Conformalized Answer Set Prediction for Knowledge Graph Embedding (2025.naacl-long)
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| Challenge: | Knowledge graph embeddings (KGE) map entities and predicates into numerical vectors, providing non-classical reasoning capabilities based on similarities and analogies between entities and relations. |
| Approach: | They propose to use knowledge graph embeddings to provide non-classical reasoning capabilities by exploiting similarities and analogies between entities and relations. |
| Outcome: | The proposed model can generate answer sets with probabilistic guarantees on four benchmark datasets and is scaled well with respect to the difficulty of the query. |
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| Challenge: | Knowledge graph embedding (KGE) methods map entities and relations from knowledge graphs into numerical vector spaces. |
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| Challenge: | Knowledge graph reasoning is an important problem for knowledge graphs. |
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| Challenge: | Existing calibration techniques are less effective under the standard closed-world assumption (CWA) and the more realistic open-world hypothesis (OWA) Existing methods are not effective under OWA and provide explanations for this discrepancy. |
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| Challenge: | Existing methods for probability calibration of knowledge graph embedding models are ill-suited for KGEs. |
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| Challenge: | Knowledge graph embeddings (KGE) models are often used to predict missing links for knowledge graphs (KGs) however, multiple KG embedds can give conflicting predictions for unseen queries. |
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| Challenge: | Existing knowledge graph embedding models suffer from Z-paradox, a deficiency in expressiveness . Embedding-based models map each entity and relation into a vector or matrix . |
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Improving Multi-hop Question Answering over Knowledge Graphs using Knowledge Base Embeddings (2020.acl-main)
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| Challenge: | Existing multi-hop KGQA methods impose heuristic neighborhood limits, which often make it much harder to answer the input NL question. |
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KGxBoard: Explainable and Interactive Leaderboard for Evaluation of Knowledge Graph Completion Models (2022.emnlp-demos)
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Haris Widjaja, Kiril Gashteovski, Wiem Ben Rim, Pengfei Liu, Christopher Malon, Daniel Ruffinelli, Carolin Lawrence, Graham Neubig
| Challenge: | Knowledge Graphs (KGs) store information in the form of (head, predicate, tail)-triples. |
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