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  • Open Access


    Expert Recommendation in Community Question Answering via Heterogeneous Content Network Embedding

    Hong Li1,*, Jianjun Li1, Guohui Li1, Rong Gao2, Lingyu Yan2

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1687-1709, 2023, DOI:10.32604/cmc.2023.035239

    Abstract Expert Recommendation (ER) aims to identify domain experts with high expertise and willingness to provide answers to questions in Community Question Answering (CQA) web services. How to model questions and users in the heterogeneous content network is critical to this task. Most traditional methods focus on modeling questions and users based on the textual content left in the community while ignoring the structural properties of heterogeneous CQA networks and always suffering from textual data sparsity issues. Recent approaches take advantage of structural proximities between nodes and attempt to fuse the textual content of nodes for modeling. However, they often fail… More >

  • Open Access


    Information Extraction Based on Multi-turn Question Answering for Analyzing Korean Research Trends

    Seongung Jo1, Heung-Seon Oh1,*, Sanghun Im1, Gibaeg Kim1, Seonho Kim2

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2967-2980, 2023, DOI:10.32604/cmc.2023.031983

    Abstract Analyzing Research and Development (R&D) trends is important because it can influence future decisions regarding R&D direction. In typical trend analysis, topic or technology taxonomies are employed to compute the popularities of the topics or codes over time. Although it is simple and effective, the taxonomies are difficult to manage because new technologies are introduced rapidly. Therefore, recent studies exploit deep learning to extract pre-defined targets such as problems and solutions. Based on the recent advances in question answering (QA) using deep learning, we adopt a multi-turn QA model to extract problems and solutions from Korean R&D reports. With the… More >

  • Open Access


    ALBERT with Knowledge Graph Encoder Utilizing Semantic Similarity for Commonsense Question Answering

    Byeongmin Choi1, YongHyun Lee1, Yeunwoong Kyung2, Eunchan Kim3,*

    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 71-82, 2023, DOI:10.32604/iasc.2023.032783

    Abstract Recently, pre-trained language representation models such as bidirectional encoder representations from transformers (BERT) have been performing well in commonsense question answering (CSQA). However, there is a problem that the models do not directly use explicit information of knowledge sources existing outside. To augment this, additional methods such as knowledge-aware graph network (KagNet) and multi-hop graph relation network (MHGRN) have been proposed. In this study, we propose to use the latest pre-trained language model a lite bidirectional encoder representations from transformers (ALBERT) with knowledge graph information extraction technique. We also propose to applying the novel method, schema graph expansion to recent… More >

  • Open Access


    An Ontology-Based Question Answering System for University Admissions Advising

    Thi Thanh Sang Nguyen*, Dang Huu Trong Ho, Ngoc Tram Anh Nguyen

    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 601-616, 2023, DOI:10.32604/iasc.2023.032080

    Abstract Question-Answer systems are now very popular and crucial to support human in automatically responding frequent questions in many fields. However, these systems depend on learning methods and training data. Therefore, it is necessary to prepare such a good dataset, but it is not an easy job. An ontology-based domain knowledge base is able to help to reason semantic information and make effective answers given user questions. This study proposes a novel chatbot model involving ontology to generate efficient responses automatically. A case study of admissions advising at the International University–VNU HCMC is taken into account in the proposed chatbot. A… More >

  • Open Access


    Impact of Data Quality on Question Answering System Performances

    Rachid Karra*, Abdelali Lasfar

    Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 335-349, 2023, DOI:10.32604/iasc.2023.026695

    Abstract In contrast with the research of new models, little attention has been paid to the impact of low or high-quality data feeding a dialogue system. The present paper makes the first attempt to fill this gap by extending our previous work on question-answering (QA) systems by investigating the effect of misspelling on QA agents and how context changes can enhance the responses. Instead of using large language models trained on huge datasets, we propose a method that enhances the model's score by modifying only the quality and structure of the data feed to the model. It is important to identify… More >

  • Open Access


    Answer Classification via Machine Learning in Community Question Answering

    Yue Jiang, Xinyu Zhang, Wohuan Jia, Li Xu*

    Journal on Artificial Intelligence, Vol.3, No.4, pp. 163-169, 2021, DOI:10.32604/jai.2021.027590

    Abstract As a new type of knowledge sharing platform, the community question answer website realizes the acquisition and sharing of knowledge, and is loved and sought after by the majority of users. But for multi-answer questions, answer quality assessment becomes a challenge. The answer selection in CQA (Community Question Answer) was proposed as a challenge task in the SemEval competition, which gave a data set and proposed two subtasks. Task-A is to give a question (including short title and extended description) and its answers, and divide each answer into absolutely relevant (good), potentially relevant (potential) and bad or irrelevant (bad, dialog,… More >

  • Open Access


    BERT for Conversational Question Answering Systems Using Semantic Similarity Estimation

    Abdulaziz Al-Besher1, Kailash Kumar1, M. Sangeetha2,*, Tinashe Butsa3

    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4763-4780, 2022, DOI:10.32604/cmc.2022.021033

    Abstract Most of the questions from users lack the context needed to thoroughly understand the problem at hand, thus making the questions impossible to answer. Semantic Similarity Estimation is based on relating user’s questions to the context from previous Conversational Search Systems (CSS) to provide answers without requesting the user's context. It imposes constraints on the time needed to produce an answer for the user. The proposed model enables the use of contextual data associated with previous Conversational Searches (CS). While receiving a question in a new conversational search, the model determines the question that refers to more past CS. The… More >

  • Open Access


    A Survey of Knowledge Based Question Answering with Deep Learning

    Chaoyu Deng, Guangfu Zeng, Zhiping Cai, Xiaoqiang Xiao*

    Journal on Artificial Intelligence, Vol.2, No.4, pp. 157-166, 2020, DOI:10.32604/jai.2020.011541

    Abstract The purpose of automated question answering is to let the machine understand natural language questions and give accurate answers in the form of natural language. This technology requires the machine to store a large amount of background knowledge. In recent years, the rapid development of knowledge graph has made the knowledge based question answering (KBQA) more and more popular. Traditional styles of KBQA methods mainly include semantic parsing, information extraction and vector modeling. With the development of deep learning, KBQA with deep learning has gradually become the mainstream method. This paper introduces the application of deep learning in KBQA mainly… More >

  • Open Access


    ACLSTM: A Novel Method for CQA Answer Quality Prediction Based on Question-Answer Joint Learning

    Weifeng Ma*, Jiao Lou, Caoting Ji, Laibin Ma

    CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 179-193, 2021, DOI:10.32604/cmc.2020.011969

    Abstract Given the limitations of the community question answering (CQA) answer quality prediction method in measuring the semantic information of the answer text, this paper proposes an answer quality prediction model based on the question-answer joint learning (ACLSTM). The attention mechanism is used to obtain the dependency relationship between the Question-and-Answer (Q&A) pairs. Convolutional Neural Network (CNN) and Long Short-term Memory Network (LSTM) are used to extract semantic features of Q&A pairs and calculate their matching degree. Besides, answer semantic representation is combined with other effective extended features as the input representation of the fully connected layer. Compared with other quality… More >

  • Open Access


    A Longest Matching Resource Mapping Algorithm with State Compression Dynamic Programming Optimization

    Zhang Min, Teng Haibin, Jiang Ming, Wen Tao, Tang Jingfan

    Intelligent Automation & Soft Computing, Vol.25, No.3, pp. 625-635, 2019, DOI:10.31209/2019.100000117

    Abstract Mapping from sentence phrases to knowledge graph resources is an important step for applications such as search engines, automatic question answering systems based on acknowledge base and knowledge graphs. The existing solution maps a simple phrase to a knowledge graph resource strictly or approximately from the text. However, it is difficult to detect phrases and map the composite semantic resource. This paper proposes a longest matching resource mapping scheme to solve this problem, namely, to find the longest substring in a sentence that can match the knowledge base resource. Based on this scheme, we propose an optimization algorithm based on… More >

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