Introduction（英语笔记）
（英语笔记）
/7/6 23:29:45
In years, , which help users items of from a large , have been an role in
近年来，××××××××××在各种在××××××××中发挥着越来越重要的作用，它可以帮助用户×××××××
(CNNs) have been to such as image , or
×××××××已成功应用于×××××、×××××××或×××××××等问题
然而
The of makes it in Web , from , E-, to media sites and news
推荐系统的成功使其在从搜索引擎、电子商务、社交媒体网站和新闻门户网站的Web应用程序中流行起来
毫不夸张
is the task of from (KGs) that refer to the same real-world .
××××是指×××××××××××××的任务。（一般用于第一句）
, has been paid to the of KG than for this task.
近年来，利用×××××××××解决这一问题越来越受到重视。
, GNN-based still face a .
然而，现有的×××××模型仍然面临一个关键问题。
Most to node are . The of these the for each node using --based , and do not to data, since they make on nodes in a , fixed graph [×].
大多数现有的××××××××方法本质上都是××××××。这些方法中的大多数直接使用×××××××，并且不会×××××××××，因为×××××××[×]。
The of this issue lies in the of fully the non- in the of from KGs.
解决此问题的挑战在于×××××
by this work, we an -based to node of graph- data. The idea is to the of each node in the graph, by over its , a self- . The has :
受这项最新工作的启发，我们引入了×××××××××。××××××架构有几个有趣的特性：×××××××
by the fact that the - can in both and of , we the KG which both and .
基于×××××这一事实，我们提出了一种×××××方法——方法name
A trend in deep is the ,which deals with sized data and the model to focus on the most parts of data.
最近在深度学习领域的一个研究趋势是注意力机制，它处理可变大小的数据，并鼓励模型关注数据中最显著的部分。
（注意力机制）It has the in deep and is to , such as text [×], graph[×] and image [×]
它在深层神经网络框架中已经证明了它的有效性，并被广泛应用于文本分析[××]、知识图[××]和图像处理[××]
the of in deep , it has not been in the graph for graph.
尽管××××××在深度学习中取得了成功，但在×××××的图神经网络框架中却没有考虑到。
HIN based have -mance to some , there are two major for these using meta-path based -
ties.
虽然××××××××方法在一定程度上提高了性能，但是这些××××××××方法存在两个主要问题
, fail to and a for in .
然而，目前的技术未能很好地定义和优化网络中××××××××所需的合理目标。
In this paper, we a graph , named HAN, which both of node-level and -level . In , given the node as input, we use the type- to types of node into the same space. Then the node-level is able to learn the the nodes and their meta-path based , while the -level aims to learn the of meta-paths for the task in the graph.
在本文中，我们提出了×××××××，它同时考虑了×××××××××。特别是，×××××××，我们使用×××××××××。然后，××××××××××××。
The of our work are as :
我们的工作贡献总结如下：
our paper makes the :
总体而言，本文的贡献如下：
（服务发现）An Web among , APIs, and tags. For , the and tags are not shown for the case where and APIs share the tag space. The the and APIs, while the the and APIs.
、api和标记之间的Web服务网络示例。为了简单起见，在和api共享标记空间的情况下，不会显示和标记之间的注释关系。组合关系描述了和api之间的功能依赖关系，而注释关系描述了和api之间的功能相似性。
的最后一段
In the rest of this paper, we first the work in 2, and ×××××××× and in 3. We the ×××× in 4 and show in 5. , we the paper in 6.
（未来工作） As work, we would like to
to fuse -level . In
, we can the of
of meta- paths in .
作为未来的工作，我们希望利用更好的×××来×××××。此外，我们还可以探索××××××。
