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24th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)

作者： Xue, Xingsi Tsai, Pei-Wei Fujian Univ Technol Fujian Key Lab Automot Elect & Elect Drive Fuzhou 350118 Fujian Peoples R China Fujian Univ Technol Intelligent Informat Proc Res Ctr Fuzhou 350118 Fujian Peoples R China Swinburne Univ Technol Dept Comp Sci & Software Engn Hawthorn Vic 3122 Australia

ISBN: (纸本)9783030604707;9783030604691

Literature Based Discovery (LBD) aims at building bridges between existing literatures and discovering new knowledge from them. Biomedical ontology is such a literature that provides an explicit specification on biomedical knowledge, i.e., the formal specification of the biomedical concepts and data, and the relationships between them. However, since biomedical ontologies are developed and maintained by different communities, the same biomedical information or knowledge could be defined with different terminologies or in different context, which makes the integration of them becomes a challenging problem. Biomedical ontology matching can determine the semantically identical biomedical concepts in different biomedical ontologies, which is regarded as an effective methodology to bridge the semantic gap between two biomedical ontologies. Currently, evolutionary algorithm (EA) is emerging as a good methodology for optimizing the ontology alignment. However, EA requires huge memory consumption and long runtime, which make EAbased matcher unable to efficiently match biomedical ontologies. To overcome these problems, in this paper, we define a discrete optimal model for biomedical ontology matching problem, and utilize a compact version of evolutionary algorithm (CEA) to solve it. In particular, CEA makes use of a Probability Vector (PV) to represent the population to save the memory consumption, and introduces a local search strategy to improve the algorithm's search performance. The experiment exploits Anatomy track, Large Biomed track and Disease and Phenotype track provided by the Ontology Alignment Evaluation Initiative (OAEI) to test our proposal's performance. The experimental results show that CEA-based approach can effectively reduce the runtime and memory consumption of EA-based matcher, and determine high-quality biomedical ontology alignments.

关键词： Literature Based Discovery Biomedical ontology matching compact evolutionary algorithm

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Using compact evolutionary Tabu Search algorithm for matching sensor ontologies

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SWARM AND evolutionary COMPUTATION 2019年 48卷 25-30页

作者： Xue, Xingsi Chen, Junfeng Fujian Univ Technol Coll Informat Sci & Engn Fuzhou 350118 Fujian Peoples R China Fujian Univ Technol Intelligent Informat Proc Res Ctr Fuzhou 350118 Fujian Peoples R China Fujian Univ Technol Fujian Prov Key Lab Big Data Min & Applicat Fuzhou 350118 Fujian Peoples R China Fujian Univ Technol Fujian Key Lab Automot Elect & Elect Drive Fuzhou 350118 Fujian Peoples R China Hohai Univ Coll IOT Engn Changzhou 213022 Jiangsu Peoples R China

To implement the semantic interoperability among intelligent sensor applications, it is necessary to match the identical entities across the sensor ontologies. Since sensor ontology matching problem requires matching thousands of sensor concepts and has many local optimal solutions, evolutionary algorithm (EA) becomes the state-of-the-art methodology for solving it. However, the premature convergence and long runtime are two drawbacks which make EA-based sensor ontology matchers incapable of effectively searching the optimal solution for sensor ontology matching problem. To improve the efficiency of EA-based sensor ontology matching technique, in this paper, a new optimal model of sensor ontology matching problem is first constructed, a novel sensor concept similarity measure is then presented to determine the identical sensor concepts, and finally, a problem-specific compact evolutionary Tabu Search algorithm (CETS) is presented to efficiently determine the sensor ontology alignment. In particular, CETS combines compact evolutionary algorithm (global search) and Tabu Search algorithm (local search), and this marriage between global search and local search allows keeping high solution diversity via PV (reducing the possibility of the premature convergence) and increasing the convergence speed via the local search (reducing the runtime). The experimental results show that comparing with the state-of-the-art sensor ontology matching techniques, CETS can more efficiently determine the high-quality alignments.

关键词： Semantic Sensor Web Sensor ontology matching compact evolutionary algorithm Tabu Search

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Ternary compound ontology matching for cognitive green computing

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2021年第4期18卷 4860-4870页

作者： Zheng, Wei-Min Chai, Qing-Wei Zhang, Jie Xue, Xingsi Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao 266590 Peoples R China Yulin Normal Univ Sch Comp Sci & Engn Yulin 537000 Peoples R China Fujian Univ Technol Sch Comp Sci & Math Fuzhou 350118 Peoples R China

Cognitive green computing (CGC) dedicates to study the designing, manufacturing, using and disposing of computers, servers and associated subsystems with minimal environmental damage. These solutions should provide efficient mechanisms for maximizing the efficiency of use of computing resources. evolutionary algorithm (EA) is a well-known global search algorithm, which has been successfully used to solve various complex optimization problems. However, a run of population-based EA often requires huge memory consumption, which limited their applications in the memory-limited hardware. To overcome this drawback, in this work, we propose a compact EA (CEA) for the sake of CGC, whose compact encoding and evolving mechanism is able to significantly reduce the memory consumption. After that, we use it to address the ternary compound ontology matching problem. Six testing cases that consist of nine ontologies are used to test CEA's performance, and the experimental results show its effectiveness.

关键词： cognitive green computing ternary compound ontology matching compact evolutionary algorithm

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Optimizing Ontology Alignment in Vector Space

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JOURNAL OF INTERNET TECHNOLOGY 2020年第1期21卷 15-22页

作者： Chu, Shu-Chuan XUe, Xingsi Pan, Jeng-Shyang Wu, Xiaojing Shandong Univ Sci & Technol Coll Comp Sci & Engn Jinan Peoples R China Fujian Univ Technol Fujian Key Lab Automot Elect & Elect Dr Fuzhou Peoples R China Fujian Univ Technol Coll Informat Sci & Engn Fuzhou Peoples R China Fujian Univ Technol Intelligent Informat Proc Res Ctr Fuzhou Peoples R China Fujian Univ Technol Fujian Prov Key Lab Big Data Min & Applicat Fuzhou Peoples R China

Ontology matching technique aims at determining the identical entities, which can effectively solve the ontology heterogeneity problem and implement the collaborations among ontology-based intelligent systems. Typically, an ontology consists of a set of concepts which are described by various properties, and they define a space such that each distinct concept and property represents one dimension in that space. Therefore, it is an effective way to model an ontology in a vector space, and use the vector space based similarity measure to calculate two entities' similarity. In this work, the entities' structure information is utilized to model an ontology in a vector space, and then, their linguistic information is used to reduce the number of dimensions, which can improve the efficiency of the similarity calculation and entity matching process. After that, a discrete optimization model is constructed for the ontology matching problem, and a compact evolutionary algorithm (cEA) based ontology matching technique is proposed to efficiently address it. The experiment uses the benchmark track provided by Ontology Alignment Evaluation Initiative (OAEI) to test our proposal's performance, and the comparing results with state-of-the-art ontology matching systems show that our approach can efficiently determine high-quality ontology alignments.

关键词： Ontology matching Vector space compact evolutionary algorithm

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Matching Sensor Ontologies Through compact evolutionary Tabu Search algorithm 11th

Matching Sensor Ontologies Through Compact Evolutionary Tabu...

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11th International Conference on Security, Privacy and Anonymity in Computation, Communication, and Storage (SpaCCS)

作者： Xue, Xingsi Liu, Shijian Fujian Univ Technol Coll Informat Sci & Engn Fuzhou 350118 Fujian Peoples R China Fujian Univ Technol Intelligent Informat Proc Res Ctr Fuzhou 350118 Fujian Peoples R China Fujian Univ Technol Fujian Prov Key Lab Big Data Min & Applicat Fuzhou 350118 Fujian Peoples R China Fujian Univ Technol Fujian Key Lab Automot Elect & Elect Drive Fuzhou 350118 Fujian Peoples R China

ISBN: (纸本)9783030053451;9783030053444

Although sensor ontologies are regarded as the solution to data heterogeneity on the Semantic Sensor Web (SSW), these sensor ontologies themselves introduce heterogeneity by defining the same entity with different names or in different ways. To solve this problem, it is necessary to determine the semantic identical entities between heterogeneous sensor ontologies, so-called sensor ontology matching. Due to the complexity of the sensor ontology matching process, evolutionary algorithm (EA) can present a good methodology for determining ontology alignments. To overcome the EA-based ontology matcher's shortcomings, i.e. premature convergence, long runtime and huge memory consumption, this paper present a compact evolutionary Tabu Search algorithm (CETS) to efficiently match the sensor ontologies. The experiment utilizes Ontology Alignment Evaluation Initiative (OAEI)'s bibliographic benchmark and library track, and two pairs of real sensor ontologies test CETS's performance. The experimental results show that CETS is both effective and efficient when matching ontologies with various scales and under different heterogeneous situations, and comparing with the state-of-the-art sensor ontology matching systems, CETS can significantly improve the ontology alignment's quality.

关键词： Semantic Sensor Web Sensor ontology matching compact evolutionary algorithm Tabu search

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