Name
Abstract | ||
|---|---|---|
Recent years have witnessed a proliferation of large-scale knowledge graphs, such as Freebase, YAGO, Google's Knowledge Graph, and Microsoft's Satori. Whereas there is a large body of research on mining homogeneous graphs, this new generation of information networks are highly heterogeneous, with thousands of entity and relation types and billions of instances of vertices and edges. In this tutorial, we will present the state of the art in constructing, mining, and growing knowledge graphs. The purpose of the tutorial is to equip newcomers to this exciting field with an understanding of the basic concepts, tools and methodologies, available datasets, and open research challenges. A publicly available knowledge base (Freebase) will be used throughout the tutorial to exemplify the different techniques. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1145/2623330.2630803 | KDD |
Keywords | Field | DocType |
big knowledge,general | Open research,Data science,Data mining,Graph,Knowledge graph,Information networks,Vertex (geometry),Homogeneous,Computer science,Artificial intelligence,Knowledge base,Machine learning | Conference |
Citations | PageRank | References |
18 | 0.65 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Antoine Bordes | 1 | 3289 | 157.12 |
| Evgeniy Gabrilovich | 2 | 4573 | 224.48 |