Knowledge graph completion(KGC) aims to infer missing facts from observed relational triples, which demands jointly modeling textual semantics and relational structure. Existing structure-based methods capture graph topology but often struggle with sparse entities and long-tail relations, while text-based methods exploit entity and relation descriptions but weakly model relational neighborhoods. In this paper, we propose LQ-KGC, a latent-query refinement framework that enables iterative interaction between semantic inference and structural reasoning for KGC. Instead of treating all missing triples as latent variables, LQ-KGC constructs a restricted set of semantically plausible latent queries and alternates between two refinement steps: the language model predicts latent tail distributions from textual descriptions, while the graph model incorporates high-confidence pseudo tails as pseudo edges for relation-aware message passing. Meanwhile, structure-aware graph predictions are distilled back into the language model to refine semantic representations. Experiments on WN18RR and FB15k-237 demonstrate that LQ-KGC consistently improves over text-based and graph-based baselines, with additional analyses highlighting the effectiveness of pseudo-edge augmentation and latent-query construction.

Research Replication Prediction (RRP) is the task of predicting whether a published research result can be replicated or not. Building an interpretable neural text classifier for RRP promotes the understanding of why a research paper is predicted as replicable or non-replicable and therefore makes its real-world application more reliable and trustworthy. However, the prior works on model interpretation mainly focused on improving the model interpretability at the word/phrase level, which are insufficient especially for long research papers in RRP. Furthermore, the existing methods cannot utilize a large size of unlabeled dataset to further improve the model interpretability. To address these limitations, we aim to build an interpretable neural model which can provide sentence-level explanations and apply weakly supervised approach to further leverage the large corpus of unlabeled datasets to boost the interpretability in addition to improving prediction performance as existing works have done. In this work, we propose the Variational Contextual Consistency Sentence Masking (VCCSM) method to automatically extract key sentences based on the context in the classifier, using both labeled and unlabeled datasets. Results of our experiments on RRP along with European Convention of Human Rights (ECHR) datasets demonstrate that VCCSM is able to improve the model interpretability for the long document classification tasks using the area over the perturbation curve and post-hoc accuracy as evaluation metrics.

Knowing whether a published research result can be replicated is important. Carrying out direct replication of published research incurs a high cost. There are efforts tried to use machine learning aided methods to predict scientific claims’ replicability. However, existing machine learning aided approaches use only hand-extracted statistics features such as p-value, sample size, etc. without utilizing research papers’ text information and train only on a very small size of annotated data without making the most use of a large number of unlabeled articles. Therefore, it is desirable to develop effective machine learning aided automatic methods which can automatically extract text information as features so that we can benefit from Natural Language Processing techniques. Besides, we aim for an approach that benefits from both labeled and the large number of unlabeled data. In this paper, we propose two weakly supervised learning approaches that use automatically extracted text information of research papers to improve the prediction accuracy of research replication using both labeled and unlabeled datasets. Our experiments over real-world datasets show that our approaches obtain much better prediction performance compared to the supervised models utilizing only statistic features and a small size of labeled dataset. Further, we are able to achieve an accuracy of 75.76% for predicting the replicability of research.