Fuzzy Sets and Fuzzy XML Data Models

Author(s):  
Zongmin Ma ◽  
Luyi Bai ◽  
Li Yan
Keyword(s):  
Xml Data ◽  
Author(s):  
Li Yan ◽  
Zongmin Ma ◽  
Fu Zhang
Keyword(s):  
Xml Data ◽  

2007 ◽  
Vol 63 (3) ◽  
pp. 972-996 ◽  
Author(s):  
Z.M. Ma ◽  
Li Yan

Author(s):  
Kamal Taha

There has been extensive research in XML Keyword-based and Loosely Structured querying. Some frameworks work well for certain types of XML data models while fail in others. The reason is that the proposed techniques overlook the context of elements when building relationships between the elements. The context of a data element is determined by its parent, because a data element is generally a characteristic of its parent. Overlooking the contexts of elements may result in relationships between the elements that are semantically disconnected, which lead to erroneous results. We present in this chapter a context-driven search engine called XTEngine for answering XML Keyword-based and Loosely Structured queries. XTEngine treats each set of elements consisting of a parent and its children data elements as one unified entity, and then uses context-driven search techniques for determining the relationships between the different unified entities. We evaluated XTEngine experimentally and compared it with three other search engines. The results showed marked improvement.


IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 22025-22033 ◽  
Author(s):  
Weijun Li ◽  
Li Yan ◽  
Fu Zhang ◽  
Xu Chen

2013 ◽  
Vol 39 (2) ◽  
pp. 386-396 ◽  
Author(s):  
Jian Liu ◽  
Z. M. Ma ◽  
Xue Feng

Author(s):  
Li Yan ◽  
Zongmin Ma ◽  
Fu Zhang

Data Mining ◽  
2013 ◽  
pp. 669-691 ◽  
Author(s):  
Evgeny Kharlamov ◽  
Pierre Senellart

This chapter deals with data mining in uncertain XML data models, whose uncertainty typically comes from imprecise automatic processes. We first review the literature on modeling uncertain data, starting with well-studied relational models and moving then to their semistructured counterparts. We focus on a specific probabilistic XML model, which allows representing arbitrary finite distributions of XML documents, and has been extended to also allow continuous distributions of data values. We summarize previous work on querying this uncertain data model and show how to apply the corresponding techniques to several data mining tasks, exemplified through use cases on two running examples.


Author(s):  
Elli Bleeker ◽  
Ronald Haentjens Dekker ◽  
Bram Buitendijk

This paper explores the potential of combining the Text-As-Graph (TAG) and the XML data models. It proposes a digital editing workflow in which users can model, edit, and store text in TAG, and subsequently export the textual data to XML for further analysis or publication with XML-based tools. The conversion from TAGML to XML presents several interesting challenges on a technical level as well as a philological level. Overall, we argue that there may be many pragmatic reasons to encode cultural heritage texts in XML, but we have to be mindful of the XML framework becoming synonymous with the framework in which we conceptualize text. The paper therefore dives deep into the translation from conceptual model to logical model(s) and argues in favor of understanding the affordances and limitations of the technologies we use.


2014 ◽  
Vol 43 (2) ◽  
pp. 473-495 ◽  
Author(s):  
Jian Liu ◽  
Z. M. Ma ◽  
Xue Feng
Keyword(s):  
Xml Data ◽  

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