LELA - A natural language processing system for Romanian tourism

Varga, Bernadette; Miron, Alina Dia Trambitas; Roth, Andrei; Marginean, Anca; Slavescu, Radu Razvan; Groza, Adrian

doi:10.15439/2014f323

Cited by 7 publications

(2 citation statements)

References 18 publications

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“…The query method described here is simple-a typical Boolean search is used. Thus, a more robust and efficient query could be achieved by implementing other query and search methods such as the ranking algorithm (Tran et al 2009;Zhiguo & Zhengjie 2010) and natural language query processing (Damljanovic et al 2010;Paredes-Valverde et al 2015;Tablan et al 2008;Varga et al 2014;Wang et al 2007). It is vital for users to query the system without being attached to a fixed one and to select pre-defined parameters.…”

Section: Discussionmentioning

confidence: 99%

Peer Review #2 of "Plant data visualisation using network graphs (v0.2)"

Pescott¹

2018

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Background. The amount of plant data such as taxonomical classification, morphological characteristics, ecological attributes and geological distribution in textual and image forms has increased rapidly due to emerging research and technologies. Therefore, it is crucial for experts as well as the public to discern meaningful relationships from this vast amount of data using appropriate methods. The data are often presented in lengthy texts and tables, which make gaining new insights difficult. The study proposes a visual-based representation to display data to users in a meaningful way. This method emphasises the relationships between different data sets. Method. This study involves four main steps which translate text-based results from XML serialization format into graphs. The four steps include: (1) conversion of ontological dataset as graph model data; (2) query from graph model data; (3) transformation of text-based results in XML serialization format into a graphical form; and (4) display of results to the user via a graphical user interface (GUI). Ontological data for plants and samples of trees and shrubs were used as the dataset to demonstrate how plant-based data could be integrated into the proposed data visualisation. Results. A visualisation system named PlantViz was developed. This system provides a graphical user interface that enables users to perform the query process, as well as a graphical viewer to display the results of the query in the form of a network graph. The efficiency of the developed visualisation system was measured by performing two types of user evaluations: a usability heuristics evaluation, and a query and visualisation evaluation. Discussion. The relationships between the data were visualised, enabling the users to easily infer the knowledge and correlations between data. The results from the user evaluation show that the proposed visualisation system is suitable for both expert and novice users, with or without computer skills. This technique demonstrates the practicability of using a computer assisted-tool by providing cognitive analysis for understanding relationships between data. Therefore, the results benefit not only botanists, but also novice users, especially those that are interested to know more about plants.

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Section: Discussionmentioning

confidence: 99%

Peer Review #2 of "Plant data visualisation using network graphs (v0.2)"

Pescott¹

2018

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“…The query method described here is simple—a typical Boolean search is used. Thus, a more robust and efficient query could be achieved by implementing other query and search methods such as the ranking algorithm ( Tran, Tsuji & Masuda, 2009 ; Zhiguo & Zhengjie, 2010 ) and natural language query processing ( Damljanovic, Agatonovic & Cunningham, 2010 ; Paredes-Valverde et al, 2015 ; Tablan, Damljanovic & Bontcheva, 2008 ; Varga et al, 2014 ; Wang et al, 2007 ). It is vital for users to query the system without being attached to a fixed one and to select pre-defined parameters.…”

Section: Discussionmentioning

confidence: 99%

Plant data visualisation using network graphs

Mohamad-Matrol

Chang

Abu

2018

PeerJ

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BackgroundThe amount of plant data such as taxonomical classification, morphological characteristics, ecological attributes and geological distribution in textual and image forms has increased rapidly due to emerging research and technologies. Therefore, it is crucial for experts as well as the public to discern meaningful relationships from this vast amount of data using appropriate methods. The data are often presented in lengthy texts and tables, which make gaining new insights difficult. The study proposes a visual-based representation to display data to users in a meaningful way. This method emphasises the relationships between different data sets.MethodThis study involves four main steps which translate text-based results from Extensible Markup Language (XML) serialisation format into graphs. The four steps include: (1) conversion of ontological dataset as graph model data; (2) query from graph model data; (3) transformation of text-based results in XML serialisation format into a graphical form; and (4) display of results to the user via a graphical user interface (GUI). Ontological data for plants and samples of trees and shrubs were used as the dataset to demonstrate how plant-based data could be integrated into the proposed data visualisation.ResultsA visualisation system named plant visualisation system was developed. This system provides a GUI that enables users to perform the query process, as well as a graphical viewer to display the results of the query in the form of a network graph. The efficiency of the developed visualisation system was measured by performing two types of user evaluations: a usability heuristics evaluation, and a query and visualisation evaluation.DiscussionThe relationships between the data were visualised, enabling the users to easily infer the knowledge and correlations between data. The results from the user evaluation show that the proposed visualisation system is suitable for both expert and novice users, with or without computer skills. This technique demonstrates the practicability of using a computer assisted-tool by providing cognitive analysis for understanding relationships between data. Therefore, the results benefit not only botanists, but also novice users, especially those that are interested to know more about plants.

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