A complex logic query in a knowledge graph refers to a query expressed in logic form that conveys a complex meaning, such as where did the Canadian Turing award winner graduate from? Knowledge graph reasoning-based applications, such as dialogue systems and interactive search engines, rely on the ability to answer complex logic queries as a fundamental task. In most knowledge graphs, edges are typically used to either describe the relationships between entities or their associated attribute values. An attribute value can be in categorical or numerical format, such as dates, years, sizes, etc. However, existing complex query answering (CQA) methods simply treat numerical values in the same way as they treat entities. This can lead to difficulties in answering certain queries, such as which Australian Pulitzer award winner is born before 1927, and which drug is a pain reliever and has fewer side effects than Paracetamol. In this work, inspired by the recent advances in numerical encoding and knowledge graph reasoning, we propose numerical complex query answering. In this task, we introduce new numerical variables and operations to describe queries involving numerical attribute values. To address the difference between entities and numerical values, we also propose the framework of Number Reasoning Network (NRN) for alternatively encoding entities and numerical values into separate encoding structures. During the numerical encoding process, NRN employs a parameterized density function to encode the distribution of numerical values. During the entity encoding process, NRN uses established query encoding methods for the original CQA problem. Experimental results show that NRN consistently improves various query encoding methods on three different knowledge graphs and achieves state-of-the-art results.

翻译：复杂逻辑查询是指在知识图谱中以逻辑形式表达的、传递复杂含义的查询，例如：加拿大图灵奖得主毕业于哪所大学？基于知识图谱推理的应用（如对话系统和交互式搜索引擎）将回答复杂逻辑查询作为基本任务。在大多数知识图谱中，边通常用于描述实体之间的关系或实体关联的属性值。属性值可以是分类格式或数值格式，例如日期、年份、尺寸等。然而，现有的复杂查询回答方法仅将数值以与实体相同的方式处理，这可能导致某些查询难以被回答，例如：哪一位澳大利亚普利策奖得主出生于1927年之前？以及哪种药物是止痛药且副作用少于扑热息痛？本研究受数值编码与知识图谱推理最新进展的启发，提出数值复杂查询回答方法。在此任务中，我们引入新的数值变量和操作来描述涉及数值属性值的查询。为处理实体与数值之间的差异，我们进一步提出数值推理网络框架，通过交替编码将实体与数值映射至独立的编码结构中。在数值编码过程中，NRN采用参数化密度函数对数值分布进行编码；在实体编码过程中，NRN沿用原始CQA问题中成熟的查询编码方法。实验结果表明，NRN在三种不同知识图谱上持续改进多种查询编码方法，并取得了最先进的结果。