Ontology Concept Similarity Research Articles

A Reputation Model of OSM Contributor Based on Semantic Similarity of Ontology Concepts

Due to their status as non-professionals, the reputations of Volunteered Geographic Information (VGI) contributors have a very important impact on data quality. In the process of contributor reputation evaluation in OpenStreetMap (OSM), it is very difficult to calculate the semantic similarity between object versions contributed by volunteers. Aimed at this issue, this paper proposes a model of contributor’s reputation based on semantic similarity of ontology concepts. Firstly, contributors are classified into three categories based on an improved WPCA and classification method. Then, an initial reputation is set for every OSM user in each class according to these categories and related research. Secondly, the related concept ontology is constructed for OSM entities; then, the semantic similarity of the object version is calculated according to the similarity of concept attributes and the semantic distance of concept. The contributor’s evaluation reputation is computed by synthesizing the semantic similarity, geometric similarity, and topological similarity of object versions. Thirdly, the contributor’s evaluation reputation and the initial reputation is aggregated to obtain the contributor’s reputation; finally, the OSM data of Rutland, England, is used as an example to verify the validity of our model. The experimental results show that the proposed model can obtain a more comprehensive contributor’s evaluation by fusing with the semantic similarity of ontology concept. The evaluation bias caused only by the semantic change between versions can be eliminated. Moreover, the obtained user’s reputation is positively correlated with the data quality. The contributor’s reputation evaluation method proposed in this paper is an effective method for evaluating the contributor’s reputation in OSM-like systems.

Adaptive resource prefetching with spatial–temporal and topic information for educational cloud storage systems

Prefetching proactively resources at datanodes within distribution networks plays a key role in improving the efficiency of data access for e-learning, which requires assistance from semantic knowledge on educational applications and resource popularity. To capture such information, we should exploit the characteristics of education end-users’ requests with high spatial–temporal locality. This paper aims to develop resource popularity modeling techniques for enhancing the performance of educational resource prefetching. Specifically, a novel topic model, built on an accelerated spectral clustering and an ontology concept similarity, is proposed to support resource access based on semantic features, including topic relevance and spatial–temporal locality. Using the proposed model, an adaptive prefetching inference approach is presented to associate possible popular resources in the future data requests. Also, an efficient prefetching management mechanism incorporating with replica techniques is suggested to design resource cloud storage systems for geo-distributed educational applications. Experiments over a simulation setting and a real-world case study with seven million users across China are carried out. Results demonstrate that the proposed method performs favorably compared to the state-of-the-art approaches.

A Novel Method to Associate Sensor Data with Domain Ontology

With the development of the Internet of Things, sensor ontologies have been applied to a variety of fields. Most sensor ontologies are currently built for applications in specific domains, and these ontologies are usually heterogeneous, making it difficult to share or reuse knowledge and concepts. The ontology association methods can be used to construct the semantic mapping between heterogeneous ontologies, so as to effectively determine the similarity between concepts in the ontologies. However, most of the contemporary methods do not make full use of the information that is stored in ontologies and are insufficient for the effective association. This paper proposes a novel association method based on comprehensive similarity. In our proposed method, we first use How-Net to obtain concept representation and calculate the semantic similarity of ontology concepts through sememe Tree and sememe Hierarchy. Then we calculate the structural similarity by the internal structure and the hierarchical relationship between the ontologies and remove the conceptual pairs with low relevance. Finally, we combine the semantic similarity and structural similarity to calculate the similarity matrix between ontology concepts to achieve association. The experimental results on real data show that our method can effectively associate sensor data with domain ontology by combining two different similarity calculation methods.

Analysis of gradient descent ontology iterative algorithm for geological setting

As a computational tool, ontology has wide applications in information retrieval and other disciplines. Ontology concept similarity calculation is an essential problem in these applications. In this paper, the focus is on the ontology setting such that the ontology function maps every vertex in graph into a real value. And the similarity between vertices is measured by the difference of their corresponding scores. We propose the gradient descent ontology algorithm for this setting, and the learning rates with general convex losses are determined.

Conceptual Cluster-based Large-scale Ontology Compression Approach

With the development of semantic web and ontology application, there is a large number of ontology whose scale is large and the structure is complex in different fields. The existing mapping method and mapping system perform well when dealing with the mapping between a lightweight small ontology. However, when comes to the large-scale ontology, it is full of challenges to the methods and systems。To this end, this paper proposes a method of ontology compression based on conceptual cluster to compress. Firstly, it calculates the semantic similarity and semantic correlation of ontology concepts with the DICE coefficient method and the information entropy technology to get semantic relation. Secondly according to the semantic relations, it carries on the conceptual cluster in the space so that the of semantic relations closely together in groups. The of cluster in space is reduced, and the noise concept which is independent of the mapping is removed, and the purpose of the large-scale ontology compression is realized. Experimental results show that the method is so effective that it can compress the volume of large-scale ontology in the mapping problems.

Services Rank by Semantic Similarity

Services can be described by their semantics to improve the precision of services discovery. OWL-S describes services semantic by their inputs, outputs, precondition and effects. In the frame of OWL-S, domain ontology concepts are used to describe the semantic of inputs, outputs, precondition and effects of a service in this paper. A service rank algorithm based on the semantic similarity of ontology concepts is introduced, and a travel service discovery example shows the algorithm is effective.

Piecewise function approximation and vertex partitioning schemes for multi-dividing ontology algorithm in AUC criterion setting (I)

Ontology is a useful tool with wide applications in various fields and attracts widespread attention of scholars, and ontology concept similarity calculation is an essential problem in these application algorithms. An effective method to get similarity between vertices on ontology is based on a function, which maps ontology graph into a line and maps each vertex in graph into a real-value, and the similarity is measured by the difference of their corresponding scores. The area under the receiver operating characteristics curve (AUC) criterion multi-dividing method is suitable for ontology problem. In this paper, we present piecewise constant function approximation approach for AUC criterion multi-dividing ontology algorithm and focus on vertex partitioning schemes. Using the techniques of statistical learning theory, theoretical characteristics of the approximation algorithm are provided with partitioning schemes, and a splitting rule is designed for vertex partitioning.

Learning rate of gradient descent multi-dividing ontology algorithm

As acknowledge representation model, ontology has wide applications in information retrieval and other disciplines. Ontology concept similarity calculation is a key issue in these applications. One approach for ontology application is to learn an optimal ontology score function which maps each vertex in graph into a real-value. And the similarity between vertices is measured by the difference of their corresponding scores. The multi-dividing ontology algorithm is an ontology learning trick such that the model divides ontology vertices into k parts correspond to the k classes of rates. In this paper, we propose the gradient descent multi-dividing ontology algorithm based on iterative gradient computation and yield the learning rates with general convex losses by virtue of the suitable step size and regularisation parameter selection.

A Domain Information Retrieval Method based on Pension Insurance Ontology Concept Similarity

Matching key terms is the main method for most engines for information retrieval, but this method cannot meet the needs of people who want to retrieve useful domain information. This is because that it doesn’t consider the semantic relation of resources in the context. Ontology is an effective method that can solve the problem of domain knowledge sharing and semantic understanding. In this paper, an optimization strategy is presented for information retrieval domain, and an algorithm is also proposed for ontology concept similarity calculation. Experiments are carried out on the audit guidance system, and the results show that this method significantly improves the accuracy of information retrieval based on domain ontology

Stability Analysis of Learning Algorithms for Ontology Similarity Computation

Ontology, as a useful tool, is widely applied in lots of areas such as social science, computer science, and medical science. Ontology concept similarity calculation is the key part of the algorithms in these applications. A recent approach is to make use of similarity between vertices on ontology graphs. It is, instead of pairwise computations, based on a function that maps the vertex set of an ontology graph to real numbers. In order to obtain this, the ranking learning problem plays an important and essential role, especiallyk-partite ranking algorithm, which is suitable for solving some ontology problems. A ranking function is usually used to map the vertices of an ontology graph to numbers and assign ranks of the vertices through their scores. Through studying a training sample, such a function can be learned. It contains a subset of vertices of the ontology graph. A good ranking function means small ranking mistakes and good stability. For ranking algorithms, which are in a well-stable state, we study generalization bounds via some concepts of algorithmic stability. We also find that kernel-based ranking algorithms stated as regularization schemes in reproducing kernel Hilbert spaces satisfy stability conditions and have great generalization abilities.

Characteristics of Optimal Function for Ontology Similarity Measure via Multi-dividing

As a powerful tool, ontology has been widely applied in social science, medicine science and computer science. In computer networks, especially, ontology is used for search extension, thus boost the quality of information retrieval. Ontology concept similarity calculation is an essential problem in these applications. A new method to get similarity between vertices on ontology graph is by machine learning, and multi-dividing algorithm is suitable for ontology problem. It is usually get an ontology function which maps the vertices of ontology graph to real numbers. Such function is given by learning a training sample which contains a subset of vertices of ontology graph. In this paper, we study the properties of best ontology function for this method. Some results under different loss functions are given.

Measuring Concept Similarity of Heterogeneous Ontologies in Multi-angent System

Different kinds of agents in a multi-agent system have different knowledge structure, which results in difficulties of interaction and coordination among agents. At present, ontology based knowledge representation is an effective way of resolving such difficulties, the key of which is heterogeneous ontology concept similarity measuring. In this paper, we designed an integrated heterogeneous ontology concept similarity measuring method. Experiments are conducted to demonstrate its feasibility and effectiveness.

Measuring Concept Similarities in Multimedia Ontologies: Analysis and Evaluations

The recent development of large-scale multimedia concept ontologies has provided a new momentum for research in the semantic analysis of multimedia repositories. Different methods for generic concept detection have been extensively studied, but the question of how to exploit the structure of a multimedia ontology and existing inter-concept relations has not received similar attention. In this paper, we present a clustering-based method for modeling semantic concepts on low-level feature spaces and study the evaluation of the quality of such models with entropy-based methods. We cover a variety of methods for assessing the similarity of different concepts in a multimedia ontology. We study three ontologies and apply the proposed techniques in experiments involving the visual and semantic similarities, manual annotation of video, and concept detection. The results show that modeling inter-concept relations can provide a promising resource for many different application areas in semantic multimedia processing.