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- W3135315741 abstract "Besides the challenge that a human can ask one question in many different ways, a key aspect in Question Answering approaches over Knowledge Graphs (KGQA) is to deal with the vast amount of information present in the knowledge graphs. Modern real-world knowledge graphs contain nearly millions of entities and relationships. Additionally, they are enriched with new facts every day. However, not all facts are relevant for answering particular questions, thus fostering several challenges to KGQA systems, which require interpretable and query-able data. One solution to filtering the extra data in knowledge graphs is to rely on graph summarization techniques. Graph-based summarization approaches aim to resize knowledge graphs to be more concise and precise by storing only relevant information. In this paper, we propose a framework named LAUREN that applies different summarization techniques on knowledge graphs to be used in KGQA systems. Our experiments show that LAUREN summarizes large knowledge graphs such as DBpedia by 2 million entities and its summarization still achieves the same performance on both question answering and linking tasks compared to the complete DBpedia." @default.
- W3135315741 created "2021-03-15" @default.
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- W3135315741 date "2021-01-01" @default.
- W3135315741 modified "2023-09-30" @default.
- W3135315741 title "LAUREN - Knowledge Graph Summarization for Question Answering" @default.
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- W3135315741 doi "https://doi.org/10.1109/icsc50631.2021.00047" @default.
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