Explanation knowledge expressed by a graph, especially in the graphical model, is essential to comprehend clearly all paths of effect events in causality for basic diagnosis. This research focuses on determining the effect boundary using a statistical based approach and patterns of effect events in the graph whether they are consequence or concurrence without temporal markers. All necessary causality events from texts for the graph construction are extracted on multiple clauses/EDUs (Elementary Discourse Units) which assist in determining effect-event patterns from written event sequences in documents. To extract the causality events from documents, it has to face the effect-boundary determination problems after applying verb pair rules (a causative verb and an effect verb) to identify the causality. Therefore, we propose Bayesian Network and Maximum entropy to determine the boundary of the effect EDUs. We also propose learning the effect-verb order pairs from the adjacent effect EDUs to solve the effect-event patterns for representing the extracted causality by the graph construction. The accuracy result of the explanation knowledge graph construction is 90% based on expert judgments whereas the average accuracy results from the effect boundary determination by Bayesian Network and Maximum entropy are 90% and 93%, respectively.

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The COVID-19 pandemic forced customer behaviour to shift from partial online searching and buying to full use of online services. Most of the products shipped must be assembled or adjusted for personal consumption. With different needs, customers learn to customize their consumption into an experience that is more trustworthy and practical than using an influencer. This research aimed to investigate the effect of an influencer moderating the path between the coproducer and brand’s mutual information. To fulfil this objective, we collected Thai customers’ message posts from 50 brands’ fan pages in 2020. The data preparation process used word segmentation and keyword relevance identification. Finally, the data set of 300 vectors with four measurement variables, including coproducer, influencer, mutual information, and customer value, was analysed by PROCESS Model 7 in SPSS. The results showed that the antecedent variables have positive effects on customer value, with the exception of the influencer moderating variable, which has a negative coefficient of − 0.055 ( p < 0.1). Since the moderating effect of influencers has decreased the indirect effect on customer value, traditional influencer marketing must be improved to support customers who engage in expertise consumption as coproducers.

The research aim is to construct a disease-symptom knowledge graph (DSKG) as a cause-effect knowledge graph containing disease-symptom relations as a cause-effect relation type determined from downloaded documents on medical web-board resources. Each disease-symptom relation connects a disease-name concept node (a causative-concept node) to a corresponding node having a group of correlated symptom-concept/effect-concept features as common symptom-concept/effect-concept features among some disease-name concepts. The DSKG benefits non-professionals in preliminary diagnosis through a recommender web-board. There are three main problems: how to determine symptom concepts from sentences without annotation on the documents having disease-name concepts as the documents’ topic-names; how to determine the disease-symptom relations from the documents with/without complications; and how to construct the DSKG involving high dimensional symptom-concept features after union of the correlated symptom-concept groups. Therefore, we apply a word co-occurrence pattern including medical-symptom expressions from Wikipedia including MeSH and the Lexitron Dictionary to determine the symptom concepts. The Cartesian product is applied for automatic-supervised machine learning to determine the disease-symptom relation. We propose using Principal Component Analysis for constructing the DSKG by dimensionality reduction in the symptom-concept features with minimized information loss. In contrast to previous works, the proposed approach enables the DSKG construction with precise and concise representation scores of 7.8 and 9, respectively.

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