Unstructured Data on the Web

A large number of web pages contain information about entities in lists where the lists are represented in textual form. Textual lists contain implicit records of entities. However, the field values of such records cannot easily be separated or extracted by automatic processes. This, therefore, remains a challenging research problem in the literature. Previous studies in the literature relied mainly on probabilistic graph-based models to capture the attributes and the likely structures of implicit records in a list. However, one of the important limitations of existing methods is that the structures of the records in input lists were implicitly encoded via training data which was manually created. This thesis aims to investigate novel techniques to acquire automatically information about entities from implicit records embedded in textual lists on the web. This thesis introduces a self-supervised learning framework which exploits both existing data in a knowledge base and the structural similarity between sequences in lists to build an extraction model automatically. In the proposed framework, initial labels for candidate field values are created and assigned to generate label sequences. Then, the structure of implici