Commonsense knowledge (CSK) supports a variety of AI applications, from
visual understanding to chatbots. Prior works on acquiring CSK, such as
ConceptNet, have compiled statements that associate concepts, like everyday
objects or activities, with properties that hold for most or some instances of
the concept. Each concept is treated in isolation from other concepts, and the
only quantitative measure (or ranking) of properties is a confidence score that
the statement is valid. This paper aims to overcome these limitations by
introducing a multi-faceted model of CSK statements and methods for joint
reasoning over sets of inter-related statements. Our model captures four
different dimensions of CSK statements: plausibility, typicality, remarkability
and salience, with scoring and ranking along each dimension. For example,
hyenas drinking water is typical but not salient, whereas hyenas eating
carcasses is salient. For reasoning and ranking, we develop a method with soft
constraints, to couple the inference over concepts that are related in in a
taxonomic hierarchy. The reasoning is cast into an integer linear programming
(ILP), and we leverage the theory of reduction costs of a relaxed LP to compute
informative rankings. This methodology is applied to several large CSK
collections. Our evaluation shows that we can consolidate these inputs into
much cleaner and more expressive knowledge. Results are available at
this https URL

本文提出了一种多维模型的常识知识语句，以及一种关于相关语句集合的联合推理方法。通过将推理转化为整数线性规划，采用软约束和松弛 LP 的降维成本理论来计算信息化排序，本方法可以应用于多个大型 CSK 收集，最终可以将这些内容转化为更干净和更具表现力的知识。

多方面常识知识的联合推理

Joint Reasoning for Multi-Faceted Commonsense Knowledge

Community Question Answering (cQA) forums are very popular nowadays, as they
represent effective means for communities around particular topics to share
information. Unfortunately, this information is not always factual. Thus, here
we explore a new dimension in the context of cQA, which has been ignored so
far: checking the veracity of answers to particular questions in cQA forums. As
this is a new problem, we create a specialized dataset for it. We further
propose a novel multi-faceted model, which captures information from the answer
content (what is said and how), from the author profile (who says it), from the
rest of the community forum (where it is said), and from external authoritative
sources of information (external support). Evaluation results show a MAP value
of 86.54, which is 21 points absolute above the baseline.

本研究探索了社区问答论坛中回答的真实性检查问题，并提出了一个多方面的模型来解决这个问题。该模型可以从回答内容、作者档案、社区论坛以及外部权威信息源中捕捉信息，并取得了 MAP 值 86.54 的良好评价结果。