Large-scale biomedical ontology matching with ServOMap

IRBM ◽  
2013 ◽  
Vol 34 (1) ◽  
pp. 56-59 ◽  
Author(s):  
M. Ba ◽  
G. Diallo
Keyword(s):  
Large Scale ◽  
Biomedical Ontology ◽  
Ontology Matching

Evaluating Large-Scale Biomedical Ontology Matching Over Parallel Platforms

2015 ◽  
Vol 33 (4) ◽  
pp. 415-427 ◽  
Author(s):  
Muhammad Bilal Amin ◽  
Wajahat Ali Khan ◽  
Shujaat Hussain ◽  
Dinh-Mao Bui ◽  
Oresti Banos ◽  
...  
Keyword(s):  
Large Scale ◽  
Biomedical Ontology ◽  
Ontology Matching

scholarly journals Semisupervised Learning-Based Sensor Ontology Matching

2021 ◽  
Vol 2021 ◽  
pp. 1-5
Author(s):  
Hai Zhu ◽  
Jie Zhang ◽  
Xingsi Xue
Keyword(s):  
Large Scale ◽  
Similarity Measures ◽  
Semisupervised Learning ◽  
Ontology Matching ◽  
Semantic Relationship ◽  
The Social ◽  
Matching Technique ◽  
Data Heterogeneity ◽  
Training Examples ◽  
The Internet Of Things

Sensor ontology models the sensor information and knowledge in a machine-understandable way, which aims at addressing the data heterogeneity problem on the Internet of Things (IoT). However, the existing sensor ontologies are maintained independently for different requirements, which might define the same concept with different terms or context, yielding the heterogeneity issue. Since the complex semantic relationship between the sensor concepts and the large-scale entities is to be dealt with, finding the identical entity correspondences is an error-prone task. To effectively determine the sensor entity correspondences, this work proposes a semisupervised learning-based sensor ontology matching technique. First, we borrow the idea of “centrality” from the social network to construct the training examples; then, we present an evolutionary algorithm- (EA-) based metamatching technique to train the model of aggregating different similarity measures; finally, we use the trained model to match the rest entities. The experiment uses the benchmark as well as three real sensor ontologies to test our proposal’s performance. The experimental results show that our approach is able to determine high-quality sensor entity correspondences in all matching tasks.

Semantic Synchronization in B2B Transactions

2010 ◽  
pp. 1518-1542
Author(s):  
Janina Fengel ◽  
Heiko Paulheim ◽  
Michael Rebstock
Keyword(s):  
Large Scale ◽  
Business Processes ◽  
User Participation ◽  
Ontology Matching ◽  
Ontological Engineering ◽  
Business Information Systems ◽  
Business Partners ◽  
Business Information ◽  
Mapping Technology

Despite the development of e-business standards, the integration of business processes and business information systems is still a non-trivial issue if business partners use different e-business standards for formatting and describing information to be processed. Since those standards can be understood as ontologies, ontological engineering technologies can be applied for processing, especially ontology matching for reconciling them. However, as e-business standards tend to be rather large-scale ontologies, scalability is a crucial requirement. To serve this demand, we present our ORBI Ontology Mediator. It is linked with our Malasco system for partition-based ontology matching with currently available matching systems, which so far do not scale well, if at all. In our case study we show how to provide dynamic semantic synchronization between business partners using different e-business standards without initial ramp-up effort, based on ontological mapping technology combined with interactive user participation.

scholarly journals Matching Biomedical Ontologies through Adaptive Multi-Modal Multi-Objective Evolutionary Algorithm

Biology ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1287
Author(s):  
Xingsi Xue ◽  
Pei-Wei Tsai ◽  
Yucheng Zhuang
Keyword(s):  
Evolutionary Algorithm ◽  
Large Scale ◽  
Biomedical Ontology ◽  
Biomedical Data ◽  
Biomedical Ontologies ◽  
Ontology Alignment ◽  
Multi Objective Optimization ◽  
Matching Problem ◽  
Multi Objective ◽  
Matching Techniques

To integrate massive amounts of heterogeneous biomedical data in biomedical ontologies and to provide more options for clinical diagnosis, this work proposes an adaptive Multi-modal Multi-Objective Evolutionary Algorithm (aMMOEA) to match two heterogeneous biomedical ontologies by finding the semantically identical concepts. In particular, we first propose two evaluation metrics on the alignment’s quality, which calculate the alignment’s statistical and its logical features, i.e., its f-measure and its conservativity. On this basis, we build a novel multi-objective optimization model for the biomedical ontology matching problem. By analyzing the essence of this problem, we point out that it is a large-scale Multi-modal Multi-objective Optimization Problem (MMOP) with sparse Pareto optimal solutions. Then, we propose a problem-specific aMMOEA to solve this problem, which uses the Guiding Matrix (GM) to adaptively guide the algorithm’s convergence and diversity in both objective and decision spaces. The experiment uses Ontology Alignment Evaluation Initiative (OAEI)’s biomedical tracks to test aMMOEA’s performance, and comparisons with two state-of-the-art MOEA-based matching techniques and OAEI’s participants show that aMMOEA is able to effectively determine diverse solutions for decision makers.

Biomedical Ontology Matching Through Attention-Based Bidirectional Long Short-Term Memory Network

2021 ◽  
Vol 32 (4) ◽  
pp. 14-27
Author(s):  
Xingsi Xue ◽  
Chao Jiang ◽  
Jie Zhang ◽  
Cong Hu
Keyword(s):  
Short Term Memory ◽  
Binary Classification ◽  
Biomedical Ontology ◽  
Ontology Matching ◽  
Semantic Heterogeneity ◽  
Short Term ◽  
Matching Problem ◽  
Term Memory ◽  
Memory Network ◽  
Long Short Term Memory

Biomedical ontology formally defines the biomedical entities and their relationships. However, the same biomedical entity in different biomedical ontologies might be defined in diverse contexts, resulting in the problem of biomedicine semantic heterogeneity. It is necessary to determine the mappings between heterogeneous biomedical entities to bridge the semantic gap, which is the so-called biomedical ontology matching. Due to the plentiful semantic meaning and flexible representation of biomedical entities, the biomedical ontology matching problem is still an open challenge in terms of the alignment's quality. To face this challenge, in this work, the biomedical ontology matching problem is deemed as a binary classification problem, and an attention-based bidirectional long short-term memory network (At-BLSTM)-based ontology matching technique is presented to address it, which is able to capture the semantic and contextual feature of biomedical entities. In the experiment, the comparisons with state-of-the-art approaches show the effectiveness of the proposal.

Semantic Synchronization in B2B Transactions

2014 ◽  
pp. 210-234
Author(s):  
Janina Fengel ◽  
Heiko Paulheim ◽  
Michael Rebstock
Keyword(s):  
Large Scale ◽  
Business Processes ◽  
User Participation ◽  
Ontology Matching ◽  
Ontological Engineering ◽  
Business Information Systems ◽  
Business Partners ◽  
Business Information ◽  
Mapping Technology

Despite the development of e-business standards, the integration of business processes and business information systems is still a non-trivial issue if business partners use different e-business standards for formatting and describing information to be processed. Since those standards can be understood as ontologies, ontological engineering technologies can be applied for processing, especially ontology matching for reconciling them. However, as e-business standards tend to be rather large-scale ontologies, scalability is a crucial requirement. To serve this demand, we present our ORBI Ontology Mediator. It is linked with our Malasco system for partition-based ontology matching with currently available matching systems, which so far do not scale well, if at all. In our case study we show how to provide dynamic semantic synchronization between business partners using different e-business standards without initial ramp-up effort, based on ontological mapping technology combined with interactive user participation.

A large dataset for the evaluation of ontology matching

2009 ◽  
Vol 24 (2) ◽  
pp. 137-157 ◽  
Author(s):  
Fausto Giunchiglia ◽  
Mikalai Yatskevich ◽  
Paolo Avesani ◽  
Pavel Shivaiko
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
The State ◽  
Ontology Matching ◽  
Large Dataset ◽  
Scale Evaluation ◽  
Evaluation Dataset ◽  
Matching Techniques ◽  
Web Directories

AbstractRecently, the number of ontology matching techniques and systems has increased significantly. This makes the issue of their evaluation and comparison more severe. One of the challenges of the ontology matching evaluation is in building large-scale evaluation datasets. In fact, the number of possible correspondences between two ontologies grows quadratically with respect to the numbers of entities in these ontologies. This often makes the manual construction of the evaluation datasets demanding to the point of being infeasible for large-scale matching tasks. In this paper, we present an ontology matching evaluation dataset composed of thousands of matching tasks, called TaxME2. It was built semi-automatically out of the Google, Yahoo, and Looksmart web directories. We evaluated TaxME2 by exploiting the results of almost two-dozen of state-of-the-art ontology matching systems. The experiments indicate that the dataset possesses the desired key properties, namely it is error-free, incremental, discriminative, monotonic, and hard for the state-of-the-art ontology matching systems.

A segment-based approach for large-scale ontology matching

2017 ◽  
Vol 52 (2) ◽  
pp. 467-484 ◽  
Author(s):  
Xingsi Xue ◽  
Jeng-Shyang Pan
Keyword(s):  
Large Scale ◽  
Ontology Matching
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