Summary of Capturing and Anticipating User Intents in Data Analytics Via Knowledge Graphs, by Gerard Pons et al.
Capturing and Anticipating User Intents in Data Analytics via Knowledge Graphs
by Gerard Pons, Besim Bilalli, Anna Queralt
First submitted to arxiv on: 1 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Databases (cs.DB)
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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 focuses on developing advanced data analytics tools for non-expert users, such as citizen data scientists. It proposes using Knowledge Graphs (KG) as a framework for capturing complex analytics workflows, including information about users, their intents, and feedback. The authors explore two methods: query templates to extract relevant information from the KG and link prediction with knowledge graph embeddings. The latter enhances flexibility and allows leveraging the entire structure and components of the graph. Evaluation experiments show that the proposed method can capture the graph’s structure and produce sensible suggestions for users interacting with these systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine having a super-smart assistant that helps you find important information in huge amounts of data. That’s what this paper is all about – making it easier for people without technical expertise to work with big data. The authors use something called Knowledge Graphs, which stores information not only about the data and algorithms but also about the people using them. This allows the system to provide personalized help and suggestions. Two methods are explored in the paper: one that helps extract relevant info from the graph and another that uses patterns to make predictions. The results show that this approach can be really helpful for people trying to work with big data. |
Keywords
» Artificial intelligence » Knowledge graph
