# Semisupervised Learning-Based Sensor Ontology Matching

Authors: Hai Zhu; Jie Zhang; Xingsi Xue
Journal: Security and Communication Networks
 (2021)

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## Abstract

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.


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## Body

## 1. Introduction

Sensor ontology models the sensor information and knowledge on Internet of Things (IoT) in a machine-understandable way [1]. With the help of sensor ontology, different intelligent sensor applications are able to communicate with each other, which is of help to implement their collaboration. Nowadays, more and more sensor ontologies have been developed, which are maintained independently for different requirements. One of the barriers that hampers them from communications is their heterogeneity problem; i.e., one concept could be defined in different ways [2]. Since the complex semantic relationship between the sensor concepts and the large-scale entities is to be dealt with, addressing the sensor ontology heterogeneity problem is an error-prone task. Finding all the identical sensor concept correspondences is called sensor ontology matching, which is regarded as an effective method of addressing the sensor ontology heterogeneity issue [3].With the quick development of ontology matching domain, more and more matching techniques were proposed. Most of them need to determine an effective similarity measure to distinguish the heterogeneous entities. However, due to the complex semantic relationships between the entities, there is no single similarity measure which is able to distinguish all the heterogeneous entities, and usually, multiple similarity measures need to work together. A popular aggregating strategy is to first sum the similarity values with linear weighted fashion and filter the results with a proper threshold [4]. However, it is difficult to determine a proper weight set for various matching tasks with different heterogeneous features with completely unsupervised way [5]. Hence, the machine learning-based matching techniques start to attract researchers’ attentions [6–11]. They use a set of correct correspondences to train the regression [12] or classification [13] models, which are then used to determine the final alignment. According to Ontology Alignment Evaluation Initiative (OAEI), the introduction of more learning techniques brings back little improvement on the alignment, and to find more correct correspondences, it is necessary to work with expert’s knowledge. In this work, we propose a semisupervised learning-based sensor ontology matching technique. In particular, we first require the expert to match a certain number of correct correspondences, which works as the reference alignment in the training phase; then, the Evolutionary Algorithm (EA) is used to train a model of aggregating similarity measures; finally, the obtained model is used to match the rest sensor entities in the training phase.The rest paper is arranged as follows. Section2 gives the relevant definitions. Section 3 describes in detail the training examples’ construction and EA for addressing the metamatching problem; Section 4 shows the experimental results and makes the corresponding analysis. Finally, Section 5 concludes this work and presents the future work.


## 2. Sensor Ontology Metamatching Problem

### 2.1. Concept Similarity Measure

A sensor ontology is a 3-tupleC,Pd,R, where C, Pd,andR are, respectively, the concepts’ set, data property set, and concept relationship set in the sensor domain [14, 15]. A concept similarity measure (CSM) is a function that maps two sensor concepts to a real number in [0,1], where 1 means two concepts are the same and 0 means they are totally different. Different CSMs measure the similarity value with different ontology information, and in general, they can be divided into three categories, i.e., name-based CSM, dictionary-based CSM, and datatype property-based CSM.A name-based CSM calculates the edit distance between two concepts’ names. In this work, we use the Levenshtein distance [16]. Given two concepts’ names s1 and s2, the Levenshtein distance is defined as follows:(1)LNc1,c2=max0,mins1,s2−ds1,s2mins1,s2,where s1 and s2 are, respectively, the names of two concepts c1 and c2, s1 and s2 are, respectively, their number of characters, and ds1,s2ɛ is the number of operations that convert s1 into s2.Dictionary-based CSM makes use of electronic dictionaries, such as WordNet [17], to calculate two concepts’ similarity value on their name, which is defined as follows:(2)WNc1,c2=2×ms1∩ms2ms1+ms2,where s1 and s2 are, respectively, the names of two concepts c1 and c2, and ms1 and ms2 are, respectively, their meaning sets.Datatype property-based CSM [18] makes use of two concepts’ datatype properties to calculate their similarity value, which is defined as follows:(3)DTc1,c2=∑i=1c1∑j=1c2simc1i,c2j+∑i=1c2∑j=1c1simc1i,c2jc1+c2,where c1i and c2j are, respectively, the ith and jth datatype property names of concepts c1 and c2, simc1i,c2j=maxmax0,minc1i,c2j−dc1i,c2j/minc1i,c2j,2×mc1i∩mc2j/mc1i+mc2j.


## 2.1. Concept Similarity Measure

A sensor ontology is a 3-tupleC,Pd,R, where C, Pd,andR are, respectively, the concepts’ set, data property set, and concept relationship set in the sensor domain [14, 15]. A concept similarity measure (CSM) is a function that maps two sensor concepts to a real number in [0,1], where 1 means two concepts are the same and 0 means they are totally different. Different CSMs measure the similarity value with different ontology information, and in general, they can be divided into three categories, i.e., name-based CSM, dictionary-based CSM, and datatype property-based CSM.A name-based CSM calculates the edit distance between two concepts’ names. In this work, we use the Levenshtein distance [16]. Given two concepts’ names s1 and s2, the Levenshtein distance is defined as follows:(1)LNc1,c2=max0,mins1,s2−ds1,s2mins1,s2,where s1 and s2 are, respectively, the names of two concepts c1 and c2, s1 and s2 are, respectively, their number of characters, and ds1,s2ɛ is the number of operations that convert s1 into s2.Dictionary-based CSM makes use of electronic dictionaries, such as WordNet [17], to calculate two concepts’ similarity value on their name, which is defined as follows:(2)WNc1,c2=2×ms1∩ms2ms1+ms2,where s1 and s2 are, respectively, the names of two concepts c1 and c2, and ms1 and ms2 are, respectively, their meaning sets.Datatype property-based CSM [18] makes use of two concepts’ datatype properties to calculate their similarity value, which is defined as follows:(3)DTc1,c2=∑i=1c1∑j=1c2simc1i,c2j+∑i=1c2∑j=1c1simc1i,c2jc1+c2,where c1i and c2j are, respectively, the ith and jth datatype property names of concepts c1 and c2, simc1i,c2j=maxmax0,minc1i,c2j−dc1i,c2j/minc1i,c2j,2×mc1i∩mc2j/mc1i+mc2j.


## 3. The Optimization Model of Metamatching Problem

Taken two sensor ontologies and a concept similarity measureCMSi as input, we can determine a similarity matrix Mcsmi, whose row and column are, respectively, the concepts from two sensor ontologies and the elements inside are the similarity value determined by CMSi and two corresponding concepts. On this basis, we can covert the SSM aggregating problem into their corresponding similarity matric aggregating issue, which can be defined as follows:(4)aggregateCMS1,CMS2,…=∑Iwi×Mcsmi,where wi∈0,1 and ∑Iwi=1.The sensor ontology metamatching problem is defined as follows:(5)maxfW,ts.t.W=w1,w2,…∑iwi=1wi,t∈0,1,where W=w1,w2,… is the aggregating weight set and wi∈W is the ith similarity measure’s weight, t is the threshold for filtering the final alignment, and the objective function fW,t is to evaluate the alignment’s quality determined by W and t. Assuming A is the alignment determined by W and t, R is the reference alignment provided by the expert and fW,t is equal to A’s f-measure [19], which is defined as follows:(6)recall=R∩AR,precision=R∩AA,f−measure=2×precision×recallrecall+precision,where A, R, and R∩A are, respectively, the number of correspondence in A and R and their intersection. In particular, recall measures the ratio of found correct correspondences in the reference alignment and precision calculates the ratio of correct correspondences in all the found correspondences.


## 4. Semisupervised Learning-Based Sensor Ontology Matching

Given a partial reference ontology alignment that are determined by the expert, our approach first uses EA to address the ontology metamatching problem, which trains the model of aggregating similarity measures, and then, the obtained model are used to match the rest entities. In the next, we first introduce the training example construction and then present the EA for training the model of aggregating similarity measures.


### 4.1. Training Example Construction

To ensure the training result’s quality, it is necessary to construct a training example set through determining the most representative entities in the sensor ontologies. Here, we borrow the definition of centrality from the social network [20]; i.e., the representative entities should be the central ones in the ontology hierarchy graph, which denotes the entities as nodes and their relationships as edges. To be specific, we measure a concept c’s centrality as follows:(7)centralityc=subc|+|superc,where subc and superC are, respectively, c’s direct descendant classes and all its ascendant classes, subc and superc are, respectively, the cardinality of subc and superc, and c’s centrality is the number of all its direct descendant classes and ascendant classes. We sort all the concepts in the descending order and select first 30% concepts as the representative ones. After that, we require the expert to manually match two representative concept sets from two ontologies, and we can obtain the partial reference alignment. With this reference alignment, we can train the model of aggregating similarity measures by finding the optimal aggregating weight set and the threshold with EA.


### 4.2. Evolutionary Algorithm

The real number encoding mechanism is applied in this work to improve the algorithm’s efficiency. To be specific, there aren + 1 gene bits in a chromosome, where n is the number of similarity measures and the last gene bit represents the threshold’s information. When decoding, the ith aggregating weight is genei/∑j=1ngenej. With aggregating weights and the threshold, each solution corresponds to a particular alignment, and its fitness value is equal to the alignment’s f-measure.We use the selection operator based on roulette wheel strategy and the single-point crossover. During the mutation, we first generate a random numberran Num in [0,1] if it is large than 0.5, the new gene value gene′=gene+ranNum×1−gene; otherwise, the updated gene value gene′=gene−ran Num×gene. To improve the converging speed, we also introduce the elite strategy, which replaces the solution with worse fitness value with the elite solution (the best solution found so far) at the end of each generation.


## 4.1. Training Example Construction

To ensure the training result’s quality, it is necessary to construct a training example set through determining the most representative entities in the sensor ontologies. Here, we borrow the definition of centrality from the social network [20]; i.e., the representative entities should be the central ones in the ontology hierarchy graph, which denotes the entities as nodes and their relationships as edges. To be specific, we measure a concept c’s centrality as follows:(7)centralityc=subc|+|superc,where subc and superC are, respectively, c’s direct descendant classes and all its ascendant classes, subc and superc are, respectively, the cardinality of subc and superc, and c’s centrality is the number of all its direct descendant classes and ascendant classes. We sort all the concepts in the descending order and select first 30% concepts as the representative ones. After that, we require the expert to manually match two representative concept sets from two ontologies, and we can obtain the partial reference alignment. With this reference alignment, we can train the model of aggregating similarity measures by finding the optimal aggregating weight set and the threshold with EA.


## 4.2. Evolutionary Algorithm

The real number encoding mechanism is applied in this work to improve the algorithm’s efficiency. To be specific, there aren + 1 gene bits in a chromosome, where n is the number of similarity measures and the last gene bit represents the threshold’s information. When decoding, the ith aggregating weight is genei/∑j=1ngenej. With aggregating weights and the threshold, each solution corresponds to a particular alignment, and its fitness value is equal to the alignment’s f-measure.We use the selection operator based on roulette wheel strategy and the single-point crossover. During the mutation, we first generate a random numberran Num in [0,1] if it is large than 0.5, the new gene value gene′=gene+ranNum×1−gene; otherwise, the updated gene value gene′=gene−ran Num×gene. To improve the converging speed, we also introduce the elite strategy, which replaces the solution with worse fitness value with the elite solution (the best solution found so far) at the end of each generation.


## 5. Experiment and Results

### 5.1. Experimental Configuration

In the experiment, we use the benchmark (http://oaei.ontologymatching.org/2016/results/benchmarks/index.html) and three sensor ontologies, i.e., CSIRO sensor ontology (CSIRO) (https://www.w3.org/2005/Incubator/ssn/wiki/SensorOntology2009), Sensor Network ontology (SSN) (https://www.w3.org/TR/vocab-ssn), and MMI Device ontology (MMI) (https://marinemetadata.org). The selected sensor ontologies are all widely used in SSW, which shares lots of overlapping information. Table 1 shows the detailed descriptions on all the ontologies in the testing cases.Table 1
Descriptions on testing cases.
Testing caseDescriptionOAEI’s benchmark1XXTwo identical ontologies2XXTwo ontologies with different heterogeneous characteristics3XXTwo ontologies from real applicationReal sensor ontologyCSIROCSIRO focuses on the description on the sensor grounding and operation specificationSSNSSN focuses on modelling the sensor network’s information and knowledgeMMIMMI focus on modelling the sensor knowledge in the marine domainTable2 shows the configuration of EA, which is a trade-off configuration that ensures our approach’s performance in all testing cases.Table 2
Evolutionary algorithm’s configuration.
ParameterValuePopulation size40Maximum generation2000Crossover rate0.6Mutation rate0.02We compare our method with SOBOM [21], CODI [22], ASMOV [23], and FuzzyAlign [24], which are four state-of-the-art techniques in the sensor ontology matching domain. The results of our method are the mean value of thirty independent runs.


### 5.2. Experimental Results and Analysis

We carried out the sensitive experiment to show the effectiveness of EA’s configuration, and Table3 compares in terms of f-measure among our approach and all competitors on all testing cases. The results shown in Tables 3 and 4 are the mean value on all testing cases.Table 3
Compare with sensor ontology matching techniques in terms off-measure.
Testing caseSOBOMCODIASMOVFuzzyAlignOur methodOAEI’s benchmark1011.001.001.001.001.001031.001.001.001.001.002011.001.000.941.001.002030.860.810.891.001.002040.880.760.911.001.002210.940.841.000.841.002231.000.971.001.001.002280.910.850.961.001.002300.871.001.000.951.002311.000.881.001.001.003010.720.680.800.800.893020.810.720.830.710.85Real sensor ontologyMMI vs SSN0.770.800.730.880.94CSIRO vs SSN0.780.790.750.880.95MMI vs CSIRO0.720.780.750.870.91Table 4
Sensitive experiment on evolutionary algorithm’s configuration.
Parameter nameParameter valueThe alignments’ meanf-measurePopulation size200.90400.96600.961000.96Maximum generation5000.9010000.9220000.9650000.96Crossover probability0.20.610.40.740.60.960.80.92Mutation probability0.010.820.020.960.050.850.100.71The population size and maximum generation depend on the complexity of the problem, and their recommended scopes are, respectively, [20,100] and [500,5000]. The larger value it takes, the longer runtime it needs. Our problem is a 3-dimension problem, which is not a very complicated optimization problem. As shown in Table3, the population size and maximum generation are set as 40 and 2000. Crossover probability and mutation probability, respectively, affect EA’s exploitation and exploration. If the crossover probability is too large, EA would easily suffer from the premature converge; if it is too small, the algorithm would be difficult to converge. On the contrary, if the mutation probability is too large, EA would become a stochastic algorithm; if it is too small, the algorithm tends to fall in the local optima. From the experimental results in Table 3, we can see that when crossover probability and mutation probability are, respectively, 0.6 and 0.02, the results are the best.As shown in Table4, our approach outperforms other competitors on all testing cases, which shows that it is able to effectively determine high-quality sensor ontology alignments in different matching tasks.


## 5.1. Experimental Configuration

In the experiment, we use the benchmark (http://oaei.ontologymatching.org/2016/results/benchmarks/index.html) and three sensor ontologies, i.e., CSIRO sensor ontology (CSIRO) (https://www.w3.org/2005/Incubator/ssn/wiki/SensorOntology2009), Sensor Network ontology (SSN) (https://www.w3.org/TR/vocab-ssn), and MMI Device ontology (MMI) (https://marinemetadata.org). The selected sensor ontologies are all widely used in SSW, which shares lots of overlapping information. Table 1 shows the detailed descriptions on all the ontologies in the testing cases.Table 1
Descriptions on testing cases.
Testing caseDescriptionOAEI’s benchmark1XXTwo identical ontologies2XXTwo ontologies with different heterogeneous characteristics3XXTwo ontologies from real applicationReal sensor ontologyCSIROCSIRO focuses on the description on the sensor grounding and operation specificationSSNSSN focuses on modelling the sensor network’s information and knowledgeMMIMMI focus on modelling the sensor knowledge in the marine domainTable2 shows the configuration of EA, which is a trade-off configuration that ensures our approach’s performance in all testing cases.Table 2
Evolutionary algorithm’s configuration.
ParameterValuePopulation size40Maximum generation2000Crossover rate0.6Mutation rate0.02We compare our method with SOBOM [21], CODI [22], ASMOV [23], and FuzzyAlign [24], which are four state-of-the-art techniques in the sensor ontology matching domain. The results of our method are the mean value of thirty independent runs.


## 5.2. Experimental Results and Analysis

We carried out the sensitive experiment to show the effectiveness of EA’s configuration, and Table3 compares in terms of f-measure among our approach and all competitors on all testing cases. The results shown in Tables 3 and 4 are the mean value on all testing cases.Table 3
Compare with sensor ontology matching techniques in terms off-measure.
Testing caseSOBOMCODIASMOVFuzzyAlignOur methodOAEI’s benchmark1011.001.001.001.001.001031.001.001.001.001.002011.001.000.941.001.002030.860.810.891.001.002040.880.760.911.001.002210.940.841.000.841.002231.000.971.001.001.002280.910.850.961.001.002300.871.001.000.951.002311.000.881.001.001.003010.720.680.800.800.893020.810.720.830.710.85Real sensor ontologyMMI vs SSN0.770.800.730.880.94CSIRO vs SSN0.780.790.750.880.95MMI vs CSIRO0.720.780.750.870.91Table 4
Sensitive experiment on evolutionary algorithm’s configuration.
Parameter nameParameter valueThe alignments’ meanf-measurePopulation size200.90400.96600.961000.96Maximum generation5000.9010000.9220000.9650000.96Crossover probability0.20.610.40.740.60.960.80.92Mutation probability0.010.820.020.960.050.850.100.71The population size and maximum generation depend on the complexity of the problem, and their recommended scopes are, respectively, [20,100] and [500,5000]. The larger value it takes, the longer runtime it needs. Our problem is a 3-dimension problem, which is not a very complicated optimization problem. As shown in Table3, the population size and maximum generation are set as 40 and 2000. Crossover probability and mutation probability, respectively, affect EA’s exploitation and exploration. If the crossover probability is too large, EA would easily suffer from the premature converge; if it is too small, the algorithm would be difficult to converge. On the contrary, if the mutation probability is too large, EA would become a stochastic algorithm; if it is too small, the algorithm tends to fall in the local optima. From the experimental results in Table 3, we can see that when crossover probability and mutation probability are, respectively, 0.6 and 0.02, the results are the best.As shown in Table4, our approach outperforms other competitors on all testing cases, which shows that it is able to effectively determine high-quality sensor ontology alignments in different matching tasks.


## 6. Conclusion and Future Work

To implement the collaborations among intelligent applications on IoT, it is necessary to distinguish the heterogeneous sensor entities. To find all the sensor entity correspondences, this work proposes a semisupervised learning-based sensor ontology matching technique. In the training process, the training example set is constructed by extracting the most important concepts from two ontologies, which are matched by the expert. Then, an EA-based metamatching technique is proposed to train the model of aggregating different similarity measures. Finally, the weight set and threshold in the model are used to determine the rest correspondences in the testing phase. The experimental results show the effectiveness of our approach.In the future, we will be interested in adaptively selecting the similarity measures according to the heterogeneous characteristics on two sensor ontologies to be aligned. Moreover, when the scale of the sensor ontology becomes huge, an efficiency-improving strategy should be introduced, such as the divide-and-conquer method [25].



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