Ontology Merging Research Articles

Humanitarian Assistance Ontology for Emergency Disaster Response

Massive amounts of open data related to a crisis aren't fully used to identify humanitarian needs because most of the data exists in an unstructured format, thus hindering machines in its interpretation. Automatic processing of crisis data in a short time period would provide useful information to decision makers. To address these problems, this article presents a method for merging ontologies and logic rules to represent humanitarian needs and recommend appropriate humanitarian responses. The main advantage of the method is that it identifies humanitarian needs and prioritizes humanitarian responses automatically, including health survival issues and possible responses by creating a working system during the evolution of the crisis. The method is implemented on 125 real data reports from the Hurricane Wilma crisis and compared to the actual support provided.

Shiva++: An Enhanced Graph based Ontology Matcher

With the web getting bigger and assimilating knowledge about different concepts and domains, it is becoming very difficult for simple database driven applications to capture the data for a domain. Thus developers have come out with ontology based systems which can store large amount of information and can apply reasoning and produce timely information. Thus facilitating effective knowledge management. Though this approach has made our lives easier, but at the same time has given rise to another problem. Two different ontologies assimilating same knowledge tend to use different terms for the same concepts. This creates confusion among knowledge engineers and workers, as they do not know which is a better term then the other. Thus we need to merge ontologies working on same domain so that the engineers can develop a better application over it. This paper shows the development of one such matcher which merges the concepts available in two ontologies at two levels; 1) at string level and 2) at semantic level; thus producing better merged ontologies. We have used a graph matching technique which works at the core of the system. We have also evaluated the system and have tested its performance with its predecessor which works only on string matching. Thus current approach produces better results.

Approaches of Ontological Property Mapping Based on Multi-Strategy

Ontology mapping is the technological foundation of semantic integration, ontology merging and ontology alignment. In this paper, we propose approaches of ontological property mapping based on multi-strategy. First we use different strategies to deal with data type property and object type property. Then the optimal property mapping results of multi strategies are chosen. Finally, we present experimental results with high accuracy and point out future possible research directions.

Shiva: A Framework for Graph based Ontology Matching

Since long, corporations are looking for knowledge sources which can provide structured description of data and can focus on meaning and shared understanding. Structures which can facilitate open world assumptions and can be flexible enough to incorporate and recognize more than one name for an entity. A source whose major purpose is to facilitate human communication and interoperability. Clearly, databases fail to provide these features and ontologies have emerged as an alternative choice, but corporations working on same domain tend to make different ontologies. The problem occurs when they want to share their data/knowledge. Thus we need tools to merge ontologies into one. This task is termed as ontology matching. This is an emerging area and still we have to go a long way in having an ideal matcher which can produce good results. In this paper we have shown a framework to matching ontologies using graphs.

Detection Algorithm of Semantic Inconsistency for Fuzzy Ontology Merging

Fuzzy ontology can be built to effectively deal with uncertainty and ambiguity for domain knowledge modeling. Merging multiple fuzzy local ontologies may implement semantic integration of multiple data sources and semantic interoperability between heterogeneous systems in distributed environment. In order to solve the problem of semantic inconsistency mappings for fuzzy ontology merging system, we proposed a detection algorithm of semantic inconsistency mapping which includes sub detection methods of circular semantic inconsistency, subclass-of axiom redundancy semantic inconsistency, attribute membership semantic inconsistency and disjoint axioms redundancy semantic inconsistency. With the detection algorithm of semantic inconsistency, we establish fuzzy ontology merging system in experiment.

A method of ontologies merging based on rules

Ontology has been widely applied to many fields, such as knowledge engineering and semantic web, as an effective method of knowledge expression and management. Owing to the needs of knowledge integration and communication between different ontologies, ontologies merging becomes more important. In this paper, a method of ontologies merging based on rules is proposed, which is aimed at the merging of two different ontologies in the same domain. This paper analyses the specific process and the problems that may appear during the merging process in detail, and divides the conflicts into two kinds: conventional semantic conflict and structural conflict, and then gives a concrete merging programme and conflicts solution algorithm without against the consistency of ontology. The method realises ontologies merging well, solves the conflicts in the merging process preferably and has good expansibility. The results of the experiment with OAEI2012 test set reveal that the proposed method is feasible and valid.

Reuse existing ontologies in an ontology development process - an integration-oriented ontology development methodology

Ontologies are the building block of the Semantic Web and a diverse range of ontologies of different domains have been constructed and shared by the ontology community. In this regard it is not impractical to reuse existing ontologies that are available publicly instead of constructing ontology from scratch each time. This paper proposes and develops guidelines on how to select ontology merging and integration in an ontology development process. The paper aims to address the gap in literature where little information and guidance are available in determining how to reuse ontology through the process of ontology merging and integration. In addition the paper also proposes a semi-automatic way of performing ontology integration. An application scenario has been presented to illustrate how the proposed methodology is applied.

Unification of multi-species vertebrate anatomy ontologies for comparative biology in Uberon.

BackgroundElucidating disease and developmental dysfunction requires understanding variation in phenotype. Single-species model organism anatomy ontologies (ssAOs) have been established to represent this variation. Multi-species anatomy ontologies (msAOs; vertebrate skeletal, vertebrate homologous, teleost, amphibian AOs) have been developed to represent ‘natural’ phenotypic variation across species. Our aim has been to integrate ssAOs and msAOs for various purposes, including establishing links between phenotypic variation and candidate genes.ResultsPreviously, msAOs contained a mixture of unique and overlapping content. This hampered integration and coordination due to the need to maintain cross-references or inter-ontology equivalence axioms to the ssAOs, or to perform large-scale obsolescence and modular import. Here we present the unification of anatomy ontologies into Uberon, a single ontology resource that enables interoperability among disparate data and research groups. As a consequence, independent development of TAO, VSAO, AAO, and vHOG has been discontinued.ConclusionsThe newly broadened Uberon ontology is a unified cross-taxon resource for metazoans (animals) that has been substantially expanded to include a broad diversity of vertebrate anatomical structures, permitting reasoning across anatomical variation in extinct and extant taxa. Uberon is a core resource that supports single- and cross-species queries for candidate genes using annotations for phenotypes from the systematics, biodiversity, medical, and model organism communities, while also providing entities for logical definitions in the Cell and Gene Ontologies.The ontology release files associated with the ontology merge described in this manuscript are available at: http://purl.obolibrary.org/obo/uberon/releases/2013-02-21/Current ontology release files are available always available at: http://purl.obolibrary.org/obo/uberon/releases/

Automatic Detection and Processing of Attributes Inconsistency for Fuzzy Ontologies Merging

Semantic fusion of multiple data sources and semantic interoperability between heterogeneous systems in distributed environment can be implemented through integrating multiple fuzzy local ontologies. However, ontology merging is one of the valid ways for ontology integration. In order to solve the problem of attributes inconsistency for concept mapping in fuzzy ontology merging system, we present an automatic detection algorithm of inconsistency for the range, number and membership grade of attributes between mapping concepts, and adopt corresponding processing strategy during the fuzzy ontologies merging according to the different types of attributes inconsistency. Experiment results show that with regard to merging accuracy, the fuzzy ontology merging system in which the automatic detection algorithm and processing strategy of attributes inconsistency is embedded is better than those traditional ontology merging systems like GLUE, PROMPT and Chimaera. DOI: http://dx.doi.org/10.11591/telkomnika.v11i11.3490 Full Text: PDF

The Novel Model of Domain Ontology Matching and Merging in Semantic Web Service Based on Fuzzy Concept Lattice

Ontology matching is committed to find a similarity between the semantically related ontology entities, these similarities can be used for a variety of tasks. The target of ontology merging is building a new ontology by the original two or more of ontology. Fuzzy concept lattice is a combination of concept analysis methods for the fuzzy logic and formal concept analysis. This paper presents the novel model of domain ontology matching and merging in semantic web service based on fuzzy concept lattice. Experimental results show that using fuzzy concept lattices to match ontology and merge ontology has improved significantly in the time and space complexity than the fuzzy set construction algorithm.

Ontology Fusion in High-Level-Architecture-Based Collaborative Engineering Environments

In high-level-architecture (HLA)-based distributed heterogeneous collaborative engineering environments (CEEs), the construction of federation object model files is time consuming. This paper presents an ontology fusion approach aiming at establishing a common understanding in such collaborative environments. The proposed approach has three steps: ontology mapping, ontology alignment, and ontology merging. Ontology mapping employs a top-down approach to explore all bridge relations between two terms from different ontologies based on bridge axioms and deduction rules. Ontology alignment adopts a bottom-up approach to discover implicit bridge relations between two terms from different domain ontologies based on equivalent inference. Ontology merging generates a new collaboration ontology from discovered equivalent bridge relations. It adopts an axiom-based ontology fusion strategy and takes heavy-weighted ontologies into consideration. It can find all the explicit and derived interontology relations. In a typical CEE, the proposed approach has a great potential to improve the efficiency of preparation for HLA-based collaborative engineering processes, reduce the work load for adaptive adjustment of existing platforms, and enhance the reusability and flexibility of CEEs. A case study has been conducted to validate the feasibility of the proposed approach.

Merging ontology by semantic enrichment and combining similarity measures

In this paper, we present a new approach to merge OWL ontologies by semantic enrichment of initial ontologies. This work is situated in the general context of stored information heterogeneity in a decisional system such as data, metadata and knowledge, for combination and reconciliation these forms of information by mediation. To add a semantic dimension to the merger, our approach based on semantic enrichment of initial ontologies, this is achieved by enriching initial ontologies by a set of metadata that annotate their concepts with synonyms and homonyms for each concept, via the use of WordNet, or semantic enrichment of an expert, then it generates a thesaurus for each local ontology to build the global thesaurus. Our method focuses on computing semantic similarity between concepts of ontologies, and based on a weighted combination of computing similarity methods, we use syntactic, lexical, structural and semantic techniques, for generating the correspondence matrix; from this latter we generate the merged ontology.

Ontology merging as social choice: judgment aggregation under the open world assumption

The problem of merging several ontologies has important applications in the Semantic Web, medical ontology engineering, and other domains where information from several distinct sources needs to be integrated in a coherent manner. We propose to view ontology merging as a problem of social choice, i.e., as a problem of aggregating the input of a set of individuals into an adequate collective decision. That is, we propose to view ontology merging as ontology aggregation. As a rst step in this direction, we formulate several desirable properties for ontology aggregators, we identify the incompatibility of some of these properties, and we dene and analyse several simple aggregation procedures. Our approach is closely related to work in judgment aggregation, but with the crucial dierence that we adopt an open world assumption, by distinguishing between facts not included in an agent’s ontology and facts explicitly negated in an agent’s ontology.

A Semantic-Oriented Method for Ontology Merging

Most of the methods for ontology merging need participation of domain experts and provide only partial automation with low efficiency. It is time-consuming and laborious especially when large amount of data are to be merged. In this paper, a method based on semantic is designed for ontology merging according to the case of ontologies to be merged. In this method, first the ontologies to be merged are conversed into a unified format. Then a secondary file is generated. After that semantic analysis is applied to secondary file and a dynamic SQL statements is generated automatically. At last the SQL statements are executed to finish ontology merging. It has been proved by the experiment that using this method would get good precision and high efficiency. Using this method, two ontologies Chinese Agricultural Thesaurus and AGROVOC of the Food and Agriculture Organization are merged successfully.

An Ontology Merging Method Based on Concept Lattice

An Ontology Merging Method Based on Concept Lattice

Ontology merging in the context of concept maps

Abstract - This paper proposes the approach to a concept map merging using methods and tools developed for the same task in the domain of ontologies. The developed method is based on ideas that concept maps and ontologies have structural similarities, and mutual transformations between them are possible therefore tools and methods suitable for ontologies can be applied to concept maps. Concept map merging is necessary to extend the functionality of intelligent concept map-based knowledge assessment system IKAS for reuse of captured concept maps.

Ontology Merging and Reasoning Using Paraconsistent Logics

Dealing with the inconsistencies that might arise during the ontology merging process constitutes a major challenge. The explosive nature of classical logic requires any logic-based merging effort to dissolve possible contradictions, and thus maintain consistency. In many cases, however, inconsistent information may be useful for intelligent reasoning activities. In healthcare systems, for example, inconsistent information may be required to provide a full clinical perspective, and thus any information loss is undesirable. The authors present a 4-valued logic-based merging system that exhibits inconsistency-tolerant behavior to avoid information loss.

Detection and resolution of semantic inconsistency and redundancy in an automatic ontology merging system

In recent years, researchers have been developing algorithms for the automatic mapping and merging of ontologies to meet the demands of interoperability between heterogeneous and distributed information systems. But, still state-of-the-art ontology mapping and merging systems is semi-automatic that reduces the burden of manual creation and maintenance of mappings, and need human intervention for their validation. The contribution presented in this paper makes human intervention one step more down by automatically identifying semantic inconsistencies in the early stages of ontology merging. We are detecting semantic heterogeneities that occur due to conflicts among the set of Generalized Concept Inclusions, Property Subsumption Criteria, and Constraint Satisfaction Mechanism in local heterogeneous ontologies, which become obstacles for the generation of semantically consistent global merged ontology. We present several algorithms to detect such semantic inconsistencies based on subsumption analysis of concepts and properties in local ontologies from the list of initial mappings. We provide ontological patterns for resolving these inconsistencies automatically. This results global merged ontology free from `circulatory error in class/property hierarchy', `common class between disjoint classes/properties', `redundancy of subclass/subproperty of relations' and other types of `semantic inconsistency' errors. Experiments on the real ontologies show that our algorithms save time and cost of traversing local ontologies, improve system's performance by producing only consistent accurate mappings, and reduce the users' dependability for ensuring the satisfiability of merged ontology.

A Statistical Amalgamation Approach for Ontologies

As ontology is subjective and varies in different domains, the amount of ontologies turns out to be huge but with poor compatibility. Mainstream method for ontology integration is mostly achieved by establishing mappings between ontologies. In this essay, the author put forward another way of ontology merging. After statistic machine learning on concept relations, the frequency of different ontologies appeared in concept relations reveals certainty factor and help to build a large-scale concept relations network including the statistic information and domain categories, so that the conceptions convey ed by different ontologies can be fused together and the merging concept space turns to be relatively objective. And the experiments results also help to demonstrate the feasibility of the ontology merging.

An Intelligent System Approach for Integrating Anatomical Ontologies

ABSTRACTRecent years have seen a vast amount of data generated by various biological and biomedical experiments. The storage, management and analysis of this data, is done by means of the modern bioinformatics applications and tools. One of the bioinformatics instruments used for solving these tasks, are ontologies and the apparatus they provide. Ontology as a modeling tool is a specification of a conceptualization meaning that an ontology is a formal description of the concepts and relationships that can exist for a given software system or software agent (8, 10). Anatomical (phenotypic) ontologies of various species nowadays typically contain from few thousands to few tens of thousands of terms and relations (which is a very small number compared to the count of objects and the amount of data produced by biological experiments at the molecular level, for example) but usually the semantics employed in them is enormous in scale. The major problem when using such ontologies is that they lack intelligent tools for cross-species literature searches (text mining) as well as tools aiding the design of new biological and biomedical experiments with other (notyet tested) species/organisms, based on available information about experiments already performed on certain model species/organisms.This is where the process of merging anatomical ontologies comes into use. Using specific models and algorithms for merging of such ontologies is a matter of choice. In this work a novel approach for solving this task, based on two directed acyclic graph (DAG) models and three original algorithmic procedures is presented. Based on them, an intelligent software system for merging two (and possibly more) input/source anatomical ontologies into one output/target super-ontology was designed and implemented. This system was named AnatOM (an abbreviation from “Anatomical Ontologies Merger”).In this work a short overview of ontologies is provided describing what ontologies are and why they are widely used as a tool in bioinformatics. The problem of merging anatomical ontologies of two or more different organisms is introduced and some effort has been put into explaining why it is important. A general outline is presented of the models and the method that have been developed for solving the ontologies merging problem. A high-level overview of the AnatOM program implemented by the authors as part of this work is also provided.To achieve the degree of intelligence that is needed, the AnatOM program utilizes the large amount of high-quality data (knowledge) available in several widely popular and generally recognized knowledge bases such as UMLS, FMA, and WordNet. The last one of these is a general-purpose i.e. non-specialized knowledge source. The first two are biological/biomedical ones. Their choice was based on the fact that they provide a very good foundation for building an intelligent system that performs certain comparative anatomy tasks including mapping and merging of anatomical ontologies (23).