KagNet: Knowledge-Aware Graph Networks for Commonsense Reasoning

Lin, Bill Yuchen; Chen, Xinyue; Chen, Jamin; Ren, Xiang

doi:10.48550/arxiv.1909.02151

Cited by 20 publications

(25 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Sun et al [31] leveraged relevant entities from a KB and relevant text from Wikipedia as external knowledge to answer a question. Lin et al [18] constructed a schema graph between QA-concept pairs for commonsense reasoning. In order to retrieve reasoning paths over Wikipedia, Godbole et al [13] used entity linking for multi-hop retrieval.…”

Section: Knowledge In Retrieval-based Qa Modelsmentioning

confidence: 99%

Knowledge-Aided Open-Domain Question Answering

Zhou,

Shi,

Huang

et al. 2020

Preprint

View full text Add to dashboard Cite

Open-domain question answering (QA) aims to find the answer to a question from a large collection of documents. Though many models for single-document machine comprehension have achieved strong performance, there is still much room for improving opendomain QA systems since document retrieval and answer reranking are still unsatisfactory. Golden documents that contain the correct answers may not be correctly scored by the retrieval component, and the correct answers that have been extracted may be wrongly ranked after other candidate answers by the reranking component. One of the reasons is derived from the independent principle in which each candidate document (or answer) is scored independently without considering its relationship to other documents (or answers) [28].In this work, we propose a knowledge-aided open-domain QA (KAQA) method which targets at improving relevant document retrieval and candidate answer reranking by considering the relationship between a question and the documents (termed as questiondocument graph), and the relationship between candidate documents (termed as document-document graph). The graphs are built using knowledge triples from external knowledge resources. During document retrieval, a candidate document is scored by considering its relationship to the question and other documents. During answer reranking, a candidate answer is reranked using not only its own context but also the clues from other documents. The experimental results show that our proposed method improves document retrieval and answer reranking, and thereby enhances the overall performance of open-domain question answering.

show abstract

Section: Knowledge In Retrieval-based Qa Modelsmentioning

confidence: 99%

Knowledge-Aided Open-Domain Question Answering

Zhou,

Shi,

Huang

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Due to its prominence and implicit nature, capturing commonsense knowledge holds a promise to benefit various AI applications, including those in NLP, computer vision, and planning. For instance, commonsense knowledge can be used to fill gaps and explain the predictions of a (neural) model [33], understand agent goals and causality in stories [63], or enhance robot navigation and manipulation [65].…”

Section: Introductionmentioning

confidence: 99%

“…Further distinction has been made between discriminative tasks [54,6,60], where the goal is to pick the single correct answer from a list, and generative tasks, where one has to generate one or multiple correct answers [34,7]. These tasks can be tackled by using the (entire or a subset of) training data [37,33], or in a zero-/few-shot evaluation regime [38,57].…”

Section: Introductionmentioning

confidence: 99%

Dimensions of Commonsense Knowledge

Ilievski¹,

Oltramari²,

Ma³

et al. 2021

Preprint

View full text Add to dashboard Cite

Commonsense knowledge is essential for many AI applications, including those in natural language processing, visual processing, and planning. Consequently, many sources that include commonsense knowledge have been designed and constructed over the past decades. Recently, the focus has been on large text-based sources, which facilitate easier integration with neural (language) models and application on textual tasks, typically at the expense of the semantics of the sources. Such practice prevents the harmonization of these sources, understanding their coverage and gaps, and may hinder the semantic alignment of their knowledge with downstream tasks. Efforts to consolidate commonsense knowledge have yielded partial success, but provide no clear path towards a comprehensive consolidation of existing commonsense knowledge.The ambition of this paper is to organize these sources around a common set of dimensions of commonsense knowledge. For this purpose, we survey a wide range of popular commonsense sources with a special focus on their relations. We consolidate these relations into 13 knowledge dimensions, each abstracting over more specific relations found in sources. This consolidation allows us to unify the separate sources and to compute indications of their coverage, overlap, and gaps with respect to the knowledge dimensions. Moreover, we analyze the impact of each dimension on downstream reasoning tasks that require commonsense knowledge, observing that the temporal and desire/goal dimensions are very beneficial for reasoning on current downstream tasks, while distinctness and lexical knowledge have little impact. These results reveal focus towards some dimensions in current evaluation, and potential neglect of others.

show abstract

“…Subjects and objects are usually called entities in KGs, e.g., the fact that Beijing is the capital of China can be represented by (Beijing, capitalOf, China). Knowledge graphs are now widely used in a variety of applications such as recommender systems [42,41] and question answering [21,7]. Recently knowledge graphs have drawn growing interests in both academia and industry communities [8,28,35].…”

Section: Introductionmentioning

confidence: 99%

MPLR: a novel model for multi-target learning of logical rules for knowledge graph reasoning

Wei¹,

Li²,

Xin³

et al. 2021

Preprint

View full text Add to dashboard Cite

Large-scale knowledge graphs (KGs) provide structured representations of human knowledge. However, as it is impossible to contain all knowledge, KGs are usually incomplete. Reasoning based on existing facts paves a way to discover missing facts. In this paper, we study the problem of learning logic rules for reasoning on knowledge graphs for completing missing factual triplets. Learning logic rules equips a model with strong interpretability as well as the ability to generalize to similar tasks. We propose a model called MPLR that improves the existing models to fully use training data and multi-target scenarios are considered. In addition, considering the deficiency in evaluating the performance of models and the quality of mined rules, we further propose two novel indicators to help with the problem. Experimental results empirically demonstrate that our MPLR model outperforms state-of-the-art methods on five benchmark datasets. The results also prove the effectiveness of the indicators.

show abstract

KagNet: Knowledge-Aware Graph Networks for Commonsense Reasoning

Cited by 20 publications

References 29 publications

Knowledge-Aided Open-Domain Question Answering

Knowledge-Aided Open-Domain Question Answering

Dimensions of Commonsense Knowledge

MPLR: a novel model for multi-target learning of logical rules for knowledge graph reasoning

Contact Info

Product

Resources

About