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 biome...
详细信息
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, evolutionaryalgorithm (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 evolutionaryalgorithm (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.
作者：
Xue, XingsiChen, JunfengFujian 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 ...
详细信息
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, evolutionaryalgorithm (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 compactevolutionary 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.
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 ef...
详细信息
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. evolutionaryalgorithm (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.
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. Typical...
详细信息
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.
作者：
Xue, XingsiLiu, ShijianFujian 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
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 nam...
详细信息
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, evolutionaryalgorithm (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 compactevolutionary 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.
暂无评论