A Minwise Hashing Method for Addressing Relationship Extraction from Text

2013 ◽  
pp. 216-230 ◽  
Author(s):  
David S. Batista ◽  
Rui Silva ◽  
Bruno Martins ◽  
Mário J. Silva
Keyword(s):  
Relationship Extraction ◽  
Minwise Hashing

Semantic Relation Model and Dataset for Remote Sensing Scene Understanding

2021 ◽  
Vol 10 (7) ◽  
pp. 488
Author(s):  
Peng Li ◽  
Dezheng Zhang ◽  
Aziguli Wulamu ◽  
Xin Liu ◽  
Peng Chen
Keyword(s):  
Remote Sensing ◽  
Scene Understanding ◽  
Deep Understanding ◽  
Remote Sensing Images ◽  
Convolutional Network ◽  
Scene Graph ◽  
Multi Scale ◽  
Relationship Extraction ◽  
High Level ◽  
Graph Generation

A deep understanding of our visual world is more than an isolated perception on a series of objects, and the relationships between them also contain rich semantic information. Especially for those satellite remote sensing images, the span is so large that the various objects are always of different sizes and complex spatial compositions. Therefore, the recognition of semantic relations is conducive to strengthen the understanding of remote sensing scenes. In this paper, we propose a novel multi-scale semantic fusion network (MSFN). In this framework, dilated convolution is introduced into a graph convolutional network (GCN) based on an attentional mechanism to fuse and refine multi-scale semantic context, which is crucial to strengthen the cognitive ability of our model Besides, based on the mapping between visual features and semantic embeddings, we design a sparse relationship extraction module to remove meaningless connections among entities and improve the efficiency of scene graph generation. Meanwhile, to further promote the research of scene understanding in remote sensing field, this paper also proposes a remote sensing scene graph dataset (RSSGD). We carry out extensive experiments and the results show that our model significantly outperforms previous methods on scene graph generation. In addition, RSSGD effectively bridges the huge semantic gap between low-level perception and high-level cognition of remote sensing images.

scholarly journals b-bit minwise hashing in practice

2013 ◽  
Author(s):  
Ping Li ◽  
Anshumali Shrivastava ◽  
Arnd Christian König
Keyword(s):  
Minwise Hashing

scholarly journals Data Set and Evaluation of Automated Construction of Financial Knowledge Graph

2021 ◽  
pp. 1-21
Author(s):  
Wenguang Wang ◽  
Yonglin Xu ◽  
Chunhui Du ◽  
Yunwen Chen ◽  
Yijie Wang ◽  
...  
Keyword(s):  
Financial Knowledge ◽  
Quality Data ◽  
Research Report ◽  
Knowledge Graph ◽  
Entity Extraction ◽  
Financial Industry ◽  
Data Set ◽  
Knowledge Reasoning ◽  
Relationship Extraction ◽  
Relationship Types

Abstract With the development of entity extraction, relationship extraction, knowledge reasoning, and entity linking, knowledge graph technology has been in full swing in recent years. To better promote the development of knowledge graph, especially in the Chinese language and in the financial industry, we built a high-quality data set, named financial research report knowledge graph (FR2KG), and organized the automated construction of financial knowledge graph evaluation at the 2020 China Knowledge Graph and Semantic Computing Conference (CCKS2020). FR2KG consists of 17,799 entities, 26,798 relationship triples, and 1,328 attribute triples covering 10 entity types, 19 relationship types, and 6 attributes. Participants are required to develop a constructor that will automatically construct a financial knowledge graph based on the FR2KG. In addition, we summarized the technologies for automatically constructing knowledge graphs, and introduced the methods used by the winners and the results of this evaluation.

Automatic extraction, prioritization and analysis of gut microbial metabolites from biomedical literature

2019 ◽  
Author(s):  
Quangqiu Wang ◽  
Rong Xu
Keyword(s):  
Human Genetics ◽  
Microbial Metabolism ◽  
Integrated Approach ◽  
Biomedical Literature ◽  
Human Diseases ◽  
Data Driven ◽  
Microbial Metabolites ◽  
Microbial Metabolite ◽  
Relationship Extraction ◽  
Human Genes

Abstract Background: Many diseases are driven by gene-environment interactions. One important environmental factor is the metabolic output of human gut microbiota. A comprehensive catalog of human metabolites originated in microbes is critical for data-driven approaches to understand how microbial metabolism contributes to human health and diseases. Here we present a novel integrated approach to automatically extract and analyze microbial metabolites from 28 million published biomedical records. Results: First, we classified 28,851,232 MEDLINE records into microbial metabolism-related or not. Second, candidate microbial metabolites were extracted from the classified texts. Third, we developed signal prioritization algorithms to further differentiate microbial metabolites from metabolites originated from other resources. Finally, we systematically analyzed the interactions between extracted microbial metabolites and human genes. A total of 11,846 metabolites were extracted from 28 million MEDLINE articles. The combined text classification and signal prioritization significantly enriched true positives among top: manual curation of top 100 metabolites showed a true precision of 0.55, representing a significant 38.3-fold enrichment as compared to the precision of 0.014 for baseline extraction. More importantly, 29% extracted microbial metabolites have not been captured by existing databases. Our data-driven analysis of the interactions between the extracted microbial metabolite and human genetics provides mechanistic insights into how microbiota may contribute to human diseases. Conclusions: This study represents the first effort towards building a comprehensive knowledge base of microbial metabolites, which can set the foundation for future tasks of microbial metabolite relationship extraction from literature and facilitate data-driven studies of how microbial metabolism contributes to human diseases.

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