Commonsense knowledge-graphs (CKGs) are important resources towards building machines that can 'reason' on text or environmental inputs and make inferences beyond perception. While current CKGs encode world knowledge for a large number of concepts and have been effectively utilized for incorporating commonsense in neural models, they primarily encode declarative or single-condition inferential knowledge and assume all conceptual beliefs to have the same likelihood. Further, these CKGs utilize a limited set of relations shared across concepts and lack a coherent knowledge organization structure resulting in redundancies as well as sparsity across the larger knowledge graph. Consequently, today's CKGs, while useful for a first level of reasoning, do not adequately capture deeper human-level commonsense inferences which can be more nuanced and influenced by multiple contextual or situational factors. Accordingly, in this work, we study how commonsense knowledge can be better represented by -- (i) utilizing a probabilistic logic representation scheme to model composite inferential knowledge and represent conceptual beliefs with varying likelihoods, and (ii) incorporating a hierarchical conceptual ontology to identify salient concept-relevant relations and organize beliefs at different conceptual levels. Our resulting knowledge representation framework can encode a wider variety of world knowledge and represent beliefs flexibly using grounded concepts as well as free-text phrases. As a result, the framework can be utilized as both a traditional free-text knowledge graph and a grounded logic-based inference system more suitable for neuro-symbolic applications. We describe how we extend the PrimeNet knowledge base with our framework through crowd-sourcing and expert-annotation, and demonstrate its application for more interpretable passage-based semantic parsing and question answering.
翻译:常识知识谱(CKGs)是用来建立能够“根据”文本或环境投入进行“理性”并作出超越认知的推断的机器的重要资源。 虽然目前的CKGs将世界知识编码为大量概念,并被有效地用于将常识知识纳入神经模型,但它们主要用来将常识编码为宣示性或单一条件的推断性知识,并假定所有概念信念具有同样的可能性。此外,这些CKGs使用一套概念之间共享的有限关系,缺乏一个连贯的知识组织架构,导致在更大范围的知识应用中出现冗余和模糊。因此,今天的CKGs虽然对一级推理有用,但并没有适当地捕捉到更深层次的人类常识性常识,而这些常识则可能受到多种背景或情况因素的影响。 因此,我们研究普通知识如何更好地被基于 -- (i) 利用一种不稳定的逻辑表述方法来模拟精度知识,并代表不同可能性的概念应用。 (ii) 将一个更深层次的概念和更深层次的概念概念概念用于更深层次的理论基础, 将一个更深层次的概念概念作为我们的知识基础的理论基础,作为一个更深层次的理论解释基础,作为一个更深层次的理论解释。