Journal articles: 'Instance matching'

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Xue, Xingsi, and Jianhua Liu. "A Compact Hybrid Evolutionary Algorithm for Large Scale Instance Matching in Linked Open Data Cloud." International Journal on Artificial Intelligence Tools 26, no. 04 (2017): 1750013. http://dx.doi.org/10.1142/s0218213017500130.

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Establishing correct links among the coreference ontology instances is critical to the success of Linked Open Data (LOD) cloud. However, because of the high level heterogeneity and large scale instance set, matching the coreference instances in LOD cloud is an error prone and time consuming task. To this end, in this work, we present an asymmetrical profile-based similarity measure for instance matching task, construct new optimal models for schema-level and instance-level matching problems, and propose a compact hybrid evolutionary algorithm based ontology matching approach to solve the large scale instance matching problem in LOD cloud. Finally, the experimental results of comprising our approach with the states of the art systems on the instance matching track of OAEI 2015 and real-world datasets show the effectiveness of our approach.

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LENNON, CRAIG, and BORIS PITTEL. "On the Likely Number of Solutions for the Stable Marriage Problem." Combinatorics, Probability and Computing 18, no. 3 (2009): 371–421. http://dx.doi.org/10.1017/s0963548308009607.

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An instance of a size-n stable marriage problem involves n men and n women, each individually ranking all members of opposite sex in order of preference as a potential marriage partner. A complete matching, a set of n marriages, is called stable if no unmatched man and woman prefer each other to their partners in the matching. It is known that, for every instance of marriage partner preferences, there exists at least one stable matching, and that there are instances with exponentially many stable matchings. Our focus is on a random instance chosen uniformly from among all (n!)2n possible instances. The second author had proved that the expected number of stable marriages is of order nlnn, while its likely value is of order n1/2−o(1) at least. In this paper the second moment of that number is shown to be of order (nlnn)2. The combination of the two moment estimates implies that the fraction of problem instances with roughly cnlnn solutions is at least 0.84. Whether this fraction is asymptotic to 1 remains an open question.

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Schopman, Balthasar, Shenghui Wang, Antoine Isaac, and Stefan Schlobach. "Instance-Based Ontology Matching by Instance Enrichment." Journal on Data Semantics 1, no. 4 (2012): 219–36. http://dx.doi.org/10.1007/s13740-012-0011-z.

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Zhu, Hongming, Xiaowen Wang, Yizhi Jiang, Hongfei Fan, Bowen Du, and Qin Liu. "FTRLIM: Distributed Instance Matching Framework for Large-Scale Knowledge Graph Fusion." Entropy 23, no. 5 (2021): 602. http://dx.doi.org/10.3390/e23050602.

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Instance matching is a key task in knowledge graph fusion, and it is critical to improving the efficiency of instance matching, given the increasing scale of knowledge graphs. Blocking algorithms selecting candidate instance pairs for comparison is one of the effective methods to achieve the goal. In this paper, we propose a novel blocking algorithm named MultiObJ, which constructs indexes for instances based on the Ordered Joint of Multiple Objects’ features to limit the number of candidate instance pairs. Based on MultiObJ, we further propose a distributed framework named Follow-the-Regular-Leader Instance Matching (FTRLIM), which matches instances between large-scale knowledge graphs with approximately linear time complexity. FTRLIM has participated in OAEI 2019 and achieved the best matching quality with significantly efficiency. In this research, we construct three data collections based on a real-world large-scale knowledge graph. Experiment results on the constructed data collections and two real-world datasets indicate that MultiObJ and FTRLIM outperform other state-of-the-art methods.

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Nguyen, Thành, and Rakesh Vohra. "Near-Feasible Stable Matchings with Couples." American Economic Review 108, no. 11 (2018): 3154–69. http://dx.doi.org/10.1257/aer.20141188.

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The National Resident Matching program seeks a stable matching of medical students to teaching hospitals. With couples, stable matchings need not exist. Nevertheless, for any student preferences, we show that each instance of a matching problem has a “nearby” instance with a stable matching. The nearby instance is obtained by perturbing the capacities of the hospitals. In this perturbation, aggregate capacity is never reduced and can increase by at most four. The capacity of each hospital never changes by more than two. (JEL C78, D47, I11, J41, J44)

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Moreno-Scott, Jorge Humberto, José Carlos Ortiz-Bayliss, Hugo Terashima-Marín, and Santiago Enrique Conant-Pablos. "Experimental Matching of Instances to Heuristics for Constraint Satisfaction Problems." Computational Intelligence and Neuroscience 2016 (2016): 1–15. http://dx.doi.org/10.1155/2016/7349070.

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Constraint satisfaction problems are of special interest for the artificial intelligence and operations research community due to their many applications. Although heuristics involved in solving these problems have largely been studied in the past, little is known about the relation between instances and the respective performance of the heuristics used to solve them. This paper focuses on both the exploration of the instance space to identify relations between instances and good performing heuristics and how to use such relations to improve the search. Firstly, the document describes a methodology to explore the instance space of constraint satisfaction problems and evaluate the corresponding performance of six variable ordering heuristics for such instances in order to find regions on the instance space where some heuristics outperform the others. Analyzing such regions favors the understanding of how these heuristics work and contribute to their improvement. Secondly, we use the information gathered from the first stage to predict the most suitable heuristic to use according to the features of the instance currently being solved. This approach proved to be competitive when compared against the heuristics applied in isolation on both randomly generated and structured instances of constraint satisfaction problems.

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Mehdi, Osama A., Hamidah Ibrahim, and Lilly Suriani Affendey. "Instance based Matching using Regular Expression." Procedia Computer Science 10 (2012): 688–95. http://dx.doi.org/10.1016/j.procs.2012.06.088.

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Dorneles, Carina Friedrich, Rodrigo Gonçalves, and Ronaldo dos Santos Mello. "Approximate data instance matching: a survey." Knowledge and Information Systems 27, no. 1 (2010): 1–21. http://dx.doi.org/10.1007/s10115-010-0285-0.

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Araujo, Samur, Duc Thanh Tran, Arjen P. de Vries, and Daniel Schwabe. "SERIMI: Class-Based Matching for Instance Matching Across Heterogeneous Datasets." IEEE Transactions on Knowledge and Data Engineering 27, no. 5 (2015): 1397–440. http://dx.doi.org/10.1109/tkde.2014.2365779.

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Nguyen, Khai, and Ryutaro Ichise. "Automatic Schema-Independent Linked Data Instance Matching System." International Journal on Semantic Web and Information Systems 13, no. 1 (2017): 82–103. http://dx.doi.org/10.4018/ijswis.2017010106.

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The goal of linked data instance matching is to detect all instances that co-refer to the same objects in two linked data repositories, the source and the target. Since the amount of linked data is rapidly growing, it is important to automate this task. However, the difference between the schemata of source and target repositories remains a challenging barrier. This barrier reduces the portability, accuracy, and scalability of many proposed approaches. The authors present automatic schema-independent interlinking (ASL), which is a schema-independent system that performs instance matching on repositories with different schemata, without prior knowledge about the schemata. The key improvements of ASL compared to previous systems are the detection of useful attribute pairs for comparing instances, an attribute-driven token-based blocking scheme, and an effective modification of existing string similarities. To verify the performance of ASL, the authors conducted experiments on a large dataset containing 246 subsets with different schemata. The results show that ASL obtains high accuracy and significantly improves the quality of discovered coreferences against recently proposed complex systems.

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Chen, Di, Shanshan Zhang, Wanli Ouyang, Jian Yang, and Bernt Schiele. "Hierarchical Online Instance Matching for Person Search." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (2020): 10518–25. http://dx.doi.org/10.1609/aaai.v34i07.6623.

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Person Search is a challenging task which requires to retrieve a person's image and the corresponding position from an image dataset. It consists of two sub-tasks: pedestrian detection and person re-identification (re-ID). One of the key challenges is to properly combine the two sub-tasks into a unified framework. Existing works usually adopt a straightforward strategy by concatenating a detector and a re-ID model directly, either into an integrated model or into separated models. We argue that simply concatenating detection and re-ID is a sub-optimal solution, and we propose a Hierarchical Online Instance Matching (HOIM) loss which exploits the hierarchical relationship between detection and re-ID to guide the learning of our network. Our novel HOIM loss function harmonizes the objectives of the two sub-tasks and encourages better feature learning. In addition, we improve the loss update policy by introducing Selective Memory Refreshment (SMR) for unlabeled persons, which takes advantage of the potential discrimination power of unlabeled data. From the experiments on two standard person search benchmarks, i.e. CUHK-SYSU and PRW, we achieve state-of-the-art performance, which justifies the effectiveness of our proposed HOIM loss on learning robust features.

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Aydar, Mehmet, and Serkan Ayvaz. "A Suggestion-Based RDF Instance Matching System." International Journal of Computer Theory and Engineering 9, no. 5 (2017): 380–84. http://dx.doi.org/10.7763/ijcte.2017.v9.1170.

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Mogahed, Alzeber, A. Alwan Ali, Nordin Azlin, and Zaid Abualkishik Abedallah. "An Empirical Comparative Study of Instance-based Schema Matching." Indonesian Journal of Electrical Engineering and Computer Science 10, no. 3 (2018): 1266–77. https://doi.org/10.11591/ijeecs.v10.i3.pp1266-1277.

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The main issue concern of schema matching is how to support the merging decision by providing matching between attributes of different schemas. There have been many works in the literature toward utilizing database instances to detect the correspondence between attributes. Most of these previous works aim at improving the match accuracy. We observed that no technique managed to provide an accurate matching for different types of data. In other words, some of the techniques treat numeric values as strings. Similarly, other techniques process textual instance, as numeric, and this negatively influences the process of discovering the match and compromising the matching result. Thus, a practical comparative study between syntactic and semantic techniques is needed. The study emphasizes on analyzing these techniques to determine the strengths and weaknesses of each technique. This paper aims at comparing two different instance-based matching techniques, namely: (i) regular expression and (ii) Google similarity to identify the match between attributes. Several analyses have been conducted on real and synthetic data sets to evaluate the performance of these techniques with respect to Precision (P), Recall (R) and F-Measure.

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Ghemmaz, Wafa, Fouzia Benchikha, and Maroua Bouzid. "Reusing Alignments for Discovering Instances Correspondences." International Journal of Web-Based Learning and Teaching Technologies 16, no. 4 (2021): 60–95. http://dx.doi.org/10.4018/ijwltt.20210701.oa5.

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Recently, instance matching has become a key technology to achieve interoperability over datasets, especially in linked data. Due the rapid growth of published datasets, it attracts increasingly more research interest. In this context, several approaches have been proposed. However, they do not perform well since the problem of matching instances that possess different descriptions is not addressed. On the other hand, the usage of the identity link owl:sameAs is generally predominant in linking correspondences. Unfortunately, many existing identity links are misused. In this paper, the authors discuss these issues and propose an original instance matching approach aiming to match instances that hold diverse descriptions. Furthermore, a novel link named ViewSameAs is proposed. The key improvement compared to existing approaches is alignment reuse. Thus, two novel methods are introduced: ViewSameAs-based clustering and alignment reuse based on metadata. Experiments on datasets by considering those of OAEI show that the proposed approach achieves satisfying and highly accuracy results.

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Han, Yu Xiang. "A Novel Method for Instance Level Schema Matching." Advanced Materials Research 791-793 (September 2013): 1283–88. http://dx.doi.org/10.4028/www.scientific.net/amr.791-793.1283.

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nformation integration refers to the problem of merging, coalescing and transforming autonomous heterogeneous data sources into a single global homogeneous database and providing a unified view of these data for future query processing purposes. One of the fundamental operations in the integration process is schema matching, which takes two schemas as input and produces a mapping between the attributes of the two schemas that correspond semantically to each other. Matching techniques can be grouped into two broad categories: schema-level matching and instance-level matching. In schema-level matching, we consider only the properties of schema elements, such as names, descriptions, data types, constraints and structures. For each match candidate pair of attributes, the degree of similarity is estimated by a normalized numeric value between 0 and 1. On the other hand, instance-level matching employs information available in the data contents of each table to determine the relationship between any two attributes. In this paper, we propose a statistical model to compare the likeliness of two lists of values under two attributes from separate databases, in order to derive the similarity ratio of the two attributes. Our framework provides efficient procedures to compute the degree ratio using statistical coefficients for both categorical and numeric attributes.

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Wei, Zhongcheng, Wenjie Guo, Yunping Zhang, Jieying Zhang, and Jijun Zhao. "Bidirectional matching and aggregation network for few-shot relation extraction." PeerJ Computer Science 9 (March 6, 2023): e1272. http://dx.doi.org/10.7717/peerj-cs.1272.

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Few-shot relation extraction is used to solve the problem of long tail distribution of data by matching between query instances and support instances. Existing methods focus only on the single direction process of matching, ignoring the symmetry of the data in the process. To address this issue, we propose the bidirectional matching and aggregation network (BMAN), which is particularly powerful when the training data is symmetrical. This model not only tries to extract relations for query instances, but also seeks relational prototypes about the query instances to validate the feature representation of the support set. Moreover, to avoid overfitting in bidirectional matching, the data enhancement method was designed to scale up the number of instances while maintaining the scope of the instance relation class. Extensive experiments on FewRel and FewRel2.0 public datasets are conducted and evaluate the effectiveness of BMAN.

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Wang, Xueyi, Lele Zhang, Zheng Fan, Yang Liu, Chen Chen, and Fang Deng. "From Coarse to Fine: A Matching and Alignment Framework for Unsupervised Cross-View Geo-Localization." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 8 (2025): 8024–32. https://doi.org/10.1609/aaai.v39i8.32865.

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Cross-view geo-localization aims at determining the geographic location of a query image by matching the reference images. The matching pairs can be captured from diverse perspectives, such as those from satellites and drones. Most existing methods are supervised that require input of location-labeled images or matched and unmatched image pairs for training, resulting in high labor costs. Moreover, current unsupervised methods perform instances matching directly between different perspectives with dramatic discrepancies, resulting in poor performance. To address these issues, this paper proposes a novel matching and alignment framework from coarse instance-cluster level to fine intermediate instance level for unsupervised cross-view geo-localization. We first introduces cluster-based contrastive learning, assigning pseudo-labels to the instances and generate clusters within each view. Then we design a cross-view location alignment module that fully exploits the feature relationships between instances and clusters for intra- and inter-views. Finally, we design an intermediate state transition module that facilitates further alignment between views by constructing intermediate states and bringing both views closer to the intermediate domain simultaneously. Extensive experiments demonstrate that our method surpasses state-of-the-art unsupervised cross-view geo-localization methods and even achieves comparable performance to state-of-the-art supervised methods.

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Alzeber, Mogahed, Ali A. Alwan, Azlin Nordin, and Abedallah Zaid Abualkishik. "An Empirical Comparative Study of Instance-based Schema Matching." Indonesian Journal of Electrical Engineering and Computer Science 10, no. 3 (2018): 1266. http://dx.doi.org/10.11591/ijeecs.v10.i3.pp1266-1277.

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<span lang="EN-US">The main issue concern of schema matching is how to support the merging decision by providing matching between attributes of different schemas. There have been many works in the literature toward utilizing database instances to detect the correspondence between attributes. Most of these previous works aim at improving the match accuracy. We observed that no technique managed to provide an accurate matching for different types of data. In other words, some of the techniques treat numeric values as strings. Similarly, other techniques process textual instance, as numeric, and this negatively influences the process of discovering the match and compromising the matching result. Thus, a practical comparative study between syntactic and semantic techniques is needed. The study emphasizes on analyzing these techniques to determine the strengths and weaknesses of each technique. This paper aims at comparing two different instance-based matching techniques, namely: (i) regular expression and (ii) Google similarity to identify the match between attributes. Several analyses have been conducted on real and synthetic data sets to evaluate the performance of these techniques with respect to Precision (P), Recall (R) and F-Measure.</span>

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Shu, Zhenqiu, Teng Sun, Yunwei Luo, and Zhengtao Yu. "Ambiguous Instance-Aware Contrastive Network with Multi-Level Matching for Multi-View Document Clustering." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 19 (2025): 20479–87. https://doi.org/10.1609/aaai.v39i19.34256.

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Multi-view document clustering (MvDC) aims to improve the accuracy and robustness of clustering by fully considering the complementarity of different views. However, in real-world clustering applications, most existing works suffer from the following challenges: 1) They primarily align multi-view data based on a single perspective, such as features and classes, thus ignoring the diversity and comprehensiveness of representations. 2) They treat each instance equally in cross-view contrastive learning without considering ambiguous ones, which weakens the model's discriminative ability. To address these problems, we propose an ambiguous instance-aware contrastive network with multi-level matching (AICN-MLM) for MvDC tasks. This model contains two key modules: a multi-level matching module and an ambiguous instance-aware contrastive learning module. The former attempts to align multi-view data from different perspectives, including features, pseudo-labels, and prototypes. The latter dynamically adjusts instance weights through a weight modulation function to highlight ambiguous instance pairs. Thus, our proposed method can effectively explore the consistency of multi-view document data and focus on ambiguous instances to enhance the model's discriminative ability. Extensive experimental results on several multi-view document datasets verify the effectiveness of our proposed method.

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Seddiqui, Hanif, and Masaki Aono. "Ontology Instance Matching based MPEG-7 Resource Integration." International Journal of Multimedia Data Engineering and Management 1, no. 2 (2010): 18–33. http://dx.doi.org/10.4018/jmdem.2010040102.

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Heterogeneous multimedia contents are annotated by a sharable formal conceptualization, often called ontology, and these contents, regardless of their media, become sharable resources/instances. Integration of the sharable resources and acquisition of diverse knowledge is getting researchers’ attention at a rapid pace. In this regard, MPEG-7 standard convertible to semantic Resource Description Framework (RDF) evolves for containing structured data and knowledge sources. In this paper, the authors propose an efficient approach to integrate the multimedia resources annotated by the standard of MPEG-7 schema using ontology instance matching techniques. MPEG-7 resources are usually specified explicitly by their surrounding MPEG-7 schema entities, e.g., concepts and properties, in conjunction with other linked resources. Therefore, resource integration needed schema matching as well. In this approach, the authors obtained the schema matching using their scalable ontology alignment algorithm and collected the semantically linked resources, referred to as the Semantic Link Cloud (SLC) collectively for each of the resources. Techniques were addressed to solve several data heterogeneity: value transformation, structural transformation and logical transformation. These experiments show the strength and efficiency of the proposed matching approach.

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Boehmer, Niclas, Klaus Heeger, and Stanisław Szufa. "A Map of Diverse Synthetic Stable Matching Instances." Journal of Artificial Intelligence Research 79 (April 4, 2024): 1113–66. http://dx.doi.org/10.1613/jair.1.15213.

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Focusing on Stable Roommates (SR), we contribute to the toolbox for conducting experiments for stable matching problems. We introduce the polynomial-time computable mutual attraction distance to measure the similarity of SR instances, analyze its properties, and use it to create a map of SR instances. This map visualizes 460 synthetic SR instances (each sampled from one of ten different statistical cultures) as follows: Each instance is a point in the plane, and two points are close on the map if the corresponding SR instances are similar with respect to our mutual attraction distance to each other. Subsequently, we conduct several illustrative experiments and depict their results on the map, illustrating the map’s usefulness as a non-aggregate visualization tool, the diversity of our generated dataset, and the need to use instances sampled from different statistical cultures. Lastly, we extend our approach to the bipartite Stable Marriage problem.

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Lu, Junyan, Hongguang Jia, Tie Li, Zhuqiang Li, Jingyu Ma, and Ruifei Zhu. "An Instance Segmentation Based Framework for Large-Sized High-Resolution Remote Sensing Images Registration." Remote Sensing 13, no. 9 (2021): 1657. http://dx.doi.org/10.3390/rs13091657.

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Feature-based remote sensing image registration methods have achieved great accomplishments. However, they have faced some limitations of applicability, automation, accuracy, efficiency, and robustness for large high-resolution remote sensing image registration. To address the above issues, we propose a novel instance segmentation based registration framework specifically for large-sized high-resolution remote sensing images. First, we design an instance segmentation model based on a convolutional neural network (CNN), which can efficiently extract fine-grained instances as the deep features for local area matching. Then, a feature-based method combined with the instance segmentation results is adopted to acquire more accurate local feature matching. Finally, multi-constraints based on the instance segmentation results are introduced to work on the outlier removal. In the experiments of high-resolution remote sensing image registration, the proposal effectively copes with the circumstance of the sensed image with poor positioning accuracy. In addition, the method achieves superior accuracy and competitive robustness compared with state-of-the-art feature-based methods, while being rather efficient.

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Dazzi, Estephan, Teofilo de Campos, Adrian Hilton, and Roberto M. Cesar -. "Scalable object instance recognition based on keygraph matching." Pattern Recognition Letters 114 (October 2018): 53–62. http://dx.doi.org/10.1016/j.patrec.2017.10.038.

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Nguyen, Khai, and Ryutaro Ichise. "ScLink: supervised instance matching system for heterogeneous repositories." Journal of Intelligent Information Systems 48, no. 3 (2016): 519–51. http://dx.doi.org/10.1007/s10844-016-0426-3.

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Wang, Qiang, Wenkang Zhang, Wankou Yang, Chunyan Xu, and Zhen Cui. "Prototype-guided Instance matching for multiple pedestrian tracking." Neurocomputing 538 (June 2023): 126207. http://dx.doi.org/10.1016/j.neucom.2023.03.068.

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Xu, Zhujun, and Damien Vivet. "Instance Sequence Queries for Video Instance Segmentation with Transformers." Sensors 21, no. 13 (2021): 4507. http://dx.doi.org/10.3390/s21134507.

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Existing methods for video instance segmentation (VIS) mostly rely on two strategies: (1) building a sophisticated post-processing to associate frame level segmentation results and (2) modeling a video clip as a 3D spatial-temporal volume with a limit of resolution and length due to memory constraints. In this work, we propose a frame-to-frame method built upon transformers. We use a set of queries, called instance sequence queries (ISQs), to drive the transformer decoder and produce results at each frame. Each query represents one instance in a video clip. By extending the bipartite matching loss to two frames, our training procedure enables the decoder to adjust the ISQs during inference. The consistency of instances is preserved by the corresponding order between query slots and network outputs. As a result, there is no need for complex data association. On TITAN Xp GPU, our method achieves a competitive 34.4% mAP at 33.5 FPS with ResNet-50 and 35.5% mAP at 26.6 FPS with ResNet-101 on the Youtube-VIS dataset.

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Alimudin, Akhmad, and Yoshiteru Ishida. "Matching-Updating Mechanism: A Solution for the Stable Marriage Problem with Dynamic Preferences." Entropy 24, no. 2 (2022): 263. http://dx.doi.org/10.3390/e24020263.

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We studied the stable marriage problem with dynamic preferences. The dynamic preference model allows the agent to change its preferences at any time, which may cause instability in a matching. However, preference changing in SMP instances does not necessarily break all pairs of an existing match. Sometimes, only a few couples want to change their partners, while others choose to stay with their current partners; this motivates us to find stable matching on a new instance by updating an existing match rather than restarting the matching process from scratch. By using the update mechanism, we try to minimize the revision cost when rematching occurs. The challenge when updating a matching is that a cyclic process may exist, and stable matching is never achieved. Our proposed mechanism can update a match and avoid the cyclic process.

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Sutanta, Edhy, Retantyo Wardoyo, Khabib Mustofa, and Edi Winarko. "A Hybrid Model Schema Matching Using Constraint-Based and Instance-Based." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 3 (2016): 1048. http://dx.doi.org/10.11591/ijece.v6i3.9790.

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Schema matching is an important process in the Enterprise Information Integration (EII) which is at the level of the back end to solve the problems due to the schematic heterogeneity. This paper is a summary of preliminary result work of the model development stage as part of research on the development of models and prototype of hybrid schema matching that combines two methods, namely constraint-based and instance-based. The discussion includes a general description of the proposed models and the development of models, start from requirement analysis, data type conversion, matching mechanism, database support, constraints and instance extraction, matching and compute the similarity, preliminary result, user verification, verified result, dataset for testing, as well as the performance measurement. Based on result experiment on 36 datasets of heterogeneous RDBMS, it obtained the highest P value is 100.00% while the lowest is 71.43%; The highest R value is 100.00% while the lowest is 75.00%; and F-Measure highest value is 100.00% while the lowest is 81.48%. Unsuccessful matching on the model still happens, including use of an id attribute with data type as autoincrement; using codes that are defined in the same way but different meanings; and if encountered in common instance with the same definition but different meaning.

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Sutanta, Edhy, Retantyo Wardoyo, Khabib Mustofa, and Edi Winarko. "A Hybrid Model Schema Matching Using Constraint-Based and Instance-Based." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 3 (2016): 1048. http://dx.doi.org/10.11591/ijece.v6i3.pp1048-1058.

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Schema matching is an important process in the Enterprise Information Integration (EII) which is at the level of the back end to solve the problems due to the schematic heterogeneity. This paper is a summary of preliminary result work of the model development stage as part of research on the development of models and prototype of hybrid schema matching that combines two methods, namely constraint-based and instance-based. The discussion includes a general description of the proposed models and the development of models, start from requirement analysis, data type conversion, matching mechanism, database support, constraints and instance extraction, matching and compute the similarity, preliminary result, user verification, verified result, dataset for testing, as well as the performance measurement. Based on result experiment on 36 datasets of heterogeneous RDBMS, it obtained the highest P value is 100.00% while the lowest is 71.43%; The highest R value is 100.00% while the lowest is 75.00%; and F-Measure highest value is 100.00% while the lowest is 81.48%. Unsuccessful matching on the model still happens, including use of an id attribute with data type as autoincrement; using codes that are defined in the same way but different meanings; and if encountered in common instance with the same definition but different meaning.

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Nassar, Ahmed Samy, Sébastien Lefèvre, and Jan Dirk Wegner. "Multi-View Instance Matching with Learned Geometric Soft-Constraints." ISPRS International Journal of Geo-Information 9, no. 11 (2020): 687. http://dx.doi.org/10.3390/ijgi9110687.

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We present a new approach for matching urban object instances across multiple ground-level images for the ultimate goal of city-scale mapping of objects with high positioning accuracy. What makes this task challenging is the strong change in view-point, different lighting conditions, high similarity of neighboring objects, and variability in scale. We propose to turn object instance matching into a learning task, where image-appearance and geometric relationships between views fruitfully interact. Our approach constructs a Siamese convolutional neural network that learns to match two views of the same object given many candidate image cut-outs. In addition to image features, we propose utilizing location information about the camera and the object to support image evidence via soft geometric constraints. Our method is compared to existing patch matching methods to prove its edge over state-of-the-art. This takes us one step closer to the ultimate goal of city-wide object mapping from street-level imagery to benefit city administration.

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Dass, Sameer, and Suresh Kumar. "A Review on Scalability Issues Of Ontologys Instance Matching." International Journal of Computer Sciences and Engineering 7, no. 1 (2019): 606–9. http://dx.doi.org/10.26438/ijcse/v7i1.606609.

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Huang, Zhenhang, and Ruirui Li. "Orientated Silhouette Matching for Single-Shot Ship Instance Segmentation." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 15 (2022): 463–77. http://dx.doi.org/10.1109/jstars.2021.3132005.

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Kamiyama, Naoyuki. "Modifying an instance of the super-stable matching problem." Information Processing Letters 189 (March 2025): 106549. http://dx.doi.org/10.1016/j.ipl.2024.106549.

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Dai, Ju, Pingping Zhang, Huchuan Lu, and Hongyu Wang. "Dynamic imposter based online instance matching for person search." Pattern Recognition 100 (April 2020): 107120. http://dx.doi.org/10.1016/j.patcog.2019.107120.

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Ferrara, A., A. Nikolov, J. Noessner, and F. Scharffe. "Evaluation of instance matching tools: The experience of OAEI." Journal of Web Semantics 21 (August 2013): 49–60. http://dx.doi.org/10.1016/j.websem.2013.05.004.

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Daskalaki, Evangelia, Giorgos Flouris, Irini Fundulaki, and Tzanina Saveta. "Instance matching benchmarks in the era of Linked Data." Journal of Web Semantics 39 (August 2016): 1–14. http://dx.doi.org/10.1016/j.websem.2016.06.002.

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Uzyıldırım, Furkan Eren, and Mustafa Özuysal. "Instance detection by keypoint matching beyond the nearest neighbor." Signal, Image and Video Processing 10, no. 8 (2016): 1527–34. http://dx.doi.org/10.1007/s11760-016-0966-6.

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Shao, Chao, Lin-Mei Hu, Juan-Zi Li, Zhi-Chun Wang, Tonglee Chung, and Jun-Bo Xia. "RiMOM-IM: A Novel Iterative Framework for Instance Matching." Journal of Computer Science and Technology 31, no. 1 (2016): 185–97. http://dx.doi.org/10.1007/s11390-016-1620-z.

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Abubakar, Mansir, Hazlina Hamdan, Norwati Mustapha, and Teh Noranis Mohd Aris. "Attributes Correspondence Discovery in Ontology Instance-based Matching and RDF Data Linkage using Clustering Method." International Journal of Engineering & Technology 7, no. 4.31 (2018): 290–97. http://dx.doi.org/10.14419/ijet.v7i4.31.23383.

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One important aspect of ontology instance matching process is elements or attributes discovery. It specifies element correspondences in order to produce potential matching elements; otherwise, all elements of a class in the source ontology have to be compared with all elements of class in the target ontology. This heavy comparison is time-consuming and resulted in the poor performance of the matching system and makes the matching incomplete. Matching two or more ontologies and RDF datasets requires complete instance matching so as to establish logically equivalent relation among semantically related entities of the data sources. This deems challenging because of the existence of semantic heterogeneity and presence of irregular data in the RDF data sources which makes elements discovery and feature value extractions difficult. Thus, we proposed a four-step elements discovery method that utilizes unsupervised K-Medoids clustering algorithm in discovering potential matching elements pairs. To ensure generalization, we take unsupervised Canopy Clustering method to be the baseline for our evaluation. In terms of scalability, our method outperforms the baseline method with approximately 99% in both Pair Completeness and Reduction Ratio as against 60% and 86% respectively in the baseline. In mapping pattern generation, our method also outperforms the baseline algorithm with the overall F-Measure of ~91% against ~85%. The result of comparism with other methods justifies the significance effect of clustering attributes in the initial stage of the instance matching which can save about 50% of the comparism.Â Â

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Burjons, Elisabet, Juraj Hromkovič, Rastislav Královič, Richard Královič, Xavier Muñoz, and Walter Unger. "Online Graph Coloring Against a Randomized Adversary." International Journal of Foundations of Computer Science 29, no. 04 (2018): 551–69. http://dx.doi.org/10.1142/s0129054118410058.

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We consider an online model where an adversary constructs a set of [Formula: see text] instances [Formula: see text] instead of one single instance. The algorithm knows [Formula: see text] and the adversary will choose one instance from [Formula: see text] at random to present to the algorithm. We further focus on adversaries that construct sets of [Formula: see text]-chromatic instances. In this setting, we provide upper and lower bounds on the competitive ratio for the online graph coloring problem as a function of the parameters in this model. Both bounds are linear in [Formula: see text] and matching upper and lower bound are given for a specific set of algorithms that we call “minimalistic online algorithms”.

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Duong, Trong, Geun-Sik Jo, Jason Jung, and Ngoc Nguyen. "Complexity Analysis of Ontology Integration Methodologies:a Comparative Study." JUCS - Journal of Universal Computer Science 15, no. (4) (2009): 877–97. https://doi.org/10.3217/jucs-015-04-0877.

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Most previous research on ontology integration has focused on similarity measure-ments between ontological entities, e.g., lexicons, instances, schemas and taxonomies, resulting in high computational costs of considering all possible pairs between two given ontologies. In this paper, we propose a novel approach to reducing computational complexity in ontology integration. Thereby, we address the importance and types of concepts, for priority matching anddirect matching between concepts, respectively. Identity-based similarity is computed, to avoid comparisons of all properties related to each concept, while matching between concepts. Theproblem of conflict in ontology integration has initially been explored on the instance-level and concept-level. This is useful to avoid many cases of mismatching.

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Li, Xiang, Jinglu Wang, Xiao Li, and Yan Lu. "Hybrid Instance-Aware Temporal Fusion for Online Video Instance Segmentation." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 2 (2022): 1429–37. http://dx.doi.org/10.1609/aaai.v36i2.20032.

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Recently, transformer-based image segmentation methods have achieved notable success against previous solutions. While for video domains, how to effectively model temporal context with the attention of object instances across frames remains an open problem. In this paper, we propose an online video instance segmentation framework with a novel instance-aware temporal fusion method. We first leverage the representation, \ie, a latent code in the global context (instance code) and CNN feature maps to represent instance- and pixel-level features. Based on this representation, we introduce a cropping-free temporal fusion approach to model the temporal consistency between video frames. Specifically, we encode global instance-specific information in the instance code and build up inter-frame contextual fusion with hybrid attentions between the instance codes and CNN feature maps. Inter-frame consistency between the instance codes is further enforced with order constraints. By leveraging the learned hybrid temporal consistency, we are able to directly retrieve and maintain instance identities across frames, eliminating the complicated frame-wise instance matching in prior methods. Extensive experiments have been conducted on popular VIS datasets, i.e. Youtube-VIS-19/21. Our model achieves the best performance among all online VIS methods. Notably, our model also eclipses all offline methods when using the ResNet-50 backbone.

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Faenza, Yuri, and Telikepalli Kavitha. "Quasi-Popular Matchings, Optimality, and Extended Formulations." Mathematics of Operations Research 47, no. 1 (2022): 427–57. http://dx.doi.org/10.1287/moor.2021.1139.

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Let [Formula: see text] be an instance of the stable marriage problem in which every vertex ranks its neighbors in a strict order of preference. A matching [Formula: see text] in [Formula: see text] is popular if [Formula: see text] does not lose a head-to-head election against any matching. Popular matchings generalize stable matchings. Unfortunately, when there are edge costs, to find or even approximate up to any factor a popular matching of minimum cost is NP-hard. Let [Formula: see text] be the cost of a min-cost popular matching. Our goal is to efficiently compute a matching of cost at most [Formula: see text] by paying the price of mildly relaxing popularity. Our main positive results are two bicriteria algorithms that find in polynomial time a “quasi-popular” matching of cost at most [Formula: see text]. Moreover, one of the algorithms finds a quasi-popular matching of cost at most that of a min-cost popular fractional matching, which could be much smaller than [Formula: see text]. Key to the other algorithm is a polynomial-size extended formulation for an integral polytope sandwiched between the popular and quasi-popular matching polytopes. We complement these results by showing that it is NP-hard to find a quasi-popular matching of minimum cost and that both the popular and quasi-popular matching polytopes have near-exponential extension complexity.

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Nedjah, Nadia, and Luiza De Macedo Mourelle. "Complete Pattern Matching for DNA Computing." Journal of Information & Knowledge Management 05, no. 04 (2006): 337–43. http://dx.doi.org/10.1142/s0219649206001591.

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Pattern matching is essential in many applications such as information retrieval, logic programming, theorem-proving, term rewriting and DNA-computing. It usually breaks down into two categories: root and complete pattern matching. Root matching determines whether a subject term is an instance of a pattern in a pattern set while complete matching determines whether a subject term contains a sub-term that is an instance of a pattern in a pattern set. For the sake of efficiency, root pattern matching need to be deterministic and lazy. Furthermore, complete pattern matching also needs to be parallel. Unlike root pattern matching, complete matching received little interest from the researchers of the field. In this paper, we present a novel deterministic multi-threaded complete matching method. This method subsumes a deterministic lazy root matching technique that was developped by the authors in an earlier work. We evaluate the performance of proposed method using theorem-proving and DNA-computing applications.

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Pittel, Boris. "On a Random Instance of a ‘Stable Roommates’ Problem: Likely Behavior of the Proposal Algorithm." Combinatorics, Probability and Computing 2, no. 1 (1993): 53–92. http://dx.doi.org/10.1017/s0963548300000481.

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In a set of even cardinality n, each member ranks all the others in order of preference. A stable matching is a partition of the set into n/2 pairs, with the property that no two unpaired members both prefer each other to their partners under matching. It is known that for some problem instances no stable matching exists. In 1985, Irving found an O(n2) two-phase algorithm that would determine, for any instance, whether a stable matching exists, and if so, would find such a matching. Recently, Tan proved that Irving's algorithm, with a modified second phase, always finds a stable cyclic partition of the members set, which is a stable matching when each cycle has length two. In this paper we study a likely behavior of the algorithm under the assumption that an instance of the ranking system is chosen uniformly at random. We prove that the likely number of basic steps, i.e. the individual proposals in the first phase and the rotation eliminations, involving subsets of members in the second phase, is O(n log n), and that the likely size of a rotation is O((n log n)1/2). We establish a ‘hyperbola law’ analogous to our past result on stable marriages. It states that at every step of the second phase, the product of the rank of proposers and the rank of proposal holders is asymptotic, in probability, to n3. We show that every stable cyclic partition is likely to be almost a stable matching, in the sense that at most O((n log n)1/2) members can be involved in the cycles of length three or more.

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Cseh, Ágnes, and Telikepalli Kavitha. "Popular Matchings in Complete Graphs." Algorithmica 83, no. 5 (2021): 1493–523. http://dx.doi.org/10.1007/s00453-020-00791-7.

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AbstractOur input is a complete graph G on n vertices where each vertex has a strict ranking of all other vertices in G. The goal is to construct a matching in G that is popular. A matching M is popular if M does not lose a head-to-head election against any matching $$M'$$ M ′ : here each vertex casts a vote for the matching in $$\{M,M'\}$$ { M , M ′ } in which it gets a better assignment. Popular matchings need not exist in the given instance G and the popular matching problem is to decide whether one exists or not. The popular matching problem in G is easy to solve for odd n. Surprisingly, the problem becomes $$\texttt {NP}$$ NP -complete for even n, as we show here. This is one of the few graph theoretic problems efficiently solvable when n has one parity and $$\texttt {NP}$$ NP -complete when n has the other parity.

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Li, Zikun, Shige Lu, Zhaolin Yuan, Bowen Hou, and Jilong Bian. "Interactive Instance Search: User-Centered Enhanced Image Retrieval with Learned Perceptual Image Patch Similarity." Electronics 14, no. 9 (2025): 1766. https://doi.org/10.3390/electronics14091766.

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Instance searches pertain to the identification of specific objects or scenes within a dataset that correspond to a given query image. The existing research primarily concentrates on improving the accuracy of machine-recognized instances, frequently neglecting the pivotal role of human–computer interaction. As a result, effectively searching for instances that align with user preferences continues to pose a substantial challenge. In this paper, we introduce an intuitive and efficient instance search method that incorporates human–computer interaction. Specifically, our proposed interactive instance search system includes tools that enable users to directly highlight specific instances of interest within the query image. Furthermore, we propose the use of learned perceptual image patch similarity to effectively bridge the semantic gap between low-level features and high-level semantics. Contrary to conventional metrics, such as cosine similarity, which rely on pixel-level or superficial feature comparisons, we employ deep neural networks to model perceptual differences in a hierarchical manner. The experimental results demonstrate that our approach surpasses traditional methods in terms of similarity-matching accuracy and exhibits robust performance on datasets such as Oxford5k and Paris6k.

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Assi, Ali, and Wajdi Dhifli. "Instance Matching in Knowledge Graphs through random walks and semantics." Future Generation Computer Systems 123 (October 2021): 73–84. http://dx.doi.org/10.1016/j.future.2021.04.015.

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Mounaf Mahdi, Ahmed, and Sabrina Tiun. "Utilizing WordNet and Regular Expressions for Instance-based Schema Matching." Research Journal of Applied Sciences, Engineering and Technology 8, no. 4 (2014): 460–70. http://dx.doi.org/10.19026/rjaset.8.994.

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Ihm, Sun-Young, and Young-Ho Park. "Instance-Level Subsequence Matching Method based on a Virtual Window." KIPS Transactions on Computer and Communication Systems 3, no. 2 (2014): 43–46. http://dx.doi.org/10.3745/ktccs.2014.3.2.43.

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Journal articles on the topic 'Instance matching'

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