Search-based metamodel matching with structural and syntactic measures

The use of different domain-specific modeling languages and diverse versions of the same modeling language often entails the need to translate models between the different languages and language versions. The first step in establishing a transformation between two languages is to find their corresponding concepts, i.e., finding correspondences between their metamodel elements. Although, metamodels use heterogeneous terminologies and structures, they often still describe similar language concepts. In this paper, we propose to combine structural metrics (e.g., number of properties per concept) and syntactic metrics to generate correspondences between metamodels. Because metamodel matching requires to cope with a huge search space of possible element combinations, we adapted a local and a global metaheuristic search algorithm to find the best set of correspondences between metamodels. The efficiency and effectiveness of our proposal is evaluated on different matching scenarios based on existing benchmarks. In addition, we compared our technique to state-of-the-art ontology matching and model matching approaches.     

OntoPlus: Text-driven ontology extension using ontology content,structure and co-occurrence information

This paper addresses the process of semi-automatic text-driven ontology extension using ontology content, structure and co-occurrence information. A novel OntoPlus methodology is proposed for semi-automatic ontology extension based on text mining methods. It allows for the effective extension of the large ontologies, providing a ranked list of potentially relevant concepts and relationships given a new concept (e.g., glossary term) to be inserted in the ontology. A number of experiments are conducted, evaluating measures for ranking correspondence between existing ontology concepts and new domain concepts suggested for the ontology extension. Measures for ranking are based on incorporating ontology content, structure and co-occurrence information. The experiments are performed using a well known Cyc ontology and textual material from two domains – finances and, fisheries & aquaculture. Our experiments show that the best results are achieved by combining content, structure and co-occurrence information. Furthermore, ontology content and structure seem to be more important than co-occurrence for our data in the financial domain. At the same time, ontology content and co-occurrence seem to have higher importance for our fisheries & aquaculture domain.     

形式概念分析在本体构建中的应用

随着信息共享和数据交换的范围不断扩大,语义数据集成成为数据库技术研究中的热点。本体作为共享概念的形式化说明,用于表达数据源的语义、识别和建立概念间的语义关联、达成语义一致,提供了语义异构问题的解决途径。传统的关系数据库由于具备技术成熟、应用广泛、数据管理能力强、数据安全程度高等特点,依然是数据存储的主要手段。然而,关系数据只表达了"属性/值"二元关系,不具备语义描述的能力,如何在关系数据中直接抽取数据的语义,构建本体,提炼丰富的语义信息,弥补关系数据表的缺陷,是一个重要的研究方向。文中探讨了运用形式概念分析的技术,结合数据库模式及其数据,构建本体的方法。在研究本体构建的基本方法基础上,论述了形式概念分析方法,并结合给定的数据库模式及其数据信息,演示了运用形式概念分析的方法构建本体的过程。     

Ontology selection ranking model for knowledge reuse

Ontology reuse is recommended as a key factor to develop cost-effective and high-quality ontologies because it could reduce development costs by avoiding rebuilding existing ontologies. Selecting the desired ontology from existing ontologies is essential for ontology reuse. Until now, much research on ontology selection has focused on lexical-level support. However, in these cases, it is almost impossible to find an ontology that includes all the concepts matched by the search terms at the semantic level. Finding an ontology that meets users’ needs requires a new ontology selection and ranking mechanism based on semantic similarity matching. We propose an ontology selection and ranking model consisting of selection standards and metrics based on better semantic matching capabilities. The model we propose presents two novel features different from previous research models. First, it enhances the ontology selection and ranking method practically and effectively by enabling semantic matching of taxonomy or relational linkage between concepts. Second, it identifies what measures should be used to rank ontologies in the given context and what weight should be assigned to each selection measure.     

Mapping queries to the Linking Open Data cloud: A case study using DBpedia

We introduce the task of mapping search engine queries to DBpedia, a major linking hub in the Linking Open Data cloud. We propose and compare various methods for addressing this task, using a mixture of information retrieval and machine learning techniques. Specifically, we present a supervised machine learning-based method to determine which concepts are intended by a user issuing a query. The concepts are obtained from an ontology and may be used to provide contextual information, related concepts, or navigational suggestions to the user submitting the query. Our approach first ranks candidate concepts using a language modeling for information retrieval framework. We then extract query, concept, and search-history feature vectors for these concepts. Using manual annotations we inform a machine learning algorithm that learns how to select concepts from the candidates given an input query. Simply performing a lexical match between the queries and concepts is found to perform poorly and so does using retrieval alone, i.e., omitting the concept selection stage. Our proposed method significantly improves upon these baselines and we find that support vector machines are able to achieve the best performance out of the machine learning algorithms evaluated.     

Fuzzy reasoning framework to improve semantic video interpretation

A video retrieval system user hopes to find relevant information when the proposed queries are ambiguous. The retrieval process based on detecting concepts remains ineffective in such a situation. Potential relationships between concepts have been shown as a valuable knowledge resource that can enhance the retrieval effectiveness, even for ambiguous queries. Recent researches in multimedia retrieval have focused on ontology modeling as a common framework to manage knowledge. Handling these ontologies has to cope with issues related to generic knowledge management and processing scalability. Considering these issues, we suggest a context-based fuzzy ontology framework for video content analysis and indexing. In this paper, we focused on the way in which we modeled our fuzzy ontology: First, we populate automatically the generated ontology by gathering various available video annotation datasets. Then, the ontology content was used to infer enhanced video semantic interpretation. Finally, considering user feedback, the content of the ontology was improved. Experimental results showed that our approach achieves the goal of scalability while at the same time allowing better video content semantic interpretation.     

中文地名识别与歧义消除-以中国县级以上行政区划地名为例

介绍了本体、地理本体和地名本体的基本概念,探讨了在文本工程通用框架GATE（General Architecture for Text Engineering）下,以自然语言处理为基础,借助地名本体,完成Web文本的中文地名识别与歧义消除的关键问题,设计并实现了一个原型系统。通过geo/non\|geo和geo/geo地名歧义的消除,使得识别出来的地名与地球表面具体的地理位置相对应,进而为Web文本中的中国行政区划地名赋予地理坐标和地理语义。做了验证实验,并对实验结果进行了分析。     

A cooperative tool for facilitating knowledge management

Nowadays, we can find systems and environments supporting processes of ontology building. However, these processes have not been specified enough yet. In this work, a real environment for integrating ontologies supplied by a predetermined set of (experts) users, who might be distributed through a communication network and working cooperatively in the integration process, is introduced. In this environment, the (expert) user can check for the ontology that is being produced, so he/she is able to refine his/her private ontology. Furthermore, the experts who take part of the ontology construction process are allowed to use their own terminology even for requesting information about the global-derived ontology until a specific instant after the integration.     

基于本体的飞机设计知识组织模型

为了解决飞机设计中知识信息量大和表现形式多样的问题,提出了一种基于本体的知识组织模型构建方法.起初论述了知识组织和本体的有关概念,然后在此基础上提出基于本体的ABC反馈方法,并构造飞机设计知识模型来验证研究成果的正确性和有效性,同时描述了基于本体构建知识模型的优点.     

An adaptive ontology mapping approach with neural network based constraint satisfaction

Ontology mapping seeks to find semantic correspondences between similar elements of different ontologies. It is a key challenge to achieve semantic interoperability in building the Semantic Web. This paper proposes a new generic and adaptive ontology mapping approach, called the PRIOR+, based on propagation theory, information retrieval techniques and artificial intelligence. The approach consists of three major modules, i.e., the IR-based similarity generator, the adaptive similarity filter and weighted similarity aggregator, and the neural network based constraint satisfaction solver. The approach first measures both linguistic and structural similarity of ontologies in a vector space model, and then aggregates them using an adaptive method based on their harmonies, which is defined as an estimator of performance of similarity. Finally to improve mapping accuracy the interactive activation and competition neural network is activated, if necessary, to search for a solution that can satisfy ontology constraints. The experimental results show that harmony is a good estimator of f-measure; the harmony based adaptive aggregation outperforms other aggregation methods; neural network approach significantly boosts the performance in most cases. Our approach is competitive with top-ranked systems on benchmark tests at OAEI campaign 2007, and performs the best on real cases in OAEI benchmark tests.     

Nonrigid motion analysis based on dynamic refinement of finiteelement models

We propose new algorithms for accurate nonrigid motion tracking. Given an initial model representing general knowledge of the object, a set of sparse correspondences, and incomplete or missing information about geometry or material properties, we can recover dense motion vectors using finite element models. The method is based on the iterative analysis of the differences between the actual and predicted behaviors. Unknown parameters are recovered using an iterative descent search for the best nonlinear finite element model that approximates nonrigid motion of the given object. During this search process, we not only estimate material properties, but also infer dense point correspondences from our initial set of sparse correspondences. Thus, during tracking, the model is refined which, in turn, improves tracking quality. Experimental results demonstrate the success of the proposed algorithm. Our work demonstrates the possibility of accurate quantitative analysis of nonrigid motion in range image sequences with objects consisting of multiple materials and 3D volumes     

Learning about evaluation and assessment: teacher's use of folksonomies and ontologies in an online narrative environment

Building ontology in the domain of human sciences can be a difficult process because of the different meanings given to the same key concepts in these disciplines: in fact, shared meaning is an important element in knowledge construction between members of a community. In this paper, we propose a participatory social environment called ‘EduOntoWiki’ where academic experts in the field of educational sciences develop lightweight ontologies that members of multiple communities of practice (teachers, trainers, etc.) can modify and integrate using a folksonomic and storytelling approach. In this way, real‐life narrative contexts become precious ‘tagged’ alternative representations that can contribute to the ontology construction process. Experience of the environment gave possible indications regarding its use in teachers' training courses and as a useful tool to discuss evaluation and assessment issues in teachers' professional practice.     

一种高精度鲁棒的基于直线对应的位姿估计迭代算法

2D-3D特征对应位姿估计问题算法多基于点对应,而基于直线对应求解比基于点对应求解更具有优势。从欧氏空间这一新颖角度出发,提出了一种从直线对应求解位姿估计问题的迭代算法,算法思想是先迭代求解出最优的旋转矩阵,然后再得到平移向量。针对不同的直线组情形,给出了相应的迭代初始值计算方法。仿真实验数据表明,算法具有对初始值较不敏感、高精度、鲁棒性好等特点。     

An ontology-based metamodel for multiagent-based simulations

Multiagent-based simulations enable us to validate different use-case scenarios in a lot of application domains. The idea is to develop a realistic virtual environment to test particular domain-specific procedures. This paper presents our general framework for interactive multiagent-based simulations in virtual environments. The major contribution of this paper is the integration of the notion of ontology as a core element to the design process of a behavioral simulation. The proposed metamodel describes the concepts of a multiagent simulation using situated agents moving in a semantically enriched 3D environment. The agents perceive the geometric and semantic data in the surrounding environment. They are also able to act in this environment by using high-level actions, which are described by the ontology of the environment. The concepts relating to the environment, the agent, and the entire simulation models are presented. Additionally, guidelines are given to exploit the simulation results to characterize the agents. Finally, a simple application of the metamodel is presented, based upon the use of Industry Foundation Classes.     

Using NLP Techniques to Identify Legal Ontology Components: Concepts and Relations

A method to identify ontology components is presented in this article. The method relies on Natural Language Processing (NLP) techniques to extract concepts and relations among these concepts. This method is applied in the legal field to build an ontology dedicated to information retrieval. Legal texts on which the method is performed are carefully chosen as describing and conceptualizing the legal domain. We suggest that this method can help legal ontology designers and may be used while building ontologies dedicated to other tasks than information retrieval.     

Legal concepts as inferential nodes and ontological categories

I shall compare two views of legal concepts: as nodes in inferential nets and as categories in an ontology (a conceptual architecture). Firstly, I shall introduce the inferential approach, consider its implications, and distinguish the mere possession of an inferentially defined concept from the belief in the concept’s applicability, which also involves the acceptance of the concept’s constitutive inferences. For making this distinction, the inferential and eliminative analysis of legal concepts proposed by Alf Ross will be connected to the views on theoretical concepts in science advanced by Frank Ramsey and Rudolf Carnap. Consequently, the mere comprehension of a legal concept will be distinguished from the application of the concept to a particular legal system, since application presupposes a doctrinal commitment, namely, the belief that the inferences constituting the concept hold in that system. Then, I shall consider how concepts can be characterised by defining the corresponding terms and placing them within an ontology. Finally, I shall argue that there is a tension between the inferential and the ontological approach, but that both need to be taken into account, to capture the meaning and the cognitive function of legal concepts.     

相似本体间属性映射方法的研究

在以往的本体映射技术的研究中，大多数的研究都集中于本体中的概念间的映射，而很少有人从事概念间属性映射的研究工作。然而对于一些复杂的嘘用，仅仪是概念之间的映射是不充分的。例如通过指定属性的约束条件来检索实例，就需要发现属性间的映射关系。另一方面，目前的很多本体映射方法都存在准确率低，启发信息少的问题，而属性间的映射关系可以被应用在辅助概念映射上边。所以希望能够将属性映射同概念映射集成，以达到更好的映射效果。因此该文对相似本体间的属性关系进行了研究，并在此基础上提出一种基于本体的属性映射方法。