Entity Relation Mining in Large-Scale Data

Li, Jingnan; Cai, Yi; Wang, Qixuan; Hu, Shuyue; Wang, Tao; Min, Huaqing

doi:10.1007/978-3-319-22324-7_10

Cited by 2 publications

(4 citation statements)

References 11 publications

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“…Apache Hadoop provides a platform to adopt parallelization in many to many relation extraction tasks using MapReduce. The system was evaluated with 100 GB free text and many to many relationships were identified [24]. Traditional methods are ineffective to handle data sparsity and scalability [24].…”

Section: Relation Extraction (Re)mentioning

confidence: 99%

“…The system was evaluated with 100 GB free text and many to many relationships were identified [24]. Traditional methods are ineffective to handle data sparsity and scalability [24]. Distant supervised learning, CNN and transfer learning have outperformed the existing traditional methods [23,25,26].…”

Section: Relation Extraction (Re)mentioning

confidence: 99%

“…The exponential growth of unstructured big data is making IE task more arduous. However, MapReduce has the capability to deal with large scale datasets by distributing the data into different clusters that increases the time efficiency [15,22,24]. Hence, the volume can be effectively handled using Apache Hadoop, whereas, the issues related to the variety of data needs to be focused.…”

Section: Unstructured Big Data Barriers For Iementioning

confidence: 99%

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An analytical study of information extraction from unstructured and multidimensional big data

2019

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IntroductionInformation extraction (IE) process extracts useful structured information from the unstructured data in the form of entities, relations, objects, events and many other types. The extracted information from unstructured data is used to prepare data for analysis. Therefore, the efficient and accurate transformation of unstructured data in the IE process improves the data analysis. Numerous techniques have been introduced for different data types i.e. text, image, audio, and video.The advancement in technology promoted the rapid growth of data volume in recent years. The volume, variety (structured, unstructured, and semi-structured data) and velocity of big data have also changed the paradigm of computational capabilities of the systems. IBM estimated that more than 2.5 quintillion bytes of data are generated every Abstract Process of information extraction (IE) is used to extract useful information from unstructured or semi-structured data. Big data arise new challenges for IE techniques with the rapid growth of multifaceted also called as multidimensional unstructured data. Traditional IE systems are inefficient to deal with this huge deluge of unstructured big data. The volume and variety of big data demand to improve the computational capabilities of these IE systems. It is necessary to understand the competency and limitations of the existing IE techniques related to data pre-processing, data extraction and transformation, and representations for huge volumes of multidimensional unstructured data. Numerous studies have been conducted on IE, addressing the challenges and issues for different data types such as text, image, audio and video. Very limited consolidated research work have been conducted to investigate the task-dependent and task-independent limitations of IE covering all data types in a single study. This research work address this limitation and present a systematic literature review of state-of-the-art techniques for a variety of big data, consolidating all data types. Recent challenges of IE are also identified and summarized. Potential solutions are proposed giving future research directions in big data IE. The research is significant in terms of recent trends and challenges related to big data analytics. The outcome of the research and recommendations will help to improve the big data analytics by making it more productive. which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Adnan and Akbar J Big Data (2019) 6:91 Malaysia Adnan and Akbar J Big Data (2019) 6:91 RESEARCHday. Among these statistics, it was also predicted that unstructured data from diverse sources will grow up to 90% in few years. IDC estimated that unstructured data will be 95% of the global data in 2020 with estimated 65% annual growth rate [1]. The common characteristics of unstructured data are, (i) it comes in multiple formats...

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Section: Relation Extraction (Re)mentioning

confidence: 99%

Section: Relation Extraction (Re)mentioning

confidence: 99%

Section: Unstructured Big Data Barriers For Iementioning

confidence: 99%

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An analytical study of information extraction from unstructured and multidimensional big data

2019

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“…MapReduce was used to extract many to many relationships between entities of sized 100 GB free text and a proposed solution has outperformed as compared to existing approaches. 48 The scalability and sparsity of unstructured big data make traditional methods ineffective. 32 To overcome the limitation of existing traditional methods, distant supervised learning, CNN (Convolutional Neural Network), and transfer learning have shown pretty notable results.…”

Section: Ie For Unstructured Data Analysismentioning

confidence: 99%

Limitations of information extraction methods and techniques for heterogeneous unstructured big data

Adnan

Akbar

2019

International Journal of Engineering Business Management

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During the recent era of big data, a huge volume of unstructured data are being produced in various forms of audio, video, images, text, and animation. Effective use of these unstructured big data is a laborious and tedious task. Information extraction (IE) systems help to extract useful information from this large variety of unstructured data. Several techniques and methods have been presented for IE from unstructured data. However, numerous studies conducted on IE from a variety of unstructured data are limited to single data types such as text, image, audio, or video. This article reviews the existing IE techniques along with its subtasks, limitations, and challenges for the variety of unstructured data highlighting the impact of unstructured big data on IE techniques. To the best of our knowledge, there is no comprehensive study conducted to investigate the limitations of existing IE techniques for the variety of unstructured big data. The objective of the structured review presented in this article is twofold. First, it presents the overview of IE techniques from a variety of unstructured data such as text, image, audio, and video at one platform. Second, it investigates the limitations of these existing IE techniques due to the heterogeneity, dimensionality, and volume of unstructured big data. The review finds that advanced techniques for IE, particularly for multifaceted unstructured big data sets, are the utmost requirement of the organizations to manage big data and derive strategic information. Further, potential solutions are also presented to improve the unstructured big data IE systems for future research. These solutions will help to increase the efficiency and effectiveness of the data analytics process in terms of context-aware analytics systems, data-driven decision-making, and knowledge management.

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Entity Relation Mining in Large-Scale Data

Cited by 2 publications

References 11 publications

An analytical study of information extraction from unstructured and multidimensional big data

An analytical study of information extraction from unstructured and multidimensional big data

Limitations of information extraction methods and techniques for heterogeneous unstructured big data

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