Bottom-Up Extraction and Trust-Based Refinement of Ontology Metadata

Ceravolo, Paolo; Damiani, Ernesto; Viviani, Marco

doi:10.1109/tkde.2007.23

Cited by 25 publications

(10 citation statements)

References 48 publications

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“…As for future directions, first of all, we plan to study further DISARM's performance by comparing it to more reputation models from the literature and use it in real-world applications, combining it also with Semantic Web metadata for trust [10,11]. Another direction is towards improving DISARM.…”

Section: Discussionmentioning

confidence: 99%

DISARM: A social distributed agent reputation model based on defeasible logic

Kravari

Bassiliades

2016

Journal of Systems and Software

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Intelligent Agents act in open and thus risky environments, hence making the appropriate decision about who to trust in order to interact with, could be a challenging process.As intelligent agents are gradually enriched with Semantic Web technology, acting on behalf of their users with limited or no human intervention, their ability to perform assigned tasks is scrutinized. Hence, trust and reputation models, based on interaction trust or witness reputation, have been proposed, yet they often presuppose the use of a centralized authority. Although such mechanisms are more popular, they are usually faced with skepticism, since users may question the trustworthiness and the robustness of a central authority. Distributed models, on the other hand, are more complex but they provide personalized estimations based on each agent's interests and preferences. To this end, this article proposes DISARM, a novel distributed reputation model. DISARM deals MASs as social networks, enabling agents to establish and maintain relationships, limiting the disadvantages of the common distributed approaches. Additionally, it is based on defeasible logic, modeling the way intelligent agents, like humans, draw reasonable conclusions from incomplete and possibly conflicting (thus inconclusive) information. Finally, we provide an evaluation that illustrates the usability of the proposed model.

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Section: Discussionmentioning

confidence: 99%

DISARM: A social distributed agent reputation model based on defeasible logic

Kravari

Bassiliades

2016

Journal of Systems and Software

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“…However, the "right" weighting to use may be application and even data-set dependent. Nevertheless, it can be compared and adjusted with respect to users perceptions, e.g., by having the user to provide weights for the schema items [30]. In our framework, this is ensured via a function called distribute-weight.…”

Section: Supporting Flexibility In Matching Tree Structuresmentioning

confidence: 99%

A fuzzy extension of the XPath query language

et al. 2008

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XML has become a widespread format for data exchange over the Internet. The current state of the art in querying XML data is represented by XPath and XQuery, both of which rely on Boolean conditions for node selection. Boolean selection is too restrictive when users do not use or even know the data structure precisely, e.g. when queries are written based on a summary rather than on a precise representation of the schema. In this paper we describe a XML querying framework, called FuzzyXPath, based on Fuzzy Set Theory, relying on fuzzy conditions for the definition of flexible constraints on stored data. To this end, we introduce a function called "deep-similar", which aims at substituting XPath's typical "deep-equal" function. Its goal is to provide a degree of similarity between two XML trees, assessing whether they are similar both structure-wise and content-wise. Several query examples are discussed in the field of XML-based metadata for e-learning.

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“…The methods of evaluating the similarity between ontology entities have been studied widely [3] [10]. Many different matching solutions have been proposed so far from various viewpoints, e.g., databases, information systems, and artificial intelligence.…”

Section: Related Workmentioning

confidence: 99%

“…The mapping from OWL description onto resource space includes: mapping inheritance hierarchy onto inheritance axis, mapping properties onto property axes, and mapping individuals onto resources [13]. To measure the similarity between two resource spaces, first of all, we should distinguish two kinds of axes in resource spaces, they are property axes and concept axes 3 . Moreover, the property axes can be further classified into object properties axes and datatype axes.…”

Section: B Similarity Of Coordinatesmentioning

confidence: 99%

“…It measures the minimum number of token insertions, deletions, and substitutions requires to transform one string into another using a dynamic programming algorithm. 3 It has also been called as inheritance axis in [13]. Moveover, a more compressive method for evaluating the difference between two concepts can be adapted by using external lexicons or dictionaries, such as WordNet [22].…”

Section: B Similarity Of Coordinatesmentioning

confidence: 99%

See 1 more Smart Citation

Measuring Semantic Similarity between Resource Spaces

Huang

Qiu

2008

2008 Fourth International Conference on Semantics, Knowledge and Grid

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Abstract-A Web Resource Space Model (RSM) is a semantic data model for specifying, storing, managing and locating Web resources by appropriately classifying the contents of resources. For the task of detecting and retrieving relevant RSMs, one needs means for measuring the similarity between RSMs on a canonical scale. This paper proposes a general framework to automatically measure the similarity between RSMs, we break down the overall task and analyze the similarity between resource space models from three different levels, i.e. coordinate, axis and resource space. A variety of strategies and measurements that capture those differences are presented. Moreover, we theoretically analyzed the variation of similarities about Join, Disjoin, Merge and Split operations on RSMs. The empirical results of our method on Ontology Alignment Evaluation Initiative (OAEI) benchmark data set are also present in this paper.

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Bottom-Up Extraction and Trust-Based Refinement of Ontology Metadata

Cited by 25 publications

References 48 publications

DISARM: A social distributed agent reputation model based on defeasible logic

DISARM: A social distributed agent reputation model based on defeasible logic

A fuzzy extension of the XPath query language

Measuring Semantic Similarity between Resource Spaces

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