Graph Databases for Information Retrieval
Chris Kamphuis
- 14 Apr 2020
- pp 608-612
TL;DR: This work proposes to deploy graph database management systems to implement existing and novel graph-based models for information retrieval, and investigates how data structures and algorithms for ranking should change in presence of continuous database updates.
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Abstract: Graph models have been deployed in the context of information retrieval for many years. Computations involving the graph structure are often separated from computations related to the base ranking. In recent years, graph data management has been a topic of interest in database research. We propose to deploy graph database management systems to implement existing and novel graph-based models for information retrieval. For this a unifying mapping from a graph query language to graph based retrieval models needs to be developed; extending standard graph database operations with functionality for keyword search. We also investigate how data structures and algorithms for ranking should change in presence of continuous database updates. We want to investigate how temporal decay can affect ranking when data is continuously updated. Finally, can databases be deployed for efficient two-stage retrieval approaches?
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Citations
How Can Graph Neural Networks Help Document Retrieval: A Case Study on CORD19 with Concept Map Generation
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Comparative Study of MongoDB vs Cassandra in big data analytics
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A Graph-oriented Framework for Online Analytical Processing
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TL;DR: The First International Workshop on Graph-Based Approaches in Information Retrieval (IRonGraphs 2024) focuses on the integration of graph-based methodologies into information retrieval methods.
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