Improving Multi-hop Question Answering over Knowledge Graphs using Knowledge Base Embeddings

Knowledge Graphs (KG) are multi-relational graphs consisting of entities as nodes and relations among them as typed edges. Goal of the Question Answering over KG (KGQA) task is to answer natural language queries posed over the KG. Multi-hop KGQA requires reasoning over multiple edges of the KG to arrive at the right answer. KGs are often incomplete with many missing links, posing additional challenges for KGQA, especially for multi-hop KGQA. Recent research on multi-hop KGQA has attempted to handle KG sparsity using relevant external text, which isn{'}t always readily available. In a separate line of research, KG embedding methods have been proposed to reduce KG sparsity by performing missing link prediction. Such KG embedding methods, even though highly relevant, have not been explored for multi-hop KGQA so far. We fill this gap in this paper and propose EmbedKGQA. EmbedKGQA is particularly effective in performing multi-hop KGQA over sparse KGs. EmbedKGQA also relaxes the requirement of answer selection from a pre-specified neighborhood, a sub-optimal constraint enforced by previous multi-hop KGQA methods. Through extensive experiments on multiple benchmark datasets, we demonstrate EmbedKGQA{'}s effectiveness over other state-of-the-art baselines.

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Reproducibility Reports

Jan 31 2021
[Re] Improving Multi-hop Question Answering over Knowledge Graphs using Knowledge Base Embeddings

We were able to reproduce the Hits@1 to be within ±2.4% of the reported value (in most cases). Anomalies were observed in 2 cases. 1. In MetaQA-KG-Full (3-hop) dataset. 2. WebQSP-KG-Full dataset. From our experiments on the QA model, we have found that a recent transformer architecture, SBERT[2] produced better accuracy than the original paper. Replacing RoBERTa[3] with SBERT[2] increased the absolute accuracy by ≈3.4% and ≈0.6% in the half KG and the full KG case respectively. (KG: Knowledge Graph, ”≈”: Approximately)

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