An information retrieval approach to ontology mapping

In this paper, we present a heuristic mapping method and a prototype mapping system that support the process of semi-automatic ontology mapping for the purpose of improving semantic interoperability in heterogeneous systems. The approach is based on the idea of semantic enrichment, i.e., using instance information of the ontology to enrich the original ontology and calculate similarities between concepts in two ontologies. The functional settings for the mapping system are discussed and the evaluation of the prototype implementation of the approach is reported. \ud \u

Data driven ontology evaluation

The evaluation of ontologies is vital for the growth of the Semantic Web. We consider a number of problems in evaluating a knowledge artifact like an ontology. We propose in this paper that one approach to ontology evaluation should be corpus or data driven. A corpus is the most accessible form of knowledge and its use allows a measure to be derived of the 'fit' between an ontology and a domain of knowledge. We consider a number of methods for measuring this 'fit' and propose a measure to evaluate structural fit, and a probabilistic approach to identifying the best ontology

Evaluation of the Project Management Competences Based on the Semantic Networks

The paper presents the testing and evaluation facilities of the SinPers system. The SinPers is a web based learning environment in project management, capable of building and conducting a complete and personalized training cycle, from the definition of the learning objectives to the assessment of the learning results for each learner. The testing and evaluation facilities of SinPers system are based on the ontological approach. The educational ontology is mapped on a semantic network. Further, the semantic network is projected into a concept space graph. The semantic computability of the concept space graph is used to design the tests. The paper focuses on the applicability of the system in the certification, for the knowledge assessment, related to each element of competence. The semantic computability is used for differentiating between different certification levels.testing, assessment, ontology, semantic networks, certification.

Ontology mapping by concept similarity

This paper presents an approach to the problem of mapping ontologies. The motivation for the research stems from the Diogene Project which is developing a web training environment for ICT professionals. The system includes high quality training material from registered content providers, and free web material will also be made available through the project's "Web Discovery" component. This involves using web search engines to locate relevant material, and mapping the ontology at the core of the Diogene system to other ontologies that exist on the Semantic Web. The project's approach to ontology mapping is presented, and an evaluation of this method is described

On Repairing Reasoning Reversals via Representational Refinements

Representation is a fluent. A mismatch between the real world and an agent’s representation of it can be signalled by unexpected failures (or successes) of the agent’s reasoning. The ‘real world ’ may include the ontologies of other agents. Such mismatches can be repaired by refining or abstracting an agent’s ontology. These refinements or abstractions may not be limited to changes of belief, but may also change the signature of the agent’s ontology. We describe the implementation and successful evaluation of these ideas in the ORS system. ORS diagnoses failures in plan execution and then repairs the faulty ontologies. Our automated approach to dynamic ontology repair has been designed specifically to address real issues in multi-agent systems, for instance, as envisaged in the Semantic Web

Learning Ontology Relations by Combining Corpus-Based Techniques and Reasoning on Data from Semantic Web Sources

The manual construction of formal domain conceptualizations (ontologies) is labor-intensive. Ontology learning, by contrast, provides (semi-)automatic ontology generation from input data such as domain text. This thesis proposes a novel approach for learning labels of non-taxonomic ontology relations. It combines corpus-based techniques with reasoning on Semantic Web data. Corpus-based methods apply vector space similarity of verbs co-occurring with labeled and unlabeled relations to calculate relation label suggestions from a set of candidates. A meta ontology in combination with Semantic Web sources such as DBpedia and OpenCyc allows reasoning to improve the suggested labels. An extensive formal evaluation demonstrates the superior accuracy of the presented hybrid approach

Semantic reconciliation across design and manufacturing knowledge models: a logic-based approach

Ontology-based models of product design and manufacture are becoming increasingly important in the effort towards achieving interoperability among various stakeholders within and across product lifecycle systems. However, in the eventuality of having to interoperate between multiple ontology-based models, with the intention of sharing knowledge among them, the process still remains a difficult one. Although the concept of ontology mapping/matching has been developed as a means to interoperate across ontology-based models, yet the concept has remained relatively weak in terms of its ability to enable the formalization and verification of cross-model semantic correspondences in design and manufacture. In this paper, improved concepts to achieve semantic reconciliation are being investigated in the context of the Semantic Manufacturing Interoperability Framework (SMIF). The approach uses a Common Logic-based underpinning for enabling the evaluation and verification of cross-model correspondences. The approach has been successfully tested by applying the relevant logic-based mechanisms, in order to show the reconciliation of two individually developed knowledge models. Through this, it has been demonstrated that the approach enables semantic reconciliation of important structures within ontology-based models of design and manufacture. © 2011-IOS Press and the authors. All rights reserved

Schema Matching for Large-Scale Data Based on Ontology Clustering Method

Holistic schema matching is the process of identifying semantic correspondences among multiple schemas at once. The key challenge behind holistic schema matching lies in selecting an appropriate method that has the ability to maintain effectiveness and efficiency. Effectiveness refers to the quality of matching while efficiency refers to the time and memory consumed within the matching process. Several approaches have been proposed for holistic schema matching. These approaches were mainly dependent on clustering techniques. In fact, clustering aims to group the similar fields within the schemas in multiple groups or clusters. However, fields on schemas contain much complicated semantic relations due to schema level. Ontology which is a hierarchy of taxonomies, has the ability to identify semantic correspondences with various levels. Hence, this study aims to propose an ontology-based clustering approach for holistic schema matching. Two datasets have been used from ICQ query interfaces consisting of 40 interfaces, which refer to Airfare and Job. The ontology used in this study has been built using the XBenchMatch which is a benchmark lexicon that contains rich semantic correspondences for the field of schema matching. In order to accommodate the schema matching using the ontology, a rule-based clustering approach is used with multiple distance measures including Dice, Cosine and Jaccard. The evaluation has been conducted using the common information retrieval metrics; precision, recall and f-measure. In order to assess the performance of the proposed ontology-based clustering, a comparison among two experiments has been performed. The first experiment aims to conduct the ontology-based clustering approach (i.e. using ontology and rule-based clustering), while the second experiment aims to conduct the traditional clustering approaches without the use of ontology. Results show that the proposed ontology-based clustering approach has outperformed the traditional clustering approaches without ontology by achieving an f-measure of 94% for Airfare and 92% for Job datasets. This emphasizes the strength of ontology in terms of identifying correspondences with semantic level variation