A semantic matching approach addressing multidimensional representations for web service discovery

Huang, Zhao Hui; Zhao, Wei

doi:10.1016/j.eswa.2022.118468

Cited by 9 publications

(5 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…, 2019). In this study, the Word2Vec algorithm is chosen as a tool for training word vectors (Huang and Zhao, 2022; Turgeman et al. , 2022; Lin et al.…”

Section: Methodsmentioning

confidence: 99%

“…The proposed techniques are domain-independent and do not require data training (Onan et al, 2016). For instance, TF-IDF is a weighting technique that is commonly used in data mining and information processing in order to forecast the significance of words in a corpus (Huang and Zhao, 2022). TF (term frequency) represents word frequency, indicating the frequency of words in a text, while IDF (inverse document frequency) puts forward the reverse text frequency, which is used to lower certain common but less influential words in the corpus.…”

Section: Feature Extraction and Cue Classificationmentioning

confidence: 99%

“…Afterward, the text is vectorized to perform topic classification, text clustering and other operations (Khatua et al, 2019). In this study, the Word2Vec algorithm is chosen as a tool for training word vectors (Huang and Zhao, 2022;Turgeman et al, 2022;Lin et al, 2022). Word2vec is a machine learning tool developed by Google to convert words into high-dimensional space vectors (Yao et al, 2017).…”

Section: Feature Extraction and Cue Classificationmentioning

confidence: 99%

See 2 more Smart Citations

Research on online shopping contextual cues: refining classification from text mining

Wang

Gao

Zhao

2023

APJML

View full text Add to dashboard Cite

PurposeContextual cues have become a hot research topic in the field of mobile consumer behavior, owing to the continuous rise of digital marketing. However, the complex online shopping scene makes it challenging to directly identify the association between the characteristics of contextual cues and consumer behavior. Presently, few studies have only systematically extracted and refined the types and characteristics of contextual cues. The purpose of this study is to explore the types and mechanisms of contextual cues in online shopping scenarios.Design/methodology/approachThis study uses the word2vec algorithm, grounded theory and co-occurrence cluster method, along with online shopping word-of-mouth (WOM) text and consumer behavior theory, in order to explore different types of contextual cues and its efficiency from 5,619 comment corpus.FindingsThis study puts forward the following conclusions. (1) From the perspective of online shopping, contextual cues comprise aesthetic perception cues, value perception cues, trust-dependent cues, time perception cues, memory attention cues, spatial perception cues, attribute cues and relationship cues. (2) Based on the online shopping scenarios, contextual cues and their interaction effects exert an effect on consumer satisfaction, recommendation, purchase and return behavior.Originality/valueThe study conclusions are helpful to further reveal the deep association between contextual cues and consumer behavior in the process of online shopping, thus providing practical and theoretical enlightenment for enterprises to not only effectively reshape the scene but also promote the consumers' active purchase behavior.

show abstract

“…, 2019). In this study, the Word2Vec algorithm is chosen as a tool for training word vectors (Huang and Zhao, 2022; Turgeman et al. , 2022; Lin et al.…”

Section: Methodsmentioning

confidence: 99%

Section: Feature Extraction and Cue Classificationmentioning

confidence: 99%

Section: Feature Extraction and Cue Classificationmentioning

confidence: 99%

See 1 more Smart Citation

Research on online shopping contextual cues: refining classification from text mining

Wang

Gao

Zhao

2023

APJML

View full text Add to dashboard Cite

show abstract

“…Web retrieval similarly extensively employs the principles of text matching. Huang et al [32] proposed a multi-dimensional representation neural network that incorporates TF-IDF, Word2Vec, and ELMo, effectively enhancing the performance of web retrieval. In the field of text summarization, text matching techniques are also utilized to identify duplicate text content, facilitating the removal of redundant information from summaries, which is beneficial for text mining purposes.…”

Section: Text Matchingmentioning

confidence: 99%

FinKENet: A Novel Financial Knowledge Enhanced Network for Financial Question Matching

Guo,

Liang,

Chen

et al. 2023

Entropy

View full text Add to dashboard Cite

Question matching is the fundamental task in retrieval-based dialogue systems which assesses the similarity between Query and Question. Unfortunately, existing methods focus on improving the accuracy of text similarity in the general domain, without adaptation to the financial domain. Financial question matching has two critical issues: (1) How to accurately model the contextual representation of a financial sentence? (2) How to accurately represent financial key phrases in an utterance? To address these issues, this paper proposes a novel Financial Knowledge Enhanced Network (FinKENet) that significantly injects financial knowledge into contextual text. Specifically, we propose a multi-level encoder to extract both sentence-level features and financial phrase-level features, which can more accurately represent sentences and financial phrases. Furthermore, we propose a financial co-attention adapter to combine sentence features and financial keyword features. Finally, we design a multi-level similarity decoder to calculate the similarity between queries and questions. In addition, a cross-entropy-based loss function is presented for model optimization. Experimental results demonstrate the effectiveness of the proposed method on the Ant Financial question matching dataset. In particular, the Recall score improves from 73.21% to 74.90% (1.69% absolute).

show abstract

“…discovery: Syntactic-aware, Semantic-aware, and Context-aware(Huang and Zhao, 2022;Rodríguez et al, 2016). According to the results, 7 of the 9 papers of this sub-theme use BN models based on the Semantic-aware approach (P01, P02, P04, P06, P07, P10, P69).…”

mentioning

confidence: 99%

A review on the applications of Bayesian network in web service

Guinhouya

2024

Int J Syst Assur Eng Manag

View full text Add to dashboard Cite

Web services (WS) are the preferred approach in realizing the service-oriented computing paradigm. However, this comes with challenges such as complexity and uncertainty that hinder their practical application. Bayesian networks (BNs) are one of the techniques used to address these challenges. The objective of this mapping study was to determine what is known about the use of Bayesian networks in web services research. To do this, we identified and selected rigorously 69 articles (out of the 532 identified) published on the subject in 2001-2021. We then classified and analyzed these articles by Web service themes (Service Composition, Service Management, Service Engineering), Objectives (Prediction, Description, Prescription), Types of BN (Basic, Combined, Extended), and Evaluation methods (Proof of concept, Experiment, No evaluation). In doing so, we hope to provide a clear understanding of the subject. We also identify and suggest avenues for future research.Thus, the review results can help researchers and practitioners interested by the application of BNs in WS research.

show abstract

A semantic matching approach addressing multidimensional representations for web service discovery

Cited by 9 publications

References 48 publications

Research on online shopping contextual cues: refining classification from text mining

Research on online shopping contextual cues: refining classification from text mining

FinKENet: A Novel Financial Knowledge Enhanced Network for Financial Question Matching

A review on the applications of Bayesian network in web service

Contact Info

Product

Resources

About