Towards a Definition of Knowledge Graphs

By Lisa Ehrlinger, Wolfram Wöß. International Conference on Semantic Systems, 2016

744 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/b18e4272a7b9fa2e1c970d258ab5ea99ed5e2284

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Shirui Pan
2. Philip S. Yu
3. Erik Cambria
4. Qian Li
5. Hao Peng
6. Feng Xia
7. Yangqiu Song
8. Shaoxiong Ji

Citations in the corpus

• Knowledge Graphs (2020, unknown): “, [141]), novel techniques (e.”
• The Consciousness Prior (2017, background): “…(broken down into phrases, statements or sentences, each of which involves very few words) as well as the structure of formal knowledge representations such as the sets of facts and rules studied in classical symbolic / logic AI or in ontologies and knowledge graphs [Ehrlinger and Wöß, 2016].”
• Tempura: Query Analysis with Structural Templates (2020, unknown): “, ontologies from knowledge graphs [8]).”
• Knowledge Graphs: Opportunities and Challenges (2023, methodology): “In 2012, Google first put forward Knowledge Graph by introducing their knowledge base called Google Knowledge Graph (Ehrlinger and Wöß 2016).”
• A Comprehensive Survey on Automatic Knowledge Graph Construction (2023, background): “Ehrlinger and Woß [39] further emphasize that a knowledge graph arranges information into an ontology and then enlightens novel knowledge discovery with “a reasoner”.”
• A Survey on Knowledge Graphs: Representation, Acquisition, and Applications (2020, background): “Definition 1 (Ehrlinger and Wöß [12]).”
• ASER: A Large-scale Eventuality Knowledge Graph (2019, background): “n a knowledge base are emphasized. We discuss in the context of the knowledge graph, as our knowledge is also constructed as a complex graph. For more information about terminologies, please refer to [13]. This paper is published under the Creative Commons Attribution 4.0 International (CC-BY 4.0) l”
• Higher-Order Knowledge Representations for Agentic Scientific Reasoning (2026, unknown): “In practice, knowledge is often encoded into graph networks by representing concepts as nodes and their relationships as edges, which capture pairwise correlations between structures [14, 42–44].”