Ontology Merging Research Articles

Ontology Merging Using the Weak Unification of Concepts

Knowledge representation and manipulation in knowledge-based systems typically rely on ontologies. The aim of this work is to provide a novel weak unification-based method and an automatic tool for OWL ontology merging to ensure well-coordinated task completion in the context of collaborative agents. We employ a technique based on integrating string and semantic matching with the additional consideration of structural heterogeneity of concepts. The tool is implemented in Prolog and makes use of its inherent unification mechanism. Experiments were run on an OAEI data set with a matching accuracy of 60% across 42 tests. Additionally, we ran the tool on several ontologies from the domain of robotics. producing a small, but generally accurate, set of matched concepts. These results clearly show a good capability of the method and the tool to match semantically similar concepts. The results also highlight the challenges related to the evaluation of ontology-merging algorithms without a definite ground truth.

Merging Biomedical Ontologies with BioSTransformers.

Ontologies play a key role in representing and structuring domain knowledge. In the biomedical domain, the need for this type of representation is crucial for structuring, coding, and retrieving data. However, available ontologies do not encompass all the relevant concepts and relationships. In this paper, we propose the framework SiMHOMer (Siamese Models for Health Ontologies Merging) to semantically merge and integrate the most relevant ontologies in the healthcare domain, with a first focus on diseases, symptoms, drugs, and adverse events. We propose to rely on the siamese neural models we developed and trained on biomedical data, BioSTransformers, to identify new relevant relations between concepts and to create new semantic relations, the objective being to build a new merging ontology that could be used in applications. To validate the proposed approach and the new relations, we relied on the UMLS Metathesaurus and the Semantic Network. Our first results show promising improvements for future research.

A Novel Algorithm for Multi-Criteria Ontology Merging through Iterative Update of RDF Graph

Ontology merging is an important task in ontology engineering to date. However, despite the efforts devoted to ontology merging, the incorporation of relevant features of ontologies such as axioms, individuals and annotations in the output ontologies remains challenging. Consequently, existing ontology-merging solutions produce new ontologies that do not include all the relevant semantic features from the candidate ontologies. To address these limitations, this paper proposes a novel algorithm for multi-criteria ontology merging that automatically builds a new ontology from candidate ontologies by iteratively updating an RDF graph in the memory. The proposed algorithm leverages state-of-the-art Natural Language Processing tools as well as a Machine Learning-based framework to assess the similarities and merge various criteria into the resulting output ontology. The key contribution of the proposed algorithm lies in its ability to merge relevant features from the candidate ontologies to build a more accurate, integrated and cohesive output ontology. The proposed algorithm is tested with five ontologies of different computing domains and evaluated in terms of its asymptotic behavior, quality and computational performance. The experimental results indicate that the proposed algorithm produces output ontologies that meet the integrity, accuracy and cohesion quality criteria better than related studies. This performance demonstrates the effectiveness and superior capabilities of the proposed algorithm. Furthermore, the proposed algorithm enables iterative in-memory update and building of the RDF graph of the resulting output ontology, which enhances the processing speed and improves the computational efficiency, making it an ideal solution for big data applications.

Merging Ontologies and Data from Electronic Health Records

The Electronic Health Record (EHR) is a system for collecting and storing patient medical records as data that can be mechanically accessed, hence facilitating and assisting the medical decision-making process. EHRs exist in several formats, and each format lists thousands of keywords to classify patients data. The keywords are specific and are medical jargon; hence, data classification is very accurate. As the keywords constituting the formats of medical records express concepts by means of specific jargon without definitions or references, their proper use is left to clinicians and could be affected by their background, hence the interpretation of data could become slow or less accurate than that desired. This article presents an approach that accurately relates data in EHRs to ontologies in the medical realm. Thanks to ontologies, clinicians can be assisted when writing or analysing health records, e.g., our solution promptly suggests rigorous definitions for scientific terms, and automatically connects data spread over several parts of EHRs. The first step of our approach consists of converting selected data and keywords from several EHR formats into a format easier to parse, then the second step is merging the extracted data with specialised medical ontologies. Finally, enriched versions of the medical data are made available to professionals. The proposed approach was validated by taking samples of medical records and ontologies in the real world. The results have shown both versatility on handling data, precision of query results, and appropriate suggestions for relations among medical records.

Towards building knowledge by merging multiple ontologies with CoMerger: A partitioning-based approach

Ontologies are the prime way of organizing data in the Semantic Web. Often, it is necessary to combine several, independently developed ontologies to obtain a complete representation of a domain of interest. The complementarity of existing ontologies can be leveraged by merging them. Existing approaches for ontology merging mostly implement a binary merge. However, with the growing number and size of relevant ontologies across domains, scalability becomes a central challenge. A multi-ontology merging technique offers a potential solution to this problem. We present Co Merger, a scalable multiple ontologies merging method. It takes as input a set of source ontologies and existing mappings across them and generates a merged ontology. For efficient processing, rather than successively merging complete ontologies pairwise, we group related concepts across ontologies into partitions and merge first within and then across those partitions. In both steps, user-specified subsets of generic merge requirements (GMRs) are taken into account and used to optimize outputs. The experimental results on well-known datasets confirm the feasibility of our approach and demonstrate its superiority over binary strategies. A prototypical implementation is freely accessible through a live web portal.

Meaning Negotiation Based on Merged Individual Context Ontology and Part of Semantic Web Ontology

The meaning negotiation process has become a confusing term in the pragmatic web. It does not depend only on convenience to agree, but requires an excellent choice of terms used in the negotiated domain. The presented paper proposes a meaning negotiation model using ontologies merging into several domains and contextual situations. The idea is to design meaning negotiation scenarios using ontologies merging extended into several domains. The results obtained show the impact of merging in meaning negotiation on the multi-agent systems. The influence of merging is interpreted by the dependence between two variables: the meaning negotiation step numbers and the merging ontologies. The authors observe the difference between meaning negotiation step numbers with and without merging. They conclude that merging ontologies has a positive influence on meaning negotiation in different contextual situations of scenarios. To demonstrate the influence, an observable analytical study proof under statistical control is applied.

Benchmark construction and experimental evaluations for incoherent ontologies

As the core building blocks of the Semantic Web, ontologies provide shared vocabularies and conceptual knowledge for specific application fields. At the same time, ontologies can restrict their individuals and relationships through their semantic schema. However, logical conflicts of an ontology are often inevitably unavoidable in actual application scenarios when the ontology contains any form of disjointness or negation. Generally, logical conflicts can be divided into incoherence and inconsistency. Reasoning with incoherent ontologies may obtain many redundant relationships, and incoherence is a potential cause of inconsistency which seriously affects the correctness of semantic reasoning Therefore, handling incoherence is imperative, which mainly involves the fields of conflicts detection, ontology repair and justification computation. Various incoherent ontologies are indispensable to evaluate the proposed methods for handling incoherence. We provide a survey of relevant research works to study the proposed construction methods of incoherent ontologies. It is observed that incoherent ontologies in existing works still have the following limitations: (1) Lots of web pages used to download ontologies were temporarily constructed are now inaccessible; (2) Most of the existing incoherent ontologies were constructed in a simple way such as randomly adding disjointness axioms or merging ontologies through their alignments without considering all possible incoherence cases. To address the limitations, we propose a general framework to construct incoherent ontologies and design a hybrid algorithm to instantiate this framework. With the implemented construction methods, a comprehensive benchmark containing 116 ontologies is constructed. In our evaluations, incoherent ontologies in the benchmark are measured with 11 classic metrics. We then compare 5 representative ontology debugging systems and 3 repair methods. The evaluation results reveal that these ontologies could reflect different characteristics of each ontology system and repair method. All observations could guide researchers to select incoherent ontologies. The availability of our benchmark makes a contribution to the community of ontology debugging and repair fields.

A framework for merging ontologies in the context of smart factories

Current trends, such as individualization, increasing complexity, and specialization, require digitization in engineering and production. However, digitization by itself often leads to so-called data silos, which cannot be leveraged effectively when designing and operating smart factories due to the heterogeneity of the information available. This paper presents a framework for (semi-)automatically merging the highly reusable terminological components of production ontologies in an a posteriori way. The framework combines translations, domain-specific vocabularies, and inconsistency checks with syntactic, terminological, and structural analyses to integrate knowledge representations formalized in the Web Ontology Language. Integrating heterogeneous knowledge representations in the production domain can improve support systems for engineers, increase awareness of interdependencies, and enable unambiguous communication in smart factories.

ONTOLOGY MERGING USING PROTÉGÉ – A CASE STUDY

The exponential growth of data and the boom of online businesses necessitates the need for data to be machine-readable, as humans are no longer able to manually manage the vast amounts of data. Ontologies can define concepts and relations that are amenable to processing by machines. Ontologies are created in silos and pockets of domains, and the need to merge these resources is key to universal access to multi-domain knowledge. Merging of ontologies has been explored to an extent over the last two decades, and this paper explores the extent of the tools and techniques available with a case study of merging two ontologies which are publicly available, the Person ontology and Institutional ontology, using the latest tools available on the most popular ontology editor, Protégé. It is found that automated merging tools have not been improved much over the last two decades, and the most current merging tools provided combine the two ontologies into one but do not unite or merge any of the classes or axioms which are equivalent. This can be seen in the axiom count, which does not decrease in the merged ontology, showing that no similar classes or actual axioms were merged. Protégé plugins which used to provide the semi-automatic mapping of similar classes to assist the merging process were found to be no longer available, and manual mapping by the knowledge engineer was required. This supports further research in automated ontology merging techniques.

A Meta-Mining Ontology Framework for Data Processing

Extracting knowledge from data streams received from observed objects through data mining is required in various domains. However, there is a lack of any kind of guidance on which techniques can or should be used in which contexts. Meta mining technology can help build processes of data processing based on knowledge models taking into account the specific features of the objects. This paper proposes a meta mining ontology framework that allows selecting algorithms for solving specific data mining tasks and build suitable processes. The proposed ontology is constructed using existing ontologies and is extended with an ontology of data characteristics and task requirements. Different from the existing ontologies, the proposed ontology describes the overall data mining process, used to build data processing processes in various domains, and has low computational complexity compared to others. The authors developed an ontology merging method and a sub-ontology extraction method, which are implemented based on OWL API via extracting and integrating the relevant axioms.

Multimedia context interpretation: a semantics-based cooperative indexing approach

ABSTRACT The relative ineffectiveness of semantics-based multimedia indexing systems on the Web is caused by the semantic knowledge incompleteness and semantic heterogeneity problems. Nevertheless, the need to search multimedia documents with precision on the Web is persistently growing; pressing the demand for effective and efficient indexing strategies. In this article, we present an ontology-based multimedia indexing approach that cooperatively identifies the semantic and taxonomic relations that exist between annotation words that surround multimedia documents on Webpages. In this context, multiple ontologies are jointly employed for indexing each document. We construct inverted indexes in the form of semantic networks where nodes of each network are identified and added based on a majority-voting technique, while edges represent the semantic and taxonomic relations that hold between those nodes. To alleviate the heterogeneity between the resulting networks, we employ ontology merging algorithms to integrate them into consistent networks. We also utilise concept relatedness measures to enrich the networks with semantically-relevant entities that are not recognised by the used ontologies. To validate our proposal, we have developed a prototype system based on the proposed techniques. The produced results using real-world datasets demonstrate an improvement of the effectiveness against state-of-the-art baseline metrics.

Онтология объединения информационных подсистем: принципы и примеры

The article is devoted to the issues of merging ontologies. These ontologies are used in the design of subsystems included in the corporate information system. This problem arises when it is necessary to build a united ontology for the design of information systems in the process of integrating various information solutions, as well as in the step-by-step design of a corporate information system created by different developers in different times. It is assumed that ontologies are built based on the same principles and on the basis of one thesaurus. But nevertheless, the task of a merged ontology may have significant difficulties in combining different ontologies that describe different subsystems of an enterprise. The authors investigated the formal determination of the possibility to merge ontologies without their preliminary revision. The article proposes a criterion for the possibility of merging. The method based on expert opinions for calculating the criterion is proposed. Various examples of merging different ontologies with the calculation of the proposed criterion are also considered. The novelty of the presented material lies in developing the principle and criterion for merging ontologies and a methodology for the application of this criterion.

Semantic Mapping of Component Framework Interface Ontologies for Interoperability of Vehicle Applications

Abstract Over the past few years, ontology merging, and ontology semantic alignment has gained significant interest as research topics in automotive application domain for finding solutions to semantic data heterogeneity. To accomplish the complex and novel vehicle service requirements such as autonomous driving, V2X (Vehicle-to-Vehicle communication), etc. the automotive applications involve collaborations of several platform-specific data from heterogeneous enterprises component frameworks and consequently there has been increase in data interoperability issues. At the application component level, data interoperability relies on the semantic alignment or mapping between the various component framework interfaces data models represented as XML schemas (XSD). With the XML schemas being the preferred standard for the interface description exchange between most of the automotive application domain components, however, the data interoperability between the semantically equivalent but structurally different data constructs of multiple heterogeneous XSDs stands as a challenge in the absence of an ontology-based approach. To confront this crucial requirement for data interoperability and to increase in effect the reuse of existing components through their interfaces, we propose an approach to semantically map the various component framework interface data models when expressed as ontology schemas, based on the exploration of semantic synergies. The transformation between XSD and RDF (Resource Description Framework) schema representations and the use of queries over the ontology schemas for semantic mapping are demonstrated including a real-world case study.

A novel approach for merging ontologies using formal concept analysis

Ontologies are mainly used for knowledge sharing and also as a knowledge structure. Due to the rising nature of ontologies, the method of merging information in the corporate realm turns to be critical. In the existing methods, formal concept analysis does not provide an efficient pseudo-intent calculation and does not handle large context. The proposed technique focused the issue of ontology heterogeneity that blocks the ontology interoperability and proposed a novel algorithm called pseudo-intent with backtracking-based FCA-Merge. The pseudo-intent with backtracking-based FCA-Merge algorithm performs four phases to merge the given two ontologies. In the first phase, it obtains the perfect attribute for the matching object using decision tree and pseudo intent technique. In the second phase, the obtained results are warehoused in the linked list as a formal context. In the third phase, the perfect relationship among formal contexts from the linked list has been identified using backtracking techniques. Finally, the merging phase performs the merging between the identified relations. The experimental outcome shows that the pseudo-intent with backtracking-based FCA-Merge provides 97% of precision, 82% of recall and 89% of accuracy which is better than the existing technique.

A novel approach for merging ontologies using formal concept analysis

Ontologies are mainly used for knowledge sharing and also as a knowledge structure. Due to the rising nature of ontologies, the method of merging information in the corporate realm turns to be critical. In the existing methods, formal concept analysis does not provide an efficient pseudo-intent calculation and does not handle large context. The proposed technique focused the issue of ontology heterogeneity that blocks the ontology interoperability and proposed a novel algorithm called pseudo-intent with backtracking-based FCA-Merge. The pseudo-intent with backtracking-based FCA-Merge algorithm performs four phases to merge the given two ontologies. In the first phase, it obtains the perfect attribute for the matching object using decision tree and pseudo intent technique. In the second phase, the obtained results are warehoused in the linked list as a formal context. In the third phase, the perfect relationship among formal contexts from the linked list has been identified using backtracking techniques. Finally, the merging phase performs the merging between the identified relations. The experimental outcome shows that the pseudo-intent with backtracking-based FCA-Merge provides 97% of precision, 82% of recall and 89% of accuracy which is better than the existing technique.

Educating the Information Integration Using Contextual Knowledge and Ontology Merging in Advanced Levels

This paper defines the methods of educating the information integration by the use of ontologies. For this there are two various architecture are central and peer-to-peer data integration. A ciis generally has a worldwide mapping, which gives the client a uniform interface to get to data put away in the information sources. Conversely, in piis, there are no worldwide purposes of control on the information sources. Such systems enable developers to develop an integrated hybrid contextual based system and new concepts to be introduced. This enables the retrival of the information is easier and faster. The two most significant methodologies for structure an information integration framework is global as view & local as view (lav). In the gav method, each substance in the worldwide pattern is related nearby outline. In this paper we use various ontology languages like xml, rdf, daml+oil, owl etc.

An approach to merge domain ontologies using granular computing

Granular computing is the emerging technique which performs data processing through making multiple levels of descriptions. Each level of description is expressed through granules or chunks of data also defined as information granules. The granule, the granule structure, and the granule layer are the heart of granular computing. Ontologies are vital information archives. On all disciplines of science and technology, ontologies are developed according to the requirements. Hence, the huge number of ontologies is available in the concerned domain which creates information duplication and storage problem. Merging of existing ontologies overcomes these issues. There are many merging approaches available. The existing approaches do not use granular computing for merging the ontologies. The proposed approach employs granular computing for merging the existing domain ontologies, thereby unifying multiple domain ontologies into a single representative domain ontology. For that, this research work proposes the following four granular computing processes, namely, association, isolation, purification, and reduction which can be applied over a group of similar nodes in the ontologies thereby unifying them. The proposed method achieves the ontology merging by performing two phases, namely similarity calculation phase and granular computing phase. The similarity calculation phase identifies the inter-label similarity between the labels of ontologies and computes the relevant group of nodes. Subsequently, granular computing applies association, isolation, purification, and reduction over a group of relevant nodes. The proposed approach is validated using the film industry and transportation domain ontologies and compared against its counterpart hybrid semantic similarity measure (HSSM). The results concluded that the proposed approach outperforms HSSM.

Reverse engineering approach for improving the quality of mobile applications.

BackgroundPortable-devices applications (Android applications) are becoming complex software systems that must be developed quickly and continuously evolved to fit new user requirements and execution contexts. Applications must be produced rapidly and advance persistently in order to fit new client requirements and execution settings. However, catering to these imperatives may bring about poor outline decisions on design choices, known as anti-patterns, which may possibly corrupt programming quality and execution. Thus, the automatic detection of anti-patterns is a vital process that facilitates both maintenance and evolution tasks. Additionally, it guides developers to refactor their applications and consequently enhance their quality.MethodsWe proposed a general method to detect mobile applications’ anti-patterns that can detect both semantic and structural design anti-patterns. The proposed method is via reverse-engineering and ontology by using a UML modeling environment, an OWL ontology-based platform and ontology-driven conceptual modeling. We present and test a new method that generates the OWL ontology of mobile applications and analyzes the relationships among object-oriented anti-patterns and offer methods to resolve the anti-patterns by detecting and treating 15 different design’s semantic and structural anti-patterns that occurred in analyzing of 29 mobile applications. We choose 29 mobile applications randomly. Selecting a browser is not a criterion in this method because the proposed method is applied on a design level. We demonstrate a semantic integration method to reduce the incidence of anti-patterns using the ontology merging on mobile applications.ResultsThe proposed method detected 15 semantic and structural design anti-patterns which have appeared 1,262 times in a random sample of 29 mobile applications. The proposed method introduced a new classification of the anti-patterns divided into four groups. “The anti-patterns in the class group” is the most group that has the maximum occurrences of anti-patterns and “The anti-patterns in the operation group” is the smallest one that has the minimum occurrences of the anti-patterns which are detected by the proposed method. The results also showed the correlation between the selected tools which we used as Modelio, the Protégé platform, and the OLED editor of the OntoUML. The results showed that there was a high positive relation between Modelio and Protégé which implies that the combination between both increases the accuracy level of the detection of anti-patterns. In the evaluation and analyzing the suitable integration method, we applied the different methods on homogeneous mobile applications and found that using ontology increased the detection percentage approximately by 11.3% in addition to guaranteed consistency.

A novel method for merging academic social network ontologies using formal concept analysis and hybrid semantic similarity measure

PurposeThe purpose of this paper is to merge the ontologies that remove the redundancy and improve the storage efficiency. The count of ontologies developed in the past few eras is noticeably very high. With the availability of these ontologies, the needed information can be smoothly attained, but the presence of comparably varied ontologies nurtures the dispute of rework and merging of data. The assessment of the existing ontologies exposes the existence of the superfluous information; hence, ontology merging is the only solution. The existing ontology merging methods focus only on highly relevant classes and instances, whereas somewhat relevant classes and instances have been simply dropped. Those somewhat relevant classes and instances may also be useful or relevant to the given domain. In this paper, we propose a new method called hybrid semantic similarity measure (HSSM)-based ontology merging using formal concept analysis (FCA) and semantic similarity measure.Design/methodology/approachThe HSSM categorizes the relevancy into three classes, namely highly relevant, moderate relevant and least relevant classes and instances. To achieve high efficiency in merging, HSSM performs both FCA part and the semantic similarity part.FindingsThe experimental results proved that the HSSM produced better results compared with existing algorithms in terms of similarity distance and time. An inconsistency check can also be done for the dissimilar classes and instances within an ontology. The output ontology will have set of highly relevant and moderate classes and instances as well as few least relevant classes and instances that will eventually lead to exhaustive ontology for the particular domain.Practical implicationsIn this paper, a HSSM method is proposed and used to merge the academic social network ontologies; this is observed to be an extremely powerful methodology compared with other former studies. This HSSM approach can be applied for various domain ontologies and it may deliver a novel vision to the researchers.Originality/valueThe HSSM is not applied for merging the ontologies in any former studies up to the knowledge of authors.

A Known in Advance, What Ontologies to Integrate? For Effective Ontology Merging Using K-means Clustering

A Known in Advance, What Ontologies to Integrate? For Effective Ontology Merging Using K-means Clustering