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Zhuoming Xu, Hanlin Liu, Jian Li, Qianqian Zhang and Yan Tang
Knowledge graph-based recommendation methods are a hot research topic in the field of recommender systems in recent years. As a mainstream knowledge graph-based recommendation method, the propagation-based recommendation method captures users? potential ...
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Mohamad Zamini, Hassan Reza and Minou Rabiei
Information extraction methods proved to be effective at triple extraction from structured or unstructured data. The organization of such triples in the form of (head entity, relation, tail entity) is called the construction of Knowledge Graphs (KGs). Mo...
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Cunxiang Xie, Limin Zhang and Zhaogen Zhong
In practical application, there are different knowledge graphs in different fields, such as financial graph, commodity graph, medical graph, and so on. Entity alignment technique can be applied to the fusion of multiple knowledge graphs in different doma...
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Sameh K. Mohamed, Emir Muñoz and Vit Novacek
Knowledge graph embedding (KGE) models have become popular means for making discoveries in knowledge graphs (e.g., RDF graphs) in an efficient and scalable manner. The key to success of these models is their ability to learn low-rank vector representatio...
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Hyun-Je Song, A-Yeong Kim and Seong-Bae Park
Translation-based knowledge graph embeddings learn vector representations of entities and relations by treating relations as translation operators over the entities in an embedding space. Since the translation is represented through a score function, tra...
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Xiu Li, Aron Henriksson, Martin Duneld, Jalal Nouri and Yongchao Wu
Educational content recommendation is a cornerstone of AI-enhanced learning. In particular, to facilitate navigating the diverse learning resources available on learning platforms, methods are needed for automatically linking learning materials, e.g., in...
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Tianxing Wu, Chaoyu Gao, Lin Li and Yuxiang Wang
In recent years, the scale of knowledge graphs and the number of entities have grown rapidly. Entity matching across different knowledge graphs has become an urgent problem to be solved for knowledge fusion. With the importance of entity matching being i...
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Hassan El-Hajj and Matteo Valleriani
The development of the field of digital humanities in recent years has led to the increased use of knowledge graphs within the community. Many digital humanities projects tend to model their data based on CIDOC-CRM ontology, which offers a wide array of ...
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Yong Yu, Shudong Chen, Rong Du, Da Tong, Hao Xu and Shuai Chen
Temporal knowledge graphs play an increasingly prominent role in scenarios such as social networks, finance, and smart cities. As such, research on temporal knowledge graphs continues to deepen. In particular, research on temporal knowledge graph reasoni...
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Xiaole Wang, Jiwei Qin, Shangju Deng and Wei Zeng
In recent years, the application of knowledge graphs to alleviate cold start and data sparsity problems of users and items in recommendation systems, has aroused great interest. In this paper, in order to address the insufficient representation of user a...
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Kuekyeng Kim, Yuna Hur, Gyeongmin Kim and Heuiseok Lim
In an age overflowing with information, the task of converting unstructured data into structured data are a vital task of great need. Currently, most relation extraction modules are more focused on the extraction of local mention-level relations?usually ...
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Jo?e Ro?anec, Elena Trajkova, Inna Novalija, Patrik Zajec, Klemen Kenda, Bla? Fortuna and Dunja Mladenic
Artificial intelligence models are increasingly used in manufacturing to inform decision making. Responsible decision making requires accurate forecasts and an understanding of the models? behavior. Furthermore, the insights into the models? rationale ca...
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Yuxun Lu and Ryutaro Ichise
Knowledge graph completion (KGC) models are a feasible approach for manipulating facts in knowledge graphs. However, the lack of entity types in current KGC models results in inaccurate link prediction results. Most existing type-aware KGC models require...
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Jindou Zhang and Jing Li
Combining first order logic rules with a Knowledge Graph (KG) embedding model has recently gained increasing attention, as rules introduce rich background information. Among such studies, models equipped with soft rules, which are extracted with certain ...
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Gabriel Boroghina, Dragos Georgian Corlatescu and Mihai Dascalu
We live in an era where time is a scarce resource and people enjoy the benefits of technological innovations to ensure prompt and smooth access to information required for our daily activities. In this context, conversational agents start to play a remar...
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Huajie Wang and Yinglin Wang
The natural language model BERT uses a large-scale unsupervised corpus to accumulate rich linguistic knowledge during its pretraining stage, and then, the information is fine-tuned for specific downstream tasks, which greatly improves the understanding c...
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Mohammad Daradkeh
The heterogeneity and diversity of users and external knowledge resources is a hallmark of open innovation communities (OICs). Although user segmentation in heterogeneous OICs is a prominent and recurring issue, it has received limited attention in open ...
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