Summary of Uncertainty Management in the Construction Of Knowledge Graphs: a Survey, by Lucas Jarnac et al.
Uncertainty Management in the Construction of Knowledge Graphs: a Survey
by Lucas Jarnac, Yoan Chabot, Miguel Couceiro
First submitted to arxiv on: 27 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 This paper explores the challenge of constructing Knowledge Graphs (KGs) in an uncertain world, where unreliable data sources can lead to conflicts that impact the quality of the graph. To address this issue, researchers have developed automatic methods for extracting knowledge from heterogeneous sources, which require handling uncertainty throughout integration into the KG. The authors survey state-of-the-art approaches and present constructions of open and enterprise KGs, highlighting the importance of maintaining quality in the face of uncertainty. They also discuss downstream tasks such as KG completion using embedding models, knowledge alignment, and fusion to address uncertainty in KG construction. This research has significant implications for companies that rely on KGs for applications like vocabulary sharing, Q/A systems, and recommendation systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to build a big library where you need to decide which books are trustworthy and which ones might be fake. That’s the problem this paper tries to solve with “Knowledge Graphs” (KGs). KGs help companies make sense of lots of information from different sources, like recommending movies or answering questions. But what if some of that information is wrong? The authors look at ways to automatically build KGs and deal with the uncertainty of the data. They also talk about using these graphs for things like suggesting new books to read or helping you find answers online. Overall, this research helps companies make better use of their KGs by dealing with the uncertainty of the information they contain. |
Keywords
» Artificial intelligence » Alignment » Embedding
