An "awesome list" is a marketing way to let more people find this excellent resource

• rename repo to awesome-knowledge-graphs
• add tag awesome-list so it shows up in https://github.com/topics/awesome-list
• read https://github.com/sindresorhus/awesome/blob/main/awesome.md and add one of the badges (little images)
• add your list to https://github.com/sindresorhus/awesome by following these guidelines: https://github.com/sindresorhus/awesome/blob/main/contributing.md
  • you may also read https://github.com/sindresorhus/awesome/blob/main/create-list.md
  • it addition to github, it will then show up on https://awesomelists.top, see an example: https://awesomelists.top/#repos/onurakpolat/awesome-bigdata
• add your list to https://github.com/semantalytics/awesome-semantic-web

THANKS!

Please take a look at https://github.com/husthuke/awesome-knowledge-graph and see if there are missing papers that can be added to this repo.

Future Generation Computer Systems journal is calling submission for special issue on graph-powered machine learning!

Recent years have witnessed a dramatic increase of graph applications due to advancements in information and communication technologies. In a variety of applications, such as social networks, communication networks, internet of things (IOTs), and human disease networks, graph data contains rich information and exhibits diverse characteristics. Specifically, graph data may come with the node or edge attributes showing the property of an entity or a connection, arise with signed or unsigned edges indicating the positive or negative relationships, form homogenous or heterogeneous information networks modeling different scenarios and settings. Furthermore, in these applications, the graph data is evolving and expanding more and more dynamically. The diverse, dynamic, and large-scale nature of graph data requires different data mining techniques and advanced machine learning methods. Meanwhile, the computing system evolves rapidly and becomes large-scale, collaborative and distributed, with many computing principles proposed such as cloud computing, edge computing and federated learning. Learning from big graph data in future-generation computing systems considers the effectiveness of graph learning, scalability of large-scale computing, privacy preserving under the federated computing setting with multi-source graphs, and graph dynamics. Today’s researchers have realized that novel graph learning theory, big graph specific platforms, and advanced graph processing techniques are needed. Therefore, a set of research topics such as distributed graph computing, graph stream learning, and graph embedding techniques have emerged, and applications such as graph-based anomaly detection, social recommendation, social influence analytics are becoming important issues.

Topics of Interest

The topics of interest include, but are not limited to:

• Feature Selection for Graph Data
• Distributed Computing on Big Graphs
• Dynamic and Streaming Graph Learning
• Graph Classification, Clustering, Link Prediction
• Graph Embedding
• Learning from Unattributed/Attributed Networks
• Learning from Unsigned/Signed Networks
• Learning from Homogenous/Heterogeneous Information Networks
• Anomaly Detection in Graph Data
• Sentiment Analysis
• Cyberbullying Detection in Social Networks
• Deep Learning for Graphs
• Graph Based Machine Learning
• Relational Data Analytics
• Social Recommendation
• Knowledge Graph Representation Learning
• Reasoning over Large-scale Knowledge Bases
• Temporal Knowledge Graphs
• Federated Learning with Distributed Graphs
• Social Computing
• Applications of Big Graph Learning

Submission Guidelines

All papers should be submitted to FGCS submission platform: https://www.evise.com/profile/api/navigate/FGCS. When submitting your manuscript please select the article type “VSI: GraphML-FGCS”. Please submit your manuscript before the submission deadline. All submissions deemed suitable to be sent for peer review will be reviewed by at least two independent reviewers. Once your manuscript is accepted, it will go into production, and will be simultaneously published in the current regular issue and pulled into the online Special Issue. Articles from this Special Issue will appear in different regular issues of the journal, though they will be clearly marked and branded as Special Issue articles. Please see an example here: Link. Please ensure you read the Guide for Authors before writing your manuscript. The Guide for Authors and the link to submit your manuscript is available on the Journal’s homepage.

Important Dates

Paper submission: Jul 15, 2020
Initial review feedback: Sep 15, 2020
Revision: Nov 1, 2020
Final review decision: Jan 31, 2021

Guest Editors

Shirui Pan, Monash University, Australia. [email protected]
Shaoxiong Ji, Aalto University, Finland. [email protected]
Di Jin, Tianjin University, China. [email protected]
Feng Xia, Federation University Australia, Australia. [email protected]
Philip S. Yu, University of Illinois at Chicago, USA. [email protected]

You have a typo in the article text:

To improve path search, Gardner et al. [57] introduced vector space similarity heuristics in random work by incorporating textual content, which also relieves the feature sparsity issue in PRA.

Is it possible to add links to tools and libraries for building knowledge graphs?

Neurocomputing Special Issue on Knowledge Graph Representation & Reasoning

https://www.journals.elsevier.com/neurocomputing/call-for-papers/knowledge-graph-representation-reasoning

Recent years have witnessed the release of many open-source and enterprise-driven knowledge graphs with a dramatic increase of applications of knowledge representation and reasoning in fields such as natural language processing, computer vision, and bioinformatics. With those large-scale knowledge graphs, recent research tends to incorporate human knowledge and imitate human’s ability of relational reasoning. Factual knowledge stored in knowledge bases or knowledge graphs can be utilized as a source for logical reasoning and, hence, be integrated to improve real-world applications.
Emerging embedding-based methods for knowledge graph representation have shown their ability to capture relational facts and model different scenarios with heterogenous information. By combining symbolic reasoning methods or Bayesian models, deep representation learning techniques on knowledge graphs attempt to handle complex reasoning with relational path and symbolic logic and capture the uncertainty with probabilistic inference. Furthermore, efficient representation learning and reasoning can be one of the paths towards the emulation of high-level cognition and human-level intelligence. Knowledge graphs can also be seen as a means to tackle the problem of explainability in AI. These trends naturally facilitate relevant downstream applications which inject structural knowledge into wide-applied neural architectures such as attention-based transformers and graph neural networks.
This special issue focuses on emerging techniques and trendy applications of knowledge graph representation learning and reasoning in fields such as natural language processing, computer vision, bioinformatics, and more.

Topics of Interests

The topics of this special issues include but not limited to:

• Representation learning on knowledge graphs
• Representation learning on text data
• Logical rule mining and symbolic reasoning
• Knowledge graph completion and link prediction
• Relation extraction
• Community embeddings
• Knowledge representation and reasoning over large-scale knowledge graphs
• Hybrid methods with symbolic and non-symbolic representation and reasoning
• Automatic knowledge graph construction
• Domain-specific knowledge graphs, e.g., medical knowledge graphs
• Knowledge dynamics of temporal knowledge graphs
• Time-evolving knowledge representation learning
• Question answering and dialogue systems with knowledge graphs
• Knowledge-injected sentiment analysis
• Commonsense knowledge representation and reasoning
• Knowledge graphs for neural machine translation
• Knowledge-aware recommendation systems
• Knowledge graphs for digital health, e.g., healthcare and medical diagnosis
• Few-shot relational learning on knowledge graphs
• Federated learning with multi-source graphs in decentralized settings
• Graph representation learning for structured data
• Explainable artificial intelligence with knowledge-aware models

Composition and Review Procedures

The Special Issue will consist of papers on novel methods and techniques that further develop and apply knowledge graph representation and reasoning for the development of intelligent tools, techniques, and applications. Some papers may survey various aspects of the topic. The balance between these will be adjusted to maximize the issue’s impact. Paper submissions for the special issue should follow the submission format and guidelines for regular papers and submitted at https://ees.elsevier.com/neucom. All the papers will be peer-reviewed following NEUCOM reviewing procedures. Guest editors will make an initial assessment of the suitability and scope of all submissions. Papers will be evaluated based on their originality, presentation, relevance and contributions, as well as their suitability to the special issue. Papers that either lack originality, clarity in presentation or fall outside the scope of the special issue will not be sent for review. Authors should select “SI: KGRR” when they reach the “Article Type” step in the submission process. The submitted papers must propose original research that has not been published nor currently under review in other venues. Previously published conference papers should be clearly identified by the authors at the submission stage and an explanation should be provided about how such papers have been extended. Such contributions must have at least 50% difference from the research work they stem from.

Important Dates

Paper submission: 31 August 2020

Initial review feedback: 31 October

2020 Revision: 15 January 2021

Publication date: March 2021

Guest Editors

Erik Cambria, Nanyang Technological University, Singapore

Shaoxiong Ji, Aalto University, Finland

Shirui Pan, Monash University, Australia

Philip S. Yu, University of Illinois at Chicago, USA