As the volume of data on the web grows, the web structure graph, which is a
graph representation of the web, continues to evolve. The structure of this
graph has gradually shifted from content-based to non-content-based.
Furthermore, spam data, such as noisy hyperlinks, in the web structure graph
adversely affect the speed and efficiency of information retrieval and link
mining algorithms. Previous works in this area have focused on removing noisy
hyperlinks using structural and string approaches. However, these approaches
may incorrectly remove useful links or be unable to detect noisy hyperlinks in
certain circumstances. In this paper, a data collection of hyperlinks is
initially constructed using an interactive crawler. The semantic and
relatedness structure of the hyperlinks is then studied through semantic web
approaches and tools such as the DBpedia ontology. Finally, the removal process
of noisy hyperlinks is carried out using a reasoner on the DBpedia ontology.
Our experiments demonstrate the accuracy and ability of semantic web
technologies to remove noisy hyperlinks

本文提出利用语义 Web 技术对超链接进行清理，构建 Web 结构图，并探索了噪音链接对信息检索和链接挖掘算法的影响。研究表明，语义 Web 技术可以准确地清除噪音链接。

实现一种噪声超链接移除系统：基于语义和相关性的方法

Implementation of a noisy hyperlink removal system: A semantic and relatedness approach

We propose a supervised algorithm for generating type embeddings in the same
semantic vector space as a given set of entity embeddings. The algorithm is
agnostic to the derivation of the underlying entity embeddings. It does not
require any manual feature engineering, generalizes well to hundreds of types
and achieves near-linear scaling on Big Graphs containing many millions of
triples and instances by virtue of an incremental execution. We demonstrate the
utility of the embeddings on a type recommendation task, outperforming a
non-parametric feature-agnostic baseline while achieving 15x speedup and
near-constant memory usage on a full partition of DBpedia. Using
state-of-the-art visualization, we illustrate the agreement of our
extensionally derived DBpedia type embeddings with the manually curated domain
ontology. Finally, we use the embeddings to probabilistically cluster about 4
million DBpedia instances into 415 types in the DBpedia ontology.

提出了一种监督算法来生成类型嵌入，该算法与给定实体嵌入集在相同的语义向量空间中工作。使用最先进的可视化技术，展示了算法在 DBpedia 本体论中的效果。最终，我们使用这些 embedding 将大约 4 百万个 DBpedia 实例概率聚类到 415 种类型。