Enhancing Query Answer Completeness with Query Expansion based on Synonym Predicates

Emetis, Niazmand,

doi:10.1145/3487553.3524198

Cited by 3 publications

(1 citation statement)

References 12 publications

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“…"A method that improves question answering over inadequate community-based knowledge graphs" is what the study [32] suggests. It proposes a method to handle queries effectively in order to assess how comprehensive the answers are and improve the output.…”

Section: Detailed Literaturementioning

confidence: 98%

Deep Learning Based Question Answering System (Survey)

Saeed,

ashraf,

Jhanjhi

2023

Preprint

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Deep learning-based question answering systems have transformed the discipline of natural language processing (NLP) by automating the extraction of answers from textual data. This survey paper provides a captivating overview of these systems, exploring methodologies, techniques, and architectures such as recurrent neural networks (RNNs), BERT model, and transformer models. Extractive and generative approaches are examined, alongside the challenges of handling complex questions, managing noisy input, and addressing rare or unseen words. This survey serves as a stimulating reference, offering valuable insights to researchers and practitioners, fueling innovation and advancement in question answering systems within NLP.

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Section: Detailed Literaturementioning

confidence: 98%

Deep Learning Based Question Answering System (Survey)

Saeed,

ashraf,

Jhanjhi

2023

Preprint

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Discovering Relationships Among Properties in Wikidata Knowledge Graph

Niazmand,

Vidal

2024

Lecture Notes in Computer Science

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A Survey on Association Rule Mining for Enterprise Architecture Model Discovery

Pinheiro,

Guerreiro,

Mamede

2023

Bus Inf Syst Eng

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Association Rule Mining (ARM) is a field of data mining (DM) that attempts to identify correlations among database items. It has been applied in various domains to discover patterns, provide insight into different topics, and build understandable, descriptive, and predictive models. On the one hand, Enterprise Architecture (EA) is a coherent set of principles, methods, and models suitable for designing organizational structures. It uses viewpoints derived from EA models to express different concerns about a company and its IT landscape, such as organizational hierarchies, processes, services, applications, and data. EA mining is the use of DM techniques to obtain EA models. This paper presents a literature review to identify the newest and most cited ARM algorithms and techniques suitable for EA mining that focus on automating the creation of EA models from existent data in application systems and services. It systematically identifies and maps fourteen candidate algorithms into four categories useful for EA mining: (i) General Frequent Pattern Mining, (ii) High Utility Pattern Mining, (iii) Parallel Pattern Mining, and (iv) Distribute Pattern Mining. Based on that, it discusses some possibilities and presents an exemplification with a prototype hypothesizing an ARM application for EA mining.

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Enhancing Query Answer Completeness with Query Expansion based on Synonym Predicates

Cited by 3 publications

References 12 publications

Deep Learning Based Question Answering System (Survey)

Deep Learning Based Question Answering System (Survey)

Discovering Relationships Among Properties in Wikidata Knowledge Graph

A Survey on Association Rule Mining for Enterprise Architecture Model Discovery

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