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Knowledge graphs (KGs) are of great importance in various artificial intelligence systems, such as question answering, relation extraction, and recommendation. Nevertheless, most real-world KGs are highly incomplete, with many missing relations between entities. To discover new triples (i.e., head entity, relation, tail entity), many KG completion algorithms have been proposed in recent years. However, a vast majority of existing studies often require a large number of training triples for each relation, which contradicts the fact that the frequency distribution of relations in KGs often follows a long tail distribution, meaning a majority of relations have only very few triples. Meanwhile, since most existing large-scale KGs are constructed automatically by extracting information from crowd-sourcing data using heuristic algorithms, plenty of errors could be inevitably incorporated due to the lack of human verification, which greatly reduces the performance for KG completion. To tackle the aforementioned issues, in this paper, we study a novel problem of error-aware few-shot KG completion and present a principled KG completion framework REFORM. Specifically, we formulate the problem under the few-shot learning framework, and our goal is to accumulate meta-knowledge across different meta-tasks and generalize the accumulated knowledge to the meta-test task for error-aware few-shot KG completion. To address the associated challenges resulting from insufficient training samples and inevitable errors, we propose three essential modules neighbor encoder, cross-relation aggregation, and error mitigation in each meta-task. Extensive experiments on three widely used KG datasets demonstrate the superiority of the proposed framework REFORM over competitive baseline methods.
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Corpus-Based Relation Extraction by Identifying and Refining Relation Patterns
Automated relation extraction without extensive human-annotated data is a crucial yet challenging task in text mining. Existing studies typically use lexical patterns to label a small set of high-precision relation triples and then employ distributional methods to enhance detection recall. This precision-first approach works well for common relation types but struggles with unconventional and infrequent ones. In this work, we propose a recall-first approach that first leverages high-recall patterns (e.g., a per:siblings relation normally requires both the head and tail entities in the person type) to provide initial candidate relation triples with weak labels and then clusters these candidate relation triples in a latent spherical space to extract high-quality weak supervisions. Specifically, we present a novel framework, RCLUS, where each relation triple is represented by its head/tail entity type and the shortest dependency path between the entity mentions. RCLUS first applies high-recall patterns to narrow down each relation type’s candidate space. Then, it embeds candidate relation triples in a latent space and conducts spherical clustering to further filter out noisy candidates and identify high-quality weakly-labeled triples. Finally, RCLUS leverages the above-obtained triples to prompt-tune a pre-trained language model and utilizes it for improved extraction coverage. We conduct extensive experiments on three public datasets and demonstrate that RCLUS outperforms the weakly-supervised baselines by a large margin and achieves generally better performance than fully-supervised methods in low-resource settings.
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- PAR ID:
- 10467075
- Publisher / Repository:
- Springer
- Date Published:
- Edition / Version:
- 1
- Subject(s) / Keyword(s):
- Corpus-Based Relation Extraction, Mining Relation Patterns
- Format(s):
- Medium: X
- Location:
- Torino, Italy
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
