A semantic approach for question answering using DBpedia and WordNet

Sengloiluean, Kittiphong; Arch-int, Ngamnij; Arch-int, Somjit; Thongkrau, Theerayut

doi:10.1109/jcsse.2017.8025918

Cited by 5 publications

(4 citation statements)

References 18 publications

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“…In 2017, Kittiphong Sengloiluean et al [32], Adopted a semantic approach to answering the query by using DBpedia and WordNet. The study aimed to decide the best methods for solving questions.…”

Section: Literature Reviewmentioning

confidence: 99%

State of Art for Semantic Analysis of Natural Language Processing

Maulud

Zeebaree

Jacksi

et al. 2021

QAJ

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Semantic analysis is an essential feature of the NLP approach. It indicates, in the appropriate format, the context of a sentence or paragraph. Semantics is about language significance study. The vocabulary used conveys the importance of the subject because of the interrelationship between linguistic classes. In this article, semantic interpretation is carried out in the area of Natural Language Processing. The findings suggest that the best-achieved accuracy of checked papers and those who relied on the Sentiment Analysis approach and the prediction error is minimal.

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Section: Literature Reviewmentioning

confidence: 99%

State of Art for Semantic Analysis of Natural Language Processing

Maulud

Zeebaree

Jacksi

et al. 2021

QAJ

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“…Knowledge extraction is the method of translating information in unstructured text into easily controlled classes. Essential to this is the role of named object identification and classification (NERC) [106], a method that includes recognizing proper names in texts (NER) and how to apply them to predefined categories of interest (NEC) [107]. This sort of tool analyzes sentences, but NERC is about deriving meaning from the text.…”

Section: Named Entity Recognitionmentioning

confidence: 99%

“…The conventional core collection of named individuals, formed for the shared NERC challenge, comprises Individual, Organization, Place, and Date and Time phrases, such as Barack Obama, Microsoft, New York, etc. Kittiphong Sengloiluean, et al [106]. Used a semantic approach to address queries using DBpedia and WordNet.…”

Section: Named Entity Recognitionmentioning

confidence: 99%

Review on Natural Language Processing Based on Different Techniques

Maulud

Ameen

Omar

et al. 2021

AJRCoS

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With the exponential growth of the information on the Internet, there is a high demand for making this information readable and processable by machines. For this purpose, there is a need for the Natural Language Processing (NLP) pipeline. Natural language analysis is a tool used by computers to grasp, perceive, and control human language. This paper discusses various techniques addressed by different researchers on NLP and compares their performance. The comparison among the reviewed researches illustrated that good accuracy levels haved been achieved. Adding to that, the researches that depended on the Sentiment Analysis and ontology methods achieved small prediction error. The syntactic analysis or parsing or syntax analysis is the third stage of the NLP as a conclusion to use NLP technology. This step aims to accurately mean or, from the text, you may state a dictionary meaning. Syntax analysis analyzes the meaning of the text in comparison with the formal grammatical rules.

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“…Indirect Match: If no direct match exists, query expansion is performed by exploiting external knowledge (e.g., Framester [10]). We try to match ECM concepts with WordNet synsets related to the keyword term(s) or DBpedia entities referring to Named Entity(ies) found in the question, similarly to [22]. 3.…”

Section: Answer Buildingmentioning

confidence: 99%

Toward a User-Adapted Question/Answering Educational Approach

Alzetta

Adorni

Celik

et al. 2018

Adjunct Publication of the 26th Conference on User Modeling, Adaptation and Personalization

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This paper addresses the design of a model for Question/Answering in an interactive and mobile learning environment. The learner's question can be made through vocal interaction or typed text and the answer is the generation of a personalized learning path. This takes into account the focus and type of the question and some personal features of the learner extracted both from the question and prosodic features, in case of vocal questions. The response is a learning path that preserves the precedence of the prerequisite relations and contains all the relevant concepts for answering the user's question. The main contribution of the paper is to investigate the possibility to exploit educational concept maps in a Q/A interactive learning system.

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A semantic approach for question answering using DBpedia and WordNet

Cited by 5 publications

References 18 publications

State of Art for Semantic Analysis of Natural Language Processing

State of Art for Semantic Analysis of Natural Language Processing

Review on Natural Language Processing Based on Different Techniques

Toward a User-Adapted Question/Answering Educational Approach

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