scholarly journals Matching sensor ontologies through siamese neural networks without using reference alignment

2021 ◽  
Vol 7 ◽  
pp. e602
Author(s):  
Xingsi Xue ◽  
Chao Jiang ◽  
Jie Zhang ◽  
Hai Zhu ◽  
Chaofan Yang
Keyword(s):  
Extraction Method ◽  
Semantic Information ◽  
Sensor Data ◽  
Reference Alignment ◽  
Ontology Matching ◽  
Training Process ◽  
High Quality ◽  
Matching Process ◽  
Matching Technique ◽  
Semantic Schema

Sensors have been growingly used in a variety of applications. The lack of semantic information of obtained sensor data will bring about the heterogeneity problem of sensor data in semantic, schema, and syntax levels. To solve the heterogeneity problem of sensor data, it is necessary to carry out the sensor ontology matching process to determine correspondences among heterogeneous sensor concepts. In this paper, we propose a Siamese Neural Network based Ontology Matching technique (SNN-OM) to align the sensor ontologies, which does not require the utilization of reference alignment to train the network model. In particular, a representative concepts extraction method is presented to enhance the model’s performance and reduce the time of the training process, and an alignment refining method is proposed to enhance the alignments’ quality by removing the logically conflict correspondences. The experimental results show that SNN-OM is capable of efficiently determining high-quality sensor ontology alignments.

scholarly journals Optimizing Sensor Ontology Alignment through Compact co-Firefly Algorithm

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 2056 ◽  
Author(s):  
Xingsi Xue ◽  
Junfeng Chen
Keyword(s):  
Firefly Algorithm ◽  
Gray Code ◽  
General Purpose ◽  
Sensor Data ◽  
Ontology Matching ◽  
Ontology Alignment ◽  
Matching Problem ◽  
Matching Technique ◽  
Sensor Information ◽  
Exploitation And Exploration

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.

scholarly journals Using decision trees for inductively driven semantic integration and ontology matching

2021 ◽  
Author(s):  
Bart Gajderowicz
Keyword(s):  
Decision Trees ◽  
Real World ◽  
Semantic Integration ◽  
Use Cases ◽  
Ontology Matching ◽  
Bottom Up ◽  
Matching Algorithm ◽  
Matching Process ◽  
Narrow Context

The popularity of ontologies for representing the semantics behind many real-world domains has created a growing pool of ontologies on various topics. While different ontologists, experts, and organizations create the vast majority of ontologies, often for internal use of for use in a narrow context, their domains frequently overlap in a wider context, specifically for complementary domains. To assist in the reuse of ontologies, this thesis proposes a bottom-up technique for creating concept anchors that are used for ontology matching. Anchors are ontology concepts that have been matched to concepts in an eternal ontology. The matching process is based on inductively derived decision trees rules for an ontology that are compared with rules derived for external ontologies. The matching algorithm is intended to match taxomonies, ontologies which define subsumption relations between concepts, with an associated database used to derive the decision trees. This thesis also introduces several algorithm evolution measures, and presents a set of use cases that demonstrate the strengths and weaknesses of the matching process.

scholarly journals Using decision trees for inductively driven semantic integration and ontology matching

2021 ◽  
Author(s):  
Bart Gajderowicz
Keyword(s):  
Decision Trees ◽  
Real World ◽  
Semantic Integration ◽  
Use Cases ◽  
Ontology Matching ◽  
Bottom Up ◽  
Matching Algorithm ◽  
Matching Process ◽  
Narrow Context

The popularity of ontologies for representing the semantics behind many real-world domains has created a growing pool of ontologies on various topics. While different ontologists, experts, and organizations create the vast majority of ontologies, often for internal use of for use in a narrow context, their domains frequently overlap in a wider context, specifically for complementary domains. To assist in the reuse of ontologies, this thesis proposes a bottom-up technique for creating concept anchors that are used for ontology matching. Anchors are ontology concepts that have been matched to concepts in an eternal ontology. The matching process is based on inductively derived decision trees rules for an ontology that are compared with rules derived for external ontologies. The matching algorithm is intended to match taxomonies, ontologies which define subsumption relations between concepts, with an associated database used to derive the decision trees. This thesis also introduces several algorithm evolution measures, and presents a set of use cases that demonstrate the strengths and weaknesses of the matching process.

scholarly journals An Iterative Automatic Final Alignment Method in the Ontology Matching System

2018 ◽  
Vol 42 (1) ◽  
pp. 39-61 ◽  
Author(s):  
Marko Gulić ◽  
Marin Vuković
Keyword(s):  
Heterogeneous Data ◽  
Data Sources ◽  
Ontology Matching ◽  
Alignment Method ◽  
Automatic Adjustment ◽  
Matching Process ◽  
Heterogeneous Data Sources ◽  
Final Alignment

Ontology matching plays an important role in the integration of heterogeneous data sources that are described by ontologies. In order to determine correspondences between ontologies, a set of matchers can be used. After the execution of these matchers and the aggregation of the results obtained by these matchers, a final alignment method is executed in order to select appropriate correspondences between entities of compared ontologies. The final alignment method is an important part of the ontology matching process because it directly determines the output result of this process. In this paper we improve our iterative final alignment method by introducing an automatic adjustment of final alignment threshold as well as a new rule for determining false correspondences with similarity values greater than adjusted threshold. An evaluation of the method is performed on the test ontologies of the OAEI evaluation contest and a comparison with other final alignment methods is given.

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.

scholarly journals EXTRACTION METHODS OF CLEANING OF MOTOR FUEL

2019 ◽  
Vol 62 (10) ◽  
pp. 30-39
Author(s):  
Sabina A. Seidova
Keyword(s):  
Organic Solvents ◽  
Extraction Method ◽  
Motor Fuel ◽  
Extraction Methods ◽  
National Academy Of Sciences ◽  
Selective Solvent ◽  
High Quality ◽  
Motor Fuels ◽  
Temperature And Pressure ◽  
Academy Of Sciences

In paper the results of the carried out analysis of literary data on preparation of motor fuels of high quality by extraction method of purification of the corresponding oil distillates with use of compounds of various class as a selective solvent have been presented. In particular, the results of comparative analysis of existing methods of the extraction purification of distillates of motor fuels from unnecessary components – aromatic hydrocarbons, sulphur-containing compounds and resinous substances with use of organic solvents and ion-liquid compositions as a selective solvent have been presented. The advantage of the extraction method of purification of motor fuels determined by possibility of the process at low temperature and pressure, by absence of necessity of application of the expensive catalysts, by possibility of regeneration and reuse of solvent, etc. in comparison with widely used hydrogenation method has been shown. The lacks of the organic solvents used as an extractant have been also listed and due to the ecological problems the use of non-volatile, thermally stable ion-liquid compositions as a selective solvent in the processes of purification of the distillates, intended for preparation a high quality target products, such as diesel fuel, gasoline, base oils for various purposes has been substantiated. In paper the results of systematic investigations carried out at the Institute of Petrochemical Processes of Azerbaijan National Academy of Sciences with the participation of the authors themselves, concerning the selective purification of the oil fractions of various composition and viscosity with use of ionic liquids synthesized on the basis of formic and acetic acids composition have been also presented. By carried out analysis it has been shown the perspectivity of application of the ion-liquid compositions as an extractant in the processes of the selective purification of the oil distillates.

scholarly journals Within and Between Shot Information Utilisation in Video Key Frame Extraction

2011 ◽  
Vol 10 (03) ◽  
pp. 247-259 ◽  
Author(s):  
Dianting Liu ◽  
Mei-Ling Shyu ◽  
Chao Chen ◽  
Shu-Ching Chen
Keyword(s):  
Extraction Method ◽  
Video Sequence ◽  
Video Retrieval ◽  
Video Browsing ◽  
High Quality ◽  
Key Frame Extraction ◽  
Key Frame ◽  
Video Shot ◽  
Key Frames ◽  
Candidate Set

In consequence of the popularity of family video recorders and the surge of Web 2.0, increasing amounts of videos have made the management and integration of the information in videos an urgent and important issue in video retrieval. Key frames, as a high-quality summary of videos, play an important role in the areas of video browsing, searching, categorisation, and indexing. An effective set of key frames should include major objects and events of the video sequence, and should contain minimum content redundancies. In this paper, an innovative key frame extraction method is proposed to select representative key frames for a video. By analysing the differences between frames and utilising the clustering technique, a set of key frame candidates (KFCs) is first selected at the shot level, and then the information within a video shot and between video shots is used to filter the candidate set to generate the final set of key frames. Experimental results on the TRECVID 2007 video dataset have demonstrated the effectiveness of our proposed key frame extraction method in terms of the percentage of the extracted key frames and the retrieval precision.

A Semantic-Based Ontology Matching Process for PDMS

2009 ◽  
pp. 124-135 ◽  
Author(s):  
Carlos Eduardo Pires ◽  
Damires Souza ◽  
Thiago Pachêco ◽  
Ana Carolina Salgado
Keyword(s):  
Ontology Matching ◽  
Matching Process
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