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

Extracting information from academic PDF documents is crucial for numerous indexing, retrieval, and analysis use cases. Choosing the best tool to extract specific content elements is difficult because many, technically diverse tools are available, but recent performance benchmarks are rare. Moreover, such benchmarks typically cover only a few content elements like header metadata or bibliographic references and use smaller datasets from specific academic disciplines. We provide a large and diverse evaluation framework that supports more extraction tasks than most related datasets. Our framework builds upon DocBank, a multi-domain dataset of 1.5 M annotated content elements extracted from 500 K pages of research papers on arXiv. Using the new framework, we benchmark ten freely available tools in extracting document metadata, bibliographic references, tables, and other content elements from academic PDF documents. GROBID achieves the best metadata and reference extraction results, followed by CERMINE and Science Parse. For table extraction, Adobe Extract outperforms other tools, even though the performance is much lower than for other content elements. All tools struggle to extract lists, footers, and equations. We conclude that more research on improving and combining tools is necessary to achieve satisfactory extraction quality for most content elements. Evaluation datasets and frameworks like the one we present support this line of research. We make our data and code publicly available to contribute toward this goal. 

/pdf/a-benchmark-of-pdf-information-extraction-tools-using-a-1z173uci.pdf

A Benchmark of PDF Information Extraction Tools Using a Multi-task and Multi-domain Evaluation Framework for Academic Documents

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 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

Metadata extraction from scholarly PDF documents is the fundamental work of publishing, archiving, digital library construction, bibliometrics, and scientific competitiveness analysis and evaluations. However, different scholarly PDF documents have different layout and document elements, which make it impossible to compare different extract approaches since testers use different source of test documents even if the documents are from the same journal or conference. Therefore, standard datasets based performance evaluation of various extraction approaches can setup a fair and reproducible comparison. In this paper we present a dataset, namely, PARDA(Pdf Analysis and Recognition DAtaset), for performance evaluation and analysis of scholarly documents, especially on metadata extraction, such as title, authors, affiliation, author-affiliation-email matching, year, date, etc. The dataset covers computer science, physics, life science, management, mathematics, and humanities from various publishers including ACM, IEEE, Springer, Elsevier, arXiv, etc. And each document has distinct layouts and appearance in terms of formatting of metadata. We also construct the ground truth metadata in Dublin Core XML format and BibTex format file associated this dataset.

PARDA: A Dataset for Scholarly PDF Document Metadata Extraction Evaluation

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Implicit Semantics Based Metadata Extraction and Matching of Scholarly Documents

PARDA: A Dataset for Scholarly PDF Document Metadata Extraction Evaluation