scholarly journals Decentralized Construction of Knowledge Graphs for Deep Recommender Systems Based on Blockchain-Powered Smart Contracts

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 136951-136961 ◽  
Author(s):  
Shuai Wang ◽  
Chenchen Huang ◽  
Juanjuan Li ◽  
Yong Yuan ◽  
Fei-Yue Wang
Keyword(s):  
Recommender Systems ◽  
Smart Contracts ◽  
Construction Of Knowledge ◽  
Knowledge Graphs

scholarly journals MOVIE RECOMMENDATION USING KNOWLEDGE GRAPH

2020 ◽  
pp. 99-103
Author(s):  
Navin Tatyaba Gopal ◽  
Anish Raj Khobragade
Keyword(s):  
Neural Network ◽  
Recommender Systems ◽  
Weighted Graph ◽  
Cold Start ◽  
Structured Data ◽  
Label Propagation ◽  
Knowledge Graph ◽  
Movie Recommendation ◽  
Knowledge Graphs ◽  
Prior Application

The Knowledge graphs (KGs) catches structured data and relationships among a bunch of entities and items. Generally, constitute an attractive origin of information that can advance the recommender systems. But, present methodologies of this area depend on manual element thus don’t permit for start to end training. This article proposes, Knowledge Graph along with Label Smoothness (KG-LS) to offer better suggestions for the recommender Systems. Our methodology processes user-specific entities by prior application of a function capability that recognizes key KG-relationships for a specific user. In this manner, we change the KG in a specific-user weighted graph followed by application of a graph neural network to process customized entity embedding. To give better preliminary predisposition, label smoothness comes into picture, which places items in the KG which probably going to have identical user significant names/scores. Use of, label smoothness gives regularization above the edge weights thus; we demonstrate that it is comparable to a label propagation plan on the graph. Additionally building-up a productive usage that symbolizes solid adaptability concerning the size of knowledge graph. Experimentation on 4 datasets shows that our strategy beats best in class baselines. This process likewise accomplishes solid execution in cold start situations where user-entity communications remain meager.

Low-Quality Error Detection for Noisy Knowledge Graphs

2021 ◽  
Vol 32 (4) ◽  
pp. 48-64
Author(s):  
*Chenyang Bu ◽  
Xingchen Yu ◽  
Yan Hong ◽  
Tingting Jiang
Keyword(s):  
Error Detection ◽  
Data Sources ◽  
Construction Process ◽  
Automatic Construction ◽  
Construction Of Knowledge ◽  
Unstructured Text ◽  
Multiple Data ◽  
Extraction Processes ◽  
Knowledge Graphs ◽  
Type Information

The automatic construction of knowledge graphs (KGs) from multiple data sources has received increasing attention. The automatic construction process inevitably brings considerable noise, especially in the construction of KGs from unstructured text. The noise in a KG can be divided into two categories: factual noise and low-quality noise. Factual noise refers to plausible triples that meet the requirements of ontology constraints. For example, the plausible triple <New_York, IsCapitalOf, America> satisfies the constraints that the head entity “New_York” is a city and the tail entity “America” belongs to a country. Low-quality noise denotes the obvious errors commonly created in information extraction processes. This study focuses on entity type errors. Most existing approaches concentrate on refining an existing KG, assuming that the type information of most entities or the ontology information in the KG is known in advance. However, such methods may not be suitable at the start of a KG's construction. Therefore, the authors propose an effective framework to eliminate entity type errors. The experimental results demonstrate the effectiveness of the proposed method.

scholarly journals Enhancing explanations in recommender systems with knowledge graphs

2018 ◽  
Vol 137 ◽  
pp. 211-222 ◽  
Author(s):  
Vincent Lully ◽  
Philippe Laublet ◽  
Milan Stankovic ◽  
Filip Radulovic
Keyword(s):  
Recommender Systems ◽  
Knowledge Graphs

scholarly journals Mining Semantic Knowledge Graphs to Add Explainability to Black Box Recommender Systems

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 110563-110579 ◽  
Author(s):  
Mohammed Alshammari ◽  
Olfa Nasraoui ◽  
Scott Sanders
Keyword(s):  
Recommender Systems ◽  
Semantic Knowledge ◽  
Black Box ◽  
Knowledge Graphs

scholarly journals Construction of Knowledge Graphs for Maritime Dangerous Goods

2019 ◽  
Vol 11 (10) ◽  
pp. 2849 ◽  
Author(s):  
Qi Zhang ◽  
Yuanqiao Wen ◽  
Chunhui Zhou ◽  
Hai Long ◽  
Dong Han ◽  
...  
Keyword(s):  
Domain Knowledge ◽  
Knowledge Structure ◽  
Intelligent Transportation ◽  
Knowledge Graph ◽  
Retrieval Process ◽  
Construction Of Knowledge ◽  
Dangerous Goods ◽  
New Knowledge ◽  
Transport Safety ◽  
Knowledge Graphs

Dangerous goods occupy an important proportion in international shipping, and government and enterprises pay a lot of attention to transport safety. There are a wide variety of dangerous goods, and the knowledge involved is extensive and complex. Organizing and managing this knowledge plays an important role in the safe transportation of dangerous goods. The knowledge graph is a mass of brand-new knowledge management technologies that provide powerful technical support for integrating domain knowledge and solving the problem of the “knowledge island.” This paper first introduces the knowledge of maritime dangerous goods (MDG); constructs a three-layer knowledge structure of MDG, dividing this knowledge into two categories; uses ontology to express the concepts, entities, and relations of MDG; and puts forward the representation methods of the conceptual layer and entity layer and designs them in detail. Finally, the knowledge graph of maritime dangerous goods (KGMDG) is constructed. Furthermore, we demonstrate the knowledge visualization, retrieval, and automatic judgment of segregation requirement based on KGMDG. It is proved that KGMDG does not only help to simplify the retrieval process of professional knowledge and to promote intelligent transportation but is also conducive to the sharing, dissemination, and utilization of MDG knowledge.

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