Ontology Mapping Systems Research Articles

GRO and WeGO - algorithmic approaches to integrate the heterogeneous databases and enhance the evaluation of ontology mapping systems in the semantic web

GRO and WeGO - algorithmic approaches to integrate the heterogeneous databases and enhance the evaluation of ontology mapping systems in the semantic web

Aligning the large-scale ontologies on schema-level for weaving Chinese linked open data

This study purposes a data field and sequence alignment-based ontology mapping architecture, as ontology mapping is an important way to solve the problem of ontology heterogeneous; meanwhile, it also can be considered as a typical scenario on finding the schema-level links in linked open data (LOD). Current researches on LOD is mainly focused on the level of instances so that the task on finding schema-level links between LOD datasets is being ignored. Furthermore, nowadays the knowledge described in Chinese is also an important part of online knowledge base; unfortunately, current Chinese ontology mapping systems have low efficiency and availability, there is still lack of relevant system for large-scale Chinese ontology mapping task in LOD environment. In order to solve this problem, firstly, based on the improved nuclear field-like potential function, we compress the size of unaligned large-scale Chinese ontology. Secondly, we use the sequence alignment algorithm to compute similarity between the concepts. Finally, we compare our system to other typical concept mapping algorithms. Results prove it can reduce the scale of mapping task effectively and also have higher overall performance. This study may have important practical significance for promoting Chinese knowledge sharing, reusing and interoperation.

Word-Sense Disambiguation for Ontology Mapping: Concept Disambiguation using Virtual Documents and Information Retrieval Techniques

Ontology mapping is a crucial task for the facil- itation of information exchange and data integration. A mapping system can use a variety of similarity measures to determine concept correspondences. This paper proposes theintegrationofword-sensedisambiguationtechniquesinto lexical similarity measures. We propose a disambiguation methodology which entails the creation of virtual documents from concept and sense definitions, including their neigh- bourhoods.Thespecifictermsareweightedaccordingtotheir origin within their respective ontology. The document simi- larities between the concept document and sense documents are used to disambiguate the concept meanings. First, we evaluate to what extent the proposed disambiguation method can improve the performance of a lexical similarity metric. We observe that the disambiguation method improves the performance of each tested lexical similarity metric. Next, we demonstrate the potential of a mapping system utilizing the proposed approach through the comparison with con- temporary ontology mapping systems. We observe a high performance on a real-world data set. Finally, we evaluate how the application of several term-weighting techniques on the virtual documents can affect the quality of the gen- erated alignments. Here, we observe that weighting terms according to their ontology origin leads to the highest per- formance.

Establishing agent trust for contradictory evidence by means of fuzzy voting model: An ontology mapping case study

This paper introduces a novel trust assessment formalism for contradicting evidence in the context of multi-agent ontology mapping. Evidence combination using the Dempster rule tend to ignore contradictory evidence and the contemporary approaches for managing these conflicts introduce additional computation complexity i.e. increased response time of the system. On the Semantic Web, ontology mapping systems that need to interact with end users in real time cannot afford prolonged computation. In this work, we have made a step towards the formalisation of eliminating contradicting evidence, to utilise the original Dempster’s combination rule without introducing additional complexity. Our proposed solution incorporates the fuzzy voting model to the Dempster–Shafer theory. Finally, we present a case study where we show how our approach improves the ontology mapping problem.

Ontology Mapping for Dynamic Multiagent Environment

Ontologies are essential for the realization of the Semantic Web, which in turn relies on the ability of systems to identify and exploit relationships that exist between and within ontologies. As ontologies can be used to represent different domains, there is a high need for efficient ontology matching techniques that can allow information to be easily shared between different heterogeneous systems. There are various systems were proposed recently for ontology mapping. Ontology mapping is a prerequisite for achieving heterogeneous data integration on the Semantic Web. The vision of the Semantic Web implies that a large number of ontologies present on the web need to be aligned before one can make use of them. At the same time, these ontologies can be used as domain-specific background knowledge by the ontology mapping systems to increase the mapping precision. However, these ontologies can differ in representation, quality, and size that pose different challenges to ontology mapping. In this paper, we analyzed the various challenges of recently introduced Multi-Agent Ontology Mapping Framework, DSSim and we have integrated an efficient feature called QoS-Web Services Composition with DSSsim. ie we have improved this framework with QoS based Service Compositions Mechanism. From our experimental results, it is established that this developed QoS based Web Services Compositions Mechanism for Multiagent Ontology Mapping Framework minimizing uncertain reasoning and improves matching time, which are encouraging results of our proposed work..

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.

Multiagent Ontology Mapping Framework for the Semantic Web

Ontology mapping is a prerequisite for achieving heterogeneous data integration on the Semantic Web. The vision of the Semantic Web implies that a large number of ontologies present on the web need to be aligned before one can make use of them, for example, a question answering on the Semantic Web. At the same time, these ontologies can be used as domain-specific background knowledge by the ontology mapping systems to increase the mapping precision. However, these ontologies can differ in representation, quality, and size that pose different challenges to ontology mapping. In this paper, we analyze these challenges and introduce a multiagent ontology mapping framework that has been designed to operate effectively in the Semantic Web environment.

Towards ensuring Satisfiability of Merged Ontology

Abstract The last decade has seen researchers developing efficient algorithms for the 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. Our methodology detects inconsistencies based on structural mismatches that occur due to conflicts among the set of Generalized Concept Inclusions, and Disjoint Relations due to the differences between disjoint partitions in the local heterogeneous ontologies. We present novel methodologies to detect and repair semantic inconsistencies from the list of initial mappings. This results in global merged ontology free from ‘circulatory error in class/property hierarchy’, „common class/instance between disjoint classes error’, ‘redundancy of subclass/subproperty relations’, ‘redundancy of disjoint relations’ and other types of „semantic inconsistency’ errors. In this way, our methodology saves time and cost of traversing local ontologies for the validation of mappings, improves performance by producing only consistent accurate mappings, and reduces the user dependability for ensuring the satisfiability and consistency of merged ontology. The experiments show that the newer approach with automatic inconsistency detection yields a significantly higher precision.

AN ANALYSIS OF MULTIPLE SIMILARITY MEASURES FOR ONTOLOGY MAPPING PROBLEM

This paper presents an analysis of similarity measures for the ontology mapping problem. To that end, 48 similarity measures such as string matching and knowledge based similarities that have been widely used in ontology mapping systems are defined. The similarity measures are investigated by discriminant analysis with a real-world data set. As a result, it was possible to identify 22 effective similarity measures for the ontology mapping problem out of 48 possible similarity measures. The identified measures have a wide variety in the type of similarity. To test whether the identified similarity measures are effective for the problem, experiments were conducted with all 48 similarity measures and the 22 identified similarity measures by using two major machine learning methods, decision tree and support vector machine. The experimental results show that the performance of the 48 cases and the 22 cases is almost the same regardless of the machine learning method. This implies that effective features for the ontology mapping problem were successfully identified.