Relation extraction is an efficient way of mining the extraordinary wealth of
human knowledge on the Web. Existing methods rely on domain-specific training
data or produce noisy outputs. We focus here on extracting targeted relations
from semi-structured web pages given only a short description of the relation.
We present GraphScholarBERT, an open-domain information extraction method based
on a joint graph and language model structure. GraphScholarBERT can generalize
to previously unseen domains without additional data or training and produces
only clean extraction results matched to the search keyword. Experiments show
that GraphScholarBERT can improve extraction F1 scores by as much as 34.8\%
compared to previous work in a zero-shot domain and zero-shot website setting.

关系抽取是一种从网络上挖掘人类知识的高效方式，本文提出了一种名为 GraphScholarBERT 的开放领域信息抽取方法，它能够从半结构化的网页中提取目标关系，并能在未见过的领域中进行泛化，无需额外数据或训练，并且与搜索关键词完全匹配。实验证明，GraphScholarBERT 在零射击域和零射击网站情境下，相比以往的方法，可以提高抽取的 F1 分数达 34.8%。

结合语言和图模型进行 Web 上的半结构化信息提取

Combining Language and Graph Models for Semi-structured Information  Extraction on the Web

The rapid growth of web pages and the increasing complexity of their
structure poses a challenge for web mining models. Web mining models are
required to understand the semi-structured web pages, particularly when little
is known about the subject or template of a new page. Current methods migrate
language models to the web mining by embedding the XML source code into the
transformer or encoding the rendered layout with graph neural networks.
However, these approaches do not take into account the relationships between
text nodes within and across pages. In this paper, we propose a new approach,
ReXMiner, for zero-shot relation extraction in web mining. ReXMiner encodes the
shortest relative paths in the Document Object Model (DOM) tree which is a more
accurate and efficient signal for key-value pair extraction within a web page.
It also incorporates the popularity of each text node by counting the
occurrence of the same text node across different web pages. We use the
contrastive learning to address the issue of sparsity in relation extraction.
Extensive experiments on public benchmarks show that our method, ReXMiner,
outperforms the state-of-the-art baselines in the task of zero-shot relation
extraction in web mining.

本文提出了一种基于 DOM 树短路路径编码，并结合多个页面上文本节点出现的频率以及对比学习处理关系稀疏问题的新方法 ReXMiner，用于零样本关系抽取，并在公共基准测试上表现出胜过现有方法的效果。

基于相对 XML 路径的多模态方法实现 Web 挖掘中零样本关系抽取

Towards Zero-shot Relation Extraction in Web Mining: A Multimodal  Approach with Relative XML Path

The availability of charging stations is an important factor for promoting
electric vehicles (EVs) as a carbon-friendly way of transportation. Hence, for
city planners, the crucial question is where to place charging stations so that
they reach a large utilization. Here, we hypothesize that the utilization of EV
charging stations is driven by the proximity to points-of-interest (POIs), as
EV owners have a certain limited willingness to walk between charging stations
and POIs. To address our research question, we propose the use of web mining:
we characterize the influence of different POIs from OpenStreetMap on the
utilization of charging stations. For this, we present a tailored interpretable
model that takes into account the full spatial distributions of both the POIs
and the charging stations. This allows us then to estimate the distance and
magnitude of the influence of different POI types. We evaluate our model with
data from approx. 300 charging stations and 4,000 POIs in Amsterdam,
Netherlands. Our model achieves a superior performance over state-of-the-art
baselines and, on top of that, is able to offer an unmatched level of
interpretability. To the best of our knowledge, no previous paper has
quantified the POI influence on charging station utilization from real-world
usage data by estimating the spatial proximity in which POIs are relevant. As
such, our findings help city planners in identifying effective locations for
charging stations.

通过网络挖掘数据，本文提出一种可解释的模型来研究不同兴趣点距离充电站的影响，帮助城市规划师寻找电动汽车充电站的有效位置。

利用网络挖掘确定电动汽车充电站的位置

Web Mining to Inform Locations of Charging Stations for Electric  Vehicles

Keyphrases are useful for a variety of purposes, including summarizing,
indexing, labeling, categorizing, clustering, highlighting, browsing, and
searching. The task of automatic keyphrase extraction is to select keyphrases
from within the text of a given document. Automatic keyphrase extraction makes
it feasible to generate keyphrases for the huge number of documents that do not
have manually assigned keyphrases. A limitation of previous keyphrase
extraction algorithms is that the selected keyphrases are occasionally
incoherent. That is, the majority of the output keyphrases may fit together
well, but there may be a minority that appear to be outliers, with no clear
semantic relation to the majority or to each other. This paper presents
enhancements to the Kea keyphrase extraction algorithm that are designed to
increase the coherence of the extracted keyphrases. The approach is to use the
degree of statistical association among candidate keyphrases as evidence that
they may be semantically related. The statistical association is measured using
web mining. Experiments demonstrate that the enhancements improve the quality
of the extracted keyphrases. Furthermore, the enhancements are not
domain-specific: the algorithm generalizes well when it is trained on one
domain (computer science documents) and tested on another (physics documents).

该论文介绍了一种 Kea 关键短语提取算法新的优化方法，通过使用 Web 挖掘获得的统计关联程度作为短语间语义关系的指标，提高了提取出的关键短语的语义连贯性，不受领域限制且结果良好