A Strategy for Automated Meaning Negotiation in Distributed Information Retrieval

Ermolayev, Vadim; Keberle, Natalya; Matzke, Wolf-Ekkehard; Vladimirov, Vladimir

doi:10.1007/11574620_17

Cited by 11 publications

(8 citation statements)

References 7 publications

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“…However, it is a promising approach also for such tasks as ontology evolution, refinement and instance migration especially in decentralised settings where manipulating of distributed heterogeneous ontologies is a necessary subtask. (Ermolayev et al, 2005) elaborate a strategy for automated meaning negotiation. Similarly to (Atencia and Schorlemmer, 2008) their approach aims at aligning ontologies by parts (contexts) that are relevant to a particular negotiation encounter.…”

Section: Agent-based Solutions For Ontology Alignmentmentioning

confidence: 99%

A Survey on Agent-Based Ontology Alignment

Davidovsky¹,

Ermolayev

Tolok³

2012

Proceedings of the 4th International Conference on Agents and Artificial Intelligence

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Abstract:Ontologies today are increasingly used as consensual knowledge representations in many distributed applications. However, if a system of knowledge based nodes is decentralized, the ontologies at those nodes differ. Therefore the alignment of knowledge representations is required. One of the promising approaches to solve this heterogeneity is the use of agents for aligning knowledge representations. The paper presents a brief survey of the approaches to agent-based ontology alignment. The analysis of these approaches is grounded on the analysis of the requirements to ontology alignments by typical applications that address semantic heterogeneity in open and decentralized settings.

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Section: Agent-based Solutions For Ontology Alignmentmentioning

confidence: 99%

A Survey on Agent-Based Ontology Alignment

Davidovsky¹,

Ermolayev

Tolok³

2012

Proceedings of the 4th International Conference on Agents and Artificial Intelligence

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“…The analysis was carried out as follows. We compared UML diagrams of the ontological contexts [9] of the corresponding concepts and documented the differences. Discovered differences were further cross-checked in the owl 6 files of the ontologies.…”

Section: Instance Transformation Patterns and Rulesmentioning

confidence: 99%

Evaluation of Semi-automated Ontology Instance Migration

Davidovsky¹,

Ermolayev²,

Matzke

et al. 2010

Studies in Computational Intelligence

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Ontology instance migration is one of the challenges in knowledge management. It becomes even more complex in distributed settings when, for example, several autonomous agents use partial assertional knowledge in a domain that is formalized by different though semantically overlapping descriptive theories. Agents exchange instances of their ontologies when cooperate. Such an exchange is essentially the migration of the assertional part of an ontology to other ontologies owned by different agents. The paper presents our method and tool support for migrating instances between different semantically overlapping ontologies. The method is based on the use of manually coded formal rules describing the changes between the input and the output ontologies. The tool to support the process is implemented as a plug-in to Cadence ProjectNavigator software. The main contribution of the paper is in presenting the results of the evaluation of this tool. It reports about the set-up for our evaluation experiments, the metrics used for measuring the quality of instance migration, the ontologies that have been chosen as the experimental data, and the evaluation results. Evaluation results are satisfactory and suggest some directions for the future work.

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“…Furthermore, using appropriate reverse-engineering techniques (such as She et al, 2011;Dumitru et al, 2011;Yang et al, 2009), we can construct such feature models from existing requirement documents and source code with the help of domain experts. Second, research on distributed information retrieval (Ermolayev et al 2005) suggests that similar elements in two different knowledge models usually have similar structural contexts. These two lines of research suggest that in addition to the "local" properties (such as lexical descriptions) of features and program elements the structural context of features and program elements can serve as the other important factor for determining the relevance between features and program elements.…”

Section: Introductionmentioning

confidence: 99%

Improving feature location using structural similarity and iterative graph mapping

Peng

Xing

Tan

et al. 2013

Journal of Systems and Software

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Abstract. Locating program element(s) relevant to a particular feature is an important step in efficient maintenance of a software system. The existing feature location techniques analyze each feature independently and perform a one-time analysis after being provided an initial input. As a result, these techniques are sensitive to the quality of the input. In this paper, we propose to address the above issues in feature location using an iterative context-aware approach. The underlying intuition is that features are not independent of each other, and the structure of source code resembles the structure of features. The distinguishing characteristics of the proposed approach are: (1) it takes into account the structural similarity between a feature and a program element to determine feature-element relevance; (2) it employs an iterative process to propagate the relevance of the established mappings between a feature and a program element to the neighboring features and program elements. We evaluate our approach using two different systems, DirectBank, a small-scale industry financial system, and Linux kernel, a large-scale open-source operating system. Our evaluation suggests that the proposed approach is more robust and can significantly increase the recall of feature location with only a minor decrease of precision.

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A Strategy for Automated Meaning Negotiation in Distributed Information Retrieval

Cited by 11 publications

References 7 publications

A Survey on Agent-Based Ontology Alignment

A Survey on Agent-Based Ontology Alignment

Evaluation of Semi-automated Ontology Instance Migration

Improving feature location using structural similarity and iterative graph mapping

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