Open Information Extraction (OIE) methods extract a large number of OIE
triples (noun phrase, relation phrase, noun phrase) from text, which compose
large Open Knowledge Bases (OKBs). However, noun phrases (NPs) and relation
phrases (RPs) in OKBs are not canonicalized and often appear in different
paraphrased textual variants, which leads to redundant and ambiguous facts. To
address this problem, there are two related tasks: OKB canonicalization (i.e.,
convert NPs and RPs to canonicalized form) and OKB linking (i.e., link NPs and
RPs with their corresponding entities and relations in a curated Knowledge Base
(e.g., DBPedia). These two tasks are tightly coupled, and one task can benefit
significantly from the other. However, they have been studied in isolation so
far. In this paper, we explore the task of joint OKB canonicalization and
linking for the first time, and propose a novel framework JOCL based on factor
graph model to make them reinforce each other. JOCL is flexible enough to
combine different signals from both tasks, and able to extend to fit any new
signals. A thorough experimental study over two large scale OIE triple data
sets shows that our framework outperforms all the baseline methods for the task
of OKB canonicalization (OKB linking) in terms of average F1 (accuracy).

本文提出了一个基于因子图模型的框架 JOCL，该框架整合了 OKB 规范化和链接两个关键任务，可以灵活地结合不同的信号，并能够扩展以适应任何新信号。在两个大规模 OIE 三元组数据集上进行的彻底实验研究表明，我们的框架在 OKB 规范化和链接任务方面的平均 F1（准确性）方面均优于所有基线方法。

联合开放知识库规范化和链接

Joint Open Knowledge Base Canonicalization and Linking

We propose a novel factor graph model for argument mining, designed for
settings in which the argumentative relations in a document do not necessarily
form a tree structure. (This is the case in over 20% of the web comments
dataset we release.) Our model jointly learns elementary unit type
classification and argumentative relation prediction. Moreover, our model
supports SVM and RNN parametrizations, can enforce structure constraints (e.g.,
transitivity), and can express dependencies between adjacent relations and
propositions. Our approaches outperform unstructured baselines in both web
comments and argumentative essay datasets.

本研究提出了一种新的因子图模型，用于处理不一定形成树结构的文本论述关系，可以学习最基本的单元分类和论证关系预测，支持 SVM 和 RNN 参数化，可以强制执行结构约束并表示相邻关系和命题之间的依赖关系，并在网络评论和论辩文章数据集上优于非结构化基准线。