Jeff Z. Pan

University of Aberdeen, Aberdeen, Scotland, United Kingdom

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Publications (168)26.9 Total impact

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    ABSTRACT: As mobile devices proliferate and their computational power has increased rapidly over recent years, mobile applications have become a popular choice for visitors to enhance their travelling experience. However, most tourist mobile apps currently use narratives generated specifically for the app and often require a reliable Internet connection to download data from the cloud. These requirements are difficult to achieve in rural settings where many interesting cultural heritage sites are located. Although Linked Data has become a very popular format to preserve historical and cultural archives, it has not been applied to a great extent in tourist sector. In this paper we describe an approach to using Linked Data technology for enhancing visitors' experience in rural settings. In particular, we present CURIOS Mobile, the implementation of our approach and an initial evaluation from a case study conducted in the Western Isles of Scotland.
    Lecture Notes in Computer Science 01/2015; 8943:129. DOI:10.1007/978-3-319-15615-6_10 · 0.51 Impact Factor
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    ABSTRACT: This paper presents a lightweight approach to representing inexact dates on the semantic web, in that it imposes minimal ontological commitments on the ontology author and provides data that can be queried using standard approaches. The approach is presented in the context of a significant need to represent inexact dates but the heavyweight nature of existing proposals which can handle such information. Approaches to querying the represented information and an example user interface for creating such information are presented.
    Lecture Notes in Computer Science 01/2015; 8943:187. DOI:10.1007/978-3-319-15615-6_14 · 0.51 Impact Factor
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    ABSTRACT: In this work we deal with the problem of TBox learning from incomplete semantic web data. TBox, or conceptual schema, is the backbone of a Description Logic (DL) ontology, but is always difficult to be obtained. Existing approaches either fail in getting correct results under incompleteness or learn results that are not enough to resolve the incompleteness. We propose to transform TBox learning in DL into inference in the extension of Bayesian Description Logic Network (abbreviated as BelNet+), whereby the structure in the data is leveraged when evaluating the relationships between two concepts. BelNet+, integrating the probabilistic inference capability of Bayesian Networks with the logical formalism of DL ontologies – Description Logics, supports promising inference. In this paper, we firstly explain the details of BelNet+ and introduce a TBox learning approach based on BelNet+. In order to overcome the drawbacks of current evaluation metrics, we then propose a novel evaluation framework conforming to the Open World Assumption (OWA) generally made in the semantic web. Finally the results from empirical studies on comparisons with the state-of-the-art TBox learners verify the effectiveness of our approach.
    Knowledge-Based Systems 11/2014; 75. DOI:10.1016/j.knosys.2014.11.004 · 3.06 Impact Factor
  • International journal on Semantic Web and information systems 10/2014; 10(4):1-16. DOI:10.4018/ijswis.2014100101 · 0.39 Impact Factor
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    ABSTRACT: Forgetting is an important tool for reducing ontologies by eliminating some redundant concepts and roles while preserving sound and complete reasoning. Attempts have previously been made to address the problem of forgetting in relatively simple description logics (DLs), such as DL-Lite and extended . However, the issue of forgetting for ontologies in more expressive DLs, such as and OWL DL, is largely unexplored. In particular, the problem of characterizing and computing forgetting for such logics is still open. In this paper, we first define semantic forgetting about concepts and roles in ontologies and state several important properties of forgetting in this setting. We then define the result of forgetting for concept descriptions in , state the properties of forgetting for concept descriptions, and present algorithms for computing the result of forgetting for concept descriptions. Unlike the case of DL-Lite, the result of forgetting for an ontology does not exist in general, even for the special case of forgetting in TBoxes. This makes the problem of computing the result of forgetting in more challenging. We address this problem by defining a series of approximations to the result of forgetting for ontologies and studying their properties. Our algorithms for computing approximations can be directly implemented as a plug-in of an ontology editor to enhance its ability of managing and reasoning in (large) ontologies.
    Computational Intelligence 05/2014; 30(2). DOI:10.1111/j.1467-8640.2012.00442.x · 0.87 Impact Factor
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    ABSTRACT: Nowadays, people are increasingly concerned about smog disaster and the caused health hazard. However, the current methods for big smog analysis are usually based on the traditional lagging data sources or merely adopt physical environment observations, which limit the methods' accuracy and usability. The discipline of Web Science, the research fields of which include web of people and web of devices, provides real time web data as well as novel web data analysis approaches. In this paper, both social web data and device web data are proposed for smog disaster analysis. Firstly, we utilize social web data to define and calculate Individual Public Health Indexes (IPHIs) for smog caused health hazard quantification. Secondly, we integrate social web data and device web data to build standard health hazard rating reference and train smog-health models for health hazard prediction. Finally, we apply the rating reference and models to online and location-sensitive smog disaster monitoring, which can better guide people's behaviour and government's strategy design for disaster mitigation.
    Web Science Track of The 23rd international conference on World Wide Web; 04/2014
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    ABSTRACT: Datalog+/- is a family of emerging ontology languages that can be used for representing and reasoning over lightweight ontologies in Semantic Web. In this paper, we propose an approach to performing belief base revision for Datalog+/- ontologies. We define a kernel based belief revision operator for Datalog+/- and study its properties using extended postulates, as well as an algorithm to revise Datalog+/- ontologies. Finally, we give the complexity results by showing that query answering for a revised linear Datalog+/- ontology is tractable.
    01/2014: pages 175-186;
  • Akanimo Samuel Okure, Jeff Z. Pan
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    ABSTRACT: The availability of streaming information is progressively increasing, thanks to the knowledge management technologies such as ontologies that captures knowledge about this information. The semantic web initiative provides standards such as RDF and OWL, the two most powerful Semantic Web knowledge representation languages. The OWL adds semantics to schemas, and allows for more expressivity than RDF. As far as we know, there are existing querying services, designed for an swering queries over streaming data in RDF format, but there is none for the OWL EL ontological stream, in order to address this short comings for OWL EL ontological streams, in this paper, we focus primarily on the problem of querying and reasoning over OWL ontological streams, specifically how to design and evaluate Continuous SPARQL query over OWL EL ontological streams, in which the queries are evaluated under more expressive semantics of OWL. We address semantic issues, by introducing the add and erase semantics, maintaining relevant erasure for special use. We propose a general and flexible architecture, for querying more expressive OWL ontological streams with the help of a stream reasoner in near real time. This is our first step towards the design and implementation of such a system.
    2013 International Conference on Advanced Computer Science Applications and Technologies (ACSAT); 12/2013
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    ABSTRACT: Recent years have seen a dramatic growth of semantic web on the data level, but unfortunately not on the schema level, which contains mostly concept hierarchies. Theshortage of schemas makes the semantic web data difficult to be used in many semantic web applications, so schemas learningfrom semantic web data becomes an increasingly pressing issue. In this paper we propose a novel schemas learning approach -BelNet, which combines description logics (DLs) with Bayesian networks. In this way BelNet is capable to understand andcapture the semantics of the data on the one hand, and tohandle incompleteness during the learning procedure on theother hand. The main contributions of this work are: (i)we introduce the architecture of BelNet, and correspondinglypropose the ontology learning techniques in it, (ii) we compare the experimental results of our approach with the state-of-the-art ontology learning approaches, and provide discussions from different aspects.
    Proceedings of the 2013 IEEE 25th International Conference on Tools with Artificial Intelligence; 11/2013
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    ABSTRACT: Recently, the appearing disaster of severe smog has been attacking many cities in China such as the capital Beijing. The chief culprit of China smog, namely PM2.5, is affected by various factors including air pollutants, weather, climate, geographical location, urbanization, etc. To analyze the factors, we collect about 35,000,000 air quality records and about 30,000,000 weather records from the sensors in 77 China's cities in 2013. Moreover, two big data sets named Geoname and DBPedia are also combined for the data of climate, geographical location and urbanization. To deal with big spatio-temporal data for big smog analysis, we propose a MapReduce-based framework named BigSmog. It mainly conducts parallel correlation analysis of the factors and scalable training of artificial neural networks for spatio-temporal approximation of the concentration of PM2.5. In the experiments, BigSmog displays high scalability for big smog analysis with big spatio-temporal data. The analysis result shows that the air pollutants influence the short-term concentration of PM2.5 more than the weather and the factors of geographical location and climate rather than urbanization play a major role in determining a city's long-term pollution level of PM2.5. Moreover, the trained ANNs can accurately approximate the concentration of PM2.5.
    Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data; 11/2013
  • Freddy Lécué, Jeff Z. Pan
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    ABSTRACT: Recently, ontology stream reasoning has been introduced as a multidisciplinary approach, merging synergies from Artificial Intelligence, Database, World-Wide-Web to reason on semantic augmented data streams. Although knowledge evolution and real-time reasoning have been largely addressed in ontology streams, the challenge of predicting its future (or missing) knowledge remains open and yet unexplored. We tackle predictive reasoning as a correlation and interpretation of past semantics-augmented data over exogenous ontology streams. Consistent predictions are constructed as Description Logics entailments by selecting and applying relevant cross-streams association rules. The experiments have shown accurate prediction with real and live stream data from Dublin City in Ireland.
    Proceedings of the Twenty-Third international joint conference on Artificial Intelligence; 08/2013
  • Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers); 08/2013
  • Jeff Z. Pan, Yuan Ren, Honghan Wu, Man Zhu
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    ABSTRACT: Due to the increasing volume of and interconnections between semantic datasets, it becomes a challenging task for novice users to know what are included in a dataset, how they can make use of them, and particularly, what queries should be asked. In this paper we analyse several types of candidate insightful queries and propose a framework to generate such queries and identify their relations. To verify our approach, we implemented our framework and evaluated its performance with benchmark and real world datasets.
    Proceedings of the seventh international conference on Knowledge capture; 06/2013
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    ABSTRACT: A limitation of standard Description Logics is its inability to reason with uncertain and vague knowledge. Although probabilistic and fuzzy extensions of DLs exist, which provide an explicit representation of uncertainty, they do not provide an explicit means for reasoning about second order uncertainty. Dempster-Shafer theory of evidence (DST) overcomes this weakness and provides means to fuse and reason about uncertain information. In this paper, we combine DL-Lite with DST to allow scalable reasoning over uncertain semantic knowledge bases. Furthermore, our formalism allows for the detection of conflicts between the fused information and domain constraints. Finally, we propose methods to resolve such conflicts through trust revision by exploiting evidence regarding the information sources. The effectiveness of the proposed approaches is shown through simulations under various settings.
    SPIE Defense, Security, and Sensing; 05/2013
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    ABSTRACT: Processes in software development generally have two facets. They can be model objects, as described in Sect. 4.2, and also workflows, as described in Sect. 4.3. In this chapter, we analyse typical problems in process modelling and develop ontology reasoning technologies to address them in the ODSD infrastructure. We show how different ontological representation of process models can be constructed for different purposes and how reasoning can be applied to guarantee the consistency of models.
    Ontology-Driven Software Development, Edited by Jeff Z. Pan, Steffen Staab, Uwe Aßmann, Jürgen Ebert, Yuting Zhao, 01/2013: chapter Ontology Reasoning for Process Models: pages 219-252; Springer Berlin Heidelberg., ISBN: 978-3-642-31226-7
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    ABSTRACT: The web of data has continued to expand thanks to the principles of Linked Data, increasing its impact on the web both in its depth and range of data sources. However tools allowing ordinary web users to contribute to this web of data are still lacking. In this paper we propose Linked Data CMS, an approach allowing existing web content management system (CMS) software to be configured to display a web site based on a group of ontology classes, by making use of a configuration to map ontological entities to CMS entities. We have implemented a prototype of our Linked Data CMS approach using the popular Drupal CMS. This approach provides the tools for semantic web application developers to rapidly develop an entire website based on linked data, while allowing ordinary web users to contribute directly to the web of data using familiar CMS tools.
    3nd Joint International Semantic Technology Conference (JIST2013); 01/2013
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    ABSTRACT: This chapter reports about the software process guidance in ontology-driven software development (ODSD), one of the core ontology-enabled services of the ODSD environments. Ontology-driven software process guidance amounts to a significant step forward in software engineering in general (cf. Fig. 1.1 on p. 3). Its role is to guide developers through a complex software development process by providing information about the consistency of artefacts and about the tasks to be accomplished to reach a particular development goal.
    Ontology-Driven Software Development, Edited by Jeff Z. Pan, Steffen Staab, Uwe Aßmann, Jürgen Ebert, Yuting Zhao, 01/2013: chapter Ontology-Guided Software Engineering in the MOST Workbench: pages 293-318; Springer Berlin Heidelberg., ISBN: 978-3-642-31226-7
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    ABSTRACT: In order to directly reason over inconsistent OWL 2 DL ontologies, this paper considers linear order inference which comes from propositional logic. Consequences of this inference in an inconsistent ontology are defined as consequences in a certain consistent sub-ontology. This paper proposes a novel framework for compiling an OWL 2 DL ontology to a Horn propositional program so that the intended consistent sub-ontology for linear order inference can be approximated from the compiled result in polynomial time. A tractable method is proposed to realize this framework. It guarantees that the compiled result has a polynomial size. Experimental results show that the proposed method computes the exact intended sub-ontology for almost all test cases, while it is significantly more efficient and scalable than state-of-the-art exact methods.
    Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01; 12/2012
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    ABSTRACT: Requirements Engineering (RE) is essential to a software project. As the result of RE process, software Requirement Speci�ca- tions (SRS) should be consistent, correct and complete. However, the acquisition, speci�cation and evolution of requirements from dierent stakeholders or sources usually leads to incomplete, ambiguous, and faulty requirements. This may become an incalculable risk for the whole project and a disaster for the �nal software product. In this paper we present a method to improve the quality of a SRS semi-automatically. Facilitated by ontology reasoning techniques, we describe how to detect and repair faulty information in the SRS. Furthermore, we also provide various metrics to measure the quality of the SRS at any time during the RE process. Finally, we generalize our approach to be applicable for any information captured in an ontology.
    Proceedings of the 8th International Workshop on Semantic Web Enabled Software Engineering (SWESE 2012; 12/2012