Multi-hop reasoning over real-life knowledge graphs (KGs) is a highly
challenging problem as traditional subgraph matching methods are not capable to
deal with noise and missing information. To address this problem, it has been
recently introduced a promising approach based on jointly embedding logical
queries and KGs into a low-dimensional space to identify answer entities.
However, existing proposals ignore critical semantic knowledge inherently
available in KGs, such as type information. To leverage type information, we
propose a novel TypE-aware Message Passing (TEMP) model, which enhances the
entity and relation representations in queries, and simultaneously improves
generalization, deductive and inductive reasoning. Remarkably, TEMP is a
plug-and-play model that can be easily incorporated into existing
embedding-based models to improve their performance. Extensive experiments on
three real-world datasets demonstrate TEMP's effectiveness.

本文提出了一种新颖的基于 TypE-aware 信息传递（TEMP）模型，它通过加强查询中的实体和关系表示，并同时提高了概括推理、演绎推理和归纳推理，来利用知识库中关键的语义知识，从而提高现有嵌入式模型的性能。实验结果表明其有效性。

知识图谱多跳推理的类型感知嵌入

Type-aware Embeddings for Multi-Hop Reasoning over Knowledge Graphs

Multihop reasoning remains an elusive goal as existing multihop benchmarks
are known to be largely solvable via shortcuts. Can we create a question
answering (QA) dataset that, by construction, \emph{requires} proper multihop
reasoning? To this end, we introduce a bottom-up approach that systematically
selects composable pairs of single-hop questions that are connected, i.e.,
where one reasoning step critically relies on information from another. This
bottom-up methodology lets us explore a vast space of questions and add
stringent filters as well as other mechanisms targeting connected reasoning. It
provides fine-grained control over the construction process and the properties
of the resulting $k$-hop questions. We use this methodology to create
MuSiQue-Ans, a new multihop QA dataset with 25K 2-4 hop questions. Relative to
existing datasets, MuSiQue-Ans is more difficult overall (3x increase in
human-machine gap), and harder to cheat via disconnected reasoning (e.g., a
single-hop model has a 30 point drop in F1). We further add unanswerable
contrast questions to produce a more stringent dataset, MuSiQue-Full. We hope
our datasets will help the NLP community develop models that perform genuine
multihop reasoning.

通过底层方法选择单跳问题对，构建了 MuSiQue-Ans 数据集，包含 2-4 跳问题，比现有数据集更具挑战性，难以通过断开推理进行作弊，旨在帮助自然语言处理社区开发真正具备多跳推理能力的模型。

MuSiQue: 通过单跳问题构建实现多跳问题

MuSiQue: Multihop Questions via Single-hop Question Composition

We present a new resource for the NLP community, namely a large (3.5M+
sentence) knowledge base of *generic statements*, e.g., "Trees remove carbon
dioxide from the atmosphere", collected from multiple corpora. This is the
first large resource to contain *naturally occurring* generic sentences, as
opposed to extracted or crowdsourced triples, and thus is rich in high-quality,
general, semantically complete statements. All GenericsKB sentences are
annotated with their topical term, surrounding context (sentences), and a
(learned) confidence. We also release GenericsKB-Best (1M+ sentences),
containing the best-quality generics in GenericsKB augmented with selected,
synthesized generics from WordNet and ConceptNet. In tests on two existing
datasets requiring multihop reasoning (OBQA and QASC), we find using GenericsKB
can result in higher scores and better explanations than using a much larger
corpus. This demonstrates that GenericsKB can be a useful resource for NLP
applications, as well as providing data for linguistic studies of generics and
their semantics. GenericsKB is available at
this https URL

我们提供了一个新的资源 —— 大型（3.5M + 句子）通用语句知识库，它包含从多个语料库中收集的自然生成的语句。这是第一个包含自然生成通用语句的大型资源，同时富含优质、通用、语义完整的语句。在两个需要多跳推理的现有数据集（OBQA 和 QASC）的测试中，使用 GenericsKB 可以得到比使用更大语料库更高的得分和更好的解释。这表明 GenericsKB 不仅可以成为 NLP 应用的有用资源，还可以为通用语句及其语义的语言研究提供数据。