DeMO: An Ontology for Discrete-event Modeling and Simulation

Several fields have created ontologies for their subdomains. For example, the biological sciences have developed extensive ontologies such as the Gene Ontology, which is considered a great success. Ontologies could provide similar advantages to the Modeling and Simulation community. They provide a way to establish common vocabularies and capture knowledge about a particular domain with community-wide agreement. Ontologies can support significantly improved (semantic) search and browsing, integration of heterogeneous information sources, and improved knowledge discovery capabilities. This paper discusses the design and development of an ontology for Modeling and Simulation called the Discrete-event Modeling Ontology (DeMO), and it presents prototype applications that demonstrate various uses and benefits that such an ontology may provide to the Modeling and Simulation community.     

Learning to Share Meaning in a Multi-Agent System

The development of the semantic Web will require agents to use common domain ontologies to facilitate communication of conceptual knowledge. However, the proliferation of domain ontologies may also result in conflicts between the meanings assigned to the various terms. That is, agents with diverse ontologies may use different terms to refer to the same meaning or the same term to refer to different meanings. Agents will need a method for learning and translating similar semantic concepts between diverse ontologies. Only until recently have researchers diverged from the last decade's common ontology paradigm to a paradigm involving agents that can share knowledge using diverse ontologies. This paper describes how we address this agent knowledge sharing problem of how agents deal with diverse ontologies by introducing a methodology and algorithms for multi-agent knowledge sharing and learning in a peer-to-peer setting. We demonstrate how this approach will enable multi-agent systems to assist groups of people in locating, translating, and sharing knowledge using our Distributed Ontology Gathering Group Integration Environment (DOGGIE) and describe our proof-of-concept experiments. DOGGIE synthesizes agent communication, machine learning, and reasoning for information sharing in the Web domain.     

Toward analyzing impact of disjoint axioms for merging heterogeneous ontologies

In recent years, the question on Automatic Ontology Merging (AOM) become challenging to address for the researchers. Our research and development for the Disjoint Knowledge Perservation based Automatic Ontology Merging (DKP-AOM) is a milestone in the same direction. This paper provides a more specific discussion about disjoint knowledge axioms in DKP-AOM and makes an assessment of our merge algorithm that looks-up within disjoint partitions of concept hierarchies of ontologies. The significant use of disjoint knowledge is corroborated by testing conference and vertebrate ontologies. The results reveal that disjoint knowledge axioms help identifying initial inaccurate mappings and remove ambiguity when the concept with same symbolic identifier has a different meaning in different local ontologies in the process of ontology merging. Disjoint axioms separate the knowledge in distinct chunks and enable concept matching within the boundaries of sub-hierarchies of the entire ontology concept hierarchy. While finding matches between concepts of ontologies, disjoint partitions with the disjoint knowledge about concepts in source ontologies minimize the search space and reduce the runtime complexity of ontology merging. We also discuss encouraging results obtained by our DKP-AOM system within the OAEI 2015 campaign.     

From Web Data to Visualization via Ontology Mapping

In this paper, we propose a novel approach for automatic generation of visualizations from domain‐specific data available on the web. We describe a general system pipeline that combines ontology mapping and probabilistic reasoning techniques. With this approach, a web page is first mapped to a Domain Ontology, which stores the semantics of a specific subject domain (e.g., music charts). The Domain Ontology is then mapped to one or more Visual Representation Ontologies, each of which captures the semantics of a visualization style (e.g., tree maps). To enable the mapping between these two ontologies, we establish a Semantic Bridging Ontology, which specifies the appropriateness of each semantic bridge. Finally each Visual Representation Ontology is mapped to a visualization using an external visualization toolkit. Using this approach, we have developed a prototype software tool, SemViz, as a realisation of this approach. By interfacing its Visual Representation Ontologies with public domain software such as ILOG Discovery and Prefuse, SemViz is able to generate appropriate visualizations automatically from a large collection of popular web pages for music charts without prior knowledge of these web pages.     

ADOL: a novel framework for automatic domain ontology learning

Ontology, as a semantic representation of a shared conceptualization, makes knowledge machine-readable and easy to spread. One of its typical applications is used to develop e-learning systems with Educational Ontology. Ontology can help students master knowledge architecture of required subjects and make scattered courseware more systematic. A big challenge is how to construct Educational Ontology to describe systematic knowledge of different subjects automatically. Currently, most of the ontologies are developed and extended manually, which requires the developers to possess certain professional knowledge and is time-consuming. In this paper, a framework to construct and extend Educational Ontology automatically is proposed.2 The proposed ontology learning framework, called ‘ADOL,’ can convert domain textbooks into a corresponding ontology automatically and efficiently. A case study on High School Physics shows that our approach is feasible and efficient.

     

Reasoning of fuzzy relational databases with fuzzy ontologies

A significant interest developed regarding the problem of describing databases with expressive knowledge representation techniques in recent years, so that database reasoning may be handled intelligently. Therefore, it is possible and meaningful to investigate how to reason on fuzzy relational databases (FRDBs) with fuzzy ontologies. In this paper, we first propose a formal approach and an automated tool for constructing fuzzy ontologies from FRDBs, and then we study how to reason on FRDBs with constructed fuzzy ontologies. First, we give their respective formal definitions of FRDBs and fuzzy Web Ontology Language (OWL) ontologies. On the basis of this, we propose a formal approach that can directly transform an FRDB (including its schema and data information) into a fuzzy OWL ontology (consisting of the fuzzy ontology structure and instance). Furthermore, following the proposed approach, we implement a prototype construction tool called FRDB2FOnto. Finally, based on the constructed fuzzy OWL ontologies, we investigate how to reason on FRDBs (e.g., consistency, satisfiability, subsumption, and redundancy) through the reasoning mechanism of fuzzy OWL ontologies, so that the reasoning of FRDBs may be done automatically by means of the existing fuzzy ontology reasoner.© 2012 Wiley Periodicals, Inc.     

RiMOM: A Dynamic Multistrategy Ontology Alignment Framework

Ontology alignment identifies semantically matching entities in different ontologies. Various ontology alignment strategies have been proposed; however, few systems have explored how to automatically combine multiple strategies to improve the matching effectiveness. This paper presents a dynamic multistrategy ontology alignment framework, named RiMOM. The key insight in this framework is that similarity characteristics between ontologies may vary widely. We propose a systematic approach to quantitatively estimate the similarity characteristics for each alignment task and propose a strategy selection method to automatically combine the matching strategies based on two estimated factors. In the approach, we consider both textual and structural characteristics of ontologies. With RiMOM, we participated in the 2006 and 2007 campaigns of the Ontology Alignment Evaluation Initiative (OAEI). Our system is among the top three performers in benchmark data sets.     

A Web Ontology Service to facilitate interoperability within a Spatial Data Infrastructure: Applicability to discovery

Ontologies are used within the context of Spatial Data Infrastructures to denote a formally represented knowledge that is used to improve data sharing and information retrieval. Given the increasing relevance of semantic interoperability in this context, this work presents the specification and development of a Web Ontology Service (WOS), based on the OGC Web Service Architecture specification, whose purpose is to facilitate the management and use of lexical ontologies. Additionally, this work shows how to integrate this service with Spatial Data Infrastructure discovery components in order to obtain a better classification of resources and an improvement in information retrieval performance.     

Ontology Libraries on the Web: Status and Prospects

This paper reviews the use of computer ontologies for Internet data linking and knowledge representation. We discuss a trend in organizing ontology libraries and servers for the joint development of ontologies and their application. Ontology libraries are regarded as public Web resources. We pay special attention to the design of ontology libraries, their debugging, and directions for their development.     

Ontology learning: state of the art and open issues

Ontology is one of the fundamental cornerstones of the semantic Web. The pervasive use of ontologies in information sharing and knowledge management calls for efficient and effective approaches to ontology development. Ontology learning, which seeks to discover ontological knowledge from various forms of data automatically or semi-automatically, can overcome the bottleneck of ontology acquisition in ontology development. Despite the significant progress in ontology learning research over the past decade, there remain a number of open problems in this field. This paper provides a comprehensive review and discussion of major issues, challenges, and opportunities in ontology learning. We propose a new learning-oriented model for ontology development and a framework for ontology learning. Moreover, we identify and discuss important dimensions for classifying ontology learning approaches and techniques. In light of the impact of domain on choosing ontology learning approaches, we summarize domain characteristics that can facilitate future ontology learning effort. The paper offers a road map and a variety of insights about this fast-growing field.     

语义集成:本体映射方法研究

本体是客观世界知识的表现形式,随着语义Web研究的深入,研究者们构建了越来越多的本体,如何实现本体之间的知识共享和重用,成为了语义Web发展的关键。文中对本体映射的方法进行了研究,系统阐述了本体及本体映射的定义、本体映射中的相似度计算和本体映射框架等。如何减少本体映射中的人工干预,实现本体的半自动化或自动化映射将是该领域的发展方向。     

A Web Ontology Service to facilitate interoperability within a Spatial Data Infrastructure: Applicability to discovery

Ontologies are used within the context of Spatial Data Infrastructures to denote a formally represented knowledge that is used to improve data sharing and information retrieval. Given the increasing relevance of semantic interoperability in this context, this work presents the specification and development of a Web Ontology Service (WOS), based on the OGC Web Service Architecture specification, whose purpose is to facilitate the management and use of lexical ontologies. Additionally, this work shows how to integrate this service with Spatial Data Infrastructure discovery components in order to obtain a better classification of resources and an improvement in information retrieval performance.     

本体进化及维护的研究总结

本体是共享概念的规范、精确的描述。本体本身并不是静态的模型,所以它必须能够捕获意思和关系的变化。这样,本体的映射和进化就成为本体学习和发展的一个基本任务。本体进化就是获得新的信息和知识时适当地维护和扩充已有本体。     

A simulation model for eliciting scheduling knowledge: an application to the precast manufacturing process

This paper discusses the process of eliciting scheduling knowledge from a simulation model and the development of a dynamic modelling approach to the scheduling process in the precast concrete industry. Due to the problems associated with eliciting scheduling knowledge from an ‘expert’ in the precast industry or perhaps in most of the manufacturing industries, simulation is used to complement human knowledge in this paper. Such knowledge will be used for online support to advise production schedulers and for further development of the simulation model by incorporating the knowledge in the model and making it more dynamic. The paper suggests that dynamic selection of scheduling rules during real-time operation has been recognised as a promising approach to the scheduling process in the precast industry. For this strategy to work effectively, sufficient knowledge is required to enable the model to predict the most effective scheduling rule to meet current factory status. The paper concludes that if the knowledge rules are used effectively, they could be a considerable managerial tool for exploring and improving managerial practices. Recommendations have been made regarding the development of a more realistic and practical scheduling system.     

Onyx: A new Canvas-based tool for visualizing biomedical and health ontologies

Ontologies provide formal, machine-readable, and human-interpretable representations of domain knowledge. Therefore, ontologies have come into question with the development of Semantic Web technologies. People who want to use ontologies need an understanding of the ontology, but this understanding is very difficult to attain if the ontology user lacks the background knowledge necessary to comprehend the ontology or if the ontology is very large. Thus, software tools that facilitate the understanding of ontologies are needed. Ontology visualization is an important research area because visualization can help in the development, exploration, verification, and comprehension of ontologies. This paper introduces the design of a new ontology visualization tool, which differs from traditional visualization tools by providing important metrics and analytics about ontology concepts and warning the ontology developer about potential ontology design errors. The tool, called Onyx, also has advantages in terms of speed and readability. Thus, Onyx offers a suitable environment for the representation of large ontologies, especially those used in biomedical and health information systems and those that contain many terms. It is clear that these additional functionalities will increase the value of traditional ontology visualization tools during ontology exploration and evaluation.     

An ontology-based framework to support intelligent data analysis of sensor measurements

In the past years, the large availability of sensed data highlighted the need of computer-aided systems that perform intelligent data analysis (IDA) over the obtained data streams. Temporal abstractions (TAs) are key to interpret the principle encoded within the data, but their usefulness depends on an efficient management of domain knowledge. In this article, an ontology-based framework for IDA is presented. It is based on a knowledge model composed by two existing ontologies (Semantic Sensor Network ontology (SSN), SWRL Temporal Ontology (SWRLTO)) and a new developed one: the Temporal Abstractions Ontology (TAO). SSN conceptualizes sensor measurements, thus enabling a full integration with semantic sensor web (SSW) technologies. SWRLTO provides temporal modeling and reasoning. TAO has been designed to capture the semantic of TAs. These ontologies have been aligned through DOLCE Ultra-Lite (DUL) upper ontology, boosting the integration with other domains. The resulting knowledge model has a modular design that facilitates the integration, exchange and reuse of its constitutive parts. The framework is sketched in a chemical plant case study. It is shown how complex temporal patterns that combine several variables and representation schemes can be used to infer process states and/or conditions.