Rethinking GNN-based Entity Alignment on Heterogeneous Knowledge Graphs: New Datasets and A New Method

The development of knowledge graph (KG) applications has led to a rising need for entity alignment (EA) between heterogeneous KGs that are extracted from various sources. Recently, graph neural networks (GNNs) have been widely adopted in EA tasks due to GNNs' impressive ability to capture structure information. However, we have observed that the oversimplified settings of the existing common EA datasets are distant from real-world scenarios, which obstructs a full understanding of the advancements achieved by recent methods. This phenomenon makes us ponder: Do existing GNN-based EA methods really make great progress? In this paper, to study the performance of EA methods in realistic settings, we focus on the alignment of highly heterogeneous KGs (HHKGs) (e.g., event KGs and general KGs) which are different with regard to the scale and structure, and share fewer overlapping entities. First, we sweep the unreasonable settings, and propose two new HHKG datasets that closely mimic real-world EA scenarios. Then, based on the proposed datasets, we conduct extensive experiments to evaluate previous representative EA methods, and reveal interesting findings about the progress of GNN-based EA methods. We find that the structural information becomes difficult to exploit but still valuable in aligning HHKGs. This phenomenon leads to inferior performance of existing EA methods, especially GNN-based methods. Our findings shed light on the potential problems resulting from an impulsive application of GNN-based methods as a panacea for all EA datasets. Finally, we introduce a simple but effective method: Simple-HHEA, which comprehensively utilizes entity name, structure, and temporal information. Experiment results show Simple-HHEA outperforms previous models on HHKG datasets.Comment: 11 pages, 6 figure

On the Evolution of Knowledge Graphs: A Survey and Perspective

Knowledge graphs (KGs) are structured representations of diversified knowledge. They are widely used in various intelligent applications. In this article, we provide a comprehensive survey on the evolution of various types of knowledge graphs (i.e., static KGs, dynamic KGs, temporal KGs, and event KGs) and techniques for knowledge extraction and reasoning. Furthermore, we introduce the practical applications of different types of KGs, including a case study in financial analysis. Finally, we propose our perspective on the future directions of knowledge engineering, including the potential of combining the power of knowledge graphs and large language models (LLMs), and the evolution of knowledge extraction, reasoning, and representation

Unsupervised Deep Keyphrase Generation

Keyphrase generation aims to summarize long documents with a collection of salient phrases. Deep neural models have demonstrated remarkable success in this task, with the capability of predicting keyphrases that are even absent from a document. However, such abstractiveness is acquired at the expense of a substantial amount of annotated data. In this paper, we present a novel method for keyphrase generation, AutoKeyGen, without the supervision of any annotated doc-keyphrase pairs. Motivated by the observation that an absent keyphrase in a document may appear in other places, in whole or in part, we construct a phrase bank by pooling all phrases extracted from a corpus. With this phrase bank, we assign phrase candidates to new documents by a simple partial matching algorithm, and then we rank these candidates by their relevance to the document from both lexical and semantic perspectives. Moreover, we bootstrap a deep generative model using these top-ranked pseudo keyphrases to produce more absent candidates. Extensive experiments demonstrate that AutoKeyGen outperforms all unsupervised baselines and can even beat a strong supervised method in certain cases

The Impact of Green Finance on China’s Agricultural Trade

Enhancing the effectiveness of green development of the agricultural trade economy with green finance is a practical need to promote the healthy development of agricultural trade. This manuscript empirically analyzes the impact of green finance on China’s agricultural products import and export trade by using provincial-level panel data from 30 Chinese provinces from 2001–2019. The findings show that: (1) Green finance positively impacts China’s agricultural import and export trade at the 1% significant level, expanding the scale of agricultural imports and exports. (2) The positive impact of green finance on China’s agricultural import and export trade is heterogeneous across regions. Accordingly, this paper puts forward policy suggestions such as strengthening the support of green finance to agriculture, focusing on the improvement of green total factor productivity in agriculture, and promoting synergistic regional development through the implementation of differentiation of green finance

CT‐determined low skeletal muscle mass predicts worse overall survival of gastric cancer in patients with cachexia

Abstract Background There were controversies for the association between computed tomography (CT)‐determined low skeletal muscle mass (SMM) and overall survival (OS) in gastric cancer (GC). In this study, we investigated whether cachexia could be a potential confounding variable for this issue. Methods We retrospectively collected the patients of GC in our institution between July 2016 and January 2021. Preoperative SMM was determined by analyzing the skeletal muscle index of L3 with abdominal CT, and the cut‐offs for low SMM were defined as <52.4 (men) and < 38.5 cm2/m2 (women), respectively. Overall survival (OS) was the primary endpoint. Results Of the 255 included GC patients, 117 (46%) were classified as having low SMM. Those with low SMM were associated with a higher level of circulating interleukin 6 and C reactive protein but a lower level of albumin than those of normal SMM. The univariate analysis showed that low SMM, tumor‐node‐metastasis (TNM) stage, body mass index (BMI), postoperative chemotherapy, and cachexia were significantly associated with OS, while in the multivariate analysis, only low SMM and TNM stage were significantly associated with OS. Kaplan–Meier survival curves with log‐rank tests indicated that low SMM significantly predicted worse OS of GC. After grouping by cachexia, the low SMM significantly predicted worse OS in patients with cachexia instead of those without cachexia. Conclusions CT‐determined low SMM predicts worse OS of GC in patients with cachexia instead of those without cachexia, and greater attention should be paid to such patients with synchronous low SMM and cachexia

Image_1_Prognostic value of cachexia index in patients with colorectal cancer: A retrospective study.tiff

BackgroundCurrent diagnostic criteria for cancer cachexia are inconsistent, and arguments still exist about the impact of cachexia on the survival of patients with colorectal cancer. In this study, we aim to investigate the prognostic value of a novel cachexia indicator, the cachexia index (CXI), in patients with colorectal cancer.MethodsThe CXI was calculated as skeletal muscle index (SMI) × serum albumin/neutrophil-lymphocyte ratio. The cut-off value of CXI was determined by the receiver operating characteristic (ROC) curves and Youden’s index. The major outcomes were major complications, overall survival (OS), and recurrence-free survival (RFS).ResultsA total of 379 patients (234 men and 145 women) were included. The ROC curves indicated that CXI had a significantly diagnostic capacity for the detection of major complications. Based on Youden’s index, there were 231 and 148 patients in the low and high CXI groups, respectively. Patients in the low CXI group had significantly older age, lower BMI, and a higher percentage of cachexia and TNM stage II+III. Besides, Patients in low CXI group were associated with a significantly higher rate of major complications, blood transfusion, and longer length of stay. Logistic regression analysis indicated that low CXI, cachexia, and coronary heart disease were independent risk factors for the major complications. Kaplan Meier survival curves indicated that patients with high CXI had a significantly more favorable OS than those with low CXI, while no significant difference was found in RFS between the two groups. Besides, there were no significant differences in OS or RFS between patients with and without cachexia. The univariate and multivariate Cox regression analysis indicated that older age, low CXI, and coronary heart disease instead of cachexia were associated with a decreased OS.ConclusionCXI was better than cachexia in predicting OS and could be a useful prognostic indicator in patients with colorectal cancer, and greater attention should be paid to patients with low CXI.</p

M1-like TAMs are required for the efficacy of PD-L1/PD-1 blockades in gastric cancer

The efficacy of PD-1/PD-L1 blockades is heterogeneous in different molecular subtypes of gastric cancer (GC). In this study, we analyzed relevant clinical trials to identify the molecular subtypes associated with the efficacy of PD-1/PD-L1 blockades, and public datasets, patient samples, and GC cell lines were used for investigating potential mechanisms. We found that GC with EBV-positive, MSI-H/dMMR, TMB-H or PIK3CA mutant subtype had enhanced efficacy of PD-L1/PD-1 blockades. Also, differentially expressed genes of these molecular subtypes shared the same gene signature and functional annotations related to immunity. Meanwhile, CIBERSORT identified that the overlapping landscapes of tumor-infiltrating immune cells in the four molecular subtypes were mainly M1-like macrophages (M1). The relationships between M1 and clinical characteristics, M1, and gene signatures associated with PD-1/PD-L1 blockades also revealed that M1 was associated with improved prognosis and required for the efficacy of PD-L1/PD-1 blockades in GC. We identified that tumor-infiltrating CD68+CD163− macrophages could represent M1 calculated by CIBERSORT in clinical application, and CXCL9, 10, 11/CXCR3 axis was involved in the mechanism of CD68+CD163− macrophages in the enhanced efficacy of PD-L1/PD-1 blockades. In conclusion, CD68+CD163− macrophages are required for the efficacy of PD-L1/PD-1 blockades and expand the applicable candidates in GC patients without the molecular subtypes