Abstract

Neuro-Symbolic Artificial Intelligence (AI) focuses on integrating symbolic and sub-symbolic systems to enhance the performance and explainability of predictive models. Symbolic and sub-symbolic approaches differ fundamentally in how they represent data and make use of data features to reach conclusions. Neuro-symbolic systems have recently received significant attention in the scientific community. However, despite efforts in neural-symbolic integration, symbolic processing can still be better exploited, mainly when these hybrid approaches are defined on top of knowledge graphs. This work is built on the statement that knowledge graphs can naturally represent the convergence between data and their contextual meaning (i.e., knowledge). We propose a hybrid system that resorts to symbolic reasoning, expressed as a deductive database, to augment the contextual meaning of entities in a knowledge graph, thus, improving the performance of link prediction implemented using knowledge graph embedding (KGE) models. An entity context is defined as the ego network of the entity in a knowledge graph. Given a link prediction task, the proposed approach deduces new RDF triples in the ego networks of the entities corresponding to the heads and tails of the prediction task on the knowledge graph (KG). Since knowledge graphs may be incomplete and sparse, the facts deduced by the symbolic system not only reduce sparsity but also make explicit meaningful relations among the entities that compose an entity ego network. As a proof of concept, our approach is applied over a KG for lung cancer to predict treatment effectiveness. The empirical results put the deduction power of deductive databases into perspective. They indicate that making explicit deduced relationships in the ego networks empowers all the studied KGE models to generate more accurate links.

中文翻译：

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用于链接预测的知识图神经符号系统

摘要

神经符号人工智能（AI）专注于集成符号和子符号系统，以增强预测模型的性能和可解释性。符号方法和子符号方法在表示数据和利用数据特征得出结论的方式上有根本的不同。神经符号系统最近受到科学界的广泛关注。然而，尽管在神经符号集成方面做出了努力，但符号处理仍然可以得到更好的利用，主要是当这些混合方法是在知识图之上定义时。这项工作建立在知识图可以自然地表示数据与其上下文含义（即知识）之间的收敛的声明之上。我们提出了一种混合系统，采用符号推理（表示为演绎数据库）来增强知识图中实体的上下文含义，从而提高使用知识图嵌入（KGE）模型实现的链接预测的性能。实体上下文被定义为知识图中实体的自我网络。给定一个链接预测任务，所提出的方法在与知识图（KG）上预测任务的头和尾相对应的实体的自我网络中推导出新的 RDF 三元组。由于知识图可能不完整且稀疏，符号系统推导的事实不仅减少了稀疏性，而且使构成实体自我网络的实体之间具有明确的有意义的关系。作为概念证明，我们的方法应用于肺癌的 KG，以预测治疗效果。实证结果验证了演绎数据库的演绎能力。他们表明，在自我网络中明确推断关系可以使所有研究的 KGE 模型生成更准确的链接。