一种关系模式到OWL DL本体的翻译方法

本体技术是数据可以达到语义层次交换的关键,如何将当前各类数据形成本体知识库表示是一个非常重要的问题。针对这个问题,以一种关系模式到一种语义扩展ER模型的正确性可满足转换算法为基础,提出了一种通过数据库反向工程到OWL DL本体的翻译算法,说明了该算法使得转换是正确性可满足的,并实验实现验证了算法。     

Geometric constraint solving with geometric transformation

This paper proposes two algorithms for solving geometric constraint systems. The first algorithm is for constrained systems without loops and has linear complexity. The second algorithm can solve constraint systems with loops. The latter algorithm is of quadratic complexity and is complete for constraint problems about simple polygons. The key to it is to combine the idea of graph based methods for geometric constraint solving and geometric transformations coming from rule-based methods.     

Learning the semantics of structured data sources

Information sources such as relational databases, spreadsheets, XML, JSON, and Web APIs contain a tremendous amount of structured data that can be leveraged to build and augment knowledge graphs. However, they rarely provide a semantic model to describe their contents. Semantic models of data sources represent the implicit meaning of the data by specifying the concepts and the relationships within the data. Such models are the key ingredients to automatically publish the data into knowledge graphs. Manually modeling the semantics of data sources requires significant effort and expertise, and although desirable, building these models automatically is a challenging problem. Most of the related work focuses on semantic annotation of the data fields (source attributes). However, constructing a semantic model that explicitly describes the relationships between the attributes in addition to their semantic types is critical.We present a novel approach that exploits the knowledge from a domain ontology and the semantic models of previously modeled sources to automatically learn a rich semantic model for a new source. This model represents the semantics of the new source in terms of the concepts and relationships defined by the domain ontology. Given some sample data from the new source, we leverage the knowledge in the domain ontology and the known semantic models to construct a weighted graph that represents the space of plausible semantic models for the new source. Then, we compute the top k candidate semantic models and suggest to the user a ranked list of the semantic models for the new source. The approach takes into account user corrections to learn more accurate semantic models on future data sources. Our evaluation shows that our method generates expressive semantic models for data sources and services with minimal user input. These precise models make it possible to automatically integrate the data across sources and provide rich support for source discovery and service composition. They also make it possible to automatically publish semantic data into knowledge graphs.     

Singularity Analysis of Geometric Constraint Systems

Singularity analysis in an important subject of the geometric constraint satisfaction problem.In this paper,three kinds of singularities are described and corresponding identifcation methods are presented for both under0constrained systems and over-constrained systems,Another special but common singularity for under-constrained geometric systems,pseudo-singularity,is analyzed.Pseudo-singularity is caused by a variety of constraint mathching of under-constrained systems and can be removed by improving constraint distribution.To avoid pseudo-singularity and decide redundant constraints adaptively,a differentiaiton algorithm is proposed in the paper.Its corrctness and effciency have been validated through its practical applications in a 2D/3D geometric constraint solver CBA.     

通用几何约束系统统一建模研究

在几何约束和几何实体的基本约束和欧拉参数表达的基础上,研究了通用几何约束系统的统一建模问题。通过对三维几何实体姿态约束和位置约束解耦性的分析,抽象出球实体、盒体和球盒体三种基本几何实体表达空间几何实体,并以基本约束的组合表达几何约束,形成几何约束模型特有的层次结构;并以有向图管理几何约束系统,可以清晰地反映姿态约束和位置约束的解耦性,实现约束系统的细粒度分解,得到规模更小的求解序列,实现高效求解。方法实现于原型系统WhutVAS中。     

非一致OWL本体的推理方法研究

针对目前OWL推理机无法在非一致本体上进行推理的问题,提出了一种基于回答集程序的非一致本体推理方式,将OWL本体翻译为一类逻辑程序：HEX回答集程序,把本体推理问题规约为回答集程序求解问题。并通过在非一致本体上的推理实验,验证了该类方法的有效性。     

基于几何约束投票的图像特征匹配

为了改进单纯依靠相似度的图像特征匹配效果,提出了一种特征匹配方法,采用投票的方法在特征匹配的过程中引入几何约束。首先根据特征的描述向量进行初步的快速匹配,得到候选特征对以后通过投票的手段在特征之间相似度矩阵中引入一种鲁棒的几何约束,并通过自适应阈值过滤的方法获得图像特征匹配。在实验中验证了该方法对图像特征匹配正确与否具有较强的区分能力,在与已有技术接近的计算时间内得到了更高的匹配准确度。基于几何约束投票的图像特征匹配比单纯依靠相似度具有更好的匹配效果。     

Development of a product configuration system with an ontology-based approach

Product configuration is a crucial means to implement the mass customization paradigm by assembling a set of customizable components to satisfy both customers’ needs and technical constraints. With the aim of enabling efficient and effective development of product configuration systems by reusing configuration knowledge, an ontology-based approach to modeling product configuration knowledge is presented in this paper. The ontology-based product configuration models are hierarchically organized. At the lower level, a configuration meta-model is defined. Based on this meta-model, domain-specific configuration knowledge can be derived by reusing or inheriting the classes or relations in the meta-model. Configuration models are formalized using OWL (Ontology Web Language), an ontology representation language developed by W3C. As a result, configuration models have well-defined semantics due to the logic semantics of OWL, making it possible to automatically detect inconsistencies of configuration knowledge bases. Furthermore, configuration constraints are represented in SWRL, a rule language based on OWL. Finally, actual configuration processes are carried out using JESS, a rule engine for the Java platform, by mapping OWL-based configuration facts and SWRL-based configuration constraints into JESS facts and JESS rules, respectively. The proposed methodology is illustrated with an example for configuring the ranger drilling machine.     

Associative retrieval in spatial big data based on spreading activation with semantic ontology

The opportunities associated with big data have helped generate significant interest, and big data analytics has emerged as an important area of study for both practitioners and researchers. For example, traditional cause–effect analysis and conditional retrieval fall short in dealing with data that are so large and complex. Associative retrieval, on the other hand, has been identified as a potential technique for big data. In this paper, we integrate the spreading activation (SA) algorithm and the ontology model in order to promote the associative retrieval of big data. In our approach, constraints based on variant weights of semantic links are considered with the aim of improving the spreading-activation process and ensuring the accuracy of search results. Semantic inference rules are also introduced to the SA algorithm to find latent spreading path and help obtain results which are more relevant. Our theoretical and experimental analysis demonstrate the utility of this approach.     

Why good data analysts need to be critical synthesists. Determining the role of semantics in data analysis

In this article, we critically examine the role of semantic technology in data driven analysis. We explain why learning from data is more than just analyzing data, including also a number of essential synthetic parts that suggest a revision of George Box’s model of data analysis in statistics. We review arguments from statistical learning under uncertainty, workflow reproducibility, as well as from philosophy of science, and propose an alternative, synthetic learning model that takes into account semantic conflicts, observation, biased model and data selection, as well as interpretation into background knowledge. The model highlights and clarifies the different roles that semantic technology may have in fostering reproduction and reuse of data analysis across communities of practice under the conditions of informational uncertainty. We also investigate the role of semantic technology in current analysis and workflow tools, compare it with the requirements of our model, and conclude with a roadmap of 8 challenging research problems which currently seem largely unaddressed.     

Efficient management and storage of a multiversion OWL 2 DL domain ontology

Ontology is the chief technology to model domain knowledge and fix its heterogeneity. Knowledge evolution is unavoidable in all fields, and ontology should reflect such an evolution while preserving its consistency. The access to an ontology evolution history is also a crucial need. Thereby, ontology versions should be stored efficiently. To address these requirements, some works focused only on managing ontology inconsistency. To this end, they adopted an a posteriori approach that checks inconsistency after its occurrence. Others have mainly focused on some ontology versioning aspects other than the storage and the consistency preservation issues. Specifically, the state of the art storage strategies perform well some typical queries at the cost of a high storage space. Strategies with low space overhead lose the main specificity of ontology: the knowledge semantics modelling. So, it is useful to address all these shortcomings. This paper presents an approach that allows generating consistent OWL 2 DL ontology versions in a priori way. It also proposes an efficient storage strategy that preserves the semantics aspect of ontology and performs well typical queries with a low storage space. A protégé plug‐in is developed to show the feasibility of the main ideas presented in this paper.     

交互更新模式的量子遗传算法的几何约束求解

目的针对传统量子遗传算法无法充分利用种群中未成熟个体信息的不足,提出了基于交互更新模式的量子遗传算法(IUMQGA)并应用于几何约束求解中。方法几何约束问题的约束方程组可转化为优化模型,因此约束求解问题可以转化为优化问题。采用将遗传算法与量子理论相结合的量子遗传算法,使用双串量子染色体结构,使用交互更新策略将遗传算法中的交叉操作利用量子门变换来实现,根据不同情况采用不同的交互更新策略。这里的交互,指的是两个个体进行信息交换的过程,该过程用以产生新的个体。这不仅增加了个体间信息的交换而且充分利用了种群中未成熟个体的信息,提高了算法的收敛速度。结果通过非线性方程实例和几何约束实例测试并与其他方法比较表明,基于交互更新模式的量子遗传算法求解几何约束问题具有更好的求解精度和求解速率。双圆外公切线问题实例中,IUMQGA算法比QGA算法稳定;单圆填充问题和双圆外公切线问题实例中,通过实验求得各变量的最优值与其相应的精确值的误差在1E-2以下。结论采用交互更新模式的量子遗传算法可以很好地求解几何约束问题。     

Translating the Foundational Model of Anatomy into OWL

The Foundational Model of Anatomy (FMA) represents the result of manual and disciplined modeling of the structural organization of the human body. It is a tremendous resource in bioinformatics that facilitates sharing of information among applications that use anatomy knowledge. The FMA was developed in Protégé and the Protégé frames language is the canonical representation language for the FMA. We present a translation of the original Protégé frame representation of the FMA into OWL. Our effort is complementary to the earlier efforts to represent FMA in OWL and is focused on two main goals: (1) representing only the information that is explicitly present in the frames representation of the FMA or that can be directly inferred from the semantics of Protégé frames; (2) representing all the information that is present in the frames representation of the FMA, thus producing an OWL representation for the complete FMA. Our complete representation of the FMA in OWL consists of two components: an OWL DL component that contains the FMA constructs that are compatible with OWL DL; and an OWL Full component that imports the OWL DL component and adds the FMA constructs that OWL DL does not allow.     

OWL DL的形式化基础研究

W3C正在制定的OWL DL是一种面向语义Web的知识表示标记语言，具有较强的知识表达能力并适合大规模应用的推理效率，是语义Web领域对适合语义Web应用的知识表示标记语言进行研究的最新综合．在知识表示领域，为了对知识表达能力和推理效率做合适的折衷，进行了大量关于描述逻辑的研究，本文以描述逻辑作为OWL DL的形式化基础，详细分析了OWL DL和描述逻辑间的对应关系，用描述逻辑的语义解释了OWL DL的各个基本元素，从而可以将描述逻辑领域研究的大量成果应用到OWL DL上来，为进一步研究OWL DL的知识表示、推理等问题奠定了基础．     

带约束本体语义扩展的信息过滤算法

提出一种基于本体的信息过滤方法。该方法通过本体实现形式化语义描述,并对原始输入条件进行带约束规则的本体语义扩展。进而为了实现语义匹配,给出了信息向量语义描述及权重计算方法。最终,实现基于语义相似度计算的信息过滤。实验证明,该方法是有效的。     

Developing taxonomy for the domain ontology of construction contractual semantics: A case study on the AIA A201 document

In construction contractual management, sharing experts’ domain knowledge through ontology is a good way to narrow the knowledge gap between the domain experts and the construction team. However, little work has been done on ontology taxonomy development in this domain. Based on a literature review on sharing domain knowledge, taxonomy development methods and the essence of construction contracts, this study proposes a synthesized methodology for taxonomy development in the domain of construction contractual semantics. This methodology is based on an ontological model extracted from definitions found in the contract, and uses common root concepts as the initial root concept classes, and includes the iterative development and competency questions approaches as well. In the case study, using the research results from pilot studies, the proposed methodology was applied to the AIA A201 General Conditions of the Contract for Construction (2007) document at the textual level. As a result, a taxonomy was developed which was used to determine the validity of the proposed methodology. The taxonomy development methodology and the developed taxonomy itself are both valuable contributions in the quest to further develop ontology-based applications for sharing domain knowledge about construction contract semantics.     

Conditioning and preconditioning of the variational data assimilation problem

Numerical weather prediction (NWP) centres use numerical models of the atmospheric flow to forecast future weather states from an estimate of the current state. Variational data assimilation (VAR) is used commonly to determine an optimal state estimate that miminizes the errors between observations of the dynamical system and model predictions of the flow. The rate of convergence of the VAR scheme and the sensitivity of the solution to errors in the data are dependent on the condition number of the Hessian of the variational least-squares objective function. The traditional formulation of VAR is ill-conditioned and hence leads to slow convergence and an inaccurate solution. In practice, operational NWP centres precondition the system via a control variable transform to reduce the condition number of the Hessian. In this paper we investigate the conditioning of VAR for a single, periodic, spatially-distributed state variable. We present theoretical bounds on the condition number of the original and preconditioned Hessians and hence demonstrate the improvement produced by the preconditioning. We also investigate theoretically the effect of observation position and error variance on the preconditioned system and show that the problem becomes more ill-conditioned with increasingly dense and accurate observations. Finally, we confirm the theoretical results in an operational setting by giving experimental results from the Met Office variational system.     

Visualising the effects of ontology changes and studying their understanding with ChImp

Due to the Semantic Web’s decentralised nature, ontology engineers rarely know all applications that leverage their ontology. Consequently, they are unaware of the full extent of possible consequences that changes might cause to the ontology. Our goal is to lessen the gap between ontology engineers and users by investigating ontology engineers’ understanding of ontology changes’ impact at editing time. Hence, this paper introduces the Protégé plugin ChImp which we use to reach our goal. We elicited requirements for ChImp through a questionnaire with ontology engineers. We then developed ChImp according to these requirements and it displays all changes of a given session and provides selected information on said changes and their effects. For each change, it computes a number of metrics on both the ontology and its materialisation. It displays those metrics on both the originally loaded ontology at the beginning of the editing session and the current state to help ontology engineers understand the impact of their changes.We investigated the informativeness of materialisation impact measures, the meaning of severe impact, and also the usefulness of ChImp in an online user study with 36 ontology engineers. We asked the participants to solve two ontology engineering tasks – with and without ChImp (assigned in random order) – and answer in-depth questions about the applied changes as well as the materialisation impact measures. We found that ChImp increased the participants’ understanding of change effects and that they felt better informed. Answers also suggest that the proposed measures were useful and informative. We also learned that the participants consider different outcomes of changes severe, but most would define severity based on the amount of changes to the materialisation compared to its size. The participants also acknowledged the importance of quantifying the impact of changes and that the study will affect their approach of editing ontologies.     

Instance matching benchmarks in the era of Linked Data

The goal of this survey is to present the state of the art instance matching benchmarks for Linked Data. We introduce the principles of benchmark design for instance matching systems, discuss the dimensions and characteristics of an instance matching benchmark, provide a comprehensive overview of existing benchmarks, as well as benchmark generators, discuss their advantages and disadvantages, as well as the research directions that should be exploited for the creation of novel benchmarks, to answer the needs of the Linked Data paradigm.