Semantic annotation is the process of adding semantic metadata to resources. Semantic metadata is data concerning the meaning of entities and the relationships that exist. Semantic annotation cannot be performed without an ontology suitable for the task. In this research paper, we describe the design, implementation, and evaluation of a lexical ontology for Arabic semantic relations. The main purpose of the ontology is to facilitate the task of semantic annotation of the Arabic textual content. The ontology was evaluated for usability and usefulness using a prototype system for the automated semantic annotation of Arabic text. The results of the evaluation indicated that the ontology was fit for the purpose of semantic annotation of Arabic text with lexical relations. The evaluation has also revealed important findings and recommendations for designing Arabic semantic annotation tools.
Trust has become an increasingly important issue given society's growing reliance on electronic transactions. Peer-to-peer (P2P) networks are among the main electronic transaction environments affected by trust issues due to the freedom and anonymity of peers (users) and the inherent openness of these networks. A malicious peer can easily join a P2P network and abuse its peers and resources, resulting in a large-scale failure that might shut down the entire network. Therefore, a plethora of researchers have proposed trust management systems to mitigate the impact of the problem. However, due to the problem's scale and complexity, more research is necessary. The algorithm proposed here, HierarchTrust, attempts to create a more reliable environment in which the selection of a peer provider of a file or other resource is based on several trust values represented in hierarchical form. The values at the top of the hierarchical form are more trusted than those at the lower end of the hierarchy. Trust, in HierarchTrust, is generally calculated based on the standard deviation. Evaluation via simulation showed that HierarchTrust produced a better success rate than the well-established EigenTrust algorithm.
Although the process of semantic annotation of Arabic Web content is essential for the realization of the Semantic Web, the process of semantic annotation cannot be performed without an ontology suitable for the task. In this paper, we describe the design, implementation and evaluation of the SemTree ontology for lexical semantic relations. The ontology was evaluated for usefulness using a prototype system for lexical semantic annotation of Arabic text. Results of the evaluation indicate that the ontology was fit for the purpose of semantic annotations with lexical relations. The evaluation also reveled important recommendations for designing Arabic semantic annotation tools.
In P2P networks, self-organizing anonymous peers share different resources without a central entity controlling their interactions. Peers can join and leave the network at any time, which opens the door to malicious attacks that can damage the network. Therefore, trust management systems that can ensure trustworthy interactions between peers are gaining prominence. This paper proposes AntTrust, a trust management system inspired by the ant colony. Unlike other ant-inspired algorithms, which usually adopt a problem-independent approach, AntTrust follows a problem-dependent (problem-specific) heuristic to find a trustworthy peer in a reasonable time. It locates a trustworthy file provider based on four consecutive trust factors: current trust, recommendation, feedback, and collective trust. Three rival trust management paradigms, namely, EigenTrust, Trust Network Analysis with Subjective Logic (TNA-SL), and Trust Ant Colony System (TACS), were tested to benchmark the performance of AntTrust. The experimental results demonstrate that AntTrust is capable of providing a higher and more stable success rate at a low running time regardless of the percentage of malicious peers in the network.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2025 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.