ComQA: Question Answering Over Knowledge Base via Semantic Matching

Jin, Hai; Luo, Yi; Gao, Chenjing; Tang, Xunzhu; Yuan, Pingpeng

doi:10.1109/access.2019.2918675

Cited by 35 publications

(20 citation statements)

References 24 publications

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“…The features used help with the approach of portability that is an added advantage of this proposed system. H. Jin et al provides the end-users with an excellent natural language interface and helps them to overcome the complexity of the underlying KB [15]. This QA system allows people who do not have any prior knowledge about the KBs and can get answers for even complex questions.…”

Section: Related Workmentioning

confidence: 99%

Question Answering System Over Semantic Web

Dhandapani

Viswanathan

2021

IEEE Access

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The Semantic Web contains a large amount of data in the form of knowledge bases. Question Answering system is the most promising way of retrieving data from the available knowledge base to the end-users, to get the appropriate result for their questions. Although many systems have been developed over the years, it remains a challenge that most systems yet to require improvements to increase the accuracy for correct interpretation of the question and provide an answer. Many Question Answering systems convert the questions into triples which are mapped to the Knowledge base from which answer is derived. However, these triples do not express the semantic representation of the question, due to which the answers cannot be located. To handle this, a template-based approach is proposed which classifies the question types and finds appropriate SPARQL query templates for each type including comparatives and superlatives. The SPARQL query built is executed in the DBpedia endpoint and results are obtained. Compared with other factoid question answering systems, the proposed approach has the potential to deal with a large number of question types, including comparatives and superlatives. Also, the experimental evaluations of the system performed on the QALD-8 dataset present good performance and can help users to find answers to their questions.

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Section: Related Workmentioning

confidence: 99%

Question Answering System Over Semantic Web

Dhandapani

Viswanathan

2021

IEEE Access

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“…The EM is the ratio that represents the extent to which the results predicted by the model and the answer are fully matched. The F 1 score (9) is the harmonic mean of precision, calculated by (7), and recall, calculated by (8). True Positive (TP) represents that the value of the actual class is 'yes,' and the value of the predicted class is also 'yes.'…”

Section: ) Metricsmentioning

confidence: 99%

“…Therefore, NLP has been actively studied, wherein it has demonstrated sufficient performance in various tasks such as machine reading comprehension (MRC) [6]- [8], machine translation [9]- [11], and natural language inference [12], [13]. MRC, which has recently received a The associate editor coordinating the review of this manuscript and approving it for publication was Arianna Dulizia .…”

Section: Introductionmentioning

confidence: 99%

Improved Machine Reading Comprehension Using Data Validation for Weakly Labeled Data

2020

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Machine reading comprehension (MRC) is a natural language processing task wherein a given question is answered according to a holistic understanding of a given context. Recently, many researchers have shown interest in MRC, for which a considerable number of datasets are being released. Datasets for MRC, which are composed of the context-query-answer triple, are designed to answer a given query by referencing and understanding a readily-available, relevant context text. The TriviaQA dataset is a weakly labeled dataset, because it contains irrelevant context that forms no basis for answering the query. The existing syntactic data cleaning method struggles to deal with the contextual noise this irrelevancy creates. Therefore, a semantic data cleaning method using reasoning processes is necessary. To address this, we propose a new MRC model in which the TriviaQA dataset is validated and trained using a high-quality dataset. The data validation method in our MRC model improves the quality of the training dataset, and the answer extraction model learns with the validated training data, because of our validation method. Our proposed method showed a 4.33% improvement in performance for the TriviaQA Wiki, compared to the existing baseline model. Accordingly, our proposed method can address the limitation of irrelevant context in MRC better than the human supervision. INDEX TERMS Computational and artificial intelligence, data validation, natural language processing, neural networks, machine reading comprehension, weak label.

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“…Jin et al proposed a bidirectional long-and short-term memory neural network based on the twin network structure for text similarity. The network traverses the entire text through two LSTM networks, comprehensively considers the context information of each word, extracts the characteristics of the sentence, and completes the text similarity [13]. Lu et al applied the weighting mechanism to the field of network text processing.…”

Section: Introductionmentioning

confidence: 99%

Semantic Matching Efficiency of Supply and Demand Text on Cross-Border E-Commerce Online Technology Trading Platforms

Chen

2021

Wireless Communications and Mobile Computing

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With the innovation of global trade business models, more and more foreign trade companies are transforming and developing in the direction of cross-border e-commerce. However, due to the limitation of platform language processing and analysis technology, foreign trade companies encounter many bottlenecks in the process of transformation and upgrading. From the perspective of the semantic matching efficiency of e-commerce platforms, this paper improves the logical and technical problems of cross-border e-commerce in the operation process and uses semantic matching efficiency as the research object to conduct experiments on the QQP dataset. We propose a graph network text semantic analysis model TextSGN based on semantic dependency analysis for the problem that the existing text semantic matching method does not consider the semantic dependency information between words in the text and requires a large amount of training data. The model first analyzes the semantic dependence of the text and performs word embedding and one-hot encoding on the nodes (single words) and edges (dependencies) in the semantic dependence graph. On this basis, in order to quickly mine semantic dependencies, an SGN network block is proposed. The network block defines the way of information transmission from the structural level to update the nodes and edges in the graph, thereby quickly mining semantics dependent information allows the network to converge faster, train classification models on multiple public datasets, and perform classification tests. The experimental results show that the accuracy rate of TextSGN model in short text classification reaches 95.2%, which is 3.6% higher than the suboptimal classification method; the accuracy rate is 86.16%, the F 1 value is 88.77%, and the result is better than other methods.

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ComQA: Question Answering Over Knowledge Base via Semantic Matching

Cited by 35 publications

References 24 publications

Question Answering System Over Semantic Web

Question Answering System Over Semantic Web

Improved Machine Reading Comprehension Using Data Validation for Weakly Labeled Data

Semantic Matching Efficiency of Supply and Demand Text on Cross-Border E-Commerce Online Technology Trading Platforms

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