引用本文:王腊红,陈家赢,汪善勤,王天巍,谭黄元.基于本体的土壤系统分类逻辑表达与检索[J].土壤学报,2020,57(6):1378-1386. DOI:10.11766/trxb201904090429
WANG Lahong,CHEN Jiaying,WANG Shanqin,WANG Tianwei,TAN Huangyuan.Logic Expression and Retrieval of Soil Taxonomy Based on Pedon[J].Acta Pedologica Sinica,2020,57(6):1378-1386. DOI:10.11766/trxb201904090429
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基于本体的土壤系统分类逻辑表达与检索
王腊红1, 陈家赢1, 2,汪善勤1, 2, 王天巍1, 2,谭黄元1
1.华中农业大学资源与环境学院;2.农业农村部长江中下游耕地保育重点实验室
摘要:
中国土壤系统分类(Chinese Soil Taxonomy, CST)是建立在诊断层和诊断特性基础上的定量化土壤分类体系,它的不断成熟为实现土壤类型自动化检索提供了理论基础。野外土壤描述与采样规范的形成为土壤分类的语义规范提供了依据。目前,我国已出现一系列基于CST的土壤类型检索系统,但仍存在以下问题。首先,现有的土壤类型检索系统仅注重分类规则的表达,忽略了诊断对象、土壤类型与检索框架(推理过程)的耦合性对系统更新的影响。其次,土壤信息的载体并不是单一的,从空间结构上可分为土壤层次(Horizon)、剖面(Profile)、单个土体(Pedon)和聚合土体(h),但现有的检索系统并未将上述结构区分开来,不利于土壤信息的管理。最后,现有的检索系统均是通过传统计算机语言表达土壤特征的范围以及土壤特征之间复杂的逻辑关系,表达方式繁琐且冗余。因此,本文引入本体概念,以土壤地理学和CST规则为理论基础,分析土壤实体的空间结构及其与土壤类型、诊断对象之间的相互关系,在此基础上建立了关于土壤实体、土壤特征和CST对象(土壤类型与诊断对象)的本体模型,并定义了相应的谓词逻辑来表达三类本体模型的逻辑、隶属关系。本文采用Python语言实现了本体模型和谓词逻辑模型,研发了CST中土纲到亚类的检索系统,并采用湖北省土系调查数据完成系统测试。
关键词:  土壤系统分类  本体  谓词逻辑  检索  诊断对象
基金项目:国家自然科学基金项目(41877071,41101192,41471179)、国家科技基础性工作专项项目(2014FY110200A16)、国家重点研发计划项目(2016YFD0800907)
Logic Expression and Retrieval of Soil Taxonomy Based on Pedon
WANG Lahong1,CHEN Jiaying1, 2,WANG Shanqin1, 2,WANG Tianwei1, 2, TAN Huangyuan1
1. College of Resources and Environment, Huazhong Agricultural University;2. Key Laboratory of Arable Land Conservation (Middle and Lower Reaches of Yangtze River), Ministry of Agriculture and Rural Affairs of the People’s Republic of China
Abstract:
【Objective】At present, certain progress has been made in the research on a Chinese Soil Taxonomy (CST)-based Soil Type Retrieval System, but the research has come across the following problems: (1) Ignorance of the impact of the coupling of diagnosis object, soil type and retrieval framework (Inference Process) on updating of the system; (2) Lack of consideration of division of the spatial structure of soil information carriers, which is unconducive to management of the soil information; and (3) Scope matching of the soil characteristics always expressed in traditional conditional nested statement and logical relationships between soil characteristics, making retrieval language cumbersome and redundant.【Method】In order to improve the above situations, this study introduced the concept of ontology, set soil geography and CST classification rules as its theoretic bases, analyzed spatial structure of soil entities and their relationship with CST objects (soil types and diagnostic objects) and eventually on such a basis, established an ontological model for relationship between soil entities and CST objects. To standardize the expression of soil characteristics, this paper defined soil attribute models, and divided soil characteristics into two categories, i.e. ordinary soil characteristics and complex soil characteristics. Moreover, this paper also defined corresponding predicate logics to express the relationships between types or models in logic and membership.【Result】This paper used Python language to realize construction of the ontology models and definition of the predicate logics, developed a type retrieval system covering all the four levels (Order, Suborder, group and subgroup), and cited the data of some single pedons representative of the Soil Series of China (Hubei volume) for test. And the test not only helped determine types of the pedons at all the four levels, but also recorded the entire retrieval processes, which facilitated analysis of the result later.【Conclusion】To compare with other existing retrieval models, this ontological model divides the carrier of soil information into four categories, i.e. Horizon, Profile, Pedon, and Polypedon, which facilitates management of the soil information and reduces complexity of the classification rules. Besides, the retrieval model separates the rules from the framework and can be characterized by high cohesion and low coupling, so that the model can better support the retrieval system in updating and expanding. The retrieval framework no longer takes soil characteristics as retrieval object, but encapsulates soil characteristics into soil entities, diagnostic objects, and soil types, thus elevating retrieval objects up to the level of category, which tallies more with human cognition. In brief, a method of constructing a retrieval model is proposed from the perspective of ontology in this paper. It can solve some problems of the existing retrieval models to a certain extent. However, the issue of semantics of soil characteristics and quantification of soil morphological characteristics remains to be a topic to work on. Therefore, to further improve the model, further efforts should be made in the next phase of the research to establish a soil characteristics semantic dictionary with complete metadata and information and introduce uncertain information as system auxiliary information.
Key words:  Soil taxonomy  Ontology  Predicate logic  Retrieval  Diagnosis object