Summary of An Ad-hoc Graph Node Vector Embedding Algorithm For General Knowledge Graphs Using Kinetica-graph, by B. Kaan Karamete and Eli Glaser
An Ad-hoc graph node vector embedding algorithm for general knowledge graphs using Kinetica-Graph
by B. Kaan Karamete, Eli Glaser
First submitted to arxiv on: 22 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel method for generating general graph node embeddings from knowledge graph representations. The approach combines several indicators, including local affinity and remote structural relevance, to capture nodal similarities. These indicators are flattened into a one-dimensional vector space, enabling the use of similarity functions for finding similar nodes. A novel loss function is defined as the sum of pairwise square differences between assumed embeddings and ground truth estimates. Ground truth is estimated using Jaccard similarity and overlapping labels. The paper demonstrates a multi-variate stochastic gradient descent (SGD) algorithm to minimize the average error using random sampling. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand how to turn knowledge graphs into useful tools for finding similar things in big networks. It uses special measures to capture different kinds of relationships between nodes, like how close they are or what labels they share. These measures are combined into a single set of numbers that can be used to find similar nodes. The paper also introduces a new way to measure the error between our predictions and the real answers. This new approach helps us improve our results by adjusting the importance of each relationship type. |
Keywords
* Artificial intelligence * Knowledge graph * Loss function * Stochastic gradient descent * Vector space
