Digital heritage portal interfaces are generally similar to digital library and search engine interfaces in displaying search results as a list of brief metadata records. The knowledge organization and search result display of these systems are item-centric, with little support for identifying relationships between items. This paper proposes a knowledge graph system and visualization interface as a promising solution for digital heritage systems to support users in browsing related items, understanding the relationships between items, and synthesizing a narrative on an issue. The paper discusses design issues for the knowledge graph, graph database, and graph visualization, and offers recommendations based on the authors’ experience in developing three knowledge graph systems for archive and digital humanities resources: the Zubir Said personal archive collection at the Nanyang Academy of Fine Arts, Singapore; Singapore Pioneers social network; and Polyglot Medicine knowledge graph of Asian traditional and herbal medicine. Lessons learned from a small user study are incorporated in the discussion.

Many types of argument claims and supports (premises) are made in a research paper. The study analyzed the types of claims and supports used in the Abstract and Introduction sections of sociology research papers, and the common argument patterns. Seventy papers were sampled from ten sociology journals with the highest impact factor in InCites Journal Citation Reports. The research papers were categorized into three types of research: Investigative research, Development and evaluation research, and Descriptive research. A coding scheme of claims and supports was developed. Sequential association rule mining was used to identify textual argument patterns. The study identified significant differences in argument profile across the three types of research papers, and differences in argument patterns. For example, Investigative research papers typically follow the basic argument pattern of (General statement or Research gap) … Research objective … Research method … Research result … (General result or Research contribution/recommendation) in the However, Development & Evaluation research and Descriptive research Abstracts often modify the basic pattern with Concept/theory/model-related claims and Research idea/approach. Methodology contributions of the paper include the coding scheme for argument claims and supports in research papers, and the method of analyzing argument patterns. Different types of sociology research tend to follow different argument strategies. As there is substantial overlap between argument elements and rhetorical functions, the common argument patterns identified in this study suggest common rhetorical move/step patterns as well.

Extracting and organizing knowledge from the agricultural crop disease research literature are challenging tasks because of the heterogeneous terminologies, complicated symptom descriptions, and unstructured nature of scientific documents. In this study, we developed a multi-stage natural language processing (NLP) pipeline to automate knowledge extraction, organization, and integration from the agricultural research literature into a domain-consistent crop disease knowledge graph. The model combines transformer-based sentence embeddings with variational deep clustering to extract topics, which are further refined via facet-aware relevance scoring for sentence selection to be included in the summary. Lexicon-guided named entity recognition helps in the precise identification and normalization of terms for crops, diseases, symptoms, etc. Relation extraction based on a combination of lexical, semantic, and contextual features leads to the meaningful generation of triplets for the knowledge graph. The experimental results show that the method yielded consistently good results at each stage of the knowledge extraction process. Among the combinations of embedding and deep clustering methods, SciBERT + VaDE achieved the best clustering results. The extraction of representative sentences for disease symptoms, control/treatment, and prevention obtained high F1-scores of around 0.8. The resulting knowledge graph has high node coverage and high relation completeness, as well as high precision and recall in triplet generation. The multi-stage NLP pipeline effectively converts unstructured agricultural research texts into a coherent and semantically rich knowledge graph, providing a basis for further research in crop disease analysis, knowledge retrieval, and data-driven decision support in agricultural informatics.

Public opinion on online platforms now plays an important role in agricultural markets, which have always been unpredictable. Although sentiment analysis has been widely applied to agricultural texts, most existing studies typically focus only on classification accuracy without connecting results to actual market intelligence systems, especially in multilingual contexts. This paper introduces a reliability-aware transformer-based framework for analyzing sentiment in agricultural market intelligence across multiple languages. The framework leverages weakly supervised multilingual transformers to extract sentiment signals from large-scale unlabeled Thai and English texts about major agricultural commodities found online. To enhance robustness under weak supervision, the framework incorporates reliability-aware mechanisms, including confidence-based pseudo-label filtering, cross-source consistency refinement, and expert-guided calibration to reduce noise and account for bias between different data sources. Sentiment predictions are further aligned with market intelligence objectives through reliability-weighted aggregation, yielding interpretable sentiment indices that enable cross-lingual and cross-source comparability. We tested the framework extensively using a multilingual agricultural corpus derived from social media and news coverage of agriculture. The results show consistent improvements over both classical machine learning approaches and standard multilingual transformer baselines. Additional ablation studies and sensitivity analyses confirmed that reliability-aware mechanisms, particularly confidence thresholding, play a crucial role in getting the right balance between label quality and data coverage. Overall, the results indicate that reliability-aware multilingual sentiment analytics provide robust and actionable insights for agricultural market monitoring and policy analysis.

Researchers have been working on different aspects of representing scientific research papers as machine-processable knowledge graphs. This study extends that work to the domain of social science research papers—specifically aiming to construct scholarly knowledge graphs that represent research results together with their supporting arguments, showing how research claims are incrementally constructed following the discourse structure of the paper. The study analyzed 30 sociology research papers that aimed to find or confirm cause-effect relationships between concepts. It analyzed the causal structure of the research result statements in the papers and investigated what additional information is provided by other statements in the papers performing the argument/rhetorical functions of general statement, topic centrality, literature review, research gap, research objective, research method, research result, and research contribution. A cause-effect information frame that lists all possible roles in a causal information structure was used to guide the analysis. We show that a scholarly knowledge graph representing the research results in a social science research paper must include information extracted from multiple statements in the paper, to provide a comprehensive view of the constructed knowledge. Together, these statements, taken in sequence, form the argument flow of the paper, showing how knowledge is constructed. The study also implemented a prototype graph visualization application to help users examine the causal structure of the research result statements and how that structure is incrementally constructed when the information content of the other statements is viewed in sequence. This tool enables readers to explore conflicting or unexpected results, trace how cause-effect relationships are generalized or specialized, and understand the overall causal information structure accumulated across the paper.