A Personalized Machine-Learning-Enabled Method for Efficient Research in Ethnopharmacology. The Case of the Southern Balkans and the Coastal Zone of Asia Minor

Axiotis, Evangelos; Kontogiannis, Andreas; Kalpoutzakis, Eleftherios; Giannakopoulos, George

doi:10.3390/app11135826

Cited by 3 publications

(3 citation statements)

References 27 publications

(26 reference statements)

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“…Bifulco, Cirillo, Esposito, Guadagni and Polese [5] released a document presenting an intelligent system which is tested especially in the field of e-procurement to support organizations in the focused crawling of artefacts (such as calls for tender, equipment, policies, market trends, and so on) of interest from the web, semantically matching them against internal big data and knowledge sources [5]. A comparable document is also found in the field of ethnopharmacology written by Axiotis, Kontogiannis, Kalpoutzaki and Giannakopoulos [6]. Alarte and Silva [7] discuss the problem of only extracting the relevant content from a website, i.e., ignoring content such as menus, advertisements, copyright notices, and comments.…”

Section: Related Workmentioning

confidence: 99%

Building a Technology Recommender System Using Web Crawling and Natural Language Processing Technology

et al. 2022

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Finding, retrieving, and processing information on technology from the Internet can be a tedious task. This article investigates if technological concepts such as web crawling and natural language processing are suitable means for knowledge discovery from unstructured information and the development of a technology recommender system by developing a prototype of such a system. It also analyzes how well the resulting prototype performs in regard to effectivity and efficiency. The research strategy based on design science research consists of four stages: (1) Awareness generation; (2) suggestion of a solution considering the information retrieval process; (3) development of an artefact in the form of a Python computer program; and (4) evaluation of the prototype within the scope of a comparative experiment. The evaluation yields that the prototype is highly efficient in retrieving basic and rather random extractive text summaries from websites that include the desired search terms. However, the effectivity, measured by the quality of results is unsatisfactory due to the aforementioned random arrangement of extracted sentences within the resulting summaries. It is found that natural language processing and web crawling are indeed suitable technologies for such a program whilst the use of additional technology/concepts would add significant value for a potential user. Several areas for incremental improvement of the prototype are identified.

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Section: Related Workmentioning

confidence: 99%

Building a Technology Recommender System Using Web Crawling and Natural Language Processing Technology

et al. 2022

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“…Observe that in our binary reward function setting the harvest rate is equivalent to the agent's average cumulative reward, and thus to an RL metric [34]. Also, we discuss the number of fetched web sites (domains), considering that a web site is relevant if it contains at least one relevant web page.…”

Section: Evaluation Metricsmentioning

confidence: 99%

Tree-based Focused Web Crawling with Reinforcement Learning

Kontogiannis¹,

Kelesis²,

Pollatos³

et al. 2021

Preprint

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A focused crawler aims at discovering as many web pages relevant to a target topic as possible, while avoiding irrelevant ones; i.e. maximizing the harvest rate. Reinforcement Learning (RL) has been utilized to optimize the crawling process, yet it deals with huge state and action spaces, which can constitute a serious challenge. In this paper, we propose TRES, an end-to-end RL-empowered framework for focused crawling. Unlike other approaches, we properly model a crawling environment as a Markov Decision Process, by representing the state as a subgraph of the Web and actions as its expansion edges. TRES adopts a keyword expansion strategy based on the cosine similarity of keyword embeddings. To learn a reward function, we propose a deep neural network, called Kw-BiLSTM, leveraging the discovered keywords. To reduce the time complexity of selecting a best action, we propose Tree-Frontier, a two-fold decision tree, which also speeds up training by discretizing the state and action spaces. Experimentally, we show that TRES outperforms state-of-the-art methods in terms of harvest rate by at least 58%, while it has competitive results in the domain maximization. Our implementation code can be found on https://github.com/ddaedalus/TRES.

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“…They reported that Machine learning-powered research improved the effectiveness and efficiency of the domain expert by 3.1 and 5.14 times, respectively. This was done by fetching 420 relevant ethnopharmacological documents in only seven hours versus an estimated 36 hours of human effort [ 26 ]. The current study documented ethnomedicinal knowledge of medicinal plants in the Shahrbabak region in the southeastern part of Iran, within Kerman Province.…”

Section: Introductionmentioning

confidence: 99%

Exploring the power of data mining for uncovering traditional medicinal plant knowledge: A case study in Shahrbabak, Iran

Bibak,

Heydari,

Sadat-Hosseini

2024

PLoS ONE

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The present study recorded indigenous knowledge of medicinal plants in Shahrbabak, Iran. We described a method using data mining algorithms to predict medicinal plants’ mode of application. Twenty-oneindividuals aged 28 to 81 were interviewed. Firstly, data were collected and analyzed based on quantitative indices such as the informant consensus factor (ICF), the cultural importance index (CI), and the relative frequency of citation (RFC). Secondly, the data was classified by support vector machines, J48 decision trees, neural networks, and logistic regression. So, 141 medicinal plants from 43 botanical families were documented. Lamiaceae, with 18 species, was the dominant family among plants, and plant leaves were most frequently used for medicinal purposes. The decoction was the most commonly used preparation method (56%), and therophytes were the most dominant (48.93%) among plants. Regarding the RFC index, the most important species are Adiantum capillus-veneris L. and Plantago ovata Forssk., while Artemisia auseri Boiss. ranked first based on the CI index. The ICF index demonstrated that metabolic disorders are the most common problems among plants in the Shahrbabak region. Finally, the J48 decision tree algorithm consistently outperforms other methods, achieving 95% accuracy in 10-fold cross-validation and 70–30 data split scenarios. The developed model detects with maximum accuracy how to consume medicinal plants.

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A Personalized Machine-Learning-Enabled Method for Efficient Research in Ethnopharmacology. The Case of the Southern Balkans and the Coastal Zone of Asia Minor

Cited by 3 publications

References 27 publications

Building a Technology Recommender System Using Web Crawling and Natural Language Processing Technology

Building a Technology Recommender System Using Web Crawling and Natural Language Processing Technology

Tree-based Focused Web Crawling with Reinforcement Learning

Exploring the power of data mining for uncovering traditional medicinal plant knowledge: A case study in Shahrbabak, Iran

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