事件本体构建中几个关键问题的研究
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摘要
本体作为“共享概念模型的明确的形式化规范说明”在信息处理、自然语言理解等人工智能应用领域发挥着越来越重要的作用。目前，大部分的本体都被构建成概念与概念之间的关系体系。这样的体系不能明确支持空间和时间的关系分析，存在“网球问题”。事件本体以事件作为知识表示单元，更加符合人类认识世界的规律，而且能表示动态的变化，为克服传统本体的缺陷提供了新的解决思路，事件本体的构建研究具有重要的学术和应用价值。
在事件本体中，“事件”这一概念不再仅仅作为一个静态概念或是概念与概念之间的关系来表示，而被视为一个包括动作、对象、环境、时间等信息的知识表示单元。作为一种大粒度的知识表示单元，事件本体中的“事件”不但要描述事件之间的关系、参与事件的人与物之间的关系，同时还要表示这些参与者在事件中所扮演的角色以及事件的动态过程等内容。本文在事件六要素表示模型的基础上，围绕事件本体构建中的关键问题展开深入细致的研究，主要的内容和创新点包括：
（1）基于概念代数理论的事件中实体概念的重用以及事件动态过程表示方法：事件中的对象要素与环境要素等均为现有领域本体中已描述的实体概念，本文对认知信息学中的概念代数操作进行扩展，添加时间与信息量标记，通过扩展后的概念代数操作实现从现有本体中重用事件表示所需的实体概念。此外，事件动态过程的描述，即将事件过程中不同时间标记的事件状态描述清楚，而事件状态的表示即为事件断言要素的表示实质。运用扩展后的概念代数操作（继承、部分继承、扩展及替换）来改变不同时间标记处的事件状态。扩展认知信息学中概念代数的应用，提高了事件本体的构建效率。
（2）事件框架表达式及其概念运算解析：本文在事件六要素表示模型的基础上，提出了基于Nilsson概念代数框架表达式的事件表示方法，并对事件框架表达式的概念运算进行解析，同时划分了事件本体的上层事件类。这样的事件框架表达式既能表示事件类，又能表示事件实例，同时也可以描述事件动态过程、事件之间分类与非分类关系、事件要素与事件之间的关系、事件要素之间的关系等信息。事件框架表达式具有灵活、适用范围广、表示能力强等特点，非常适合事件知识的表示，它的提出为事件本体的存储与后期推理等应用服务提供了理论支持。
（3）基于事件结构的中文语句分析方法与面向事件的中文语料标注方法：本体的构建离不开领域知识语料库的构建，同样事件本体的构建也离不开面向事件本体构建的语料库。本文在分析了现有中文语句分析方法的不足，以及面向事件本体构建的需求后，提出一种基于事件结构的中文语句分析方法及其标注方法。该方法改进了原有CEC（Chinese Event Corpus）语料的标注方法，为事件的自动识别与分类、事件要素和事件关系的自动获取等应用提供语料信息。主要扩展的功能包括：①对非事件成分（介词、连词等）的语义功能进行分类与标注；②最大限度的涵盖了语句中的各类事件；③可以分析与描述嵌套的事件层次结构；④指明语句中各事件所对应的对象、环境、时间等信息。
（4）事件自动识别与分类策略：本文采用词典方法，从文本中识别出事件指示词共计8858个，并对识别出的事件指示词进行手工分类，构建事件指示词分类训练语料。在训练语料的支持下，运用一对一支持向量机SVM（SupportVector Machine）多分类方法对事件指示词进行分类。针对事件知识的特点，在SVM机器学习算法构建特征向量时，加入了词汇特征、词法特征、语法特征、语义特征；实验结果表明，随着有效特征的加入，事件指示词分类的效果随之提高，而将多种特征融合在一起时，特别是随着语义特征的加入，事件指示词分类效果最好，其Precision达到81.85%。在事件本体的构建过程中，事件自动识别与分类将显著降低人工处理的工作量。同时事件自动识别与分类也为基于事件本体的事件语义理解等应用打下基础。可以依据事件自动识别结果，查询事件本体中相匹配的事件类，填充自然语言中缺失的事件信息，帮助事件语义理解。
As a formal and explicit specification of shared conceptualisation, ontologyplays an increasingly important role in the application of artificial intelligencesuch as information processing and natural language understanding. At present,ontology is usually constructed as a relational system of various concepts, whichdoes not provide support for analysis in terms of time and space levels andcannot solve the “tennis problem”. Event ontology, taking event as the basic unitof knowledge, perfectly coincides with the way of human thinking. Moreover, itis able to express dynamic changes and provides a novel approach to overcomeobstacles of traditional ontology. The research on construction of event ontologyhas significant academic and practical value.
In event ontology,“event” is not merely treated as static concepts orrelations between concepts. It is the basic unit of knowledge, which containsinformation like action, object, environment, as well as time. The concept“event” in event ontology is responsible for representation of relations betweendifferent events and relations between people or things involved in event.Furthermore, it should express the roles of event participants and dynamicprocedure of event. Based upon six-tuples expression of event, this paperfocuses on the key problems in construction of event ontology. Specifically, thecontributions of this thesis are listed as following:
（1）Reuse of entities from existing ontologies and representation ofevent’s dynamic procedure based on concept algebra. In event, bothobject-element and environment-element are entities of existing ontologies. Toreuse those existing entities when constructing event ontology, the paper extendsoperations of concept algebra via adding “time flag” and “information quantity”.Representation of event’s dynamic procedure can be considered asrepresentation of a series of event’s states changing along with different time points, and then assertion-element can essentially express event’s states. Bytaking advantage of extended operations (inheritance, tailoring, extension andsubstitute) in concept algebra, we can change event’s states along with differenttime points. In short, the application of extended concept algebra in cognitiveinformatics greatly raises the efficiency when constructing event ontology.
（2）Frame-based representation model of event and analysis of conceptualoperations. This paper proposes a novel frame-based representation model ofevent on the basis of six-tuples expression, analyzes several conceptualoperations, and defines the upper classification of events. Based on Nilsson’sconcept algebra, the frame-based representation model is able to express bothevent class and instance. Furthermore, it can express dynamic procedure ofevent and the relations between events and their elements. The frame-basedrepresentation model is flexible, effective and widely applicable. It is extremelyappropriate for representation of event-based knowledge and providestheoretical support for applications like knowledge storage and reasoning.
（3）Event-based tagging and analytic technique for Chinese text. Buildingcorpuses of domain knowledge plays an important part in construction ofontology. Similarly, event-oriented corpuses tagging is essential for constructionof event ontology. In order to overcome shortcomings of existing Chineseanalytic technique and facilitate the build of event ontology, the paper presentsan event-based tagging and analytic technique for processing Chinese text. Incomparison with the tagging method of CEC (Chinese Event Corpus), itenhances the tagging ability and provides high quality corpuses for eventauto-recognition, auto-classification and retrieval of event elements or relations.The major extended functionalities are listed as following:①Classify and tagthe non-event components like preposition and auxiliary word.②Incorporateevents in text as much as possible③Ability of nested analysis for eventhierarchical structure.④Clearly denote event object, environment and time information from Chinese text.
（4）Event auto-recognition and classification strategy. We retrieve8858event denoters from a bunch of text files, manually classify them to buildtraining set, and then design a one-against-one SVM (Support Vector Machine)learner with feature vectors containing word, lexical, grammatical and semanticinformation. Experiments indicate that accuracy of classification increases afterincorporating several effective features. By combination of some key features,especially semantic features, the SVM learner works effectively with theprecision up to81.85%. In summary, auto-recognition and classification of eventsignificantly reduce manual workload and lay a solid foundation for applicationlike event-based language understanding. By taking result from eventrecognition and classification, the matched event class with abundantinformation can be retrieved from event ontology, making it convenient forevent-based language understanding.
在事件本体中，“事件”这一概念不再仅仅作为一个静态概念或是概念与概念之间的关系来表示，而被视为一个包括动作、对象、环境、时间等信息的知识表示单元。作为一种大粒度的知识表示单元，事件本体中的“事件”不但要描述事件之间的关系、参与事件的人与物之间的关系，同时还要表示这些参与者在事件中所扮演的角色以及事件的动态过程等内容。本文在事件六要素表示模型的基础上，围绕事件本体构建中的关键问题展开深入细致的研究，主要的内容和创新点包括：
（1）基于概念代数理论的事件中实体概念的重用以及事件动态过程表示方法：事件中的对象要素与环境要素等均为现有领域本体中已描述的实体概念，本文对认知信息学中的概念代数操作进行扩展，添加时间与信息量标记，通过扩展后的概念代数操作实现从现有本体中重用事件表示所需的实体概念。此外，事件动态过程的描述，即将事件过程中不同时间标记的事件状态描述清楚，而事件状态的表示即为事件断言要素的表示实质。运用扩展后的概念代数操作（继承、部分继承、扩展及替换）来改变不同时间标记处的事件状态。扩展认知信息学中概念代数的应用，提高了事件本体的构建效率。
（2）事件框架表达式及其概念运算解析：本文在事件六要素表示模型的基础上，提出了基于Nilsson概念代数框架表达式的事件表示方法，并对事件框架表达式的概念运算进行解析，同时划分了事件本体的上层事件类。这样的事件框架表达式既能表示事件类，又能表示事件实例，同时也可以描述事件动态过程、事件之间分类与非分类关系、事件要素与事件之间的关系、事件要素之间的关系等信息。事件框架表达式具有灵活、适用范围广、表示能力强等特点，非常适合事件知识的表示，它的提出为事件本体的存储与后期推理等应用服务提供了理论支持。
（3）基于事件结构的中文语句分析方法与面向事件的中文语料标注方法：本体的构建离不开领域知识语料库的构建，同样事件本体的构建也离不开面向事件本体构建的语料库。本文在分析了现有中文语句分析方法的不足，以及面向事件本体构建的需求后，提出一种基于事件结构的中文语句分析方法及其标注方法。该方法改进了原有CEC（Chinese Event Corpus）语料的标注方法，为事件的自动识别与分类、事件要素和事件关系的自动获取等应用提供语料信息。主要扩展的功能包括：①对非事件成分（介词、连词等）的语义功能进行分类与标注；②最大限度的涵盖了语句中的各类事件；③可以分析与描述嵌套的事件层次结构；④指明语句中各事件所对应的对象、环境、时间等信息。
（4）事件自动识别与分类策略：本文采用词典方法，从文本中识别出事件指示词共计8858个，并对识别出的事件指示词进行手工分类，构建事件指示词分类训练语料。在训练语料的支持下，运用一对一支持向量机SVM（SupportVector Machine）多分类方法对事件指示词进行分类。针对事件知识的特点，在SVM机器学习算法构建特征向量时，加入了词汇特征、词法特征、语法特征、语义特征；实验结果表明，随着有效特征的加入，事件指示词分类的效果随之提高，而将多种特征融合在一起时，特别是随着语义特征的加入，事件指示词分类效果最好，其Precision达到81.85%。在事件本体的构建过程中，事件自动识别与分类将显著降低人工处理的工作量。同时事件自动识别与分类也为基于事件本体的事件语义理解等应用打下基础。可以依据事件自动识别结果，查询事件本体中相匹配的事件类，填充自然语言中缺失的事件信息，帮助事件语义理解。
As a formal and explicit specification of shared conceptualisation, ontologyplays an increasingly important role in the application of artificial intelligencesuch as information processing and natural language understanding. At present,ontology is usually constructed as a relational system of various concepts, whichdoes not provide support for analysis in terms of time and space levels andcannot solve the “tennis problem”. Event ontology, taking event as the basic unitof knowledge, perfectly coincides with the way of human thinking. Moreover, itis able to express dynamic changes and provides a novel approach to overcomeobstacles of traditional ontology. The research on construction of event ontologyhas significant academic and practical value.
In event ontology,“event” is not merely treated as static concepts orrelations between concepts. It is the basic unit of knowledge, which containsinformation like action, object, environment, as well as time. The concept“event” in event ontology is responsible for representation of relations betweendifferent events and relations between people or things involved in event.Furthermore, it should express the roles of event participants and dynamicprocedure of event. Based upon six-tuples expression of event, this paperfocuses on the key problems in construction of event ontology. Specifically, thecontributions of this thesis are listed as following:
（1）Reuse of entities from existing ontologies and representation ofevent’s dynamic procedure based on concept algebra. In event, bothobject-element and environment-element are entities of existing ontologies. Toreuse those existing entities when constructing event ontology, the paper extendsoperations of concept algebra via adding “time flag” and “information quantity”.Representation of event’s dynamic procedure can be considered asrepresentation of a series of event’s states changing along with different time points, and then assertion-element can essentially express event’s states. Bytaking advantage of extended operations (inheritance, tailoring, extension andsubstitute) in concept algebra, we can change event’s states along with differenttime points. In short, the application of extended concept algebra in cognitiveinformatics greatly raises the efficiency when constructing event ontology.
（2）Frame-based representation model of event and analysis of conceptualoperations. This paper proposes a novel frame-based representation model ofevent on the basis of six-tuples expression, analyzes several conceptualoperations, and defines the upper classification of events. Based on Nilsson’sconcept algebra, the frame-based representation model is able to express bothevent class and instance. Furthermore, it can express dynamic procedure ofevent and the relations between events and their elements. The frame-basedrepresentation model is flexible, effective and widely applicable. It is extremelyappropriate for representation of event-based knowledge and providestheoretical support for applications like knowledge storage and reasoning.
（3）Event-based tagging and analytic technique for Chinese text. Buildingcorpuses of domain knowledge plays an important part in construction ofontology. Similarly, event-oriented corpuses tagging is essential for constructionof event ontology. In order to overcome shortcomings of existing Chineseanalytic technique and facilitate the build of event ontology, the paper presentsan event-based tagging and analytic technique for processing Chinese text. Incomparison with the tagging method of CEC (Chinese Event Corpus), itenhances the tagging ability and provides high quality corpuses for eventauto-recognition, auto-classification and retrieval of event elements or relations.The major extended functionalities are listed as following:①Classify and tagthe non-event components like preposition and auxiliary word.②Incorporateevents in text as much as possible③Ability of nested analysis for eventhierarchical structure.④Clearly denote event object, environment and time information from Chinese text.
（4）Event auto-recognition and classification strategy. We retrieve8858event denoters from a bunch of text files, manually classify them to buildtraining set, and then design a one-against-one SVM (Support Vector Machine)learner with feature vectors containing word, lexical, grammatical and semanticinformation. Experiments indicate that accuracy of classification increases afterincorporating several effective features. By combination of some key features,especially semantic features, the SVM learner works effectively with theprecision up to81.85%. In summary, auto-recognition and classification of eventsignificantly reduce manual workload and lay a solid foundation for applicationlike event-based language understanding. By taking result from eventrecognition and classification, the matched event class with abundantinformation can be retrieved from event ontology, making it convenient forevent-based language understanding.
引文
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