On Using Declarative Generation Rules To Deliver Linked Biodiversity Data

Akbar, Zaenal; Kartika, Yulia Aris; Saleh, Dadan Ridwan; Mustika, Hani Febri; Manik, Lindung Parningotan

doi:10.1109/icramet51080.2020.9298573

Cited by 5 publications

(3 citation statements)

References 15 publications

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“…As one of the efforts, we also have developed a knowledge-based chatbot (KBC) that students and laypeople can use to query and ask about species-related information (Akbar et al, 2020;Manik et al, 2021). A KBC refers to computer software designed to respond to users' queries or questions using information stored in a knowledge base (KB).…”

Section: Literature Reviewmentioning

confidence: 99%

Unraveling Knowledge-Based Chatbot Adoption Intention in Enhancing Species Literacy

Parningotan Manik,

Setyo Rini,

Priyanti

et al. 2024

IJIKM

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Aim/Purpose: This research investigated the determinant factors influencing the adoption intentions of Chatsicum, a Knowledge-Based Chatbot (KBC) aimed at enhancing the species literacy of biodiversity students. Background: This research was conducted to bridge the gap between technology, education, and biodiversity conservation. Innovative solutions are needed to empower individuals with knowledge, particularly species knowledge, in preserving the natural world. Methodology: The study employed a quantitative approach using the Partial Least Square Structural Equation Modeling (PLS-SEM) and sampled 145 university students as respondents. The research model combined the Task-Technology Fit (TTF) framework with elements from the Diffusion of Innovation (DOI), including relative advantage, compatibility, complexity, and observability. Also, the model introduced perceived trust as an independent variable. The primary dependent variable under examination was the intention to use the KBC. Contribution: The findings of this research contribute to a deeper understanding of the critical factors affecting the adoption of the KBC in biodiversity education and outreach, as studies in this context are limited. This study provides valuable insights for developers, educators, and policymakers interested in promoting species literacy and leveraging innovative technologies by analyzing the interplay of TTF and DOI constructs alongside perceived trust. Ultimately, this research aims to foster more effective and accessible biodiversity education strategies. Findings: TTF influenced all DOI variables, such as relative advantage, compatibility, observability, and trust positively and complexity negatively. In conclusion, TTF strongly affected usage intention indirectly. However, relative advantage, complexity, and observability insignificantly influenced the intention to use. Meanwhile, compatibility and trust strongly affected the use intention. Recommendations for Practitioners: Developers should prioritize building and maintaining chatbots that are aligned with the tasks, needs, and goals of the target users, as well as establishing trust through the assurance of information accuracy. Educators could develop tailored educational interventions that resonate with the values and preferences of diverse learners and are aligned closely with students’ learning needs, preferences, and curriculum while ensuring seamless integration with the existing educational context. Conservation organizations and policymakers could also utilize the findings of this study to enhance their outreach strategies, as the KBC is intended for students and biodiversity laypeople. Recommendation for Researchers: Researchers should explore the nuances of relationships between TTF and DOI, as well as trust, and consider the potential influence of mediating and moderating variables to advance the field of technology adoption in educational contexts. Researchers could also explore why relative advantage, complexity, and observability did not significantly impact the usage intention and whether specific user segments or contextual factors influence these relationships. Impact on Society: This research has significant societal impacts by improving species literacy, advancing technology in education, and promoting conservation efforts. Species knowledge could raise awareness regarding biodiversity and the importance of conservation, thereby leading to more informed and responsible citizens. Future Research: Future works should address the challenges and opportunities presented by KBCs in the context of species literacy enhancement, for example, interventions or experiments to influence the non-significant factors. Furthermore, longitudinal studies should investigate whether user behavior evolves. Ultimately, examining the correlation between species literacy, specifically when augmented by chatbots, and tangible conservation practices is an imperative domain in the future. It may entail evaluating the extent to which enhanced knowledge leads to concrete measures promoting biodiversity preservation.

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Section: Literature Reviewmentioning

confidence: 99%

Unraveling Knowledge-Based Chatbot Adoption Intention in Enhancing Species Literacy

Parningotan Manik,

Setyo Rini,

Priyanti

et al. 2024

IJIKM

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“…For example, a rule-based system has been used for legal reasoning (Liu et al, 2021), safety assessment (Tang et al, 2020), emergency management (Jain et al, 2021), and online communication (Akbar et al, 2014). Specifically, in the biodiversity research area, rulebased systems are also widely used, for example, for predicting the impact of land-use changes on biodiversity (Scolozzi & Geneletti, 2011), molecular biodiversity database management (Pannarale et al, 2012), or for generating linked biodiversity data (Akbar et al, 2020). Figure 3 shows the capability of the schema to infer causality and temporal relationships.…”

Section: Temporal Information Inferencementioning

confidence: 99%

Automatic Generation of Temporal Data Provenance From Biodiversity Information Systems

Akbar

Saleh

Kartika

et al. 2022

IJIKM

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Aim/Purpose: Although the significance of data provenance has been recognized in a variety of sectors, there is currently no standardized technique or approach for gathering data provenance. The present automated technique mostly employs workflow-based strategies. Unfortunately, the majority of current information systems do not embrace the strategy, particularly biodiversity information systems in which data is acquired by a variety of persons using a wide range of equipment, tools, and protocols. Background: This article presents an automated technique for producing temporal data provenance that is independent of biodiversity information systems. The approach is dependent on the changes in contextual information of data items. By mapping the modifications to a schema, a standardized representation of data provenance may be created. Consequently, temporal information may be automatically inferred. Methodology: The research methodology consists of three main activities: database event detection, event-schema mapping, and temporal information inference. First, a list of events will be detected from databases. After that, the detected events will be mapped to an ontology, so a common representation of data provenance will be obtained. Based on the derived data provenance, rule-based reasoning will be automatically used to infer temporal information. Consequently, a temporal provenance will be produced. Contribution: This paper provides a new method for generating data provenance automatically without interfering with the existing biodiversity information system. In addition to this, it does not mandate that any information system adheres to any particular form. Ontology and the rule-based system as the core components of the solution have been confirmed to be highly valuable in biodiversity science. Findings: Detaching the solution from any biodiversity information system provides scalability in the implementation. Based on the evaluation of a typical biodiversity information system for species traits of plants, a high number of temporal information can be generated to the highest degree possible. Using rules to encode different types of knowledge provides high flexibility to generate temporal information, enabling different temporal-based analyses and reasoning. Recommendations for Practitioners: The strategy is based on the contextual information of data items, yet most information systems simply save the most recent ones. As a result, in order for the solution to function properly, database snapshots must be stored on a frequent basis. Furthermore, a more practical technique for recording changes in contextual information would be preferable. Recommendation for Researchers: The capability to uniformly represent events using a schema has paved the way for automatic inference of temporal information. Therefore, a richer representation of temporal information should be investigated further. Also, this work demonstrates that rule-based inference provides flexibility to encode different types of knowledge from experts. Consequently, a variety of temporal-based data analyses and reasoning can be performed. Therefore, it will be better to investigate multiple domain-oriented knowledge using the solution. Impact on Society: Using a typical information system to store and manage biodiversity data has not prohibited us from generating data provenance. Since there is no restriction on the type of information system, our solution has a high potential to be widely adopted. Future Research: The data analysis of this work was limited to species traits data. However, there are other types of biodiversity data, including genetic composition, species population, and community composition. In the future, this work will be expanded to cover all those types of biodiversity data. The ultimate goal is to have a standard methodology or strategy for collecting provenance from any biodiversity data regardless of how the data was stored or managed.

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“…Ontology-based solutions are widely used to represent knowledge in this domain, which includes a set of terms to describe the classes in the domain and the relationships among terms [18]. For example, a schema to represent data from multiple biodiversity information systems that are available on the Web was constructed to enable Linked Biodiversity Data [19]. The International Rice Information System (IRIS) was developed to handle rice functional genomics data diversity, including genomic sequence data, molecular genetic data, expression data, and proteomic information [20].…”

Section: Related Workmentioning

confidence: 99%

An Ontology-Driven Personalized Faceted Search for Exploring Knowledge Bases of Capsicum

et al. 2021

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Capsicum is a genus of flowering plants in the Solanaceae family in which the members are well known to have a high economic value. The Capsicum fruits, which are popularly known as peppers or chili, have been widely used by people worldwide. It serves as a spice and raw material for many products such as sauce, food coloring, and medicine. For many years, scientists have studied this plant to optimize its production. A tremendous amount of knowledge has been obtained and shared, as reflected in multiple knowledge-based systems, databases, or information systems. An approach to knowledge-sharing is through the adoption of a common ontology to eliminate knowledge understanding discrepancy. Unfortunately, most of the knowledge-sharing solutions are intended for scientists who are familiar with the subject. On the other hand, there are groups of potential users that could benefit from such systems but have minimal knowledge of the subject. For these non-expert users, finding relevant information from a less familiar knowledge base would be daunting. More than that, users have various degrees of understanding of the available content in the knowledge base. This understanding discrepancy raises a personalization problem. In this paper, we introduce a solution to overcome this challenge. First, we developed an ontology to facilitate knowledge-sharing about Capsicum to non-expert users. Second, we developed a personalized faceted search algorithm that provides multiple structured ways to explore the knowledge base. The algorithm addresses the personalization problem by identifying the degree of understanding about the subject from each user. In this way, non-expert users could explore a knowledge base of Capsicum efficiently. Our solution characterized users into four groups. As a result, our faceted search algorithm defines four types of matching mechanisms, including three ranking mechanisms as the core of our solution. In order to evaluate the proposed method, we measured the predictability degree of produced list of facets. Our findings indicated that the proposed matching mechanisms could tolerate various query types, and a high degree of predictability can be achieved by combining multiple ranking mechanisms. Furthermore, it demonstrates that our approach has a high potential contribution to biodiversity science in general, where many knowledge-based systems have been developed with limited access to users outside of the domain.

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On Using Declarative Generation Rules To Deliver Linked Biodiversity Data

Cited by 5 publications

References 15 publications

Unraveling Knowledge-Based Chatbot Adoption Intention in Enhancing Species Literacy

Unraveling Knowledge-Based Chatbot Adoption Intention in Enhancing Species Literacy

Automatic Generation of Temporal Data Provenance From Biodiversity Information Systems

An Ontology-Driven Personalized Faceted Search for Exploring Knowledge Bases of Capsicum

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