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  • × author_ss:"Ma, X."
  1. Ma, X.; Carranza, E.J.M.; Wu, C.; Meer, F.D. van der; Liu, G.: ¬A SKOS-based multilingual thesaurus of geological time scale for interoperability of online geological maps (2011) 0.03
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    Abstract
    The usefulness of online geological maps is hindered by linguistic barriers. Multilingual geoscience thesauri alleviate linguistic barriers of geological maps. However, the benefits of multilingual geoscience thesauri for online geological maps are less studied. In this regard, we developed a multilingual thesaurus of geological time scale (GTS) to alleviate linguistic barriers of GTS records among online geological maps. We extended the Simple Knowledge Organization System (SKOS) model to represent the ordinal hierarchical structure of GTS terms. We collected GTS terms in seven languages and encoded them into a thesaurus by using the extended SKOS model. We implemented methods of characteristic-oriented term retrieval in JavaScript programs for accessing Web Map Services (WMS), recognizing GTS terms, and making translations. With the developed thesaurus and programs, we set up a pilot system to test recognitions and translations of GTS terms in online geological maps. Results of this pilot system proved the accuracy of the developed thesaurus and the functionality of the developed programs. Therefore, with proper deployments, SKOS-based multilingual geoscience thesauri can be functional for alleviating linguistic barriers among online geological maps and, thus, improving their interoperability.
    Content
    Article Outline 1. Introduction 2. SKOS-based multilingual thesaurus of geological time scale 2.1. Addressing the insufficiency of SKOS in the context of the Semantic Web 2.2. Addressing semantics and syntax/lexicon in multilingual GTS terms 2.3. Extending SKOS model to capture GTS structure 2.4. Summary of building the SKOS-based MLTGTS 3. Recognizing and translating GTS terms retrieved from WMS 4. Pilot system, results, and evaluation 5. Discussion 6. Conclusions Vgl. unter: http://www.sciencedirect.com/science?_ob=MiamiImageURL&_cid=271720&_user=3865853&_pii=S0098300411000744&_check=y&_origin=&_coverDate=31-Oct-2011&view=c&wchp=dGLbVlt-zSkzS&_valck=1&md5=e2c1daf53df72d034d22278212578f42&ie=/sdarticle.pdf.
  2. Ma, X.; Xue, P.; Matta, N.; Chen, Q.: Fine-grained ontology reconstruction for crisis knowledge based on integrated analysis of temporal-spatial factors (2021) 0.00
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    Abstract
    Previous studies on crisis knowledge organization mostly focused on the categorization of crisis knowledge without regarding its dynamic trend and temporal-spatial features. In order to emphasize the dynamic factors of crisis collaboration, a fine-grained crisis knowledge model is proposed by integrating temporal-spatial analysis based on ontology, which is one of the commonly used methods for knowledge organization. The reconstruction of ontologybased crisis knowledge will be implemented through three steps: analyzing temporal-spatial features of crisis knowledge, reconstructing crisis knowledge ontology, and verifying the temporal-spatial ontology. In the process of ontology reconstruction, the main classes and properties of the domain will be identified by investigating the crisis information resources. Meanwhile the fine-grained crisis ontology will be achieved at the level of characteristic representation of crisis knowledge including temporal relationship, spatial relationship, and semantic relationship. Finally, we conducted case addition and system implementation to verify our crisis knowledge model. This ontology-based knowledge organization method theoretically optimizes the static organizational structure of crisis knowledge, improving the flexibility of knowledge organization and efficiency of emergency response. In practice, the proposed fine-grained ontology is supposed to be more in line with the real situation of emergency collaboration and management. Moreover, it will also provide the knowledge base for decision-making during rescue process.
  3. Ma, X.; Cahier, J.-P.: ¬An exploratory study on semantic arrangement of VDL-based iconic knowledge tags (2014) 0.00
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    Source
    Knowledge organization. 41(2014) no.1, S.14-29
  4. Liu, Y.; Qin, C.; Ma, X.; Liang, H.: Serendipity in human information behavior : a systematic review (2022) 0.00
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    Date
    5. 6.2022 17:03:29
  5. Jiao, H.; Qiu, Y.; Ma, X.; Yang, B.: Dissmination effect of data papers on scientific datasets (2024) 0.00
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    Date
    7. 1.2024 18:24:29