A better entity detection of question for knowledge graph question answering through extracting position-based patterns

Yani, Mohammad; Krisnadhi, Adila; Budi, Indra

doi:10.1186/s40537-022-00631-1

Cited by 5 publications

(13 citation statements)

References 25 publications

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“…Research categorizes questions based on the amount of language information required for classification. Comparison experiments demonstrate the impact of various DL models on question categorization [66][67][68][69][70]. Deep transfer learning is employed to enhance question categorization, even in new domains.…”

Section: Literature Reviewmentioning

confidence: 99%

Detection of Questions from Text Data Using LSTM-Deep Learning Model

Nadir Hussain,

Dr. Sheikh Muhammad Saqib,

Hamza Arif

et al. 2024

VAWKUM trans. comput. sci.

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This paper discusses the importance of detecting questions in textual data for various applications in natural language processing (NLP), such as question answering and chatbot creation. The proposed approach employs long short-term memory (LSTM) models to accurately identify questions by leveraging the sequential nature of language.The paper highlights that LSTM models address challenges like ambiguous language and varying sentence structures. They allow the model to learn from sequential patterns, crucial for understanding the intent behind the text. The preprocessing steps, including tokenization, embedding, and padding, are detailed to prepare the data for training and testing. The study investigates the impact of hyperparameters like hidden layers, hidden states, and optimizer choice on the LSTM algorithm’s performance. In experiments on benchmark datasets, the proposed LSTM-based approach consistently outperforms conventional machine learning models, achieving a remarkable accuracy of 99.25% on the test dataset. The paper concludes by suggesting future directions, including applyingthe approach to other NLP tasks like named entity recognition, sentiment analysis, and text classification. Further optimization for specific datasets or domains is also encouraged. Overall, this research contributes to robust question detection models in NLP, with potential applications in various fields.

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Section: Literature Reviewmentioning

confidence: 99%

Detection of Questions from Text Data Using LSTM-Deep Learning Model

Nadir Hussain,

Dr. Sheikh Muhammad Saqib,

Hamza Arif

et al. 2024

VAWKUM trans. comput. sci.

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“…Meanwhile, the first two are more difficult. A previous work was proposed to address the issues of the entity detection task [2]. The first task (entity and relation detection) addressed an issue in defining the position of the detected entities in a triple for a given [2].…”

Section: Knowledge Graph Question Answeringmentioning

confidence: 99%

“…Therefore, the query constructor should try all possibilities of the position of entities and relations. A model proposed by [2] was used to address the issue. This research focused on the entity and relation linking tasks by utilizing the result of the entity detection proposed by [2].…”

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Entity and Relation Linking for Knowledge Graph Question Answering Using Gradual Searching

Adila Alfa Krisnadhi,

Mohammad Yani,

Indra Budi

2024

Jurnal Nasional Teknik Elektro dan Teknologi Informasi

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Knowledge graph question answering (KGQA) systems have an important role in retrieving data from a knowledge graph (KG). With the system, regular users can access data from a KG without the need to construct a formal SPARQL query. KGQA systems receive a natural language question (NLQ) and translate it into a SPARQL query through three main tasks, namely, entity and relation detection, entity and relation linking, and query construction. However, the translation is not trivial due to lexical gaps and entity ambiguity that may occur during entity or relation linking. This research proposed an approach based on multiclass classification of NLQ whose entity occurrences are detected into categories based on KG relations to address the lexical gap challenge. Next, to solve the entity ambiguity challenge, this research proposed a three-stage searching procedure to determine appropriate KG entities associated with the NLQ entities, given the correspondence between the NLQ and a particular KG relation. This three-stage searching consisted of text-based searching, vector-based searching, and entity and relation pairing. The proposed approach was evaluated on the SimpleQuestions and LC-QuAD 2.0 datasets. The experiments demonstrated that the proposed approach outperformed the state-of-the-art baseline. For the relation linking task, the proposed approach reached 89.87% and 74.83% recall for the SimpleQuestions and LC-QuAD 2.0, respectively. This approach also achieved 91.74% and 61.96% recall on the entity linking tasks for the SimpleQuestions and LC-QuAD 2.0, respectively.

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“…People also work on different detection approaches -few shot (Xia et al, 2021), zero shot (Xia et al, 2018), clustering frameworks (Mullick et al, 2022b). (Yani et al, 2022;Sufi and Alsulami, 2021;Zhao et al, 2021) all explore entity detection tasks. (Vanzo et al, 2019) develop a hierarchical multi-task architecture for semantic parsing sentences for cross-domain spoken dialogue systems.…”

Section: Related Workmentioning

confidence: 99%

Intent Identification and Entity Extraction for Healthcare Queries in Indic Languages

Mullick¹,

Mondal²,

Ray³

et al. 2023

Preprint

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Scarcity of data and technological limitations for resource-poor languages in developing countries like India poses a threat to the development of sophisticated NLU systems for healthcare. To assess the current status of various state-of-the-art language models in healthcare, this paper studies the problem by initially proposing two different Healthcare datasets, Indian Healthcare Query Intent-WebMD and 1mg (IHQID-WebMD and IHQID-1mg) and one real world Indian hospital query data in English and multiple Indic languages (Hindi, Bengali, Tamil, Telugu, Marathi and Gujarati) which are annotated with the query intents as well as entities. Our aim is to detect query intents and extract corresponding entities. We perform extensive experiments on a set of models in various realistic settings and explore two scenarios based on the access to English data only (less costly) and access to target language data (more expensive). We analyze context specific practical relevancy through empirical analysis. The results, expressed in terms of overall F1 score show that our approach is practically useful to identify intents and entities.

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A better entity detection of question for knowledge graph question answering through extracting position-based patterns

Cited by 5 publications

References 25 publications

Detection of Questions from Text Data Using LSTM-Deep Learning Model

Detection of Questions from Text Data Using LSTM-Deep Learning Model

Entity and Relation Linking for Knowledge Graph Question Answering Using Gradual Searching

Intent Identification and Entity Extraction for Healthcare Queries in Indic Languages

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