Automatic information extraction from online published scientific documents is useful in various applications such as tagging, web indexing and search engine optimization. As a result, automatic information extraction has become among the hottest areas of research in text mining. Although various information extraction techniques have been proposed in the literature, their efficiency demands domain specific documents with static and well-defined format. Furthermore, their accuracy is challenged with a slight modification in the format. To overcome these issues, a novel ontological framework for information extraction (OFIE) using fuzzy rule-base (FRB) and word sense disambiguation (WSD) is proposed. The proposed approach is validated with a significantly wider document domains sourced from well-known publishing services such as IEEE, ACM, Elsevier, and Springer. We have also compared the proposed information extraction approach against state-of-the-art techniques. The results of the experiment show that the proposed approach is less sensitive to changes in the document format and has a significantly better average accuracy of 89.14% and F-score as 89%.

/pdf/an-ontological-framework-for-information-extraction-from-2zrkm6szj3.pdf

An Ontological Framework for Information Extraction From Diverse Scientific Sources

An overview of cluster-based image search result organization: background, techniques, and ongoing challenges

In the big data era, it is a great challenge to identify high-level abstract features out of a flood of sci-tech literature to achieve in-depth analysis of data. The deep learning technology has developed rapidly and achieved applications in many fields, but has rarely been utilized in the research of sci-tech literature data. This article introduced the presentation method of vector space of terminologies in sci-tech literature based on the deep learning model. It explored and adopted a deep AE model to reduce the dimensionality of input word vector feature. Also put forward is the methodology of correlation analysis of sci-tech literature based on deep learning technology. The experimental results showed that the processing of sci-tech literature data could be simplified into the computation of vectors in the multi-dimensional vector space, and the similarity in vector space could be used to represent similarity in text semantics. The correlation analysis of subject contents between sci-tech literatures of the same or different types can be made using this method.

Research on Methodology of Correlation Analysis of Sci-Tech Literature Based on Deep Learning Technology in the Big Data

Object-oriented databases are becoming increasingly popular in business. Some areas of research interest include formal object-oriented database models, query optimization, analysis and design techniques, and concurrency control. Many of these issues have been well researched as they pertain to the relational model and to centralized databases. Specifically, many studies have concentrated on relational database concurrency control but few have concentrated on object-oriented database concurrency control. Once a concurrency control mechanism is proposed and implemented, the next phase is to analyze its performance using simulation techniques. This paper includes the simulation study for the object-oriented database concurrency control mechanism named O2C2. Contributions of this study include offering a method of simulating concurrency control in an object-oriented database model. This includes extending prior studies that deal only with reading or writing a single data item without implications to the rest of the database. Specifically, the method of simulating concurrency control in an object-oriented database model is applied to the mechanism O2C2. The simulation demonstrates some intuitive, yet important properties about concurrency control in object-oriented databases. For example, as the mix of transactions progresses from operating mostly with instance objects to operating with more class and method definitions, throughput decreases.

An empirical analysis of the object-oriented database concurrency control mechanism O 2 O 2

The present article aims to review and evaluate the practiced and classical techniques, tools, models, and systems concerning automatic information extraction (IE) from published scientific documents like research articles, patents, theses, technical reports, and case studies etc. IE is performed for various reasons such as better indexing, archiving, searching, and retrieving. That is mainly used by the search engines and the indexing services as well the digital libraries and semantic web. In this regard, several studies have been conducted targeting various nature of documents. The study pays special consideration to the successful IE models, algorithms and approaches applied to structural IE from published documents. To grasp this, the paper is classified into several segments and each segment covers a significant aspect of IE. Furthermore, to validate their benefits and drawbacks, a comparative study of all the approaches have been conducted in terms of various performance factors like precision, accuracy, recall and F-score. Potential areas of improvement have been emphasized as research gap for the scholars in the closely related areas. Ultimately, a comprehensive summary of the evaluation is presented in tabular form and review is concluded. It was observed that the hybrid methods outperform the other methods due to their versatile nature to address various document formats.

/pdf/assessment-of-information-extraction-techniques-models-and-1gesczh8.pdf

Assessment of Information Extraction Techniques, Models and Systems

The﻿ authors﻿ propose﻿ to﻿ use﻿ formatting﻿ templates﻿ and﻿ implicit﻿ formatting﻿ semantics﻿ information﻿ for﻿ automatic﻿ metadata﻿ identification﻿ and﻿ segmentation.﻿ The﻿ pure﻿ texts﻿ and﻿ their﻿ corresponding﻿ formatting﻿information﻿including﻿line﻿height,﻿font﻿type,﻿and﻿font﻿size,﻿are﻿recognized﻿in﻿parallel﻿to﻿ guide﻿metadata﻿identification.﻿The﻿authors﻿use﻿implicit﻿formatting﻿semantics,﻿such﻿as﻿the﻿change﻿of﻿ formatting,﻿formatting﻿templates﻿and﻿implications,﻿explicit﻿formatting﻿layouts,﻿as﻿well﻿as﻿predefined﻿ frequently﻿occurred﻿keywords﻿database﻿to﻿increase﻿the﻿extraction﻿accuracy.﻿Unlike﻿other﻿OCR-based﻿ approaches,﻿ the﻿authors﻿use﻿open﻿source﻿PDFBox﻿package﻿as﻿ the﻿basic﻿preprocessing﻿ tool﻿ to﻿get﻿ pure﻿texts﻿and﻿formatting﻿values﻿of﻿the﻿document﻿contents.﻿On﻿top﻿of﻿PDFBox﻿they﻿built﻿their﻿own﻿ pipeline﻿program,﻿namely,﻿PAXAT,﻿to﻿implement﻿their﻿approaches﻿for﻿metadata﻿extraction.﻿10177﻿ papers﻿from﻿arXiv,﻿ACM,﻿ACL﻿and﻿other﻿publicly﻿accessed﻿and﻿institution-subscribed﻿sources﻿are﻿ tested.﻿The﻿overall﻿extraction﻿accuracy﻿of﻿title,﻿authors,﻿affiliations,﻿author-affiliation﻿matching﻿are﻿ 0.9798,﻿0.9425,﻿0.9298,﻿and﻿0.9109,﻿respectively. KEywoRDS Formatting Semantics, Information Retrieval, Metadata Extraction, PDF Document, Template

Implicit Semantics Based Metadata Extraction and Matching of Scholarly Documents

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Implicit Semantics Based Metadata Extraction and Matching of Scholarly Documents