Papers with Knowledge Bases
OpenKI: Integrating Open Information Extraction and Knowledge Bases with Relation Inference (N19-1)
Copied to clipboard
| Challenge: | Existing methods for knowledge extraction and alignment are limited in quality and performance. |
| Approach: | They propose to integrate OpenIE extractions in the form of (subject, predicate, object) triples with Knowledge Bases (KB) |
| Outcome: | The proposed method improves state-of-the-art for OpenIE extractions and boosts performance on OpenIE from semi-structured data. |
Connecting Language and Knowledge with Heterogeneous Representations for Neural Relation Extraction (N19-1)
Copied to clipboard
| Challenge: | Knowledge Bases (KBs) require constant updating to reflect changes to the world they represent. |
| Approach: | They propose a framework that unifies learning of RE and KBE models . the framework is based on a relation extraction task that uses a KB relation to a phrase . |
| Outcome: | The proposed framework unifies learning of RE and KBE models, leading to significant improvements over the state-of-the-art RE framework. |
MLMLM: Link Prediction with Mean Likelihood Masked Language Model (2021.findings-acl)
Copied to clipboard
| Challenge: | Knowledge Bases (KBs) are easy to query, verifiable, and interpretable. however, they scale with man-hours and high-quality data. |
| Approach: | They propose to commit the knowledge embedded in MLMs to a KB, making it interpretable . they propose to use a mean likelihood Masked Language Model to compare the likelihood of generating different entities to perform link prediction in a tractable manner. |
| Outcome: | The proposed approach compares the likelihood of generating different entities to perform link prediction in a tractable manner. |
Fine-grained Entity Typing without Knowledge Base (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing work on fine-grained entity typing (FET) relies on knowledge bases as distant supervision, but lack of or incompleteness of KB can hinder training. |
| Approach: | They propose a two-step framework that trains FET models without accessing any knowledge base. |
| Outcome: | The proposed framework achieves competitive performance with respect to the models trained on the original KB-supervised datasets. |
Joint Knowledge Base Completion and Question Answering by Combining Large Language Models and Small Language Models (2026.acl-long)
Copied to clipboard
| Challenge: | Existing studies rely on the small language model (SLM) to enhance them jointly, and the large language model’s strong reasoning ability is ignored. |
| Approach: | They propose a framework which can make knowledge base completion and knowledge base question answering enhance each other in an iterative manner by combining the strengths of the small language model and the large language model. |
| Outcome: | The proposed framework surpasses baselines for both KBC and KBQA tasks over two public benchmark data sets. |