Dense- ATOMIC : Towards Densely-connected ATOMIC with High Knowledge Coverage and Massive Multi-hop Paths (2023)

Abstract: ATOMIC is a large-scale commonsense knowledge graph (CSKG) containing everyday if-then knowledge triplets, i.e., head event, relation, tail event. The one-hop annotation manner made ATOMIC a set of independent bipartite graphs, which ignored the numerous links between events in different bipartite graphs and consequently caused shortages in knowledge coverage and multi-hop paths. In this work, we aim to construct Dense-ATOMIC with high knowledge coverage and massive multi-hop paths. The events in ATOMIC are normalized to a consistent pattern at first. We then propose a CSKG completion method called Rel-CSKGC to predict the relation given the head event and the tail event of a triplet, and train a CSKG completion model based on existing triplets in ATOMIC. We finally utilize the model to complete the missing links in ATOMIC and accordingly construct Dense-ATOMIC. Both automatic and human evaluation on an annotated subgraph of ATOMIC demonstrate the advantage of Rel-CSKGC over strong baselines. We further conduct extensive evaluations on Dense-ATOMIC in terms of statistics, human evaluation, and simple downstream tasks, all proving Dense-ATOMIC’s advantages in Knowledge Coverage and Multi-hop Paths. Both the source code of Rel-CSKGC and Dense-ATOMIC are publicly available on https://github.com/NUSTM/Dense-ATOMIC .

## introduction atomic is a large-scale human-annotated commonsense knowledge graph focusing on the inferential knowledge in social life @xcite . it consists of nine if-then relation types describing the causes, effects, agent, stative, and theme of an event. the research on atomic has drawn more and more attention in recent years. an increasing number of downstream tasks, including * *corresponding author commonsense reasoning @xcite , storytelling @xcite , question answering @xcite , dialog generation @xcite , etc., have improved their performances by acquiring and utilizing the commonsense knowledge from atomic. currently, atomic was constructed under onehop annotations. it began with 24,000 pre-defined base events and nine relation types. for each base event and each relation, the annotators were asked to write a possible tail event based on one-hop reasoning. as shown in figure 1 , given the base event "x asks y to marry", the annotated tail events can be "loving" under the relation of "xattr", "smiles" under the relation of "xeffect", and "says yes" under the relation of "oeffect". in such a one-hop annotation manner, each base event and its related annotated tail events shape a bipartite graph containing only b-to-a links, where b denotes the base event and a denotes the annotated tail event. thereby, the whole graph of atomic can be viewed as a set of b-to-a bipartite graphs, while the b-to-b, a-to-b and a-to-a links between different bipartite graphs were almost ignored. in figure 1 , the dashed lines illustrate such missing links in atomic, e.g., an annotated tail event "in front of y" and a base event "x asks y to marry" in two different bipartite graphs miss a link of the "xintent" relation. this leads to two shortcomings of atomic. firstly, with only b-to-a links, atomic contains very few multi-hop paths, since an annotated tail event cannot become the head event of a triplet. secondly, missing b-to-b, a-to-b and a-to-a links cause unsatisfactory knowledge coverage, despite its high-quality human-annotated commonsense knowledge. both shortcomings limit the potential of atomic in practical applications. intuitively, an ideal cskg requires high knowledge coverage to meet the needs of various tasks, and massive multi-hop paths to understand the evolu- to be forgiven x asks y to marry happy says yes smiles xwant oreact xintent xeffect oeffect oreact xattr in front of y xneed loving a-to-b b-to-a a-to-a a-to-a existing links intra-cluster inter-cluster predictable unpredictable relation predicted relation b-to-b x is forgiven xafter opersona xintent x infront of y xneed x asks y to marry y is happy y says yes x smiles xafter oafter x is loving opersona opersona xintent xafter oafter xafter xafter opersona xpersona opersona opersona xneed atomic dense-atomic x kneels down in front of y x kneels down in front of y y is delightful marriage x proposes proposes marriage delightful figure 1: atomic vs. dense-atomic. firstly, dense-atomic completes many missing links in atomic, including b-to-a, b-to-b, a-to-b, and a-to-a links, e.g., missing "opersona" link between "x proposes marriage" and "y is delightful" (type: a-to-a); secondly, dense-atomic contains more multi-hop paths, e.g., a two-hop path "x asks y to marry" → "y says yes" → "x smiles" after predicting missing links on normalizd atomic. tion between different events. in this work, we aim to construct a denselyconnected atomic. the key is to complete different types of missing links, leading to denser atomic with high knowledge coverage and massive multi-hop paths. we achieve this goal through three main steps: normalizing tail events, training a relation prediction model and constructing dense-atomic. firstly, most of the annotated tail events in atomic have different patterns to the base events, so we normalize annotated tail events in atomic to a consistent pattern ("subject + verb + object"), to facilitate subsequent cskg completion. specific relations are also grouped to mitigate ambiguity. secondly, we train a relation prediction model based on a set of existing triplets in atomic to infer the missing links on the whole graph, i.e., cskg completion upon atomic. to the best of our knowledge, most of the existing studies for cskg completion utilized the translation based methods, which formalized the cskg completion as a tail event ranking task given the head event and the relation. a graph convolutional network (gcn) was mostly employed to encode the graph embeddings of events, but its performance is unsatisfactory since the sparsity of atomic limits the information propagation on the gcn @xcite . in contrast, in this work, we propose a method called rel-cskgc, which regards cskg completion as a relation prediction problem given the head event and the tail event, and accordingly train a cskg completion model based on atomic. finally, based on the cskg completion model, we construct dense-atomic by inferring the missing links on atomic. figure 1 illustrates the main differences between atomic and dense-atomic. we conduct extensive evaluations towards the rel-cskgc method and the constructed dense-atomic, respectively. first, we compare rel-cskgc with several newly proposed relation prediction methods and translation based methods. both automatic evaluation on an annotated subgraph and human evaluation on 500 sampled triplets show the advantage of rel-cskgc for completion on atomic . next, we evaluate dense-atomic from the perspectives of knowledge coverage and multi-hop paths respectively. extensive experiments are conducted in terms of statistics, human evaluation, and simple downstream tasks. the results demonstrate that dense-atomic surpasses atomic in terms of triplet counts by an order of magnitude, and multi-hop paths by more than two orders of magnitude, respectively, while at the same time maintaining its quality. normalizing tail events, training a relation prediction model, and constructing dense-atomic. ## evaluation of our rel-cskgc method in this section, we compare rel-cskgc with relation prediction and translation based methods by experimenting on a newly annotated subgraph and human evaluation. ## related work conceptnet @xcite is a largescale cskg merging various knowledge bases. aser @xcite ) contains the selectional preference knowledge extracted from more than 11 billion-token unstructured textual data. tran-somcs @xcite utilizes linguistic graphs to convert aser into the same representation as conceptnet. discos @xcite aggregates the neighboring information to distill the commonsense knowledge in aser. recent years have seen crowdsourced cskgs aiming to provide high-quality commonsense knowledge triplets. @xcite released atomic consisting of if-then knowledge triplets mainly about daily events. @xcite augmented atomic with event-centered and physicalentity triplets. glucose @xcite grounds the implicit commonsense knowledge about everyday situations in a narrative context for richer inferential content. dense-atomic unleashes the power of atomic for high knowledge coverage and multi-hop paths. prior cskg completion methods performed binary classification by scoring bilstm-encoded tuples @xcite @xcite . following translation based methods for the knowledge graph completion @xcite @xcite @xcite , @xcite additionally densified the cskg based on bert similarity and achieve promising results. @xcite and @xcite designed heuristic rules to add more edges for nodes with fewer neighbors. @xcite presented a neural-symbolic reasoner to learn logic rules during the training, making the cskg completion process interpretable. rel-cskgc differs from them in that we utilize pretrained language models to predict the relation given the head event and the tail event. similar relation prediction methods targeting at the knowledge graph completion have been proposed @xcite @xcite . to our best knowledge, we are the first to explore the relation prediction method on cskg completion. ## conclusion in this paper, we construct dense-atomic for high knowledge coverage and massive multi-hop paths and accordingly propose a cskg completion method called rel-cskgc to train a relation prediction model and infer the missing links in atomic. both automatic and human evaluation show the advantage of rel-cskgc over strong baselines. the statistics prove that dense-atomic has significantly more triplets and multi-hop paths, providing potential for high-quality downstream applications and multi-hop reasoning based on commonsense knowledge.