scholarly journals A Study on the Visual Information Extraction Process

2003 ◽  
Vol 108 (0) ◽  
pp. 283-289
Author(s):  
Romei ASAI ◽  
Hayama IMAZU
Keyword(s):  
Information Extraction ◽  
Visual Information ◽  
Extraction Process

scholarly journals Ekstraksi Informasi Halaman Web Menggunakan Pendekatan Bootstrapping pada Ontology-Based Information Extraction

2015 ◽  
Vol 9 (2) ◽  
pp. 111 ◽  
Author(s):  
Erma Susanti ◽  
Khabib Mustofa
Keyword(s):  
Information Extraction ◽  
Language Processing ◽  
Semantic Content ◽  
Extraction Process ◽  
Web Pages ◽  
Structured Information ◽  
Improved Performance ◽  
Types Of Information ◽  
Unstructured Information

AbstrakEkstraksi  informasi  merupakan suatu bidang ilmu untuk pengolahan bahasa alami, dengan cara mengubah teks tidak terstruktur menjadi informasi dalam bentuk terstruktur. Berbagai jenis informasi di Internet ditransmisikan secara tidak terstruktur melalui website, menyebabkan munculnya kebutuhan akan suatu teknologi untuk menganalisa teks dan menemukan pengetahuan yang relevan dalam bentuk informasi terstruktur. Contoh informasi tidak terstruktur adalah informasi utama yang ada pada konten halaman web. Bermacam pendekatan untuk ekstraksi informasi telah dikembangkan oleh berbagai peneliti, baik menggunakan metode manual atau otomatis, namun masih perlu ditingkatkan kinerjanya terkait akurasi dan kecepatan ekstraksi. Pada penelitian ini diusulkan suatu penerapan pendekatan ekstraksi informasi dengan mengkombinasikan pendekatan bootstrapping dengan Ontology-based Information Extraction (OBIE). Pendekatan bootstrapping dengan menggunakan sedikit contoh data berlabel, digunakan untuk memimalkan keterlibatan manusia dalam proses ekstraksi informasi, sedangkan penggunakan panduan ontologi untuk mengekstraksi classes (kelas), properties dan instance digunakan untuk menyediakan konten semantik untuk web semantik. Pengkombinasian kedua pendekatan tersebut diharapkan dapat meningkatan kecepatan proses ekstraksi dan akurasi hasil ekstraksi. Studi kasus untuk penerapan sistem ekstraksi informasi menggunakan dataset “LonelyPlanet”. Kata kunci—Ekstraksi informasi, ontologi, bootstrapping, Ontology-Based Information Extraction, OBIE, kinerja Abstract Information extraction is a field study of natural language processing by converting unstructured text into structured information. Several types of information on the Internet is transmitted through unstructured information via websites, led to emergence of the need a technology to analyze text and found relevant knowledge into structured information. For example of unstructured information is existing main information on the content of web pages. Various approaches  for information extraction have been developed by many researchers, either using manual or automatic method, but still need to be improved performance related accuracy and speed of extraction. This research proposed an approach of information extraction that combines bootstrapping approach with Ontology-Based Information Extraction (OBIE). Bootstrapping approach using small seed of labelled data, is used to minimize human intervention on information extraction process, while the use of guide ontology for extracting classes, properties and instances, using for provide semantic content for semantic web. Combining both approaches expected to increase speed of extraction process and accuracy of extraction results. Case study to apply information extraction system using “LonelyPlanet” datasets. Keywords— Information extraction, ontology, bootstrapping, Ontology-Based Information Extraction, OBIE, performance

Web Information Extraction via Web Views

2004 ◽  
pp. 227-267
Author(s):  
Wee Keong Ng ◽  
Zehua Liu ◽  
Zhao Li ◽  
Ee Peng Lim
Keyword(s):  
Information Extraction ◽  
Data Model ◽  
Information Source ◽  
Extraction Process ◽  
Web Pages ◽  
Efficient Manner ◽  
Web Information Extraction ◽  
Web Information ◽  
Definition Of ◽  
The Web

With the explosion of information on the Web, traditional ways of browsing and keyword searching of information over web pages no longer satisfy the demanding needs of web surfers. Web information extraction has emerged as an important research area that aims to automatically extract information from target web pages and convert them into a structured format for further processing. The main issues involved in the extraction process include: (1) the definition of a suitable extraction language; (2) the definition of a data model representing the web information source; (3) the generation of the data model, given a target source; and (4) the extraction and presentation of information according to a given data model. In this chapter, we discuss the challenges of these issues and the approaches that current research activities have taken to revolve these issues. We propose several classification schemes to classify existing approaches of information extraction from different perspectives. Among the existing works, we focus on the Wiccap system — a software system that enables ordinary end-users to obtain information of interest in a simple and efficient manner by constructing personalized web views of information sources.

Information Extraction from Blogs

2011 ◽  
pp. 469-487 ◽  
Author(s):  
Marie-Francine Moens
Keyword(s):  
Information Extraction ◽  
Extraction Process ◽  
Future Research ◽  
Different Sources

This chapter introduces information extraction from blog texts. It argues that the classical techniques for information extraction that are commonly used for mining well-formed texts lose some of their validity in the context of blogs. This finding is demonstrated by considering each step in the information extraction process and by illustrating this problem in different applications. In order to tackle the problem of mining content from blogs, algorithms are developed that combine different sources of evidence in the most flexible way. The chapter concludes with ideas for future research.

Supporting Binding-Sites Discovery via Iterative Database Processing

2013 ◽  
Vol 2 (3) ◽  
pp. 19-41
Author(s):  
Ran Tel-Nir ◽  
Roy Gelbard ◽  
Israel Spiegler
Keyword(s):  
Information Extraction ◽  
Binding Site ◽  
Iterative Algorithm ◽  
Spatial Data ◽  
Binding Sites ◽  
Heuristic Algorithms ◽  
Protein Structures ◽  
Three Dimensional ◽  
Extraction Process ◽  
Binary Representation

Recognition of binding sites common to a set of protein structures is important for applications such as drug design. Common methods of binding-sites are based on heuristic algorithms that use summarized spatial data and superimposition techniques. However, computational operations generally do not store intermediate data for further calculation and information extraction. The current study presents an alternative approach to binding calculation by introducing a binary representation scheme for three dimensional molecule data and a fast iterative algorithm which obviates the need to calculate and resolve spatial transformations in the binding site extraction process. This is achieved by using relational database indexing methods and an efficient iterative model. This general-purpose iterative algorithm was tested for binding small molecules. The results show that the method can be applied efficiently for binding site extraction, and bio-information extraction. This binary representation improves performance by reducing processing time by 31% compared to typical representations.

scholarly journals Implicit race attitudes modulate visual information extraction for trustworthiness judgments

PLoS ONE ◽  
2020 ◽  
Vol 15 (9) ◽  
pp. e0239305
Author(s):  
Isabelle Charbonneau ◽  
Karolann Robinson ◽  
Caroline Blais ◽  
Daniel Fiset
Keyword(s):  
Information Extraction ◽  
Visual Information

scholarly journals Scalable Evolutionary Computation for Efficient Information Extraction from Remote Sensed Imagery

2014 ◽  
Vol XL-1 ◽  
pp. 27-34
Author(s):  
L. M. Almutairi ◽  
S. Shetty ◽  
H. G. Momm
Keyword(s):  
Cloud Computing ◽  
Genetic Programming ◽  
Information Extraction ◽  
Evolutionary Computation ◽  
Programming Model ◽  
Computational Cost ◽  
Evolutionary Process ◽  
Extraction Process ◽  
Computing Environment ◽  
Cloud Computing Environment

Evolutionary computation, in the form of genetic programming, is used to aid information extraction process from high-resolution satellite imagery in a semi-automatic fashion. Distributing and parallelizing the task of evaluating all candidate solutions during the evolutionary process could significantly reduce the inherent computational cost of evolving solutions that are composed of multichannel large images. In this study, we present the design and implementation of a system that leverages cloud-computing technology to expedite supervised solution development in a centralized evolutionary framework. The system uses the MapReduce programming model to implement a distributed version of the existing framework in a cloud-computing platform. The proposed system has two major subsystems; (i) data preparation: the generation of random spectral indices; and (ii) distributed processing: the distributed implementation of genetic programming, which is used to spectrally distinguish the features of interest from the remaining image background in the cloud computing environment in order to improve scalability. The proposed system reduces response time by leveraging the vast computational and storage resources in a cloud computing environment. The results demonstrate that distributing the candidate solutions reduces the execution time by 91.58%. These findings indicate that such technology could be applied to more complex problems that involve a larger population size and number of generations.

Effective Entity Linking and Disambiguation Algorithms for User-Generated Content (UGC)

2018 ◽  
pp. 416-433
Author(s):  
Senthil Kumar Narayanasamy ◽  
Dinakaran Muruganantham
Keyword(s):  
Decision Making ◽  
Social Media ◽  
Information Extraction ◽  
Knowledge Base ◽  
Exponential Growth ◽  
Extraction Process ◽  
Unstructured Data ◽  
User Generated Content ◽  
Entity Linking ◽  
Named Entities

The exponential growth of data emerging out of social media is causing challenges in decision-making systems and poses a critical hindrance in searching for the potential information. The major objective of this chapter is to convert the unstructured data in social media into the meaningful structure format, which in return brings the robustness to the information extraction process. Further, it has the inherent capability to prune for named entities from the unstructured data and store the entities into the knowledge base for important facts. In this chapter, the authors explain the methods to identify all the critical interpretations taken over to find the named entities from Twitter streams and the techniques to proportionally link it with appropriate knowledge sources such as DBpedia.

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