scholarly journals Automated concept and relationship extraction for the semi-automated ontology management (SEAM) system

2015 ◽  
Vol 6 (1) ◽  
Author(s):  
Kristina Doing-Harris ◽  
Yarden Livnat ◽  
Stephane Meystre
Keyword(s):  
Relationship Extraction ◽  
Ontology Management

Comparative Study on Ontology Management Approaches in Semantic Web

2018 ◽  
Vol 6 (1) ◽  
pp. 132-140
Author(s):  
Ranjna Jain ◽  
◽  
Neelam Duhan ◽  
A.K.Sharma . ◽  
◽  
...  
Keyword(s):  
Semantic Web ◽  
Comparative Study ◽  
Management Approaches ◽  
Ontology Management

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.

A challenge for historical research: Making data FAIR using a collaborative ontology management environment (OntoME)

Semantic Web ◽  
2020 ◽  
pp. 1-16
Author(s):  
Francesco Beretta
Keyword(s):  
Semantic Analysis ◽  
Historical Research ◽  
Data Modelling ◽  
Fine Grained ◽  
Fair Principles ◽  
Active Research ◽  
New Research ◽  
High Level ◽  
Conceptual Data ◽  
Ontology Management

This paper addresses the issue of interoperability of data generated by historical research and heritage institutions in order to make them re-usable for new research agendas according to the FAIR principles. After introducing the symogih.org project’s ontology, it proposes a description of the essential aspects of the process of historical knowledge production. It then develops an epistemological and semantic analysis of conceptual data modelling applied to factual historical information, based on the foundational ontologies Constructive Descriptions and Situations and DOLCE, and discusses the reasons for adopting the CIDOC CRM as a core ontology for the field of historical research, but extending it with some relevant, missing high-level classes. Finally, it shows how collaborative data modelling carried out in the ontology management environment OntoME makes it possible to elaborate a communal fine-grained and adaptive ontology of the domain, provided an active research community engages in this process. With this in mind, the Data for history consortium was founded in 2017 and promotes the adoption of a shared conceptualization in the field of historical research.

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.

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