Increasing Search Quality with the Semantic Desktop in Proposal Development

Siebert, Mark; Smits, Pierre; Sauermann, Leo; Dengel, Andreas

doi:10.1007/11944935_25

Cited by 7 publications

(6 citation statements)

References 3 publications

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“…Enabling classified information to be shared as a common vocabulary across people and applications, ontologies are consequently paving the way towards building a supportive infrastructure for information exchange and discovery. Examples of ontologybased solutions are the Semantic Desktop [7], and Semantic Security Web Services (SSWS) [8].…”

Section: What Are Ontologies?mentioning

confidence: 99%

An Ontological Framework for Opportunistic Composition of IoT Systems

Nundloll

Elkhatib

Elhabbash

et al. 2020

2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies (ICIoT)

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As the number of connected devices rapidly increases, largely thanks to uptake of IoT technologies, there is significant stimulus to enable opportunistic interactions between different systems that encounter each other at run time. However, this is complicated by diversity in IoT technologies and implementation details that are not known in advance. To achieve such unplanned interactions, we use the concept of a holon to represent a system's services and requirements at a high level. A holon is a self-describing system that appears as a whole when viewed from above whilst potentially comprising multiple subsystems when viewed from below. In order to realise this world view and facilitate opportunistic system interactions, we propose the idea of using ontologies to define and program a holon. Ontologies offer the ability to classify the concepts of a domain, and use this formalised knowledge to infer new knowledge through reasoning. In this paper, we design a holon ontology and associated code generation tools. We also explore a case study of how programming holons using this approach can aid an IoT system to self-describe and reason about other systems it encounters. As such, developers can develop system composition logic at a high-level without any preconceived notions about low-level implementation details.

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Section: What Are Ontologies?mentioning

confidence: 99%

An Ontological Framework for Opportunistic Composition of IoT Systems

Nundloll

Elkhatib

Elhabbash

et al. 2020

2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies (ICIoT)

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“…Rather, they try to retrieve the files by the relations among the target files with other related files which are associated with certain conceptual backgrounds [16]. For example, this enables us to quickly find a file that contain the original data about a certain figure that is used in a specific file.…”

Section: Search Approaches With Semantic Metadatamentioning

confidence: 99%

“…For example, this enables us to quickly find a file that contain the original data about a certain figure that is used in a specific file. Such retrieval can be realized by preparing sufficient semantic metadata for the files to be retrieved [16], [17]. Furthermore, such technologies can be extended for the use of service retrievals and automated compositions of them [18].…”

Section: Search Approaches With Semantic Metadatamentioning

confidence: 99%

“…By associating semantic relations from ontologies to files, it is demanded to realize flexible semantic retrievals on searching files [16]. However, when such files are owned and maintained by their owners, the given annotations for such files and even the c 2012 Information Processing Society of Japan used ontologies themselves should less or more depend on their owners' thoughts and policies [29].…”

Section: Personalized Ontologies: Their Uses and Issuesmentioning

confidence: 99%

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A Mobile Agent Approach for P2P-based Semantic File Retrieval

Fukuta¹

2012

Journal of Information Processing

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Peer-to-peer (P2P) data sharing is a valuable approach for sharing data among people when they are belonging to different institutions. There are strong demands on both flexible, high-precision search and protection of privacy at peer-to-peer data retrievals. Especially, it is demanded for searching relevant files in P2P environment by using metadata while the terms in metadata that are used in such queries and annotations include some private information. In this paper, I present a mechanism and an analysis of P2P-based semantic file sharing and retrieval that uses mobile agents. The mechanism enables us to utilize private ontologies for flexible concept-oriented semantic searches without loss of privacy in processing semantic matching among private metadata of files and the requested semantic queries. The private ontologies are formed on a certain reference ontology with differential ontologies for personalization. In my approach, users can manage and annotate their files with their own private ontologies. Reference ontologies are used to find out semantically relevant files for the given queries that include semantic relations among existing files and the requested files. Mobile agent approach is applied for both implementing a system with less use of network bandwidth and coding it into a set of simple and small programs. I show the effectiveness of the use of private ontologies in metadata-based file retrieval. Also I show that the mobile agent approach has somewhat less overhead in execution time when the network latency is relatively high, while it is small enough even when the network is ideally fast.

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“…Then, it seems that the majority of the techniques used in natural language processing should be context aware. It is crucial not only for context-biased text access to the web information [18], but also during searching for information from the standalone databases of digital documents [11], [19], [20], [21].…”

Section: Introductionmentioning

confidence: 99%

Context-sensitive text mining with fitness leveling Genetic Algorithm

Huk

Kwiatkowski

Konieczny

et al. 2015

2015 IEEE 2nd International Conference on Cybernetics (CYBCONF)

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Contextual processing is a great challenge for information retrieval study -the most approved techniques include scanning content of HTML web pages, user supported metadata analysis, automatic inference grounded on knowledge base, or content-oriented digital documents analysis. We propose a meta-heuristic by making use of Genetic Algorithms for Contextual Search (GACS) built on genetic programming (GP) and custom fitness leveling function to optimize contextual queries in exact search that represents unstructured phrases generated by the user. Our findings show that the queries built with GACS can significantly optimize the retrieval process.

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Increasing Search Quality with the Semantic Desktop in Proposal Development

Cited by 7 publications

References 3 publications

An Ontological Framework for Opportunistic Composition of IoT Systems

An Ontological Framework for Opportunistic Composition of IoT Systems

A Mobile Agent Approach for P2P-based Semantic File Retrieval

Context-sensitive text mining with fitness leveling Genetic Algorithm

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