Semantic web technology for agent interoperability: a proposed infrastructure

Pai, Fang-Ping; Hsu, I-Ching; Chung, Yeh‐Ching

doi:10.1007/s10489-015-0690-x

Cited by 16 publications

(4 citation statements)

References 23 publications

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“…The semantic web 6,7 is proposed by Tim Berners‐Lee as “an extension of the current web in which information is given well defined meaning, better enabling computers and people to work in cooperation.” This study uses semantic web techniques, including OWL‐based ontology and jena‐based rule, 8‐10 to deduce personalized recommendations for the development of an intelligence system. How to automatically generate objects in an ontology class is an interesting research area in the field of semantic web.…”

Section: Introductionmentioning

confidence: 99%

Integrated machine learning with semantic web for open government data recommendation based on cloud computing

Hsu

Lin

2020

Softw Pract Exp

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Summary Open government data (OGD) is a type of trusted information that can be used to verify the correctness of information on social platforms. Finding interesting OGD to serve personalized needs to facilitate the development of social platforms is a challenging research topic. This study explores how to link the Taiwanese government's open data platform with Facebook and how to recommend related OGD. First, an integrated machine learning with semantic web into cloud computing framework is defined. Next, the linked data query platform (LDQP) is developed to validate its feasibility. The LDQP provides a graphical approach for personal query and links with related Facebook fan pages. LDQP automatically finds highly relevant OGD based on recent topics that users are following on Facebook when users login to Facebook via the LDQP. In this way, the LDQP query result can be dynamically adjusted and graphically displayed according to user's Facebook operations.

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Section: Introductionmentioning

confidence: 99%

Integrated machine learning with semantic web for open government data recommendation based on cloud computing

Hsu

Lin

2020

Softw Pract Exp

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“…A lack of intelligent judgment prevents current services from comparing and filtering information quickly. Many Internet services incorporate machine learning or Semantic Web technologies [5][6][7][8] to endow systems with intelligent judgment capabilities. They also use user profiles to record and filter information according to the interests of users.…”

Section: Introductionmentioning

confidence: 99%

Incorporation of Ontology-based Technologies and Web 2.0 Mashups in Cloud Computing Environment

Hsu¹,

Lyu²

2018

jcl

Self Cite

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The rapid development of the social Web has resulted in diverse Web 2.0 applications for accessing various publishing information related to social activity such as conference activities, public welfare activities, and cultural events. Due to the masses of social activities information available on the Internet, a major challenge is how to access them efficiently. The traditional keyword search is imprecise and inconvenient for web surfing to find interest information on the internet. This study addresses this issue by defining a Multi-layered Cloud Service Framework (MCSF) for integrating ontology and Web 2.0 technologies into cloud computing environment. The proposed MCSF is used to develop an Intelligent Social Activity Cloud Platform (ISACP) that provides customized social activity for personal needs. To demonstrate the feasibility of ISACP, the experimental results illustrate the efficiency and effectiveness of the proposed approach.

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“…The importance of ontologies is increasing especially in multi-agent environments since they enable agents to communicate, interact, and understand each other [2,6].…”

Section: Introductionmentioning

confidence: 99%

BOEM: A Model for Automating Detection and Evolution of Distributed Ontologies in Multi-Agent Environment

Soliman¹,

Salah²,

Hefny³

2017

IJIES

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Knowledge gives a strong support to autonomous agents in multi-agent systems and thus the evolution of agent's knowledge needs a great attention since it has a control on agents' behaviors and has effect on their decisions making. The problem is to allow agents to detect and decide whether they need more domain knowledge and allow their knowledge to evolve consistently and automatically. This paper utilizes ontologies to represent the internal knowledge of agents instead of utilizing them only as a shared conceptualization. Consequently, the paper proposes a model of bottom-up instance-driven ontology evolution that allows the internal ontologies of agents to evolve automatically and consistently in run time based on agents' interactions. Experiments are designed and implemented to evaluate our model in different situations. One of its results shows that an empty internal ontology of one agent could evolve automatically in runtime by 88.3% through its interactions with other agents. Moreover, a comparison between the proposed approach and literature review approaches is presented to compare between their different features and techniques. This paper is considered a step forward to automate ontology evolution for agents in multiagent environment.

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Semantic web technology for agent interoperability: a proposed infrastructure

Cited by 16 publications

References 23 publications

Integrated machine learning with semantic web for open government data recommendation based on cloud computing

Integrated machine learning with semantic web for open government data recommendation based on cloud computing

Incorporation of Ontology-based Technologies and Web 2.0 Mashups in Cloud Computing Environment

BOEM: A Model for Automating Detection and Evolution of Distributed Ontologies in Multi-Agent Environment

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