Perform like an Engine: A Closed-Loop Neural-Symbolic Learning Framework for Knowledge Graph Inference (2022)

Abstract: Knowledge graph (KG) inference aims to address the natural incompleteness of KGs, including rule learning-based and KG embedding (KGE) models. However, the rule learning-based models suffer from low efficiency and generalization while KGE models lack interpretability. To address these challenges, we propose a novel and effective closed-loop neural-symbolic learning framework EngineKG via incorporating our developed KGE and rule learning modules. KGE module exploits symbolic rules and paths to enhance the semantic association between entities and relations for improving KG embeddings and interpretability. A novel rule pruning mechanism is proposed in the rule learning module by leveraging paths as initial candidate rules and employing KG embeddings together with concepts for extracting more high-quality rules. Experimental results on four real-world datasets show that our model outperforms the relevant baselines on link prediction tasks, demonstrating the superiority of our KG inference model in a neural-symbolic learning fashion. The source code and datasets of this paper are available at https://github.com/ngl567/EngineKG .

## introduction typical knowledge graphs (kgs) store triple facts and some of them also contain concepts of entities @xcite . the kgs have proven to be incredibly effective for a variety of applications such as dialogue system @xcite and question answering @xcite . however, the existing kgs are always incomplete which restricts the performance of knowledge-based applications. thus, kg inference plays a vital role in completing kgs for better applications of kgs. the existing kg inference approaches are usually classified into two main categories: (1) rule learning-based models such as amie+ (galárraga et al., 2015) and anyburl @xcite mine rules from kgs and employ these rules to predict new triples by deduction. however, rule learning-based models suffer from low efficiency of the rule mining process and the poor generalization caused by the limited coverage of inference patterns. to address the above challenges, we propose a closed-loop neural-symbolic learning framework enginekg via combining an embedding-based rule learning and a rule-enhanced kge, in which paths and concepts are utilized. our model is named enginekg because it performs like an engine as shown in figure 1: (1) intake stroke. the closed-path rules (or named chain rules) are injected into the kge module (analogous to intake) to guide the procedure of learning kg embeddings, where the initial seed rules are mined by any rule learning tool, and the rule set would grow via our designed rule learning module from the first iteration. (2) compression stroke. the kge module leverages the rules and paths to learn the low-dimensional embeddings (analogous to compression) of entities and relations, improving interpretability and accuracy. (3) expansion stroke. the novel rule learning module outputs newly learned rules (analogous to exhaust) by the effective rule pruning strategy based on paths, relation embeddings and concepts. (4) exhaust stroke. update the rule set (analogous to exhaust) by merging the previous rule set and the newly learned rules for boosting kge and kg inference in the next iteration. our research makes three contributions: ## methodology in this section, we first describe the problem formulation and notation of our work in section 3.1. then, following the workflow of enginekg as shown in figure 2 , we introduce the rule-enhanced kge module in section 3.2 and the embedding-based rule learning module in section 3.3. ## conclusion and future work in this paper, we develop a novel closed-loop neural-symbolic learning framework enginekg for kg inference by jointly rule learning and kge while exploiting paths and concepts. in the kge module, both rules and paths are introduced to enhance the semantic associations and interpretability for learning the entity and relation embeddings. in the rule learning module, paths and kg embeddings together with entity concepts are leveraged in the designed rule pruning strategy to generate high-quality rules efficiently and effectively. extensive experimental results on four datasets illustrate the superiority and effectiveness of our approach compared to some state-of-the-art baselines. in the future, we will investigate combining other semantics such as contextual descriptions of entities, and attempt to apply our model to dynamic kgs.