Towards Large-Scale Unsupervised Relation Extraction from the Web

2012 ◽  
Vol 8 (3) ◽  
pp. 1-23 ◽  
Author(s):  
Bonan Min ◽  
Shuming Shi ◽  
Ralph Grishman ◽  
Chin-Yew Lin
Keyword(s):  
Information Extraction ◽  
Large Scale ◽  
Relation Extraction ◽  
Previous Method ◽  
Semantic Relations ◽  
Large Set ◽  
Semantic Classes ◽  
Extraction Algorithm ◽  
Unsupervised Algorithms ◽  
The Web

The Web brings an open-ended set of semantic relations. Discovering the significant types is very challenging. Unsupervised algorithms have been developed to extract relations from a corpus without knowing the relation types in advance, but most rely on tagging arguments of predefined types. One recently reported system is able to jointly extract relations and their argument semantic classes, taking a set of relation instances extracted by an open IE (Information Extraction) algorithm as input. However, it cannot handle polysemy of relation phrases and fails to group many similar (“synonymous”) relation instances because of the sparseness of features. In this paper, the authors present a novel unsupervised algorithm that provides a more general treatment of the polysemy and synonymy problems. The algorithm incorporates various knowledge sources which they will show to be very effective for unsupervised relation extraction. Moreover, it explicitly disambiguates polysemous relation phrases and groups synonymous ones. While maintaining approximately the same precision, the algorithm achieves significant improvement on recall compared to the previous method. It is also very efficient. Experiments on a real-world dataset show that it can handle 14.7 million relation instances and extract a very large set of relations from the Web.

Automatic Topic Ontology Construction Using Semantic Relations from WordNet and Wikipedia

2013 ◽  
Vol 9 (3) ◽  
pp. 61-89 ◽  
Author(s):  
V. Subramaniyaswamy
Keyword(s):  
Web Mining ◽  
Relation Extraction ◽  
Past Research ◽  
Semantic Relations ◽  
Svm Classifier ◽  
Ontology Construction ◽  
Knowledge Resources ◽  
Web Resources ◽  
The Past ◽  
The Web

Due to the explosive growth of web technology, a huge amount of information is available as web resources over the Internet. Therefore, in order to access the relevant content from the web resources effectively, considerable attention is paid on the semantic web for efficient knowledge sharing and interoperability. Topic ontology is a hierarchy of a set of topics that are interconnected using semantic relations, which is being increasingly used in the web mining techniques. Reviews of the past research reveal that semiautomatic ontology is not capable of handling high usage. This shortcoming prompted the authors to develop an automatic topic ontology construction process. However, in the past many attempts have been made by other researchers to utilize the automatic construction of ontology, which turned out to be challenging due to time, cost and maintenance. In this paper, the authors have proposed a corpus based novel approach to enrich the set of categories in the ODP by automatically identifying the concepts and their associated semantic relationship with corpus based external knowledge resources, such as Wikipedia and WordNet. This topic ontology construction approach relies on concept acquisition and semantic relation extraction. A Jena API framework has been developed to organize the set of extracted semantic concepts, while Protégé provides the platform to visualize the automatically constructed topic ontology. To evaluate the performance, web documents were classified using SVM classifier based on ODP and topic ontology. The topic ontology based classification produced better accuracy than ODP.

An Integrated Approach for Large-Scale Relation Extraction from the Web

2013 ◽  
pp. 163-175
Author(s):  
Naimdjon Takhirov ◽  
Fabien Duchateau ◽  
Trond Aalberg ◽  
Ingeborg Sølvberg
Keyword(s):  
Large Scale ◽  
Relation Extraction ◽  
Integrated Approach ◽  
The Web

scholarly journals CONSTRUCTION OF GREEN TIDE MONITORING SYSTEM AND RESEARCH ON ITS KEY TECHNIQUES

2018 ◽  
Vol XLII-3 ◽  
pp. 1963-1970
Author(s):  
B. Xing ◽  
J. Li ◽  
H. Zhu ◽  
P. Wei ◽  
Y. Zhao
Keyword(s):  
Remote Sensing ◽  
Information Extraction ◽  
Monitoring System ◽  
Large Scale ◽  
Microwave Remote Sensing ◽  
Automatic Generation ◽  
Green Tide ◽  
Great Loss ◽  
Distribution Boundary ◽  
Extraction Algorithm

As a kind of marine natural disaster, Green Tide has been appearing every year along the Qingdao Coast, bringing great loss to this region, since the large-scale bloom in 2008. Therefore, it is of great value to obtain the real time dynamic information about green tide distribution. In this study, methods of optical remote sensing and microwave remote sensing are employed in Green Tide Monitoring Research. A specific remote sensing data processing flow and a green tide information extraction algorithm are designed, according to the optical and microwave data of different characteristics. In the aspect of green tide spatial distribution information extraction, an automatic extraction algorithm of green tide distribution boundaries is designed based on the principle of mathematical morphology dilation/erosion. And key issues in information extraction, including the division of green tide regions, the obtaining of basic distributions, the limitation of distribution boundary, and the elimination of islands, have been solved. The automatic generation of green tide distribution boundaries from the results of remote sensing information extraction is realized. Finally, a green tide monitoring system is built based on IDL/GIS secondary development in the integrated environment of RS and GIS, achieving the integration of RS monitoring and information extraction.

scholarly journals Large-Scale Information Extraction from Textual Definitions through Deep Syntactic and Semantic Analysis

2015 ◽  
Vol 3 ◽  
pp. 529-543 ◽  
Author(s):  
Claudio Delli Bovi ◽  
Luca Telesca ◽  
Roberto Navigli
Keyword(s):  
Information Extraction ◽  
Knowledge Base ◽  
Large Scale ◽  
Semantic Analysis ◽  
Semantic Relations ◽  
High Quality ◽  
Data Sparsity ◽  
Syntactic Dependencies ◽  
Large Corpus

We present DefIE, an approach to large-scale Information Extraction (IE) based on a syntactic-semantic analysis of textual definitions. Given a large corpus of definitions we leverage syntactic dependencies to reduce data sparsity, then disambiguate the arguments and content words of the relation strings, and finally exploit the resulting information to organize the acquired relations hierarchically. The output of DefIE is a high-quality knowledge base consisting of several million automatically acquired semantic relations.

Enriching contextualized language model from knowledge graph for biomedical information extraction

2020 ◽  
Author(s):  
Hao Fei ◽  
Yafeng Ren ◽  
Yue Zhang ◽  
Donghong Ji ◽  
Xiaohui Liang
Keyword(s):  
Information Extraction ◽  
Large Scale ◽  
Language Model ◽  
Relation Extraction ◽  
Event Extraction ◽  
Entity Recognition ◽  
Language Models ◽  
Training Procedure ◽  
Biomedical Knowledge ◽  
Biomedical Texts

Abstract Biomedical information extraction (BioIE) is an important task. The aim is to analyze biomedical texts and extract structured information such as named entities and semantic relations between them. In recent years, pre-trained language models have largely improved the performance of BioIE. However, they neglect to incorporate external structural knowledge, which can provide rich factual information to support the underlying understanding and reasoning for biomedical information extraction. In this paper, we first evaluate current extraction methods, including vanilla neural networks, general language models and pre-trained contextualized language models on biomedical information extraction tasks, including named entity recognition, relation extraction and event extraction. We then propose to enrich a contextualized language model by integrating a large scale of biomedical knowledge graphs (namely, BioKGLM). In order to effectively encode knowledge, we explore a three-stage training procedure and introduce different fusion strategies to facilitate knowledge injection. Experimental results on multiple tasks show that BioKGLM consistently outperforms state-of-the-art extraction models. A further analysis proves that BioKGLM can capture the underlying relations between biomedical knowledge concepts, which are crucial for BioIE.

DERIVING PATHWAY MAPS FROM AUTOMATED TEXT ANALYSIS USING A GRAMMAR-BASED APPROACH

2006 ◽  
Vol 04 (02) ◽  
pp. 483-501 ◽  
Author(s):  
BJÖRN OLSSON ◽  
BARBARA GAWRONSKA ◽  
BJÖRN ERLENDSSON
Keyword(s):  
Text Analysis ◽  
Large Scale ◽  
Relation Extraction ◽  
Syntactic Analysis ◽  
Epistemic Status ◽  
Regulatory Pathways ◽  
Semantic Class ◽  
Extraction Algorithm ◽  
Automated Text Analysis ◽  
Parse Trees

We demonstrate how automated text analysis can be used to support the large-scale analysis of metabolic and regulatory pathways by deriving pathway maps from textual descriptions found in the scientific literature. The main assumption is that correct syntactic analysis combined with domain-specific heuristics provides a good basis for relation extraction. Our method uses an algorithm that searches through the syntactic trees produced by a parser based on a Referent Grammar formalism, identifies relations mentioned in the sentence, and classifies them with respect to their semantic class and epistemic status (facts, counterfactuals, hypotheses). The semantic categories used in the classification are based on the relation set used in KEGG (Kyoto Encyclopedia of Genes and Genomes), so that pathway maps using KEGG notation can be automatically generated. We present the current version of the relation extraction algorithm and an evaluation based on a corpus of abstracts obtained from PubMed. The results indicate that the method is able to combine a reasonable coverage with high accuracy. We found that 61% of all sentences were parsed, and 97% of the parse trees were judged to be correct. The extraction algorithm was tested on a sample of 300 parse trees and was found to produce correct extractions in 90.5% of the cases.

A FRAME WORK FOR WEB INFORMATION EXTRACTION AND ANALYSIS

2013 ◽  
Vol 7 (2) ◽  
pp. 574-579 ◽  
Author(s):  
Dr Sunitha Abburu ◽  
G. Suresh Babu
Keyword(s):  
Information Extraction ◽  
Data Extraction ◽  
Research Work ◽  
Web Pages ◽  
Web Documents ◽  
E Learning ◽  
Structured Information ◽  
Frame Work ◽  
Effective Decision ◽  
The Web

Day by day the volume of information availability in the web is growing significantly. There are several data structures for information available in the web such as structured, semi-structured and unstructured. Majority of information in the web is presented in web pages. The information presented in web pages is semi-structured.  But the information required for a context are scattered in different web documents. It is difficult to analyze the large volumes of semi-structured information presented in the web pages and to make decisions based on the analysis. The current research work proposed a frame work for a system that extracts information from various sources and prepares reports based on the knowledge built from the analysis. This simplifies  data extraction, data consolidation, data analysis and decision making based on the information presented in the web pages.The proposed frame work integrates web crawling, information extraction and data mining technologies for better information analysis that helps in effective decision making.   It enables people and organizations to extract information from various sourses of web and to make an effective analysis on the extracted data for effective decision making.  The proposed frame work is applicable for any application domain. Manufacturing,sales,tourisum,e-learning are various application to menction few.The frame work is implemetnted and tested for the effectiveness of the proposed system and the results are promising.

Adsorption Isotherm Predictions for Multiple Molecules in MOFs Using the Same Deep Learning Model

2019 ◽  
Author(s):  
Ryther Anderson ◽  
Achay Biong ◽  
Diego Gómez-Gualdrón
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Molecular Simulation ◽  
Large Scale ◽  
Learning Model ◽  
Operating Conditions ◽  
Small Subset ◽  
Screening Methods ◽  
Large Set ◽  
Metal Organic

<div>Tailoring the structure and chemistry of metal-organic frameworks (MOFs) enables the manipulation of their adsorption properties to suit specific energy and environmental applications. As there are millions of possible MOFs (with tens of thousands already synthesized), molecular simulation, such as grand canonical Monte Carlo (GCMC), has frequently been used to rapidly evaluate the adsorption performance of a large set of MOFs. This allows subsequent experiments to focus only on a small subset of the most promising MOFs. In many instances, however, even molecular simulation becomes prohibitively time consuming, underscoring the need for alternative screening methods, such as machine learning, to precede molecular simulation efforts. In this study, as a proof of concept, we trained a neural network as the first example of a machine learning model capable of predicting full adsorption isotherms of different molecules not included in the training of the model. To achieve this, we trained our neural network only on alchemical species, represented only by their geometry and force field parameters, and used this neural network to predict the loadings of real adsorbates. We focused on predicting room temperature adsorption of small (one- and two-atom) molecules relevant to chemical separations. Namely, argon, krypton, xenon, methane, ethane, and nitrogen. However, we also observed surprisingly promising predictions for more complex molecules, whose properties are outside the range spanned by the alchemical adsorbates. Prediction accuracies suitable for large-scale screening were achieved using simple MOF (e.g. geometric properties and chemical moieties), and adsorbate (e.g. forcefield parameters and geometry) descriptors. Our results illustrate a new philosophy of training that opens the path towards development of machine learning models that can predict the adsorption loading of any new adsorbate at any new operating conditions in any new MOF.</div>

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