Jeff Z. Pan

University of Aberdeen, Aberdeen, Scotland, United Kingdom

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Publications (150)14.52 Total impact

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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). · 1.00 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.
    Proceedings of the companion publication of the 23rd international conference on World wide web companion; 04/2014
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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
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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
  • 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
  • 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: 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.
    01/2013: pages 293-318; , ISBN: 978-3-642-31226-7
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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.
    01/2013: pages 219-252; , ISBN: 978-3-642-31226-7
  • 3nd Joint International Semantic Technology Conference (JIST2013); 01/2013
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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
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    ABSTRACT: Human computation systems that outsource tasks to the crowd often have to address issues associated with the quality of contributions. We are exploring the potential role of provenance to facilitate processes such as quality assessment within such systems. In this demo we present an application for managing traffic disruption reports generated by the crowd, and outline the technologies used to integrate provenance, linked data, and streams.
    Proceedings of the 4th international conference on Provenance and Annotation of Data and Processes; 06/2012
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    ABSTRACT: We describe our work exploring provenance within an open linked data ecosystem being developed in the travel/transport domain. We discuss techniques to infer provenance of sensor data, maintain provenance of third party data, and reference sources not available as linked data within a provenance record.
    Proceedings of the 4th international conference on Provenance and Annotation of Data and Processes; 06/2012
  • Jeff Z. Pan
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    ABSTRACT: John McCarthy defines Artificial Intelligence (AI) as the science and engineering of making intelligent machines, especially intelligent computer systems. To build intelligent systems, many of AI researchers, including experts in machine learning and data mining, focus on building "smart applications", which perform complex transformations and manipulations on raw data to discover insights and hidden patterns. On the other hand, the Semantic Web, which exploits the state of the art technologies (in particular ontology reasoning) from Knowledge Representation, a well established branch of AI, attempts to build intelligent systems based on "smart data", which is annotated and described by ontological vocabulary. The "smart application" and "smart data" directions constitute two major approaches to intelligent system. The former is generally the inductive approach that exploits the pattern of large amount of data. The later is generally the deductive approach that exploits the possibilities of even a small fraction of data. These two approaches complement each other in many complex scenarios: • mining technologies can be used to help discover knowledges from huge data sets; • semantic technologies can be used to check if the discovered knowledge are consistent with the existing knowledge and, if so, exploit them so as to meet application requirements.
    06/2012;
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    ABSTRACT: ABox abduction is an important reasoning facility in Description Logics DLs. It finds all minimal sets of ABox axioms, called abductive solutions, which should be added to a background ontology to enforce entailment of an observation which is a specified set of ABox axioms. However, ABox abduction is far from practical by now because there lack feasible methods working in finite time for expressive DLs. To pave a way to practical ABox abduction, this paper proposes a new problem for ABox abduction and a new method for computing abductive solutions accordingly. The proposed problem guarantees finite number of abductive solutions. The proposed method works in finite time for a very expressive DL,, which underpins the W3C standard language OWL 2, and guarantees soundness and conditional completeness of computed results. Experimental results on benchmark ontologies show that the method is feasible and can scale to large ABoxes.
    International journal on Semantic Web and information systems 04/2012; 8(2):1-33. · 0.25 Impact Factor
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    ABSTRACT: The six papers in this special issue depict the state of the art and practice of the impact of semantic technologies in the field of Software Engineering.
    IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) 01/2012; 42:1-2. · 2.55 Impact Factor
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    ABSTRACT: A matchmaking system for finding renting houses is required as the housing problem becomes serious in China and many people resort to rent a house. A semantic approach based on abductive conjunctive query answering (CQA) in Description Logic ontologies is exploited to provide more matches for a request about renting houses. Moreover, a matchmaking system based on this approach is developed. This demo will guide users to find suitable renting houses using this matchmaking system and show the advantages of the system.
    01/2012;
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    ABSTRACT: To perform matchmaking in Web-based scenarios where data are often incomplete, we propose an extended conjunctive query answering (CQA) problem, called abductive CQA problem, in Description Logic ontologies. Given a consistent ontology and a conjunctive query, the abductive CQA problem computes all abductive answers to the query in the ontology. An abductive answer is an answer to the query in some consistent ontology enlarged from the given one by adding a bounded number of individual assertions, where the individual assertions that can be added are confined by user-specified concept or role names. We also propose a new approach to matchmaking based on the abductive CQA semantics, in which offer information is expressed as individual assertions, request information is expressed as conjunctive queries, and matches for a request are defined as abductive answers to a conjunctive query that expresses the request. We propose a sound and complete method for computing all abductive answers to a conjunctive query in an ontology expressed in the Description Logic Program fragment of OWL 2 DL with the Unique Name Assumption. The feasibility of this method is demonstrated by a real-life application, rental matchmaking, which handles requests for renting houses.
    Proceedings of the 2011 joint international conference on The Semantic Web; 12/2011
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    ABSTRACT: DBpedia has been proved to be a successful structured knowledge base, and large scale Semantic Web data has been built by using DBpedia as the central interlinking-hubs of the Web of Data in English. But in Chinese, due to the heavily imbalance in size (no more than one tenth) between English and Chinese in Wikipedia, there are few Chinese linked data are published and linked to DBpedia, which hinders the structured knowledge sharing both within Chinese resources and cross-lingual resources. This paper aims at building large scale Chinese structured knowledge base from Hudong, which is one of the largest Chinese Wiki Encyclopedia websites. In this paper, an upper-level ontology schema in Chinese is first learned based on the category system and Infobox information in Hudong. Totally, there are 19542 concepts are inferred, which are organized in hierarchy with maximally 20 levels. 2381 properties with domain and range information are learned according to the attributes in the Hudong Infoboxes. Then, 802593 instances are extracted and described using the concepts and properties in the learned ontology. These extracted instances cover a wide range of things, including persons, organizations, places and so on. Among all the instances, 62679 of them are linked to identical instances in DBpedia. Moreover, the paper provides RDF dump or SPARQL to access the established Chinese knowledge base. The general upper-level ontology and wide coverage makes the knowledge base a valuable Chinese semantic resource. It not only can be used in Chinese linked data building, the fundamental work for building multi lingual knowledge base across heterogeneous resources of different languages, but also can largely facilitate many useful applications of large-scale knowledge base such as knowledge question-answering and semantic search.
    Proceedings of the 2011 joint international conference on The Semantic Web; 12/2011