Ontology Mapping Method Research Articles

A multi-strategy ontology mapping method based on cost-sensitive SVM

As the core of ontology integration, the task of ontology mapping is to find the semantic relationship between ontologies. Nevertheless, most existing ontology mapping methods only rely ontext information to calculate entity similarity, thereby disregarding semantic nuances and necessitating substantial manual intervention. Therefore, this paper introduces an ontology mapping method. Based on the traditional ontology mapping method, the process employs a deep learning model to mine the semantic information of entity concepts, entity properties and ontology structure to obtain the embedding vector. We use the similarity mechanism to calculate the similarity between different embedding vectors, and combine the similarity values obtained from multiple strategy entities into a similarity matrix. The similarity matrix serves as input to the support vector machine (SVM), and the ontology mapping problem is finally transformed into a binary classification problem. However, since the number of mapped pairs is much larger than the number of non-mapped pairs, the number of positive samples in the data set is much smaller than the number of negative samples. Therefore, based on the traditional SVM, the paper adopts cost-sensitive strategy to deal with the class imbalance problem. In comparative evaluations against contemporary ontology mapping techniques, our method demonstrates a noteworthy 5.0% enhancement in recall and a 3.0% improvement in F1 score when tested on both public benchmark datasets and domain-specific datasets.

Process knowledge graph modeling techniques and application methods for ship heterogeneous models

In the process design and reuse of marine component products, there are a lot of heterogeneous models, causing the problem that the process knowledge and process design experience contained in them are difficult to express and reuse. Therefore, a process knowledge representation model for ship heterogeneous model is proposed in this paper. Firstly, the multi-element process knowledge graph is constructed, and the heterogeneous ship model is described in a unified way. Then, the multi-strategy ontology mapping method is applied, and the semantic expression between the process knowledge graph and the entity model is realized. Finally, by obtaining implicit semantics based on case-based reasoning and checking the similarity of the matching results, the case knowledge reuse is achieved, to achieve rapid design of the process. This method provides reliable technical support for the design of ship component assembly and welding process, greatly shortens the design cycle, and improves the working efficiency. In addition, taking the double-deck bottom segment of a ship as an example, the process knowledge map of the heterogeneous model is constructed to realize the rapid design of ship process, which shows that the method can effectively acquire the process knowledge in the design case and improve the efficiency and intelligence of knowledge reuse in the process design of the heterogeneous model of a ship.

Ontology Construction and Evaluation of UAV FCMS Software Requirement Elicitation Considering Geographic Environment Factors

The quality of unmanned aerial vehicle flight control and management system (UAV FCMS) software is crucial to guaranteeing the quality of UAVs. Software requirement elicitation (SRE) is an important part of the UAV FCMS software development process. However, this activity suffers from ambiguity, heterogeneity and incompleteness; furthermore, because the use of UAVs is closely related to their geographic environment, geographic environment factors must be fully considered when conducting UAV FCMS SRE activity. In the knowledge engineering community, an ontology is an explicit specification of a conceptualization. Introducing the ontology method into the SRE process is an effective way to solve the above problems. This paper creates a UAV FCMS SRE ontology (SREO) based on domain knowledge and empirical data, as well as a geo-ontology based on geographic information metadata. Then, the paper integrates the above two ontologies into a new ontology. The goal of ontology integration is to analyze ontology concepts by adopting a hybrid ontology mapping method. The specific process analyzes the semantic similarities between the concepts of two ontologies and then decides whether to use a description logic (DL) strategy based on the analysis results. When the corresponding conditions are satisfied, the DL strategy is used to perform both direct and transitive reasoning for the relationships to achieve the ontology mapping, and the ontology integration is eventually implemented. Finally, this paper uses a criteria-based ontology evaluation approach to evaluate the quality of the newly integrated ontology. The evaluation results show that the UAV FCMS SREO considering geographic environment factors exhibits high quality. Further engineering practices also show that the SRE activities and the generated software requirement specifications (SRSs) exhibit a large increase in quality. Through the above activities, improvements to both the quality and reliability of UAV FCMS software can be achieved.

A Bilingual Ontology Mapping and Enrichment Method for Domain Ontologies in E-learning

A Bilingual Ontology Mapping and Enrichment Method for Domain Ontologies in E-learning

A learning-based ontology alignment approach using inductive logic programming

Ontologies are key concepts in the semantic web and have an impressive role which comprise the biggest and the most prominent part of the infrastructure in this realm of web research. By fast growth of the semantic web and also, the variety of its applications, ontology mapping (ontology alignment) has been transformed into a crucial issue in the realm of computer science. Several approaches are introduced for ontology alignment during these last years, but developing more accurate and efficient algorithms and finding new effective techniques and algorithms for this problem is an interesting research area since real-world applications with respect to their more complicated concepts need more efficient algorithms. In this paper, we illustrated a new ontology mapping method based on learning using Inductive Logic Programming (ILP), and show how the ILP can be used to solve the ontology mapping problem. As a matter of fact, in this approach, an ontology which is described in OWL format is interpreted to first-order logic. Then, with the use of learning based on inductive logic, the existing hidden rules and relationships between concepts are discovered and presented. Since the inductive logic has high flexibility in solving problems such as discovering relationships between concepts and links, it also can be performed effectively in solving the ontology alignment problem. Our experimental results show that this technique yield to more accurate results comparing to other matching algorithms and systems, achieving an F-measure of 95.6% and 91% on two well-known reference datasets the Anatomy and the Library, respectively.

Variable weight semantic graph‐based ontology mapping method

AbstractThe mapping method that is based on the name and structure of the ontology elements is the strategy used in most mapping methods. Methods using the name often only use the similarity between the individual elements in the ontology to predict the semantic relations between two ontologies, while the latter measure the mapping between two ontologies by means of the structural relations between the elements. The effects of these two kinds of mapping strategies are not ideal. Addressing this issue, the work presented in this paper proposes an ontology mapping approach, in which the ontology element name and structure are combined. It uses the approaches based on linguistics and distance to generate a variable weight semantic graph. On this graph, the similarity of element names and structure are calculated through iterative computation. In the process of iteration, similarity result values are constantly adjusted. The approach avoids the problem of single methods that cannot use the entire amount of ontology information; therefore, it provides a more ideal mapping result. For making full use of the message of ontology, our implementation and experimental results are provided to demonstrate the effectiveness of the mapping approach.

The uncertainty mapping of ontologies based on three-dimensional combination weight vector space model

PurposeThis paper aims to apply vector space model (VSM)-PCR model to compute the similarity of Fault zone ontology semantics, which verified the feasibility and effectiveness of the application of VSM-PCR method in uncertainty mapping of ontologies.Design/methodology/approachThe authors first define the concept of uncertainty ontology and then propose the method of ontology mapping. The proposed method fully considers the properties of ontology in measuring the similarity of concept. It expands the single VSM of concept meaning or instance set to the “meaning, properties, instance” three-dimensional VSM and uses membership degree or correlation to express the level of uncertainty.FindingsIt provides a relatively better accuracy which verified the feasibility and effectiveness of VSM-PCR method in treating the uncertainty mapping of ontology.Research limitations/implicationsThe future work will focus on exploring the similarity measure and combinational methods in every dimension.Originality/valueThis paper presents an uncertain mapping method of ontology concept based on three-dimensional combination weighted VSM, namely, VSM-PCR. It expands the single VSM of concept meaning or instance set to the “meaning, properties, instance” three-dimensional VSM. The model uses membership degree or correlation which is used to express the degree of uncertainty; as a result, a three-dimensional VSM is obtained. The authors finally provide an example to verify the feasibility and effectiveness of VSM-PCR method in treating the uncertainty mapping of ontology.

Knowledge Fusion and Synchronization over Ubiquitous Ontology Mapping

With the rapid development and popularization of web services, the available information types and structure are becoming more and more complex and challenging. Actually web services involve the need for dynamic integration and transparent knowledge integration, in light of the urgent information changing track. Under this situation, the traditional search engine and information integration cannot finish this challenge, thereby bringing the opportunity for knowledge fusion and synchronization. This paper proposes a multi-matching strategy ontology mapping method for web information, i.e., ubiquitous ontology mapping method (U-Mapping), which can be viewed as a base collection of information on multiple ontologies made to appear anytime and everywhere. This approach is usually built independently by different information providers, avoiding the grammatical and semantic conflict. Finally, the ontology case information can be utilized under the consolidation of the U-Mapping, concerning language technology and machine learning methods.

A Concept Based Ontology Mapping Method for Effective Retrieval of Bio-Medical Documents

A Concept Based Ontology Mapping Method for Effective Retrieval of Bio-Medical Documents

Ontology-based Function Optimization of S2B e-commerce Network Platform for College Students' Creative Works

Student to business(Abbr. S2B) e-commerce network platform is designed to display the college students’ creative works, and build joint channel for the creative talents of college and small and medium-sized enterprises requiring innovative resources. Firstly, this paper analyses the targets and features of S2B e-commerce platform. Secondly, the user requirements-oriented explicit functional demands are listed from two aspects of the individual users and enterprise users through investigation. In addition, implicit functional demand is obtained based on ontology. According to the method of ontology mapping, the function description by natural language is mapped to semantic elements and demand items, then the relationship of demand items coming into semantic structures. According to the method of ontology inference, the semantic structure is mapped to domain ontology of S2B e-commerce network platform, as well as the implicit functional requirements of platform are digging out. As a result, the whole functional structure tree of S2B e-commerce network platform is constructed. Finally, the detail functions of the platform are described by UML modeling tool. Through the platform the college students’ creative works are integrated and flow to meet the innovative demand of enterprises. S2B e-commerce model can make the creative resources be fully used and put forward the innovative and creative industry developing well.

An Ontology Mapping Method Based on Lexical Similarity Calculation

An Ontology Mapping Method Based on Lexical Similarity Calculation

An Ontology Mapping Method using Ontology Mapping Results as Background Knowledge

An Ontology Mapping Method using Ontology Mapping Results as Background Knowledge

A Modularized Multi-strategy Fuzzy Ontology Mapping Method

A Modularized Multi-strategy Fuzzy Ontology Mapping Method

Ontology mapping using description logic and bridging axioms

In the last decade various proposals have been made to promote fruitful and efficient collaboration among small and medium sized enterprises (SMEs) in the form of virtual enterprises (VEs). The success of VEs depends on seamless interoperability of knowledge and data sharing. Ontology implementation is becoming an essential and successful tool for VE operation but commonly ontology mapping is also required to achieve interoperability. The current state of the art in ontology mapping indicates that mapping systems require a great deal of human intervention as mapping brings various types of conflicts and inconsistencies. The ontology mapping method proposed in this paper uses description logic (DL) based bridging axioms between the ontologies. Atomic concept level similarity has been taken as input to establish the complex concepts and roles level mapping. Manufacturing and marketing enterprise ontologies are considered and their mapping has been demonstrated as an example of the proposed mapping process.

Ontology Mapping Method based on Extended Information

Ontology Mapping Method based on Extended Information

On Ontology Mapping Based on Transfer Learning

Construction of sharable semantic space by ontology mapping is the premise and foundation of semantic inquiry and automatic composition of web service. But the present method of ontology mapping relies on syntax and structural similarity, with no formal semantic involved during the process of mapping, which makes low recall rate of mapping relations and impossibility of automated reasoning. In this paper, through extension and presentation of OWL ontology relations, an ontology mapping method based on semantic connector is presented, so the semantic relations with small granularity can be obtained through transfer learning. Furthermore, the ontology transfer learning and the construction of semantic connector is emphasized to step forward to design and develop a group of semantic arithmetics with transfer learning capacities. The research in this paper provides a new method for ontology mapping, and it can be used to create a development and coordination environment of Web software.

Combination of the Improved Method for Ontology Mapping

Abstract Most of current ontology mapping methods can not treat different mapping tasks in different ways referred to the features of the input ontology. And they combine different features of ontology without full consideration of the influences on mapping results caused mapping features. In view of the above questions, this paper proposes mapping method which can use entropy decision-making method to determine the combined weight of the different features of ontology. Experiments show that this method can maintain the stability and the commonality, and improve the recall ratio and the precision ratio at the same time.

Strategies Combination of Multi-strategy Ontology Mapping based on Entropy Decision-making

Most of current ontology mapping methods can not treat different mapping tasks in different ways referred to the features of the input ontology. And they combine different mapping strategies without full consideration of the influences on mapping results caused mapping strategies. In view of the above questions, this paper proposes a dynamic mapping policy selection method which can pre-select mapping strategy and use entropy decision-making method to determine the combined weight of the selected strategy. Experiments show that this method can maintain the stability and the commonality, and improve the recall ratio and the precision ratio at the same time.

Ontology Alignment

A grand number of ontologies have been developed and are publicly accessible on the Web making techniques for mapping between various ontologies more significant. Research has been made in the area of ontology alignment, a grand number of approaches, algorithms, and tools have been developed in recent years, but are still not “perfect” and excellent knowledge. In this article, the author makes an overall view of the state of ontology alignment, including the latest research, comparing many approaches, and analyzing their strengths and drawbacks. The main motivation behind this work is the fact that despite many component matching solutions that have been developed so far, there is no integrated solution that is a clear success, which can be used for ontology mapping in all cases, making knowledge about developed ontology mapping methods and their clear classification needed.

Knowledge Representation and Ontology Mapping Methods for Product Data in Engineering Applications

This paper presents a semantic approach that uses ontologies to share knowledge related to product data in CAD/CAE applications and for integrating the design evaluation information that these applications individually provide. Our overall approach is the ontology-based adaptive design evaluation, also coined as OADE. This paper reports a piece of our ongoing work in the area of knowledge representation and ontology mapping methods. Here we design ontologies for representing product design and analysis, instantiate a source ontology with the product data, create formal ontology mapping methods, and then apply these methods to transfer the product data from the source ontology to the target one. A prototype implementation has been created using technologies such as OWL (representation language), JENA (ontology API), and PROTÉGÉ (ontology editor) to demonstrate the approach for integrating product design and assembly simulation analysis applications. This work is significant because heuristic methods based on geometry attributes, composition, and inheritance for determining mapped concepts in engineering ontologies is still very new, and not much work has been done in this area. This work will lead to the ability to create, share, and exchange knowledge for solving design evaluation challenges involving multiple applications and viewpoints.