前言

随着互联网的快速普及与发展，互联网数据以惊人的速度在全世界范围内呈现出指数级增长的态势。而数据作为客观世界在信息世界中的抽象表达，其必然带有普遍的关联性。如何从海量的异构数据中挖掘实体及其语义关联和属性，并进行知识的融合，进而构建大规模的知识图谱，为语义搜索、深度问答、文本理解等应用提供有力支撑,已成为数据管理、数据挖掘和信息抽取等领域的一个重要研究方向。相比于传统的数据集成，在面向大规模的数据和知识融合过程中，融合算法的效率、多源数据的数据质量评估和基于语义的数据和知识融合等都给现有的数据集成和知识融合技术带来了巨大的挑战。2016年《计算机研究与发展》数据融合和知识融合专题侧重大规模数据和知识的抽取、融合及应用等诸多方面，涉及到数据管理、信息抽取和知识工程等多个交叉学科领域，研究主题包括数据与知识抽取技术、歧义性消除、数据与知识融合技术、数据与知识建模、关联知识库的应用等。本期专题经过公开征文收到43篇投稿，并最终收录了7篇论文，内容涉及实体抽取、实体链接、数据融合与溯源、短文本理解、数据查询、知识表示等主题，为相关领域的研究者探讨面向大数据的数据融合和知识融合的基础理论研究及其应用、讨论该领域内最新的突破性进展、交流新的学术思想和新方法以及展望未来的发展趋势提供了很好的沟通和交流机会。

End-to-end differentiability and tensor processing unit computing to accelerate materials’ inverse design

Numerical simulations have revolutionized material design.However,although simulations excel at mapping an input material to its output property,their direct application to inverse design has traditionally been limited by their high computing cost and lack of differentiability.Here,taking the example of the inverse design of a porous matrix featuring targeted sorption isotherm,we introduce a computational inverse design framework that addresses these challenges,by programming differentiable simulation on TensorFlow platform that leverages automated end-to-end differentiation.Thanks to its differentiability,the simulation is used to directly train a deep generative model,which outputs an optimal porous matrix based on an arbitrary input sorption isotherm curve.Importantly,this inverse design pipeline leverages the power of tensor processing units(TPU)—an emerging family of dedicated chips,which,although they are specialized in deep learning,are flexible enough for intensive scientific simulations.This approach holds promise to accelerate inverse materials design.

Predicting the synthesizability of crystalline inorganic materials from the data of known material compositions

Reliably identifying synthesizable inorganic crystalline materials is an unsolved challenge required for realizing autonomous materials discovery.In this work,we develop a deep learning synthesizability model(SynthNN)that leverages the entire space of synthesized inorganic chemical compositions.By reformulating material discovery as a synthesizability classification task,SynthNN identifies synthesizable materials with 7×higher precision than with DFT-calculated formation energies.In a head-to-head material discovery comparison against 20 expert material scientists,SynthNN outperforms all experts,achieves 1.5×higher precision and completes the task five orders of magnitude faster than the best human expert.Remarkably,without any prior chemical knowledge,our experiments indicate that SynthNN learns the chemical principles of charge-balancing,chemical family relationships and ionicity,and utilizes these principles to generate synthesizability predictions.The development of SynthNN will allow for synthesizability constraints to be seamlessly integrated into computational material screening workflows to increase their reliability for identifying synthetically accessible materials.

Graph convolution machine for context-aware recommender system

The latest advance in recommendation shows that better user and item representations can be learned via performing graph convolutions on the user-item interaction graph.However,such finding is mostly restricted to the collaborative filtering(CF)scenario,where the interaction contexts are not available.In this work,we extend the advantages of graph convolutions to context-aware recommender system(CARS,which represents a generic type of models that can handle various side information).We propose Graph Convolution Machine(GCM),an end-to-end framework that consists of three components:an encoder,graph convolution(GC)layers,and a decoder.The encoder projects users,items,and contexts into embedding vectors,which are passed to the GC layers that refine user and item embeddings with context-aware graph convolutions on the user-item graph.The decoder digests the refined embeddings to output the prediction score by considering the interactions among user,item,and context embeddings.We conduct experiments on three real-world datasets from Yelp and Amazon,validating the effectiveness of GCM and the benefits of performing graph convolutions for CARS.

Preface

It is our great pleasure to edit this special section of the Journal of Computer Science and Technology （JCST）. The database field has experienced a rapid growth with increasing of data. Therefore, novel technology for covering emerging databases such as network or graph analysis, spatial or temporal data analysis, search, recommendation, and data mining is required. The goal of the section is to provide state-of-the-art research issues, challenges, new technologies, and solutions of emerging databases. This section publishes seven interesting articles related to query processing, trajectory data reduction, botnet evolution, recommendation system, bielustering, and protein structure alignment. The articles are summarized as follows.