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This paper aims to curate open research knowledge graph (ORKG) with papers related to ontology learning and define an approach using ORKG as a computer-assisted tool to organize key-insights extracted from research papers.

Action research was used to explore, test and evaluate the use of the Open Research Knowledge Graph as a computer assistant tool for knowledge acquisition from scientific papers.

To extract, structure and describe research contributions, the granularity of information should be decided; to facilitate the comparison of scientific papers, one should design a common template that will be used to describe the state of the art of a domain.

This approach is currently used to document “food information engineering,” “tabular data to knowledge graph matching” and “question answering” research problems and the “neurosymbolic AI” domain. More than 200 papers are ingested in ORKG. From these papers, more than 800 contributions are documented and these contributions are used to build over 100 comparison tables. At the end of this work, we found that ORKG is a valuable tool that can reduce the working curve of state-of-the-art research.

The term knowledge refers to the part of the world investigated by a specific discipline and that includes a specific taxonomy, vocabulary, concepts, theories, research methods and standards of justification. Our approach uses domain knowledge to improve the quality of business process models (BPMs) by exploiting the domain knowledge provided by large language models (LLMs). Among these models, ChatGPT stands out as a notable example of an LLM capable of providing in-depth domain knowledge. The lack of coverage presents a limitation in each approach, as it hinders the ability to fully capture and represent the domain’s knowledge. To solve such limitations, we aim to exploit GPT-3.5 knowledge. Our approach does not ask GPT-3.5 to create a visual representation; instead, it needs to suggest missing concepts, thus helping the modeler improve his/her model. The GPT-3.5 may need to refine its suggestions based on feedback from the modeler.

We initiate our semantic quality enhancement process of a BPM by first extracting crucial elements including pools, lanes, activities and artifacts, along with their corresponding relationships such as lanes being associated with pools, activities belonging to each lane and artifacts associated with each activity. These data are systematically gathered and structured into ArrayLists, a form of organized collection that allows for efficient data manipulation and retrieval. Once we have this structured data, our methodology involves creating a series of prompts based on each data element. We adopt three approaches to prompting: zero-shot, few-shot and chain of thoughts (CoT) prompts. Each type of prompting is specifically designed to interact with the OpenAI language model in a unique way, aiming to elicit a diverse array of suggestions. As we apply these prompting techniques, the OpenAI model processes each prompt and returns a list of suggestions tailored to that specific element of the BPM. Our approach operates independently of any specific notation and offers semi-automation, allowing modelers to select from a range of suggested options.

This study demonstrates the significant potential of prompt engineering techniques in enhancing the semantic quality of BPMs when integrated with LLMs like ChatGPT. Our analysis of model activity richness and model artifact richness across different prompt techniques and input configurations reveals that carefully tailored prompts can lead to more complete BPMs. This research is a step forward for further exploration into the optimization of LLMs in BPM development.

The limitation is the domain ontology that we are relying on to evaluate the semantic completeness of the new BPM. In our future work, the modeler will have the option to ask for synonyms, hyponyms, hypernyms or keywords. This feature will facilitate the replacement of existing concepts to improve not only the completeness of the BPM but also the clarity and specificity of concepts in BPMs.

To demonstrate our methodology, we take the “Hospitalization” process as an illustrative example. In the scope of our research, we have presented a select set of instructions pertinent to the “chain of thought” and “few-shot prompting.” Due to constraints in presentation and the extensive nature of the instructions, we have not included every detail within the body of this paper. However, they can be found in the previous GitHub link. Two appendices are given at the end. Appendix 1 describes the different prompt instructions. Appendix 2 presents the application of the instructions in our example.

In our research, we rely on the domain application knowledge provided by ChatGPT-3 to enhance the semantic quality of BPMs. Typically, the semantic quality of BPMs may suffer due to the modeler's lack of domain knowledge. To address this issue, our approach employs three prompt engineering methods designed to extract accurate domain knowledge. By utilizing these methods, we can identify and propose missing concepts, such as activities and artifacts. This not only ensures a more comprehensive representation of the business process but also contributes to the overall improvement of the model's semantic quality, leading to more effective and accurate business process management.

This study aims to investigate the applications of knowledge graphs in developing artificial intelligence (AI) assistants and chatbots by reviewing scholarly publications from different lenses and dimensions. The authors also analyze the various AI approaches used for knowledge graph-driven chatbots and discuss how implementing these techniques makes a difference in technology.

Over recent years, chatbots have emerged as a transformational force in interacting with the digital world in various domains, including customer service and personal assistants. Recently, chatbots have become a revolutionary tool for interacting with the digital world in various contexts, such as personal assistants and customer support. Incorporating knowledge graphs considerably improved the capabilities of chatbots by allowing them access to massive knowledge bases and enhancing their ability to understand queries. Furthermore, knowledge graphs enable chatbots to understand semantic links between elements and improve response quality. This study highlights the role of knowledge graphs in chatbots following a systematic review approach. They have been integrated into major health-care, education and business domains. Beyond improving information retrieval, knowledge graphs enhance the user experience and increase the range of fields in which chatbots can be used. Improving and enriching chatbot answers was also identified as one of the main advantages of knowledge graphs. This enriched response can increase user confidence and improve the accuracy of chatbot interactions, making them more trustworthy information sources.

Knowledge graph-based chatbots leverage extensive data retrieval to provide accurate and enriched responses, increasing user confidence and experience without requiring extensive training. The three major domains where knowledge graph-based chatbots have been used are health care, education and business.

Knowledge graph-based chatbots can better comprehend user queries and respond with relevant information efficiently without extensive training. Furthermore, knowledge graphs enable chatbots to understand semantic links between elements, allowing them to answer complicated and multi-faceted questions. This semantic comprehension improves response quality, making chatbots more successful in providing accurate and valuable information in various domains. Furthermore, knowledge graphs enable chatbots to provide consumers with individualized experiences by storing and recalling individual preferences, history or previous encounters. This study analyzes the role of knowledge graphs in chatbots following a systematic review approach. This study reviewed state-of-the-art articles to understand where and how chatbots have used knowledge graphs. The authors found health care, business and education as three main areas in which knowledge-graph-based chatbots have been mostly used. Chatbots have been developed in text, voice and visuals using various machine learning models, particularly natural language pocessing, to develop recommender systems to recommend suitable items, content or services based on user preferences and item associations.

This paper provides a comprehensive review of the current state of the field in using knowledge graphs in chatbots, focusing on machine learning models, domains and communication channels. The study highlights the prevalence of text and voice channels over visual ones and identifies research gaps and future directions. The paper’s insights can inform the design and development of chatbots using knowledge graphs and benefit both researchers and practitioners in AI, natural language processing and human–computer interaction. The paper is of interest to professionals in domains such as health care, education and business.

Green knowledge management (GKM) has become a more prominent research topic because of its ability to balance business sustainability, performance and society's well-being. The purpose of this paper is to study how GKM literature evolved before and after two major events: the introduction of sustainable development goals (SDGs) and the first conceptualization of GKM. In this paper, GKM is holistically examined following the stages of the knowledge management cycle, a framework for organizational knowledge-processing phases.

This study performed a bibliometric analysis of 1,274 papers related to GKM from 1995 until January 2024.

Over the three decades, this research outlined the intertwined relationships between core themes in the domain such as knowledge management in the context of corporate social responsibilities, sustainable development (SD), competitive advantage and so on, and popular theories. GKM evolved from an “industrial and technical view” of knowledge management to a more emerging perspective of a “social process.” Emerging themes were identified such as green innovation, information security or organizational learning sub-themes with key technologies like block-chain, big data analytics and artificial intelligence. Future research can explore themes such as green knowledge integration, green entrepreneurship, green supply chain and green knowledge integration capabilities.

This review offers practitioners a holistic picture of GKM to tackle emerging environmental concerns and increase businesses' competitive advantages. This study provides insights into the future practices of GKM, incorporating emerging technological advancement, to gain green intellectual capital and build dynamic capabilities for sustainability.

To the best of authors’ knowledge, this study is the first to provide a comprehensive picture of the GKM literature, from its earliest forms of corporate social responsibility and SD until the introduction of SDGs, and in combination with the evolution of knowledge management cycle stages.

The purpose of this research study is to improve sentiment analysis (SA) at the aspect level, which is accomplished through two independent goals of aspect term and opinion extraction and subsequent sentiment classification.

The proposed architecture uses neighborhood and dependency tree-based relations for target opinion extraction, a domain–ontology-based knowledge management system for aspect term extraction, and deep learning techniques for classification.

The authors use different deep learning architectures to test the proposed approach of both review and aspect levels. It is reported that Vanilla recurrent neural network has an accuracy of 83.22%, long short-term memory (LSTM) is 89.87% accurate, Bi-LSTM is 91.57% accurate, gated recurrent unit is 65.57% accurate and convolutional neural network is 82.33% accurate. For the aspect level analysis, ρ_aspect comes out to be 0.712 and Δ²_aspect is 0.384, indicating a marked improvement over previously reported results.

This study suggests a novel method for aspect-based SA that makes use of deep learning and domain ontologies. The use of domain ontologies allows for enhanced aspect identification, and the use of deep learning algorithms enhances the accuracy of the SA task.

In recent decades, interest in digital transformation (DX) within the architecture, engineering, and construction (AEC) industry has significantly increased. Despite the existence of several literature reviews on DX research, there remains a notable lack of systematic quantitative and visual investigations into the structure and evolution of this field. This study aims to address this gap by uncovering the current state, key topics, keywords, and emerging areas in DX research specific to the AEC sector.

Employing a holistic review approach, this study undertook a thorough and systematic analysis of the literature concerning DX in the AEC industry. Utilizing a bibliometric analysis, 3,656 papers were retrieved from the Web of Science spanning the years 1990–2023. A scientometric analysis was then applied to these publications to discern patterns in publication years, geographical distribution, journals, authors, citations, and keywords.

The findings identify China, the USA, and England as the leading contributors in the field of DX in AEC sector. Prominent keywords include “building information modeling”, “design”, “system”, “framework”, “adoption”, “model”, “safety”, “internet of things”, and “innovation”. Emerging areas of interest are “deep learning”, “embodied energy”, and “machine learning”. A cluster analysis of keywords reveals key research themes such as “deep learning”, “smart buildings”, “virtual reality”, “augmented reality”, “smart contracts”, “sustainable development”, “building information modeling”, “big data”, and “3D printing”.

This study is among the earliest to provide a comprehensive scientometric mapping of the DX field. The findings presented here have significant implications for both industry practitioners and the scientific community, offering a thorough overview of the current state, prominent keywords, topics, and emerging areas within DX in the AEC industry. Additionally, this research serves as an invaluable reference and guideline for scholars interested in this subject.

This study aims to perform the bibliometric analysis of the customer engagement (CE) literature, highlights the major research themes and classifies the subdomains. The study also identifies antecedents and consequences, as well as dimension evolution, and suggests future research directions.

This study used a comprehensive bibliometric approach using Scopus data from 2002 to January 2024. Advanced analytical techniques, including bibliometric and cocitation analysis using R and bibexcel, were used. In addition, machine learning (ML)-based Latent Dirichlet Allocation (LDA) was used to extract latent themes.

This study reveals the domain’s past trend and present research scenario. The thematic analysis of CE is classified into three phases. Document cocitation analysis provided four broad clusters: conceptualization and operationalization, value creation through engagement, building relationships with brands and engagement-social media interface. The antecedents and consequences are categorized and presented along with the evolution of the multidimensional nature of CE.

This study adds to the literature in two key ways. First, the entire scholarly production has been compiled into one frame. Second, multiple methods were used to unravel citation, cocitation and textual data. Furthermore, ML-based LDA was used to extract latent themes from clusters and future research directions were proposed.

The purpose of this Real Impact Research Article is to empirically explore one of the most controversial and elusive concepts in knowledge management research – practical wisdom. It develops a 10-dimensional practical wisdom construct and tests it within the nomological network of counterproductive and productive knowledge behavior.

A survey instrument was created based on the extant literature. A model was developed and tested by means of Partial Least Squares with data obtained from 200 experienced employees recruited from CloudResearch Connect crowdsourcing platform.

Practical wisdom is a multidimensional construct that may be operationalized and measured like other well-established knowledge management concepts. Practical wisdom guides employee counterproductive and productive knowledge behavior: it suppresses knowledge sabotage and knowledge hiding (whether general, evasive, playing dumb, rationalized or bullying) and promotes knowledge sharing. While all proposed dimensions contribute to employee practical wisdom, particularly salient are subject matter expertise, moral purpose in decision-making, self-reflection in the workplace and external reflection in the workplace. Unexpectedly, practical wisdom facilitates knowledge hoarding instead of reducing it.

Managers should realize that possessing practical wisdom is not limited to a group of select, high-level executives. Organizations may administer the practical wisdom questionnaire presented in this study to their workers to identify those who score the lowest, and invest in employee training programs that focus on the development of those attributes pertaining to the practical wisdom dimensions.

The concept of practical wisdom is a controversial topic that has both detractors and supporters. To the best of the author’s knowledge, this is the first large-scale empirical study of practical wisdom in the knowledge management domain.

This study aims to adopt a systematic review approach to examine the existing literature on law and LLMs.It involves analyzing and synthesizing relevant research papers, reports and scholarly articles that discuss the use of LLMs in the legal domain. The review encompasses various aspects, including an analysis of LLMs, legal natural language processing (NLP), model tuning techniques, data processing strategies and frameworks for addressing the challenges associated with legal question-and-answer (Q&A) systems. Additionally, the study explores potential applications and services that can benefit from the integration of LLMs in the field of intelligent justice.

This paper surveys the state-of-the-art research on law LLMs and their application in the field of intelligent justice. The study aims to identify the challenges associated with developing Q&A systems based on LLMs and explores potential directions for future research and development. The ultimate goal is to contribute to the advancement of intelligent justice by effectively leveraging LLMs.

To effectively apply a law LLM, systematic research on LLM, legal NLP and model adjustment technology is required.

This study contributes to the field of intelligent justice by providing a comprehensive review of the current state of research on law LLMs.

This study aims to examine core professional competencies required by organizational learning (OL) field, using the main knowledge, skills and abilities (KSA) theoretical framework for job candidates in Israel.

An analysis was conducted on 100 job postings from two online platforms using content analysis techniques. The job offers were evaluated according to criteria established by prior research conducted in the USA.

The findings indicate that job announcements appear for three main professions in the field of learning in organizations in Israel: learning designer, learning developer and instructional designer. Most of the offers are for full-time jobs, without requiring a relevant academic degree or previous experience. In comparison to the US employment market, in Israel the demand for OL professionals necessitates communication abilities in English, macro development skills, knowing how to manage professional training and mastery of learning through innovative technology such as augmented reality/virtual reality. The findings also indicated which competencies are most recently required in the OL branch in Israel and the significant differences in KSA necessary for OL professionals in each of the three identified professions.

The study highlighted critical elements of the OL professional field and has implications for OL professionals seeking employment and human resources (HR) recruiters seeking them. Job seekers need to know the current job market requirements in the OL field, and HR recruiters need to know what is happening in the current job market. This can be done by following updated job offers in the OL field and responding quickly to changes. The findings also have implications for the educational/academic aspect of the OL teachers in various settings and inform them to refine the content of their syllabus and course content in accordance with the current requirements of the job market in the field of OL.

The study is based on the KSA theoretical framework and analysis of the OL US job market according to Wang et al.’s (2021) work. This study presents the Israel OL job market and discuss the authors’ critical view on Wang et al.’s work.