PromptKG: A Prompt Learning Framework for Knowledge Graph Representation Learning and Application

Knowledge Graphs (KGs) often have two characteristics: heterogeneous graph structure and text-rich entity/relation information. KG representation models should consider graph structures and text semantics, but no comprehensive open-sourced framework is mainly designed for KG regarding informative text description. In this paper, we present PromptKG, a prompt learning framework for KG representation learning and application that equips the cutting-edge text-based methods, integrates a new prompt learning model and supports various tasks (e.g., knowledge graph completion, question answering, recommendation, and knowledge probing). PromptKG is publicly open-sourced at https://github.com/zjunlp/PromptKG with long-term technical support.Comment: Work in progres

Decision Diagram Based Symbolic Algorithm for Evaluating the Reliability of a Multistate Flow Network

Evaluating the reliability of Multistate Flow Network (MFN) is an NP-hard problem. Ordered binary decision diagram (OBDD) or variants thereof, such as multivalued decision diagram (MDD), are compact and efficient data structures suitable for dealing with large-scale problems. Two symbolic algorithms for evaluating the reliability of MFN, MFN_OBDD and MFN_MDD, are proposed in this paper. In the algorithms, several operating functions are defined to prune the generated decision diagrams. Thereby the state space of capacity combinations is further compressed and the operational complexity of the decision diagrams is further reduced. Meanwhile, the related theoretical proofs and complexity analysis are carried out. Experimental results show the following: (1) compared to the existing decomposition algorithm, the proposed algorithms take less memory space and fewer loops. (2) The number of nodes and the number of variables of MDD generated in MFN_MDD algorithm are much smaller than those of OBDD built in the MFN_OBDD algorithm. (3) In two cases with the same number of arcs, the proposed algorithms are more suitable for calculating the reliability of sparse networks

From Discrimination to Generation: Knowledge Graph Completion with Generative Transformer

Knowledge graph completion aims to address the problem of extending a KG with missing triples. In this paper, we provide an approach GenKGC, which converts knowledge graph completion to sequence-to-sequence generation task with the pre-trained language model. We further introduce relation-guided demonstration and entity-aware hierarchical decoding for better representation learning and fast inference. Experimental results on three datasets show that our approach can obtain better or comparable performance than baselines and achieve faster inference speed compared with previous methods with pre-trained language models. We also release a new large-scale Chinese knowledge graph dataset AliopenKG500 for research purpose. Code and datasets are available in https://github.com/zjunlp/PromptKG/tree/main/GenKGC.Comment: Accepted by WWW 2022 Poste

Construction and Applications of Billion-Scale Pre-trained Multimodal Business Knowledge Graph

Business Knowledge Graphs (KGs) are important to many enterprises today, providing factual knowledge and structured data that steer many products and make them more intelligent. Despite their promising benefits, building business KG necessitates solving prohibitive issues of deficient structure and multiple modalities. In this paper, we advance the understanding of the practical challenges related to building KG in non-trivial real-world systems. We introduce the process of building an open business knowledge graph (OpenBG) derived from a well-known enterprise, Alibaba Group. Specifically, we define a core ontology to cover various abstract products and consumption demands, with fine-grained taxonomy and multimodal facts in deployed applications. OpenBG is an open business KG of unprecedented scale: 2.6 billion triples with more than 88 million entities covering over 1 million core classes/concepts and 2,681 types of relations. We release all the open resources (OpenBG benchmarks) derived from it for the community and report experimental results of KG-centric tasks. We also run up an online competition based on OpenBG benchmarks, and has attracted thousands of teams. We further pre-train OpenBG and apply it to many KG- enhanced downstream tasks in business scenarios, demonstrating the effectiveness of billion-scale multimodal knowledge for e-commerce. All the resources with codes have been released at \url{https://github.com/OpenBGBenchmark/OpenBG}.Comment: OpenBG. Work in Progres

Predictive and prognostic markers from endoscopic ultrasound with biopsies during definitive chemoradiation therapy in esophageal squamous cell carcinoma

Abstract Introduction Endoscopic ultrasound (EUS) may play a role in evaluating treatment response after definitive chemoradiation therapy (dCRT) for esophageal squamous cell carcinoma (ESCC). This study explored the prognostic markers of EUS with biopsies and developed two nomograms for survival prediction. Methods A total of 821 patients newly diagnosed with ESCC between January 2015 and December 2019 were reviewed. We investigated the prognostic value of the changes in tumor imaging characteristics and histopathological markers by an interim response evaluation, including presence of stenosis, ulceration, tumor length, tumor thickness, lumen involvement, and tumor remission. Independent prognostic factors of progression-free survival (PFS) and overall survival (OS) were determined using Cox regression analysis and further selected to build two nomogram models for survival prediction. The receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) were used to respectively assess its discriminatory capacity, predictive accuracy, and clinical usefulness. Results A total of 155 patients were enrolled in this study and divided into the training (109 cases) and testing (46 cases) cohorts. Tumor length, residual tumor thickness, reduction in tumor thickness, lumen involvement, and excellent remission (ER) of spatial luminal involvement in ESCC (ER/SLI) differed significantly between responders and non-responders. For patients undergoing dCRT, tumor stage (P = 0.001, 0.002), tumor length (P = 0.013, 0.008), > 0.36 reduction in tumor thickness (P = 0.004, 0.004) and ER/SLI (P = 0.041, 0.031) were independent prognostic markers for both PFS and OS. Time-dependent ROC curves, calibration curves, and DCA indicated that the predicted survival rates of our two established nomogram models were highly accurate. Conclusion Our nomogram showed high accuracy in predicting PFS and OS for ESCC after dCRT. External validation and complementation of other biomarkers are needed in further studies

Image_3_Comparison of dynamic changes in the peripheral CD8+ T cells function and differentiation in ESCC patients treated with radiotherapy combined with anti-PD-1 antibody or concurrent chemoradiotherapy.jpeg

ObjectiveThe systematic immune status of cancer patients undergoing immunotherapy is little known. We prospectively identified the function and differentiation traits of peripheral CD8+ T cells based on our phase 1b clinical trial (NCT03222440) of radiotherapy combined with camrelizumab in patients with locally advanced esophageal squamous cell carcinoma (ESCC) and compared it with concurrent chemoradiotherapy (CCRT).Methods19 and 18 patients were included in the cohort of radiotherapy plus camrelizumab and cohort of CCRT treatment. By using flow cytometry, we evaluated the expression levels of PD-1, Eomes, T-bet and IFN-γ (function), CD38 and HLA-DR (activation), and differentiation subsets classified according to the expression levels of CD45RA and CD62L in peripheral CD8+ T cells before and during treatment.ResultsEffective binding of anti-PD-1 antibody camrelizumab with PD-1 on CD8+ T cells was detected during treatment. Both two treatments elevated the expression levels of activation molecules CD38 and HLA-DR on CD8+ T cells. PD-1+CD8+ T cells had more activation features than PD-1-CD8+ T cells in two groups and the treatments did not alter these differences. The two treatments activated both PD-1+ and PD-1- CD8+ T cells. PD-1+CD8+ T cells had less Naïve and TEMRA but more Tcm and Tem than PD-1-CD8+ T cells in two groups and both two treatments changed the ratio of memory T cells in PD-1+ and PD-1- cells. RT plus camrelizumab treatment reduced Naïve T cells and TEMRA subsets both in PD-1+ and PD-1- CD8+ T cells while elevated Tcm subset in PD-1+CD8+ T cells and Tem subset in PD-1-CD8+ T cells. CCRT elevated Tcm subset and reduced TEMRA subset in PD-1-CD8+ T cells while did not change any subset in PD-1+CD8+ T cells. Furthermore, patients undergoing radiotherapy plus immunotherapy were found to obtain better prognosis than those receiving CCRT.ConclusionsThis study identified the dynamic changes of systematic immune status of patients undergoing treatment. The two treatments had similar activation effects on peripheral CD8+ T cells with different PD-1 properties but had different effects on their differentiation status. These results provided potential clues to the reasons underlying the difference in prognosis of the two treatments.</p

New trapezoid-shaped Frisch-grid ionization chamber for low-energy particle measurements

A new trapezoid-shaped Frisch-grid ionization chamber (TFG-IC) has been built as a part of a $$\varDelta {E}-E$$ telescope system for the detection and identification of charged particles at energies down to a few MeV. To study the effect of the drift electric field uniformity, two types of sealed windows, namely a pair of SSA (split-strip aluminized mylar film) and a pair of DSA (double-sided aluminized mylar film) sealed windows have been investigated. The detector’s performances were studied using a standard $$^{241}$$Am source at different gas pressures, and the total energy-deposit resolution achieved is about 1.1%(FWHM). The $$\varDelta {E}-E$$ telescope, which was composed of TFG-IC and a DSSSD (double-sided silicon strip detector), has been tested using a three-component $$\alpha$$ source and the $$^{241}$$Am source under laboratory conditions. The results show that the energy resolution with the SSA sealed windows which provide uniform drift electric field has a smaller fluctuation than that with the DSA ones; the fluctuations are about 1% and 4% for the former and the latter, respectively. Simulations using the COMSOL software also confirmed the electric-field distortion at the edge of the detector with the DSA windows. A correlation curve between energy resolution and energy deposit of charged particles at various gas pressures and for two gas species is derived for TFG-IC with the SSA sealed windows using the measurement with the $$^{241}$$Am source. Incorporating the above results, we performed Monte Carlo simulations to evaluate the particle-identification capability of the telescope. The results show that the telescope can be extended to the identification of low-energy particles