What Makes a Good Collaborative Knowledge Graph: Group Composition and Quality in Wikidata

COVID-19 (short for coronavirus disease 2019) is an emerging infectious disease that has had a tremendous impact on our daily lives. Globally, there have been over 95 million cases of COVID-19 and 2 million deaths across 191 countries and regions. The rapid spread and severity of COVID-19 call for a monitoring dashboard that can be developed quickly in an adaptable manner. Wikidata is a free, collaborative knowledge graph, collecting structured data about various themes, including that of COVID-19. We present COVIWD, a COVID-19 Wikidata dashboard, which provides a one-stop information/visualization service for topics related to COVID-19, ranging from symptoms and risk factors to comparison of cases and deaths among countries. The dashboard is one of the first that leverages open knowledge graph technologies, namely, RDF (for data modeling) and SPARQL (for querying), to give a live, concise snapshot of the COVID-19 pandemic. The use of both RDF and SPARQL enables rapid and flexible application development. COVIWD is available at http://coviwd.org.

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Provenance Information in a Collaborative Knowledge Graph: An Evaluation of Wikidata External References

Lecture Notes in Computer Science - The Semantic Web – ISWC 2017 ◽

10.1007/978-3-319-68288-4_32 ◽

2017 ◽

pp. 542-558 ◽

Cited By ~ 3

Author(s):

Alessandro Piscopo ◽

Lucie-Aimée Kaffee ◽

Chris Phethean ◽

Elena Simperl

Keyword(s):

Knowledge Graph ◽

Provenance Information ◽

Collaborative Knowledge

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Explainable Reasoning over Knowledge Graphs for Recommendation

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v33i01.33015329 ◽

2019 ◽

Vol 33 ◽

pp. 5329-5336 ◽

Cited By ~ 63

Author(s):

Xiang Wang ◽

Dingxian Wang ◽

Canran Xu ◽

Xiangnan He ◽

Yixin Cao ◽

...

Keyword(s):

Knowledge Base ◽

State Of The Art ◽

Recurrent Network ◽

User Preferences ◽

Knowledge Graph ◽

Complementary Information ◽

Sequential Dependencies ◽

Factorization Machine ◽

Knowledge Graphs ◽

Collaborative Knowledge

Incorporating knowledge graph into recommender systems has attracted increasing attention in recent years. By exploring the interlinks within a knowledge graph, the connectivity between users and items can be discovered as paths, which provide rich and complementary information to user-item interactions. Such connectivity not only reveals the semantics of entities and relations, but also helps to comprehend a user’s interest. However, existing efforts have not fully explored this connectivity to infer user preferences, especially in terms of modeling the sequential dependencies within and holistic semantics of a path.In this paper, we contribute a new model named Knowledgeaware Path Recurrent Network (KPRN) to exploit knowledge graph for recommendation. KPRN can generate path representations by composing the semantics of both entities and relations. By leveraging the sequential dependencies within a path, we allow effective reasoning on paths to infer the underlying rationale of a user-item interaction. Furthermore, we design a new weighted pooling operation to discriminate the strengths of different paths in connecting a user with an item, endowing our model with a certain level of explainability. We conduct extensive experiments on two datasets about movie and music, demonstrating significant improvements over state-of-the-art solutions Collaborative Knowledge Base Embedding and Neural Factorization Machine.

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A Method of Recommending Physical Education Network Course Resources Based on Collaborative Filtering Technology

Scientific Programming ◽

10.1155/2021/9531111 ◽

2021 ◽

Vol 2021 ◽

pp. 1-9

Author(s):

Zhihao Zhang

Keyword(s):

Collaborative Filtering ◽

Dimensional Space ◽

Learning System ◽

Personalized Recommendation ◽

Knowledge Graph ◽

Learning Resources ◽

Search Methods ◽

E Learning ◽

Predicted Values ◽

Collaborative Knowledge

Through the current research on e-learning, it is found that the present e-learning system applied to the recommendation activities of learning resources has only two search methods: Top-N and keywords. These search methods cannot effectively recommend learning resources to learners. Therefore, the collaborative filtering recommendation technology is applied, in this paper, to the process of personalized recommendation of learning resources. We obtain user content and functional interest and predict the comprehensive interest of web and big data through an infinite deep neural network. Based on the collaborative knowledge graph and the collaborative filtering algorithm, the semantic information of teaching network resources is extracted from the collaborative knowledge graph. According to the principles of the nearest neighbor recommendation, the course attribute value preference matrix (APM) is obtained first. Next, the course-predicted values are sorted in descending order, and the top T courses with the highest predicted values are selected as the final recommended course set for the target learners. Each course has its own online classroom; the teacher will publish online class details ahead of time, and students can purchase online access to the classroom number and password. The experimental results show that the optimal number of clusters k is 9. Furthermore, for extremely sparse matrices, the collaborative filtering technique method is more suitable for clustering in the transformed low-dimensional space. The average recommendation satisfaction degree of collaborative filtering technology method is approximately 43.6%, which demonstrates high recommendation quality.

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Collaborative Knowledge-Enhanced Recommendation with Self-Supervisions

Mathematics ◽

10.3390/math9172129 ◽

2021 ◽

Vol 9 (17) ◽

pp. 2129

Author(s):

Zhiqiang Pan ◽

Honghui Chen

Keyword(s):

Neural Network ◽

State Of The Art ◽

User Preferences ◽

The State ◽

The Self ◽

Knowledge Graph ◽

Model Optimization ◽

Interaction Graph ◽

Benchmark Datasets ◽

Collaborative Knowledge

Knowledge-enhanced recommendation (KER) aims to integrate the knowledge graph (KG) into collaborative filtering (CF) for alleviating the sparsity and cold start problems. The state-of-the-art graph neural network (GNN)–based methods mainly focus on exploiting the connectivity between entities in the knowledge graph, while neglecting the interaction relation between items reflected in the user-item interactions. Moreover, the widely adopted BPR loss for model optimization fails to provide sufficient supervisions for learning discriminative representation of users and items. To address these issues, we propose the collaborative knowledge-enhanced recommendation (CKER) method. Specifically, CKER proposes a collaborative graph convolution network (CGCN) to learn the user and item representations from the connection between items in the constructed interaction graph and the connectivity between entities in the knowledge graph. Moreover, we introduce the self-supervised learning to maximize the mutual information between the interaction- and knowledge-aware user preferences by deriving additional supervision signals. We conduct comprehensive experiments on two benchmark datasets, namely Amazon-Book and Last-FM, and the experimental results show that CKER can outperform the state-of-the-art baselines in terms of recall and NDCG on knowledge-enhanced recommendation.

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