Levels versus changes: Information contents of textual information

2021 ◽  
Author(s):  
Kotaro Miwa
Keyword(s):  
Textual Information ◽  
Information Contents

Method of semantic search and analysis of information

2020 ◽  
pp. 75-80
Author(s):  
A.L. Ogarok
Keyword(s):  
Computer Systems ◽  
Linguistic Analysis ◽  
Semantic Search ◽  
Textual Information ◽  
Formalized Description

The methodology of semantic search and analysis of information is considered. The results of the analysis of various approaches to solving the problem of a complete linguistic analysis of textual information in computer systems are presented. A formalized description of the method of semantic search and analysis of information is given.

scholarly journals Shall I Work with Them? A Knowledge Graph-Based Approach for Predicting Future Research Collaborations

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 664
Author(s):  
Nikos Kanakaris ◽  
Nikolaos Giarelis ◽  
Ilias Siachos ◽  
Nikos Karacapilidis
Keyword(s):  
Language Processing ◽  
Scientific Knowledge ◽  
Link Prediction ◽  
Performance Metrics ◽  
Future Research ◽  
Knowledge Graph ◽  
Prediction Problem ◽  
Textual Information ◽  
Research Collaborations ◽  
Processing Techniques

We consider the prediction of future research collaborations as a link prediction problem applied on a scientific knowledge graph. To the best of our knowledge, this is the first work on the prediction of future research collaborations that combines structural and textual information of a scientific knowledge graph through a purposeful integration of graph algorithms and natural language processing techniques. Our work: (i) investigates whether the integration of unstructured textual data into a single knowledge graph affects the performance of a link prediction model, (ii) studies the effect of previously proposed graph kernels based approaches on the performance of an ML model, as far as the link prediction problem is concerned, and (iii) proposes a three-phase pipeline that enables the exploitation of structural and textual information, as well as of pre-trained word embeddings. We benchmark the proposed approach against classical link prediction algorithms using accuracy, recall, and precision as our performance metrics. Finally, we empirically test our approach through various feature combinations with respect to the link prediction problem. Our experimentations with the new COVID-19 Open Research Dataset demonstrate a significant improvement of the abovementioned performance metrics in the prediction of future research collaborations.

ETINE: Enhanced Textual Information Network Embedding

2021 ◽  
Vol 220 ◽  
pp. 106917
Author(s):  
Wenfeng Liu ◽  
Maoguo Gong ◽  
Zedong Tang
Keyword(s):  
Information Network ◽  
Network Embedding ◽  
Textual Information

scholarly journals Genetic Diversity and Population Structures in Chinese Miniature Pigs Revealed by SINE Retrotransposon Insertion Polymorphisms, a New Type of Genetic Markers

Animals ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 1136
Author(s):  
Cai Chen ◽  
Xiaoyan Wang ◽  
Wencheng Zong ◽  
Enrico D’Alessandro ◽  
Domenico Giosa ◽  
...  
Keyword(s):  
Genetic Analysis ◽  
Genetic Markers ◽  
Structure Analysis ◽  
Genetic Distances ◽  
Population Genetic Analysis ◽  
Miniature Pigs ◽  
Population Structure Analysis ◽  
Information Contents ◽  
Population Structures ◽  
New Type

RIPs have been developed as effective genetic markers and popularly applied for genetic analysis in plants, but few reports are available for domestic animals. Here, we established 30 new molecular markers based on the SINE RIPs, and applied them for population genetic analysis in seven Chinese miniature pigs. The data revealed that the closed herd (BM-clo), inbreeding herd (BM-inb) of Bama miniature pigs were distinctly different from the BM-cov herds in the conservation farm, and other miniature pigs (Wuzhishan, Congjiang Xiang, Tibetan, and Mingguang small ear). These later five miniature pig breeds can further be classified into two clades based on a phylogenetic tree: one included BM-cov and Wuzhishan, the other included Congjiang Xiang, Tibetan, and Mingguang small ear, which was well-supported by structure analysis. The polymorphic information contents estimated by using SINE RIPs are lower than the predictions based on microsatellites. Overall, the genetic distances and breed-relationships between these populations revealed by 30 SINE RIPs generally agree with their evolutions and geographic distributions. We demonstrated the potential of SINE RIPs as new genetic markers for genetic monitoring and population structure analysis in pigs, which can even be extended to other livestock animals.

Knowledge-aware Multi-modal Adaptive Graph Convolutional Networks for Fake News Detection

2021 ◽  
Vol 17 (3) ◽  
pp. 1-23
Author(s):  
Shengsheng Qian ◽  
Jun Hu ◽  
Quan Fang ◽  
Changsheng Xu
Keyword(s):  
Social Media ◽  
Visual Information ◽  
Representation Learning ◽  
Fake News ◽  
Unified Framework ◽  
Model Learning ◽  
Convolutional Network ◽  
Textual Information ◽  
Convolutional Networks ◽  
Real World Datasets

In this article, we focus on fake news detection task and aim to automatically identify the fake news from vast amount of social media posts. To date, many approaches have been proposed to detect fake news, which includes traditional learning methods and deep learning-based models. However, there are three existing challenges: (i) How to represent social media posts effectively, since the post content is various and highly complicated; (ii) how to propose a data-driven method to increase the flexibility of the model to deal with the samples in different contexts and news backgrounds; and (iii) how to fully utilize the additional auxiliary information (the background knowledge and multi-modal information) of posts for better representation learning. To tackle the above challenges, we propose a novel Knowledge-aware Multi-modal Adaptive Graph Convolutional Networks (KMAGCN) to capture the semantic representations by jointly modeling the textual information, knowledge concepts, and visual information into a unified framework for fake news detection. We model posts as graphs and use a knowledge-aware multi-modal adaptive graph learning principal for the effective feature learning. Compared with existing methods, the proposed KMAGCN addresses challenges from three aspects: (1) It models posts as graphs to capture the non-consecutive and long-range semantic relations; (2) it proposes a novel adaptive graph convolutional network to handle the variability of graph data; and (3) it leverages textual information, knowledge concepts and visual information jointly for model learning. We have conducted extensive experiments on three public real-world datasets and superior results demonstrate the effectiveness of KMAGCN compared with other state-of-the-art algorithms.

scholarly journals Automatic Extraction of Adverse Drug Reactions from Summary of Product Characteristics

2021 ◽  
Vol 11 (6) ◽  
pp. 2663
Author(s):  
Zhengru Shen ◽  
Marco Spruit
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Adverse Drug Reactions ◽  
Language Processing ◽  
European Medicines Agency ◽  
Drug Reactions ◽  
Clinical Practices ◽  
Product Characteristics ◽  
Textual Information ◽  
Summary Of Product Characteristics

The summary of product characteristics from the European Medicines Agency is a reference document on medicines in the EU. It contains textual information for clinical experts on how to safely use medicines, including adverse drug reactions. Using natural language processing (NLP) techniques to automatically extract adverse drug reactions from such unstructured textual information helps clinical experts to effectively and efficiently use them in daily practices. Such techniques have been developed for Structured Product Labels from the Food and Drug Administration (FDA), but there is no research focusing on extracting from the Summary of Product Characteristics. In this work, we built a natural language processing pipeline that automatically scrapes the summary of product characteristics online and then extracts adverse drug reactions from them. Besides, we have made the method and its output publicly available so that it can be reused and further evaluated in clinical practices. In total, we extracted 32,797 common adverse drug reactions for 647 common medicines scraped from the Electronic Medicines Compendium. A manual review of 37 commonly used medicines has indicated a good performance, with a recall and precision of 0.99 and 0.934, respectively.

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