Summary of Rcone: Rough Cone Embedding For Multi-hop Logical Query Answering on Multi-modal Knowledge Graphs, by Mayank Kharbanda et al.
RConE: Rough Cone Embedding for Multi-Hop Logical Query Answering on Multi-Modal Knowledge Graphs
by Mayank Kharbanda, Rajiv Ratn Shah, Raghava Mutharaju
First submitted to arxiv on: 21 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 proposes a novel approach, called RConE, for querying Multi-Modal Knowledge Graphs (MMKGs). The method is designed to capture the dense information contained in these graphs and answer queries that involve complex logical constructs. Unlike previous works that focus on path-based question answering, this model uses First Order Logic (FOL) operators to execute queries. The approach first identifies candidate entities containing the answer and then finds the solution within those entities. The evaluation of RConE on four publicly available MMKGs shows that it outperforms the current state-of-the-art. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to ask questions about things like movies, music, and books that have many different types of information connected together. Right now, computers are good at answering simple questions, but they struggle when the question is complicated or involves multiple pieces of information. The researchers made a special computer model called RConE that can handle these kinds of complex questions. It works by looking for clues in the data and then finding the answer within those clues. This new approach was tested on four big collections of connected data and showed it could do better than other methods. |
Keywords
» Artificial intelligence » Multi modal » Question answering
