Domain-specific Knowledge Research Articles

How does artificial intelligence affect the business context? A bibliometric analysis

We conducted a descriptive bibliometric analysis to examine scientific production, identify the most influential publications, and identify the most and least researched topics in four specific knowledge domains. We used a quantitative, descriptive, and correlational research approach to scientific production to carry out the analysis, which involved extracting 7,937 articles from the Web of Science and distributing them into three search equations. Using SciMAT v1.1.04 software, we processed the data and conducted a descriptive analysis of scientific production, enabling the creation of maps highlighting scientists with the most and least researched topics. The analysis of published articles, author performance, most productive journals, and most cited articles provided a detailed view of the dominant trends and approaches in the fields of Artificial Intelligence and business. The analysis showed that there is a significant evolution in the scholarly output, with themes such as "Value Creation", "Artificial Intelligence", "Business Intelligence", "E-Commerce", "Decision Making" and "Management" emerging as central in different periods, indicating their continued importance. Additionally, we note the inclusion of emerging themes like 'Customer Experience', 'Chatbots', 'Internet of Things', and 'Machine Learning', which reflect the dynamics and evolution of research concerns over time. The results of the analysis have significant implications for business policy and strategy formulation. Understanding emerging trends can help organizations make informed decisions and proactively adapt to changes in the artificial intelligence and sustainability landscape.

Skill development for the future supply chain workforce: Identifying key areas

This review paper examines skill development for the future supply chain workforce, emphasizing the importance of identifying key areas and implementing effective strategies. The evolving supply chain landscape, influenced by technological advancements, globalization, and changing consumer demands, necessitates a proactive approach to talent development. Key skill areas identified include technical skills (e.g., data analytics, automation proficiency), soft skills (e.g., adaptability, critical thinking), and domain-specific knowledge (e.g., sustainability practices, regulatory compliance). Strategies for skill development encompass experiential learning, online platforms, and collaborative partnerships. The paper highlights the role of employers, educational institutions, and policymakers in promoting continuous learning and upskilling. A forward-looking outlook predicts further evolution in supply chain roles, emphasizing the need for a competent and agile workforce. Stakeholders are urged to prioritize investment in skill development initiatives to ensure industry competitiveness and resilience. Keywords: Skill Development, Supply Chain Workforce, Future Trends, Technical Skills, Soft Skills, Domain-Specific Knowledge.

Exploring Scientific Jargon: Diverse Translation Theories for Conveying Computer Scientific Terms into Arabic

Translating computer scientific terms into Arabic represents a challenge that necessitates not only linguistic expertise but also a nuanced understanding of both source and target languages. Despite the increasing demand for accurate scientific translation in Arabic-speaking communities, the process is far from being simple. This study embarks on an exploration of scientific translation with a particular focus on computer scientific terms. It aims to identify the challenges encountered by translators and check if the application of translation theories is practical for creating new terms in Arabic. The study is significant as it addresses the pressing need for accurate scientific translation into Arabic language which contributes to the enhancement of communication and knowledge dissemination. An examination of various translation strategies helped to uncover the applicability of translation theories in this context. By scrutinizing the linguistic nuances, we seek to unravel the complexities inherent in translating computer scientific terms into Arabic. Findings not only shed light on the practical application of translation theories but also underscore the importance of context and domain-specific knowledge in achieving accurate and sensitive translations. Ultimately, this study contributes to the broader discourse on scientific translation by offering recommendations for enhancing the precision and fluency of computer scientific translation into Arabic, thereby facilitating the seamless exchange of knowledge across linguistic boundaries.

Increasing the Reliability of Software Systems Using a Large-Language-Model-Based Solution for Onboarding

Software systems are often maintained by a group of experienced software developers in order to ensure that faults that may bring the system down are less likely. Large turnover in organizations such as CERN makes it important to think of ways of onboarding newcomers on a technical project rapidly. This paper focuses on optimizing the way that people get up-to-speed on the business logic and technologies used on the project by using a knowledge-imbued large language model that is enhanced using domain-specific knowledge from the group or team’s internal documentation. The novelty of this approach is the gathering of all of these different open-source methods for developing a chatbot and using it in an industrial use-case.

Retrieval-augmented generation using domain-specific text: a pilot study

The natural language processing (NLP) field has witnessed remarkable advancements with the advent of large language models (LLMs) like GPT, Gemini, Claude, etc. These models are trained on vast amounts of text data, allowing them to generate human-like responses for various tasks. However, despite their impressive capabilities, LLMs have limitations in their ability to incorporate and reason over external knowledge that is not in their training data. This limitation of LLMs is particularly evident in the case of specific domain knowledge. This situation has given rise to the concept of retrieval augmented generation (RAG), an approach that combines the generative power of LLMs with the ability to retrieve and integrate relevant information from external knowledge sources. This research attempts to use RAG as a module in an application designed to answer questions concerning a specific domain, namely social philosophy/philosophy of management, using a published book from the respective domain as an external source. The paper analyzes the mentioned application output, draws conclusions, and traces future directions to improve the accuracy of the output.

Detection of Lowering in Sport Climbing Using Orientation-Based Sensor-Enhanced Quickdraws: A Preliminary Investigation.

Climbing gyms aim to continuously improve their offerings and make the best use of their infrastructure to provide a unique experience for their clients, the climbers. One approach to achieve this goal is to track and analyze climbing sessions from the beginning of the ascent until the climber's descent. Detecting the climber's descent is crucial because it indicates when the ascent has ended. This paper discusses an approach that preserves climber privacy (e.g., not using cameras) while considering the convenience of climbers and the costs to the gyms. To this aim, a hardware prototype has been developed to collect data using accelerometer sensors attached to a piece of climbing equipment mounted on the wall, called a quickdraw, which connects the climbing rope to the bolt anchors. The sensors are configured to be energy-efficient, making them practical in terms of expenses and time required for replacement when used in large quantities in a climbing gym. This paper describes the hardware specifications, studies data measured by the sensors in ultra-low power mode, detects sensors' orientation patterns during descent on different routes, and develops a supervised approach to identify lowering. Additionally, the study emphasizes the benefits of multidisciplinary feature engineering, combining domain-specific knowledge with machine learning to enhance performance and simplify implementation.

What is it that you say you do here? Advocating for the critical role of data scientists in research infrastructure.

Clinical and academic research continues to become more complex as our knowledge and technology advance. A substantial and growing number of specialists in biostatistics, data science, and library sciences are needed to support these research systems and promote high-caliber research. However, that support is often marginalized as optional rather than a fundamental component of research infrastructure. By building research infrastructure, an institution harnesses access to tools and support/service centers that host skilled experts who approach research with best practices in mind and domain-specific knowledge at hand. We outline the potential roles of data scientists and statisticians in research infrastructure and recommend guidelines for advocating for the institutional resources needed to support these roles in a sustainable and efficient manner for the long-term success of the institution. We provide these guidelines in terms of resource efficiency, monetary efficiency, and long-term sustainability. We hope this work contributes to-and provides shared language for-a conversation on a broader framework beyond metrics that can be used to advocate for needed resources.

Advancing Sustainable Manufacturing: Reinforcement Learning with Adaptive Reward Machine Using an Ontology-Based Approach

Sustainable manufacturing practices are crucial in job shop scheduling (JSS) to enhance the resilience of production systems against resource shortages and regulatory changes, contributing to long-term operational stability and environmental care. JSS involves rapidly changing conditions and unforeseen disruptions that can lead to inefficient resource use and increased waste. However, by addressing these uncertainties, we can promote more sustainable operations. Reinforcement learning-based job shop scheduler agents learn through trial and error by receiving scheduling decisions feedback in the form of a reward function (e.g., maximizing machines working time) from the environment, with their primary challenge being the handling of dynamic reward functions and navigating uncertain environments. Recently, Reward Machines (RMs) have been introduced to specify and expose reward function structures through a finite-state machine. With RMs, it is possible to define multiple reward functions for different states and switch between them dynamically. RMs can be extended to incorporate domain-specific prior knowledge, such as task-specific objectives. However, designing RMs becomes cumbersome as task complexity increases and agents must react to unforeseen events in dynamic and partially observable environments. Our proposed Ontology-based Adaptive Reward Machine (ONTOADAPT-REWARD) model addresses these challenges by dynamically creating and modifying RMs based on domain ontologies. This adaptability allows the model to outperform a state-of-the-art baseline algorithm in resource utilization, processed orders, average waiting time, and failed orders, highlighting its potential for sustainable manufacturing by optimizing resource usage and reducing idle times.

Optimizing Tourism Accommodation Offers by Integrating Language Models and Knowledge Graph Technologies

Online platforms have become the primary means for travellers to search, compare, and book accommodations for their trips. Consequently, online platforms and revenue managers must acquire a comprehensive comprehension of these dynamics to formulate a competitive and appealing offerings. Recent advancements in natural language processing, specifically through the development of large language models, have demonstrated significant progress in capturing the intricate nuances of human language. On the other hand, knowledge graphs have emerged as potent instruments for representing and organizing structured information. Nevertheless, effectively integrating these two powerful technologies remains an ongoing challenge. This paper presents an innovative deep learning methodology that combines large language models with domain-specific knowledge graphs for classification of tourism offers. The main objective of our system is to assist revenue managers in the following two fundamental dimensions: (i) comprehending the market positioning of their accommodation offerings, taking into consideration factors such as accommodation price and availability, together with user reviews and demand, and (ii) optimizing presentations and characteristics of the offerings themselves, with the intention of improving their overall appeal. For this purpose, we developed a domain knowledge graph covering a variety of information about accommodations and implemented targeted feature engineering techniques to enhance the information representation within a large language model. To evaluate the effectiveness of our approach, we conducted a comparative analysis against alternative methods on four datasets about accommodation offers in London. The proposed solution obtained excellent results, significantly outperforming alternative methods.

A unified approach to publish semantic annotations of agricultural documents as knowledge graphs

The research results presented in this paper were obtained as part of the D2KAB project (Data to Knowledge in Agriculture and Biodiversity) which aims to develop semantic web-based tools to describe and make agronomical data actionable and accessible following the FAIR principles. We focus on constructing domain-specific Knowledge Graphs (KGs) from textual data sources, using Natural Language Processing (NLP) techniques to extract and structure relevant entities. Our approach is based on the formalization of a semantic data model using common linked open vocabularies such as the Web Annotation Ontology (OA) and the Provenance Ontology (PROV). The model was developed by formulating motivating scenarios and competency questions from domain experts. This model has been used to construct three different KGs from three distinct corpora: PubMed scientific publications on wheat and rice genetics and phenotyping, and French agricultural alert bulletins. The named entities to be recognized include genes, phenotypes, traits, genetic markers, taxa and phenological stages normalized using semantic resources such as the Wheat Trait and Phenotype Ontology (WTO), the French Crop Usage (FCU) thesaurus and the Plant Phenological Description Ontology (PPDO). Named entities were extracted using different NLP approaches and tools. The relevance of the semantic model was validated by implementing experts questions as SPARQL queries to be answered on the constructed RDF knowledge graphs. Our work demonstrates how domain-specific vocabularies and systematic querying of KGs can reveal hidden interactions and support agronomists in navigating vast amounts of data. The resources and transformation pipelines developed are publicly available in Git repositories.

Instruct Pix-to-3D: Instructional 3D object generation from a single image

We study a challenging task, creating the instructional 3D object based on a single image and human instruction. Although existing methods utilize pre-trained text-to-image diffusion models to optimize Neural Radiance Fields (NeRF) and achieve remarkable results, they are inherently challenged in addressing this task due to two inherent obstacles. First, the written instruction is mismatched or uncorrelated with target images in the distribution of pre-trained text-to-image diffusion models, which leads to insufficient supervision. Another is editing 3D content starting from a single image, which is an ill-posed problem as the image only provides partial information. To tackle the difficulties, we propose an Instruct Pixel-to-3D framework, which exploits the supervisory signals from the given instructions and integrates the domain-specific knowledge drawn from 2D and 3D diffusion models. Specifically, to address the first challenge, we propose instruction guidance to facilitate meticulous image editing using fine-grained instructions and regulate the novel views of 3D objects using positional prompts. To tackle the second difficulty, we propose diffusion guidance for 3D generation, which optimizes the NeRF employing dual guidances: a 2D diffusion guidance derived from the score distillation sampling (SDS) loss and a 3D diffusion guidance embodied by the Zero-1-to-3 loss. Moreover, Instruct Pix-to-3D adopts a coarse-to-fine training strategy aimed at mitigating the complexities inherent in reconstructing scenes from sparse viewpoints. Qualitative and quantitative experiments on the DTU dataset and in-the-wild images demonstrate that our approach could synthesize high-quality and high-fidelity 3D content based on a single image and human instruction.

Nursing practices on sexual health for people with neurological disorders: A narrative review.

To identify and describe nursing practices on the sexual health of people with neurological disorders. Narrative review. Data were extracted from 1 January 2002, to 20 May 2021. Inclusion criteria were nursing practices, sexual health and people with neurological disorders. The main outcome measures were: context of nursing practice implementation (assumptions, knowledge, strategies and skills), facilitators of and barriers to addressing and treating the sexual health of people with neurological disorders, and benefits of nursing practices in sexual health. PRISMA reporting guidelines were used. PubMed, Embase, ScienceDirect and CINAHL. In total, 926 articles were identified and nine were included. The involvement of nurses was recommended in most studies. Assumptions about the impact of neurology on sexuality and nurse's role in sexual healthcare, biopsychosocial knowledge, and skills (ethical, interpersonal, and technical) were highlighted. We found that the modes of knowledge proposed by Carper were mobilized in an unequal way. Sexual difficulties were the key focus and eroticization concerns were not addressed in any of the articles. Several studies advocate nursing intervention; however, few accurately present, detail and evaluate sexual health nursing practices of patients with neurological pathologies. Literature describes practices structured around disorders rather than the potentials, fails to address the brake of eroticism and provides little information on the results of interventions. Developing teaching programs on sexual health in nursing programs may be necessary if nurses are to support a diverse range of patients in an inclusive and positive manner. These programs should highlight the domain-specific knowledge that is mobilized. Sexual health is a fundamental human right. Alterations in the nervous system have shown to affect sexual health, however, it is not often discussed among patients with neurological disorders, who are rarely provided with sexual health counselling. Our findings may impact healthcare professionals engaged in care with these patients. PRISMA. No patient or public contribution.

How Being Inside or Outside of Buildings Affects the Causal Relationship between Weather and Pain among People Living with Chronic Pain

Abstract Although many people believe their pain fluctuates with weather conditions, both weather and pain may be associated with time spent outside. For example, pleasant weather may mean that people spend more time outside doing physical activity and are exposed to the weather, leading to more (or less) pain, and poor weather or severe pain may keep people inside, sedentary, and not exposed to the weather. We conducted a smartphone study where participants with chronic pain reported daily pain severity, as well as time spent outside. We address the relationship between four weather variables (temperature, dewpoint temperature, pressure, and wind speed) and pain by proposing a three-step approach to untangle their effects: (i) propose a set of plausible directed acyclic graphs (DAGs) that account for potential roles of time spent outside (e.g., collider, effect modifier, mediator); (ii) analyze the compatibility of the observed data with the assumed model; and (iii) identify the most plausible model by combining evidence from the observed data and domain-specific knowledge. We found that the data do not support time spent outside as a collider or mediator of the relationship between weather variables and pain. On the other hand, time spent outside modifies the effect between temperature and pain, as well as wind speed and pain, with the effect being absent on days that participants spent inside and present if they spent some or all of the day outside. Our results show the utility of using directed acyclic graphs for studying causal inference. Significance Statement Three-quarters of people living with chronic pain believe that weather influences their pain. However, people staying inside would not be exposed to the weather outside, and good weather may mean that people are more active outside, leading to more or less pain. To obtain data to calculate how the amount of time spent outside affects the weather–pain relationship, we conducted a 15-month smartphone study collecting daily pain reports and nearby weather for nearly 5000 participants in the United Kingdom. We found that time spent outside modifies the relationship between temperature/wind speed and pain, showing the importance of accounting for other factors when investigating the association between weather and chronic pain, which could guide future research into pain mitigation and management.

Development and initial validation of a data quality evaluation tool in obstetrics real-world data through HL7-FHIR interoperable Bayesian networks and expert rules.

The increasing prevalence of electronic health records (EHRs) in healthcare systems globally has underscored the importance of data quality for clinical decision-making and research, particularly in obstetrics. High-quality data is vital for an accurate representation of patient populations and to avoid erroneous healthcare decisions. However, existing studies have highlighted significant challenges in EHR data quality, necessitating innovative tools and methodologies for effective data quality assessment and improvement. This article addresses the critical need for data quality evaluation in obstetrics by developing a novel tool. The tool utilizes Health Level 7 (HL7) Fast Healthcare Interoperable Resources (FHIR) standards in conjunction with Bayesian Networks and expert rules, offering a novel approach to assessing data quality in real-world obstetrics data. A harmonized framework focusing on completeness, plausibility, and conformance underpins our methodology. We employed Bayesian networks for advanced probabilistic modeling, integrated outlier detection methods, and a rule-based system grounded in domain-specific knowledge. The development and validation of the tool were based on obstetrics data from 9 Portuguese hospitals, spanning the years 2019-2020. The developed tool demonstrated strong potential for identifying data quality issues in obstetrics EHRs. Bayesian networks used in the tool showed high performance for various features with area under the receiver operating characteristic curve (AUROC) between 75% and 97%. The tool's infrastructure and interoperable format as a FHIR Application Programming Interface (API) enables a possible deployment of a real-time data quality assessment in obstetrics settings. Our initial assessments show promised, even when compared with physicians' assessment of real records, the tool can reach AUROC of 88%, depending on the threshold defined. Our results also show that obstetrics clinical records are difficult to assess in terms of quality and assessments like ours could benefit from more categorical approaches of ranking between bad and good quality. This study contributes significantly to the field of EHR data quality assessment, with a specific focus on obstetrics. The combination of HL7-FHIR interoperability, machine learning techniques, and expert knowledge presents a robust, adaptable solution to the challenges of healthcare data quality. Future research should explore tailored data quality evaluations for different healthcare contexts, as well as further validation of the tool capabilities, enhancing the tool's utility across diverse medical domains.

A framework for measuring electricity price literacy: Application to South Korea

Consumers’ knowledge of electricity prices and its effect on their behaviors have been widely studied, yet the comparison and generalization of these findings are challenging due to variations in definition and survey items. This paper proposes a comprehensive framework to assess Electricity Price Literacy by introducing a functional representation that encompasses various electricity pricing systems and categorizing into three knowledge domains to systematically organize previous literature's knowledge measures. To demonstrate the practicality of this framework, a survey was administered to a nationwide sample of 4214 electricity consumers in South Korea. The survey results highlight variations in electricity price literacy across different domains and reveal varying relationships between domain-specific knowledge and behaviors, thereby highlighting the need for a decomposed measure of price knowledge.

Optimizing Video Queries with Declarative Clues

Video Database Management Systems (VDBMS) leverage advancements in computer vision and deep learning for efficient video data analysis and retrieval. This paper introduces the concept of user-specified Clues, allowing users to incorporate domain-specific knowledge, referred to as Clues, into query optimization. Clues are expressed as Clue types, each associated with optimization rules, and applied to queries through Clue instances. The extensible ClueVQS system we present to incorporate these ideas, optimizes queries automatically, utilizing Clues to improve processing efficiency. We also introduce algorithms to optimize queries using Clues allowing for trade-offs between speed and query accuracy. Our proposals and system address challenges such as data-dependent Clue effectiveness, limiting search space, and accuracy-efficiency trade-offs. Detailed experimental results demonstrate query speedups of up to two orders of magnitude compared to other applicable approaches, and a reduction of the query optimizer time by up to 95% while respecting user-specified accuracy constraints, showcasing the effectiveness of the proposed framework.

Anomaly-based error and intrusion detection in tabular data: No DNN outperforms tree-based classifiers

Recent years have seen a growing involvement of researchers and practitioners in crafting Deep Neural Networks (DNNs) that seem to outperform existing machine learning approaches for solving classification problems as anomaly-based error and intrusion detection. Undoubtedly, classifiers may be very diverse among themselves, and choosing one or another is typically due to the specific task and target system. Designing and training the optimal tabular data classifier requires extensive experimentation, sensitivity analyses, big datasets, and domain-specific knowledge that may not be available at will or considered a non-strategical asset by many companies and stakeholders. This paper compares, using a total of 23 public datasets: i) traditional (tree-based, statistical) supervised classifiers, ii) DNNs that are specifically designed for classifying tabular data, iii) DNNs for image classification that are applied to tabular data after converting data points into images, alone and as ensembles. Experimental results and related discussions show clear advantages in adopting tree-based classifiers for anomaly-based error and intrusion detection in tabular data as they outperform their competitors, including DNNs. Then, individual classifiers are compared against ensembles using different combinations of the classifiers considered in this study as base-learners, providing a unified final response through many meta-learning strategies. Results show that there is no benefit in building ensembles instead of using a tree-based classifier as Random Forests, eXtreme Gradient Boosting or Extra Trees. The paper concludes that anomaly-based error and intrusion detectors for critical systems should use the old (but gold) tree-based classifiers, which are also easier to fine-tune, and understand; plus, they require less time and resources to learn their model.

Integration of background knowledge for automatic detection of inconsistencies in gene ontology annotation.

Biological background knowledge plays an important role in the manual quality assurance (QA) of biological database records. One such QA task is the detection of inconsistencies in literature-based Gene Ontology Annotation (GOA). This manual verification ensures the accuracy of the GO annotations based on a comprehensive review of the literature used as evidence, Gene Ontology (GO) terms, and annotated genes in GOA records. While automatic approaches for the detection of semantic inconsistencies in GOA have been developed, they operate within predetermined contexts, lacking the ability to leverage broader evidence, especially relevant domain-specific background knowledge. This paper investigates various types of background knowledge that could improve the detection of prevalent inconsistencies in GOA. In addition, the paper proposes several approaches to integrate background knowledge into the automatic GOA inconsistency detection process. We have extended a previously developed GOA inconsistency dataset with several kinds of GOA-related background knowledge, including GeneRIF statements, biological concepts mentioned within evidence texts, GO hierarchy and existing GO annotations of the specific gene. We have proposed several effective approaches to integrate background knowledge as part of the automatic GOA inconsistency detection process. The proposed approaches can improve automatic detection of self-consistency and several of the most prevalent types of inconsistencies. https://github.com/jiyuc/de-inconsistency.

BioCoder: a benchmark for bioinformatics code generation with large language models.

Pretrained large language models (LLMs) have significantly improved code generation. As these models scale up, there is an increasing need for the output to handle more intricate tasks and to be appropriately specialized to particular domains. Here, we target bioinformatics due to the amount of domain knowledge, algorithms, and data operations this discipline requires. We present BioCoder, a benchmark developed to evaluate LLMs in generating bioinformatics-specific code. BioCoder spans much of the field, covering cross-file dependencies, class declarations, and global variables. It incorporates 1026 Python functions and 1243 Java methods extracted from GitHub, along with 253 examples from the Rosalind Project, all pertaining to bioinformatics. Using topic modeling, we show that the overall coverage of the included code is representative of the full spectrum of bioinformatics calculations. BioCoder incorporates a fuzz-testing framework for evaluation. We have applied it to evaluate various models including InCoder, CodeGen, CodeGen2, SantaCoder, StarCoder, StarCoder+, InstructCodeT5+, GPT-3.5, and GPT-4. Furthermore, we fine-tuned one model (StarCoder), demonstrating that our training dataset can enhance the performance on our testing benchmark (by >15% in terms of Pass@K under certain prompt configurations and always >3%). The results highlight two key aspects of successful models: (i) Successful models accommodate a long prompt (>2600 tokens) with full context, including functional dependencies. (ii) They contain domain-specific knowledge of bioinformatics, beyond just general coding capability. This is evident from the performance gain of GPT-3.5/4 compared to the smaller models on our benchmark (50% versus up to 25%). All datasets, benchmark, Docker images, and scripts required for testing are available at: https://github.com/gersteinlab/biocoder and https://biocoder-benchmark.github.io/.

The emerging landscape of halal tourism in the digital era: an IT perspective

PurposeThe purpose of this paper is to provide a comprehensive literature review on the application of information technology (IT) in Halal tourism. Halal tourism is a segment of the tourism business catering to Muslims in accordance with Islamic law, which has significant potential for economic growth and is an important export market for the global tourism industry.Design/methodology/approachFollowing the guidelines of Kitchenham, this study developed four research questions to achieve its objective. A systematic literature review was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses methodology, collecting and assessing 163 English-language publications published between 2016 and 2022, resulting in 18 relevant documents.FindingsThe review identified nine key research topics related to IT in Halal tourism, which are organized into three major themes: technology-enhanced Halal tourism, digital marketing and communications in Halal tourism and quality and satisfaction in Halal tourism.Originality/valueThis paper fills a gap in the existing literature by synthesizing the current research on the use of IT in Halal tourism. It also proposes potential areas for future research, such as developing artificial intelligence-powered applications for Halal tourism through the creation of a domain-specific knowledge graph.