Rule-Based Agricultural Knowledge Fusion in Web Information Integration

Xie, Naiming; Wang, Wensheng; Yang, Xiaorong; Li-hua, Jiang

doi:10.1166/sl.2012.1874

Cited by 7 publications

(3 citation statements)

References 9 publications

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“…Following the above content fusion approaches, another rule-based method was proposed for Q&A field which analysed multiple answers and combined them into one final answer. This method was tested in the agricultural product price information service system and performed well in terms of improving answer accuracy and user satisfaction (Nengfu et al 2012).…”

Section: Rule-based Patternmentioning

confidence: 99%

“…Agriculture was another field in which researchers applied the knowledge fusion technique. An information access interface based on knowledge fusion was developed to combine agricultural answers from different information sources including search engines to obtain one single answer (Nengfu et al 2012). This study focused on solving information content conflict and extension conflict by defining knowledge fusion rules, and it proved to be effective for enhancing answer correctness and user satisfaction in providing agricultural product price information services.…”

Section: Semantic Web Developmentmentioning

confidence: 99%

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Knowledge fusion through academic articles: a survey of definitions, techniques, applications and challenges

Wang

Saberi

et al. 2020

Scientometrics

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The ever growing volume of academic articles stresses the need for a new generation of knowledge management method to intelligently reuse the academic knowledge and facilitate the development of scientific research. Knowledge fusion (KF) serves a key element of such method addressing those needs, and breakthrough progress has taken place in the field of KF. This brings a great opportunity for the academic community to expedite the process of literature review and automatically retrieve the required knowledge from academic publications. Therefore, a survey reviewing the KF studies in terms of the related technologies and applications for valuable insights to reuse academic knowledge, which is missing from the state-of-the-art literature, is in need. Motivated to bridge this gap, this paper conducts a systematic survey reviewing the existing studies on KF, meanwhile discussing the opportunities and challenges of applying KF through academic articles. To this end, we revisit the definitions of knowledge and KF in the context of academic articles, and summarise the fusion patterns and their usage in existing applications. Furthermore, we review the techniques and applications of KF, especially those with academic articles as sources of knowledge. Finally, we discuss the challenges and future directions in order to bring new insights to researchers and practitioners to deepen their understanding of knowledge fusion and to develop versatile functions.

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Section: Rule-based Patternmentioning

confidence: 99%

Section: Semantic Web Developmentmentioning

confidence: 99%

Knowledge fusion through academic articles: a survey of definitions, techniques, applications and challenges

Wang

Saberi

et al. 2020

Scientometrics

View full text Add to dashboard Cite

show abstract

“…answers to their query under redundancy/lack of information (e.g., Dumais et al 2002;Nengfu et al 2012;Preece et al 1999). Some researchers deal with knowledge fusion of multiple knowledge sources to build a new knowledge base (Craven et al 2000;Gou et al 2005;Kuo et al 2003).…”

Section: Introductionmentioning

confidence: 99%

Knowledge Fusion in Context-Aware Decision Support Systems

Smirnov

Levashova

Shilov

2014

Proceedings of the International Conference on Knowledge Engineering and Ontology Development

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The paper discusses knowledge fusion processes with reference to context-aware decision support. It extends the previous research work on context-based knowledge fusion patterns. The contribution of this paper is service-oriented implementation of the context-aware decision support system for emergency management. This system was used in the previous work as the basis for revealing the knowledge fusion patterns. The presentation is accompanied by examples from a fire response scenario. 2 KNOWLEDGE FUSION Recently, many research focus on knowledge fusion. Depending on the application domains the focus of knowledge fusion becomes more specific. Knowledge fusion technology supports intelligent query-answering systems aiming at providing the users with nonrecurring, consistent, unambiguous 186 Smirnov A., Levashova T. and Shilov N.. Knowledge Fusion in Context-Aware Decision Support Systems.

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Context-Aware Knowledge Fusion for Decision Support

Smirnov

Levashova

Shilov

2016

Context-Enhanced Information Fusion

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The purpose of this chapter is to investigate knowledge fusion processes with reference to context-aware decision support. Various knowledge fusion processes and their possible outcomes are analyzed. A context-aware decision support system for emergency management serves as a possible application in which knowledge fusion processes go on. This system provides fused outputs from different knowledge sources. It relies upon context model, which is the key to fuse information/knowledge and to generate useful decisions. The discussion is complemented by examples from a fire response scenario.Keywords Context-aware decision support Á Constraint-based ontology Á Information fusion model Á Knowledge fusion Á Emergency management Á Fire response IntroductionInformation fusion deals with various forms of information integration (aggregation, union, merging, etc.) from multiple sources. Some time ago, sources of data and information (databases, sensors, etc.) [1] due to observed shortcomings. Currently, this model is the most popular among other information fusion models. Initially proposed for the military applications, now it is widely used in civil domains as well, such as business or medicine. The levels with the JDL/DFG model are: source pre-processing/subject assessment (level 0), object assessment (level 1), situation assessment (level 2), impact assessment/threat refinement (level 3), process refinement (level 4), and user refinement/cognitive refinement (level 5). Through its different levels, the model divides the fusion processes according to the different levels of abstraction of the data fused and the different problems the data fusion is applicable to (e.g., characteristic estimation vs. situation recognition and analysis). The model does not prescribe a strict ordering of the processes and the fusion levels, and the levels are not always discrete and may overlap. The JDL/DFG model is useful for visualizing the data fusion process, facilitating discussion and common understanding, and important for system-level information fusion design [2].Recently, research on information fusion extended the set of input sources with ontologies, text documents, the web, etc. That is, the focus of data and information fusion has changed to knowledge fusion.Information fusion and knowledge fusion are tightly related [3]. Referring to various perspectives on information fusion (e.g., [2,[4][5][6][7][8]), this technology is aimed at facilitation of situation awareness and improvement of decision making. The main result of information fusion is a new meaningful piece of information that is beneficial to decision making in less uncertainty, more preciseness, and/or more comprehensibility then the contributing parts [9][10][11].The objective of knowledge fusion is to integrate information and knowledge from multiple sources into some common knowledge that may be used for decision making and problem solving or may provide a better insight and understanding of the situation under consideration [12][13][14][15]. Knowledge f...

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Rule-Based Agricultural Knowledge Fusion in Web Information Integration

Cited by 7 publications

References 9 publications

Knowledge fusion through academic articles: a survey of definitions, techniques, applications and challenges

Knowledge fusion through academic articles: a survey of definitions, techniques, applications and challenges

Knowledge Fusion in Context-Aware Decision Support Systems

Context-Aware Knowledge Fusion for Decision Support

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