Temporal Knowledge Representation Research Articles

τSQWRL: A TSQL2-Like Query Language for Temporal Ontologies Generated from JSON Big Data

Temporal ontologies allow to represent not only concepts, their properties, and their relationships, but also time-varying information through explicit versioning of definitions or through the four-dimensional perdurantist view. They are widely used to formally represent temporal data semantics in several applications belonging to different fields (e.g., Semantic Web, expert systems, knowledge bases, big data, and artificial intelligence). They facilitate temporal knowledge representation and discovery, with the support of temporal data querying and reasoning. However, there is no standard or consensual temporal ontology query language. In a previous work, we have proposed an approach named τJOWL (temporal OWL 2 from temporal JSON, where OWL 2 stands for “OWL 2 Web Ontology Language” and JSON stands for “JavaScript Object Notation”). τJOWL allows (1) to automatically build a temporal OWL 2 ontology of data, following the Closed World Assumption (CWA), from temporal JSON-based big data, and (2) to manage its incremental maintenance accommodating their evolution, in a temporal and multi-schema-version environment. In this paper, we propose a temporal ontology query language for rJOWL, named rSQWRL (temporal SQWRL), designed as a temporal extension of the ontology query language-Semantic Query-enhanced Web Rule Language (SQWRL). The new language has been inspired by the features of the consensual temporal query language TSQL2 (Temporal SQL2), well known in the temporal (relational) database community. The aim of the proposal is to enable and simplify the task of retrieving any desired ontology version or of specifying any (complex) temporal query on time-varying ontologies generated from time-varying big data. Some examples, in the Internet of Healthcare Things (IoHT) domain, are provided to motivate and illustrate our proposal.

Fixing the inconsistencies of continuous changing operations in fuzzy spatiotemporal RDF graph

Fuzzy spatiotemporal data models have been used to support spatial and temporal knowledge representation and reasoning in the presence of fuzziness. In the meantime, Resource Description Framework (RDF), the most relevant data representation and exchange standard on Semantic Web, has become a trend for representing fuzzy spatiotemporal data. However, due to the dynamic changes of fuzzy spatiotemporal data, fuzzy spatiotemporal RDF graphs are more likely to violate predefined spatial and temporal constraints than the general RDF graph, which causes data inconsistencies. At present, consistency problems in RDF graphs have been widely studied, but their studies consider only discrete changing operations of fuzzy spatiotemporal data, while researches on consistencies of continuous changing operations are still open issues. In this paper, we study the types of inconsistency caused by continuous changing operations and propose the corresponding fixing rules to the problems of inconsistencies. Then, we propose the corresponding fixing approaches to checking and fixing fuzzy spatiotemporal RDF graphs according to the inconsistent states. Finally, the experimental results show that our proposed algorithms can significantly fix inconsistencies of continuous changing operations in fuzzy spatiotemporal RDF graphs.

Computational complexity of hybrid interval temporal logics

Interval logics are very expressive temporal formalisms, but reasoning with them is often undecidable or has high computational complexity. As a result, a vast number of approaches limiting their expressive power—in order to obtain better computational behaviour—have been introduced. Unfortunately, due to such restrictions, interval logics often lose referentiality, that is, the capacity to refer to specific time intervals, which is crucial for temporal representation and reasoning. The computational price that needs to be paid in order to regain referentiality is not well studied and our research aims to fill this gap. In particular we study the main interval temporal logic, called the Halpern-Shoham logic, and its low complexity modifications. To regain referentiality in these modifications, we extend the language with the hybrid machinery—nominals and satisfaction operators—and classify the obtained logics according to their computational complexity. We show that such a hybridisation often makes tractable logics intractable but not undecidable. This allows us to construct hybrid interval temporal logics which are referential as well as maintain a good compromise between expressiveness and complexity; it makes them valuable formalisms for temporal knowledge representation. We also introduce a class of models which, due to a specific interplay between the interpretation of modal operators and a structure of time, makes reasoning in interval logics computationally hard even in the absence of the hybrid machinery.

MODEL OF GENERALIZED REPRESENTATION OF TEMPORAL KNOWLEDGE FOR TASKS OF SUPPORT OF ADMINISTRATIVE DECISIONS

The subject of the research is the processes of building and using temporal knowledge, which determine the order of implementation of managerial decisions within the framework of organizational management. The goal is to develop a temporal knowledge representation model to determine the sequence of control actions over time as part of a management decision, taking into account the level of the organizational hierarchy at which these actions are implemented. To achieve this goal, the problems of structuring the factors determining the differences in the presentation and use of temporal knowledge, as well as the development of a generalized model of temporal knowledge to support managerial decisions, are solved. It is shown that the key differences between temporal and causal dependencies are associated with the use of a combination of formal and informal knowledge of performers about causal relationships between control actions as part of a management decision to build a sequence of actions in time, as well as the probabilistic properties of temporal relationships. A generalized model of temporal knowledge is proposed, designed to support managerial decisions. The model includes many generalized facts, as well as a set of temporal and hierarchical relationships between these facts. Facts reflect known states of the control object. Each generalized fact is formed on the basis of a conjunction of elementary facts. An elementary fact sets the value of a certain property of a control object. Temporal relationships determine the sequence of facts over time. The organizational hierarchy level for facts is set using logical operations. In practical terms, the proposed model is focused on building a temporal knowledge base. Conclusion based on such a knowledge base makes it possible to determine the probable sequence of control actions that lead to a sequence of transitions between states, which allows achieving the target state of the control object. This makes it possible to form a set of possible alternative options for implementing a managerial decision in the event of abnormal conditions of the control object.

Development of information technology for the automated construction and expansion of the temporal knowledge base in the tasks of supporting management decisions

The object of research is the process of knowledge base (KB) constructing, which involves the development of a formal presentation of knowledge, knowledge extraction, verification of their consistency and inclusion in the KB. The implementation of such a process is a necessary condition to use KB in systems supporting management decisions at the tactical and strategic levels of organizational management. However, there is currently a discrepancy between the practical need for implementation, knowledge-based support for managerial decisions under uncertainty, taking into account the temporal aspect of managing action and also the possibilities of existing techniques and technologies of interactive and automated construction of the KB. The analysis of the research object testifies to the possibility of automated construction of the KB to support managerial decisions using temporal dependencies. The latter can be obtained based on the analysis of the sequence of states corresponds to the behavior of an organizational system as an object of management. Temporal dependencies between states represent the knowledge of control actions that have been implemented in managing decisions. The logical-probabilistic model of temporal knowledge representation is improved by taking into account the hierarchical description of the management solution context, which makes it possible to simplify the construction of this solution. The proposed model provides the ability to support a rational choice from a variety of admissible management decisions by the probability of transition to the target state of the control object. The method of automated construction and support of the temporal KB based on the account of the attributive description of the control object state and the context of the management solution is improved. The method involves the rapid formation and verification of the logical-probabilistic representation of temporal knowledge to support management decisions. The information technology of automated construction and replenishment of temporal KBs is developed. Technology combines the capability of generating representation patterns and semantic verification of knowledge. The verification is performed by a specialist in the subject area. The automatic construction of weighted temporal rules based on the detection of dependencies in known state sequences of the control object. This makes it possible to quickly identify the new temporal dependencies for the subject area and bring them to the KB after semantic verification by an expert.

Formalization and acquisition of temporal knowledge for decision support in medical processes

Background: In medical practice, long term interventions are common and they require timely planning of the involved processes. Unfortunately, evidence-based statements about time are hard to find in Clinical Practice Guidelines (CPGs) and in other sources of medical knowledge. At the same time, health care centers use medical records and information systems to register data about clinical processes and patients, including time information about the encounters, prescriptions, and other clinical actions. Consequently, medical records and health care information systems are promising sources of data from which we can detect temporal medical knowledge.Objective: The objectives were to (1) Analyze and classify the sorts of time constraints in medical processes, (2) Propose a formalism to represent these sorts of clinical time constraints, (3) Use these formalisms to enable the automatic generation of temporal models from clinical data, and (4) Study the adherence of these intervention models to CPG recommendations.Methods: In order to achieve these objectives, we carried out four studies: The identification of the sort of times involved in the long-term diagnostic and therapeutic medical procedures of fifty patients, the supervision of the indications about time contained in six CPGs on chronic diseases, the study of the time structures of two standard data models, as well as ten languages to computerize CPGs. Based on the provided studies, we synthesized two representation formalisms: Micro- and macro-temporality. We developed three algorithms for automatic generation of generalized time constraints in the form of micro- and macro-temporalities from clinical databases, which were double tested.Results: A full classification of time constraints for medical procedures is proposed. Two formalisms called micro- and macro-temporality are introduced and validated to represent these time constraints. Time constraints were generated automatically from the data about 8781 Arterial Hypertension (AH) patients. The generated macro-temporalities restricted visits to be between 1–7 weeks, whereas CPGs recommend 2–4 weeks. Micro-temporal constraints on drug-dosage therapies distinguished between the initial dosage and the target dosage, with visits every 1–6 weeks, and 2–5 months, respectively. Our algorithms obtained semi-complete maps of dosage increments and the maximum dosages for 7 drug types. Data-based time limits for lifestyle change counsels and blood pressure (BP) check-ups were fixed to 6 and 3 months, for patients with low- and high-BP, respectively, when CPGs specify a general 3–6 month range.Conclusions: Experience-based temporal knowledge detected using our algorithms complements the evidence-based knowledge about clinical procedures contained in the CPGs. Our temporal model is simple and highly descriptive when dealing with general or specific time constraints’ representations, offering temporal knowledge representation of varying detail. Therefore, it is capable of capturing all the temporal knowledge we can find in medical procedures, when dealing with chronic diseases. With our model and algorithms, an adherence analysis emerges naturally to detect CPG-compliant interventions, but also deviations whose causes and possible rationales can call into question CPG recommendations (e.g., our analysis of AH patients showed that the time between visits recommended by CPGs were too long for a proper drug therapy decision, dosage titration, or general follow-up).

Automated acquisition, representation and processing of temporal knowledge in dynamic integrated expert systems

This paper analyzes the results of automated knowledge base construction for dynamic integrated expert systems on the basis of the so-called combined method of knowledge acquisition with temporal extensions.

Development of the temporal knowledge representation model for computer support of consumer goodd production processes at multi-nomenclature machine-building enterprise

The object of research is the processes associated with the organization of the production of consumer goods (CG) at the multi-nomenclature machine-building enterprise (MME). These processes are considered in the aspect of automating the production of consumer goods by creating a special model of knowledge about the current state of production processes. One of the most problematic issues is the complex automation of combined processes for the production of basic products and CGs at MMEs. The solution of this question required the improvement of the formal apparatus for modeling time dependencies. In the course of the research, discrete mathematics methods, in particular set theory and mathematical logic, are used, as well as tools that are known in knowledge engineering, in particular production systems.A temporal model of knowledge and technology is combined with the knowledge base of the decision support system. The peculiarity of this model is the possibility of adequate reflection of asynchrony and parallelism, which is inherent in the processes of CGs production at MMEs. The overall effect of the implementation of the developed model of knowledge will be achieved by reducing the incidence of disruptions in the production program, preserving the professional knowledge of experienced MME specialists in the production of consumer goods. In addition, the use of the temporal knowledge model as part of the intellectual core of the decision support system will ensure the reduction of the MME costs in the organization of production of consumer goods.

Fixing inconsistencies of fuzzy spatiotemporal XML data

Fuzzy spatiotemporal data models have been used to support spatial and temporal knowledge representation and reasoning in the presence of fuzziness. In the meantime, XML is expected to become the next generation standard language for exchanging data over the Internet, which will become a trend to represent fuzzy spatiotemporal data based on XML. However, fuzzy spatiotemporal XML documents may contain inconsistencies violating predefined spatial and temporal constraints, which cause the data inconsistency problems. Although those consistency problems in XML documents have been widely studied, their studies only take the general data into account, and the studies on consistencies of fuzzy spatiotemporal data are still open issues. In this paper we put forward solutions to the problems of inconsistencies in fuzzy spatiotemporal XML documents. We also analyze inconsistent states which are named discontinuity overlap or cycle of the temporal labels of some incoming edges. Then, we put forward the corresponding approaches to checking and fixing fuzzy spatiotemporal XML documents according to the inconsistent states. Finally, the experimental results show that our proposed algorithms can fix inconsistencies of fuzzy spatiotemporal XML documents significantly.

Combining ontological and temporal formalisms for composite activity modelling and recognition in smart homes

Activity recognition is essential in providing activity assistance for users in smart homes. While significant progress has been made for single-user single-activity recognition, it still remains a challenge to carry out real-time progressive composite activity recognition. This paper introduces a hybrid ontological and temporal approach to composite activity modelling and recognition by extending existing ontology-based knowledge-driven approach. The compelling feature of the approach is that it combines ontological and temporal knowledge representation formalisms to provide powerful representation capabilities for activity modelling. The paper describes in detail ontological activity modelling which establishes relationships between activities and their involved entities, and temporal activity modelling which defines relationships between constituent activities of a composite activity. As an essential part of the model, the paper also presents methods for developing temporal entailment rules to support the interpretation and inference of composite activities. In addition, this paper outlines an integrated architecture for composite activity recognition and elaborated a unified activity recognition algorithm which can support the recognition of simple and composite activities. The approach has been implemented in a feature-rich prototype system upon which testing and evaluation have been conducted. Initial experimental results have shown average recognition accuracy of 100% and 88.26% for simple and composite activities, respectively.

An intelligent neuro fuzzy temporal knowledge representation model for mining temporal patterns

Representation of temporal knowledge and analysis of temporal data is becoming a good practice for effective classification and prediction. Various semantic levels on knowledge representation schemes have been measured for temporal data. The existing Fuzzy Cognitive Maps FCMs facilitate modeling dynamic systems for knowledge representation and reasoning under uncertainty. However, the FCMs are constructed manually and are constrained by the human experts' validation for assessing its reliability and they are lacking in considering temporal features necessary for reasoning in medical applications. This paper proposes a new temporal mining system known as Fuzzy Temporal Cognitive Map FTCM, which defines a complete discrete temporal extension and fuzzy inference mechanism of FCM. In FTCM, the temporal dependencies of concepts during a particular time interval are measured. This work aims to reduce the complexities of dynamic modeling of a complex causal system by proposing a four layer fuzzy neural network to construct FTCM from the temporal data. In this proposed model, a fuzzy temporal mutual subsethood operator is used to measure the activation spread in the FTCM for automatic quantification of causalities. This FTCM is designed for a set of temporal clinical records, which can be further used for inferencing and prediction in medical diagnosis by generating a set of fuzzy temporal rules using Allen's temporal relationships and fuzzy temporal rules.

Axiomatic theories of the ontology of time in GFO

Time is a pervasive notion of high impact in information systems and computer science altogether. Respective understandings of the domain of time are fundamental for numerous areas, frequently in combination with closely related entities such as events, changes and processes. The conception and representation of time entities and reasoning about temporal data and knowledge are thus significant research areas. Each representation of temporal knowledge bears ontological commitments concerning time. Thus it is important to base temporal representations on a foundational ontology that covers general categories of time entities.In this article we introduce and discuss two consecutive ontologies of time that have been developed for the top-level ontology General Formal Ontology (GFO). The first covers intervals, named chronoids, and time boundaries of chronoids, as a kind of time points. One important specialty of time boundaries is their ability to coincide with other time boundaries. The second theory extends the first one by additionally addressing time regions, i.e., mereological sums of chronoids.Both ontologies are partially inspired by ideas of Franz Brentano, especially from his writings about the continuum. In particular, we view continuous time intervals as a genuine phenomenon which should not be identified with intervals (sets) of real numbers. On these grounds the resulting ontologies allow for proposing novel contributions to several problematic issues in temporal representation and reasoning, among others, the Dividing Instant Problem and the problem of persistence and change.Following our general approach to ontology development, both ontologies are axiomatized as formal theories in first-order logic and are analyzed metalogically. We prove the consistency of both ontologies, and completeness and decidability for one. Moreover, standard time theories with points and intervals are covered by both theories.

Event order generation using Reference Event based qualitative Temporal (REseT) relations in Time Event Ontology

AbstractOWL (Web Ontology Language) is the standard language for Semantic Web and is used in defining ontologies for Web. Temporal event data are ubiquitous in nature. Temporal data can be represented qualitatively using temporal relations in OWL, enabling temporal ordering of events which plays a vital role in task planners. The basic Allen’s temporal interval relations can be used to describe relations in OWL. Allen’s interval algebra is a well known formalism used to represent and reason the temporal knowledge. In this work, Allen’s interval algebra is extended by Reference Event based Temporal (REseT) relations to reduce the ambiguity in the before relation. The extended formalism is used in the representation of relations between time intervals and the viability of ordering of events in ontology is elucidated. This paper proposes a temporal knowledge representation and reasoning based event ordering system which helps in the temporal ordering of events. The advantage of this method is that it does not introduce any additional constructs in OWL and hence the existing reasoning tools and DL based query languages are capable of generating the linear order of events. The system is investigated experimentally using the COW (Correlates of War) dataset and has been evaluated using the Percent_ Similarity measure.

Discovering metric temporal constraint networks on temporal databases

In this paper, we propose the ASTPminer algorithm for mining collections of time-stamped sequences to discover frequent temporal patterns, as represented in the simple temporal problem (STP) formalism: a representation of temporal knowledge as a set of event types and a set of metric temporal constraints among them. To focus the mining process, some initial knowledge can be provided by the user, also expressed as an STP, that acts as a seed pattern for the searching procedure. In this manner, the mining algorithm will search for those frequent temporal patterns consistent with the initial knowledge. Health organisations demand, for multiple areas of activity, new computational tools that will obtain new knowledge from huge collections of data. Temporal data mining has arisen as an active research field that provides new algorithms for discovering new temporal knowledge. An important point in defining different proposals is the expressiveness of the resulting temporal knowledge, which is commonly found in the bibliography in a qualitative form. ASTPminer develops an Apriori-like strategy in an iterative algorithm where, as a result of each iteration i, a set of frequent temporal patterns of size i is found that incorporates three distinctive mechanisms: (1) use of a clustering procedure over distributions of temporal distances between events to recognise similar occurrences as temporal patterns; (2) consistency checking of every combination of temporal patterns, which ensures the soundness of the resultant patterns; and (3) use of seed patterns to allow the user to drive the mining process. To validate our proposal, several experiments were conducted over a database of time-stamped sequences obtained from polysomnography tests in patients with sleep apnea-hypopnea syndrome. ASTPminer was able to extract well-known temporal patterns corresponding to different manifestations of the syndrome. Furthermore, the use of seed patterns resulted in a reduction in the size of the search space, which reduced the number of possible patterns from 2.1×10⁷ to 1219 and reduced the number of frequent patterns found from 1167 to 340, thereby increasing the efficiency of the mining algorithm. A temporal data mining technique for discovering frequent temporal patterns in collections of time-stamped event sequences is presented. The resulting patterns describe different and distinguishable temporal arrangements among sets of event types in terms of repetitive appearance and similarity of the dispositions between the same events. ASTPminer allows users to participate in the mining process by introducing domain knowledge in the form of a temporal pattern using the STP formalism. This knowledge constrains the search to patterns consistent with the provided pattern and improves the performance of the procedure.

A Framework for Mining Signatures from Event Sequences and Its Applications in Healthcare Data

This paper proposes a novel temporal knowledge representation and learning framework to perform large-scale temporal signature mining of longitudinal heterogeneous event data. The framework enables the representation, extraction, and mining of high-order latent event structure and relationships within single and multiple event sequences. The proposed knowledge representation maps the heterogeneous event sequences to a geometric image by encoding events as a structured spatial-temporal shape process. We present a doubly constrained convolutional sparse coding framework that learns interpretable and shift-invariant latent temporal event signatures. We show how to cope with the sparsity in the data as well as in the latent factor model by inducing a double sparsity constraint on the β-divergence to learn an overcomplete sparse latent factor model. A novel stochastic optimization scheme performs large-scale incremental learning of group-specific temporal event signatures. We validate the framework on synthetic data and on an electronic health record dataset.

Automated generation of patient-tailored electronic care pathways by translating computer-interpretable guidelines into hierarchical task networks

This paper describes a methodology which enables computer-aided support for the planning, visualization and execution of personalized patient treatments in a specific healthcare process, taking into account complex temporal constraints and the allocation of institutional resources. To this end, a translation from a time-annotated computer-interpretable guideline (CIG) model of a clinical protocol into a temporal hierarchical task network (HTN) planning domain is presented. The proposed method uses a knowledge-driven reasoning process to translate knowledge previously described in a CIG into a corresponding HTN Planning and Scheduling domain, taking advantage of HTNs known ability to (i) dynamically cope with temporal and resource constraints, and (ii) automatically generate customized plans. The proposed method, focusing on the representation of temporal knowledge and based on the identification of workflow and temporal patterns in a CIG, makes it possible to automatically generate time-annotated and resource-based care pathways tailored to the needs of any possible patient profile. The proposed translation is illustrated through a case study based on a 70 pages long clinical protocol to manage Hodgkin's disease, developed by the Spanish Society of Pediatric Oncology. We show that an HTN planning domain can be generated from the corresponding specification of the protocol in the Asbru language, providing a running example of this translation. Furthermore, the correctness of the translation is checked and also the management of ten different types of temporal patterns represented in the protocol. By interpreting the automatically generated domain with a state-of-art HTN planner, a time-annotated care pathway is automatically obtained, customized for the patient's and institutional needs. The generated care pathway can then be used by clinicians to plan and manage the patients long-term care. The described methodology makes it possible to automatically generate patient-tailored care pathways, leveraging an incremental knowledge-driven engineering process that starts from the expert knowledge of medical professionals. The presented approach makes the most of the strengths inherent in both CIG languages and HTN planning and scheduling techniques: for the former, knowledge acquisition and representation of the original clinical protocol, and for the latter, knowledge reasoning capabilities and an ability to deal with complex temporal and resource constraints. Moreover, the proposed approach provides immediate access to technologies such as business process management (BPM) tools, which are increasingly being used to support healthcare processes.

A general framework for time-aware decision support systems

In this paper we present a general framework for time-aware decision support systems. The framework uses the state-of-the-art tOWL language for the representation of temporal knowledge and enables temporal reasoning over the information that is represented in a knowledge base. Our approach uses state-of-the-art Semantic Web technology for handling temporal data. Through such an approach, the designer of a system can focus on the application intelligence rather than enforcing/checking data related restrictions manually. Also, there is an increased support for reuse of temporal reasoning tools across applications. We illustrate the applicability of our framework by building a market recommendations aggregation system. This system automatically collects market recommendations from online sources and, based on the past performance of the analysts that issued a recommendation, generates an aggregated recommendation in the form of a buy, hold, or sell advice. We illustrate the flexibility of our proposed system by implementing multiple methods for the aggregation of market recommendations.

Learning and Representing Temporal Knowledge in Recurrent Networks

The effective integration of knowledge representation, reasoning, and learning in a robust computational model is one of the key challenges of computer science and artificial intelligence. In particular, temporal knowledge and models have been fundamental in describing the behavior of computational systems. However, knowledge acquisition of correct descriptions of a system's desired behavior is a complex task. In this paper, we present a novel neural-computation model capable of representing and learning temporal knowledge in recurrent networks. The model works in an integrated fashion. It enables the effective representation of temporal knowledge, the adaptation of temporal models given a set of desirable system properties, and effective learning from examples, which in turn can lead to temporal knowledge extraction from the corresponding trained networks. The model is sound from a theoretical standpoint, but it has also been tested on a case study in the area of model verification and adaptation. The results contained in this paper indicate that model verification and learning can be integrated within the neural computation paradigm, contributing to the development of predictive temporal knowledge-based systems and offering interpretable results that allow system researchers and engineers to improve their models and specifications. The model has been implemented and is available as part of a neural-symbolic computational toolkit.

Representation and application of interval-gap-based time ontology

Interval-gap-based theory was proposed to extend OWL-Time to represent the situation of a single event with gap-intervals and ontology.The concept and temporal relation in ontology were defoned based on interval-gap-based theory and the application of this representation in the domain was specified by the representation of transport event in logistics.The reasoner for the extension was built by defining the primitives rules and extending the operation on inference model in Jena.The experiments on the reasoner not only verify the correctness of the reasoner but also ensure the correctness of temporal knowledge represented in ontology and rules.With the method in this paper the problem of interval-gap-based temporal knowledge representation and reasoning is solved.

Dynamic Logic Model of Time Axes in Temporal Database

PDF HTML阅读 XML下载 导出引用 引用提醒 时态数据库时间轴的动态逻辑模型 DOI: 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: Supported by the National Natural Science Foundation of China under Grant Nos.60673135, 60736020 (国家自然科学基金); the Program for New Century Excellent Talents in University of China under Grant No.NCET-04-0805 (新世纪优秀人才支持计划); the Natural Science Foundation of Guangdong Province of China under Grant Nos.7003721, 04105503 (广东省自然科学基金); the Science-Technology Plan of Guangdong Province of China under Grant Nos.2005B10101041, 2007B010200052 (广东省科技攻关计划) Dynamic Logic Model of Time Axes in Temporal Database Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:尽管在1994年,Gabbay等人论证了时态逻辑的公理化系统和证明论方法是不适合于时态数据库查询语言建模的,但是仍需要通过对时间轴的公理化建模,利用公理化系统的可靠和完全等性质对时间轴作“细精度”的语义刻画.只有这样才能准确地在数据库中反映时间和时间属性的本质特点,并应用于与时间轴模型直接相关的时态查询语言.因此,从分析时间轴的性质出发,对时间轴的序关系和谓词逻辑性质进行了研究和阐述.随后分别用经典的时态逻辑Tense Logic和动态逻辑对时态数据库的时间轴进行了公理化建模,这样刻画的目的是为了“细精度”地体现时间轴的特点,并借助逻辑的方法对其分析.在TDB(temporal database)时间轴的动态逻辑建模部分,相对于原时态逻辑系统较为静态,着重处理了时间的动态性,并参照了Tense Logic中的Lin.Z系统,将其转化为动态Lin.Z系统,在其中添加了参数化处理.该参数化的处理是基于动作执行的,主要工作在动作指数的数值化和函数化两个方面,其结果体现了时态数据库中规则生存周期和“Now”节点的一些特点以及知识表达和解决方法,研究结果将对后续时态知识表达和时态数据库查询语言的研究起到积极的作用. Abstract:Although axiomatic systems and proof method for temporal logic have found so far relatively few applications in the query language modeling of temporal database and that was proved by Gabbay, et al in 1994, the model of time axes still must be built axiomatically, which owns soundness and completeness and depicts the time axes in fine grain. Thus the essential characteristics of time and temporal attributes in database can be described exactly, and the result is used in the temporal querying. So the research starts from essential characteristics of time attributes. At first, this paper expounds the order relation and first order logic properties of time axes. Secondly, this paper axiomatically models that using Tense Logic and dynamic logic, which aims at reflecting the properties of axes in fine grain by logical analysis. In the part of dynamic logic modeling, respecting to the static of temporal logic system, this paper mostly deals with the dynamics in the new system. Based on Lin.Z system in Tense Logic, this paper makes out the dynamic Lin.Z system, which has some parameters and functions. The parameters of this system are based on the action, which helps the action exponential numerical and functional. The results of that embody the properties and representation method of the rule’s lifecycle and point “Now” in temporal database, which positively helps the research in the field of temporal knowledge representation and temporal querying subsequently. 参考文献 相似文献 引证文献