Jeff Z. Pan

University of Aberdeen, Aberdeen, Scotland, United Kingdom

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Publications (181)37.45 Total impact

  • Jeff Z. Pan · Yuan Ren · Yuting Zhao
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    ABSTRACT: Today's ontology applications require efficient and reliable description logic (DL) reasoning services. Expressive DLs usually have high worst case complexity while tractable DLs are restricted in terms of expressive power. This brings a new challenge: can users use expressive DLs to build their ontologies and still enjoy the efficient services as in tractable languages? Approximation has been considered as a solution to this challenge; however, traditional approximation approaches have limitations in terms of performance and usability. In this paper, we present a tractable approximate reasoning framework for OWL 2 that improves efficiency and guarantees soundness. Evaluation on ontologies from benchmarks and real-world use cases shows that our approach can do reasoning on complex ontologies efficiently with a high recall.
    No preview · Article · Jan 2016
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    Full-text · Conference Paper · Nov 2015
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    Full-text · Conference Paper · Nov 2015
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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.
    Full-text · Article · Jan 2015 · Lecture Notes in Computer Science
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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.
    Full-text · Article · Jan 2015 · Lecture Notes in Computer Science
  • Man Zhu · Zhiqiang Gao · Jeff Z. Pan · Yuting Zhao · Ying Xu · Zhibin Quan
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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.
    No preview · Article · Nov 2014 · Knowledge-Based Systems
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    Full-text · Article · Oct 2014 · International journal on Semantic Web and information systems
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    Jun Zhao · Honghan Wu · Jeff Z Pan

    Full-text · Conference Paper · Jul 2014
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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.
    No preview · Article · May 2014 · Computational Intelligence
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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.
    No preview · Conference Paper · Apr 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.
    No preview · Chapter · Jan 2014
  • 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.
    No preview · Conference Paper · Dec 2013
  • Man Zhu · Zhiqiang Gao · Jeff Z. Pan · Yuting Zhao · Ying Xu · Zhibin Quan
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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.
    No preview · Conference Paper · Nov 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.
    No preview · Conference Paper · Nov 2013
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    ABSTRACT: Decision making processes over large military coalition networks involve querying over multiple dynamic and distributed data sources. Data that is implicit, but relevant to the decision making process, can be inferred by ontological reasoning. Evaluation of such reasoning tasks in a decentralized setting involves redistribution of data across the network to answer multiple join sub-queries. However, it is important to minimize the associated communication costs while querying over constrained military coalition networks with limited bandwidth. We present a baseline approach for distributed reasoning over a set of nodes of a peer-to-peer lightweight dynamic distributed federated database, GaianDB. We present a scalability study of the baseline approach with varying size of data and varying number of data sources. We also present our experience in identifying the performance bottlenecks, and discuss possible optimization techniques to minimize reasoning and communication costs.
    Full-text · Conference Paper · Oct 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.
    No preview · Conference Paper · Aug 2013
  • Zhigang Wang · Zhixing Li · Juanzi Li · Jie Tang · Jeff Z. Pan
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    ABSTRACT: Wikipedia infoboxes are a valuable source of structured knowledge for global knowledge sharing. However, infobox information is very incomplete and imbalanced among the Wikipedias in different languages. It is a promising but challenging problem to utilize the rich structured knowledge from a source language Wikipedia to help complete the missing infoboxes for a target language. In this paper, we formulate the problem of cross-lingual knowledge extraction from multilingual Wikipedia sources, and present a novel framework, called Wiki-CiKE, to solve this problem. An instancebased transfer learning method is utilized to overcome the problems of topic drift and translation errors. Our experimental results demonstrate that WikiCiKE outperforms the monolingual knowledge extraction method and the translation-based method.
    No preview · Conference Paper · Aug 2013
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    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.
    Full-text · Conference Paper · Jun 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.
    No preview · Conference Paper · May 2013
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    Katja Siegemund · Yuting Zhao · Jeff Z. Pan · Uwe Aßmann

    Full-text · Dataset · Apr 2013