Using Memetic Algorithm for Instance Coreference Resolution

Xue, Xingsi; Wang, Yuping

doi:10.1109/tkde.2015.2475755

Cited by 102 publications

(40 citation statements)

References 26 publications

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“…Moreover, we will improve CcFA based approach to match the large-scale sensor ontologies, which is a challenge in the ontology matching domain. Another challenge in ontology matching domain is the problem of Instance Coreference Resolution (ICR) [53] in the sensor network domain, which requires matching large-scale sensor instances in the Linked Open Data cloud (LOD). Currently, there is no SIA-based technique that could effectively solve ICR, and we are also interested in addressing this challenge with CcFA.…”

Section: Discussionmentioning

confidence: 99%

Optimizing Sensor Ontology Alignment through Compact co-Firefly Algorithm

Xue

Chen

2020

Sensors

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Semantic Sensor Web (SSW) links the semantic web technique with the sensor network, which utilizes sensor ontology to describe sensor information. Annotating sensor data with different sensor ontologies can be of help to implement different sensor systems’ inter-operability, which requires that the sensor ontologies themselves are inter-operable. Therefore, it is necessary to match the sensor ontologies by establishing the meaningful links between semantically related sensor information. Since the Swarm Intelligent Algorithm (SIA) represents a good methodology for addressing the ontology matching problem, we investigate a popular SIA, that is, the Firefly Algorithm (FA), to optimize the ontology alignment. To save the memory consumption and better trade off the algorithm’s exploitation and exploration, in this work, we propose a general-purpose ontology matching technique based on Compact co-Firefly Algorithm (CcFA), which combines the compact encoding mechanism with the co-Evolutionary mechanism. Our proposal utilizes the Gray code to encode the solutions, two compact operators to respectively implement the exploiting strategy and exploring strategy, and two Probability Vectors (PVs) to represent the swarms that respectively focuses on the exploitation and exploration. Through the communications between two swarms in each generation, CcFA is able to efficiently improve the searching efficiency when addressing the sensor ontology matching problem. The experiment utilizes the Conference track and three pairs of real sensor ontologies to test our proposal’s performance. The statistical results show that CcFA based ontology matching technique can effectively match the sensor ontologies and other general ontologies in the domain of organizing conferences.

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Section: Discussionmentioning

confidence: 99%

Optimizing Sensor Ontology Alignment through Compact co-Firefly Algorithm

Xue

Chen

2020

Sensors

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“…Due to the complex and time-consuming nature of the ontology matching process, EA-based methods could present a good methodology for obtaining ontology alignments and indeed have already been applied to solve the ontology alignment problem by reaching acceptable results [ 10 ]. Different from other EA based approaches [ 11 – 13 ] which models the ontology alignment process as a meta-matching problem, i.e.…”

Section: Related Workmentioning

confidence: 99%

Using Compact Coevolutionary Algorithm for Matching Biomedical Ontologies

Xue

Chen

et al. 2018

Computational Intelligence and Neuroscience

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Over the recent years, ontologies are widely used in various domains such as medical records annotation, medical knowledge representation and sharing, clinical guideline management, and medical decision-making. To implement the cooperation between intelligent applications based on biomedical ontologies, it is crucial to establish correspondences between the heterogeneous biomedical concepts in different ontologies, which is so-called biomedical ontology matching. Although Evolutionary algorithms (EAs) are one of the state-of-the-art methodologies to match the heterogeneous ontologies, huge memory consumption, long runtime, and the bias improvement of the solutions hamper them from efficiently matching biomedical ontologies. To overcome these shortcomings, we propose a compact CoEvolutionary Algorithm to efficiently match the biomedical ontologies. Particularly, a compact EA with local search strategy is able to save the memory consumption and runtime, and three subswarms with different optimal objectives can help one another to avoid the solution's bias improvement. In the experiment, two famous testing cases provided by Ontology Alignment Evaluation Initiative (OAEI 2017), i.e. anatomy track and large biomed track, are utilized to test our approach's performance. The experimental results show the effectiveness of our proposal.

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“…Multiobjective optimization problems (MOPs) [1][2][3][4] which have conflicting objectives exist widely in the real world. Different from single-objective optimization problems, there are a series of optimal solutions for a MOP.…”

Section: Introductionmentioning

confidence: 99%

A Multiobjective Brain Storm Optimization Algorithm Based on Decomposition

Dai

Lei

2019

Complexity

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Brain storm optimization (BSO) algorithm is a simple and effective evolutionary algorithm. Some multiobjective brain storm optimization algorithms have low search efficiency. This paper combines the decomposition technology and multiobjective brain storm optimization algorithm (MBSO/D) to improve the search efficiency. Given weight vectors transform a multiobjective optimization problem into a series of subproblems. The decomposition technology determines the neighboring clusters of each cluster. Solutions of adjacent clusters generate new solutions to update population. An adaptive selection strategy is used to balance exploration and exploitation. Besides, MBSO/D compares with three efficient state-of-the-art algorithms, e.g., NSGAII and MOEA/D, on twenty-two test problems. The experimental results show that MBSO/D is more efficient than compared algorithms and can improve the search efficiency for most test problems.

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Using Memetic Algorithm for Instance Coreference Resolution

Cited by 102 publications

References 26 publications

Optimizing Sensor Ontology Alignment through Compact co-Firefly Algorithm

Optimizing Sensor Ontology Alignment through Compact co-Firefly Algorithm

Using Compact Coevolutionary Algorithm for Matching Biomedical Ontologies

A Multiobjective Brain Storm Optimization Algorithm Based on Decomposition

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