We present the Open Predicate Query Language (OPQL); a method for
constructing a virtual KB (VKB) trained entirely from text. Large Knowledge
Bases (KBs) are indispensable for a wide-range of industry applications such as
question answering and recommendation. Typically, KBs encode world knowledge in
a structured, readily accessible form derived from laborious human annotation
efforts. Unfortunately, while they are extremely high precision, KBs are
inevitably highly incomplete and automated methods for enriching them are far
too inaccurate. Instead, OPQL constructs a VKB by encoding and indexing a set
of relation mentions in a way that naturally enables reasoning and can be
trained without any structured supervision. We demonstrate that OPQL
outperforms prior VKB methods on two different KB reasoning tasks and,
additionally, can be used as an external memory integrated into a language
model (OPQL-LM) leading to improvements on two open-domain question answering
tasks.

提出了一种 Open Predicate Query Language（OPQL）的方法，用于从文本中构建虚拟 KB（VKB），它不需要结构化的训练，可以用于语言模型，同时在两个开放领域问题回答任务中表现出色。

使用开放谓词关系在虚拟知识库上进行推理

Reasoning Over Virtual Knowledge Bases With Open Predicate Relations

Extracting entities and relations for types of interest from text is
important for understanding massive text corpora. Traditionally, systems of
entity relation extraction have relied on human-annotated corpora for training
and adopted an incremental pipeline. Such systems require additional human
expertise to be ported to a new domain, and are vulnerable to errors cascading
down the pipeline. In this paper, we investigate joint extraction of typed
entities and relations with labeled data heuristically obtained from knowledge
bases (i.e., distant supervision). As our algorithm for type labeling via
distant supervision is context-agnostic, noisy training data poses unique
challenges for the task. We propose a novel domain-independent framework,
called CoType, that runs a data-driven text segmentation algorithm to extract
entity mentions, and jointly embeds entity mentions, relation mentions, text
features and type labels into two low-dimensional spaces (for entity and
relation mentions respectively), where, in each space, objects whose types are
close will also have similar representations. CoType, then using these learned
embeddings, estimates the types of test (unlinkable) mentions. We formulate a
joint optimization problem to learn embeddings from text corpora and knowledge
bases, adopting a novel partial-label loss function for noisy labeled data and
introducing an object "translation" function to capture the cross-constraints
of entities and relations on each other. Experiments on three public datasets
demonstrate the effectiveness of CoType across different domains (e.g., news,
biomedical), with an average of 25% improvement in F1 score compared to the
next best method.

本文介绍 “CoType”，一个数据驱动的文本分割算法和一种冠以知识库标签的实体及关系识别方法。实验表明，其在不同领域具有 25% 的 F1 得分提高。