Semantic Ambiguous Query Formulation Using Statistical Linguistics Technique

Kadir, Rabiah Abdul; Yauri, Rufai Aliyu; Azman, Azreen

doi:10.22452/mjcs.sp2018no1.4

Cited by 5 publications

(4 citation statements)

References 17 publications

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“…Moreover, the performances of different and the same data structure types are discussed, the different indexes are described, and the appropriate index type for semantic search under different conditions is determined. In [18], query ambiguity resolution by query expansion Malaysian Journal of Computer Science, Vol. 35 (1), 2022 using a statistical linguistic technique during semantic query formulation is described to retrieve relevant answers from a Holy Quran ontology.…”

Section: Literature Reviewmentioning

confidence: 99%

A Method for Improving Reasoning and Realization Problem Solving in Descriptive Logic- Based and Ontology-Based Reasoners

Shokohinia¹,

Dideban²

2022

MJCS

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Recently, many methods have been developed for representing knowledge, reasoning, and result extraction extracting results based on the respective domain knowledge in question. Despite the ontological success in knowledge representation, the reasoning method has faces some challenges. The main challenge in ontology reasoning methods is the failure in solving realization problems in the reasoning process. Apart from the complexity of solving realization problems, this already daunting challenge is compounded by computational complexity the time complexity of the solving realization problem solving process problems is equal to that of NEXP TIME. This important issue problem is achieved solved by solving the subsumption and satisfiability problems. Thus, to solve the realization problem, we first partition the ontology or extract partitions related to the query. Then, the satisfiability problem is solved by extracting partitions, and all concepts related to the query are extracted. This study proposes a method to overcome this problem, where a new solution is proposed with an appropriate time position. Finally, the efficiency of the proposed method, is evaluated against other reasoning engines, and the results show optimized performance vis-a-vis previous studies.

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Section: Literature Reviewmentioning

confidence: 99%

A Method for Improving Reasoning and Realization Problem Solving in Descriptive Logic- Based and Ontology-Based Reasoners

Shokohinia¹,

Dideban²

2022

MJCS

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“…There are number of query expansion approaches studied in the past, however, the scope of this study is limited to focus on linguistic approaches. In the paper [22], authors have proposed a statistical linguistic technique for ambiguous query reformulation. Here, the query is first normalized to identify concepts using a knowledge base and stored it in the container which is further used by a system to match between noun query tokens.…”

Section: Query Expansionmentioning

confidence: 99%

Leveraging Neural Network Phrase Embedding Model for Query Reformulation in Ad-Hoc Biomedical Information Retrieval

Bhopale

Tiwari

2021

MJCS

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This study presents a spark enhanced neural network phrase embedding model to leverage query representation for relevant biomedical literature retrieval. Information retrieval for clinical decision support demands high precision. In recent years, word embeddings have been evolved as a solution to such requirements. It represents vocabulary words in low-dimensional vectors in the context of their similar words; however, it is inadequate to deal with semantic phrases or multi-word units. Learning vector embeddings for phrases by maintaining word meanings is a challenging task. This study proposes a scalable phrase embedding technique to embed multi-word units into vector representations using a state-of-the-art word embedding technique, keeping both word and phrase in the same vectors space. It will enhance the effectiveness and efficiency of query language models by expanding unseen query terms and phrases for the semantically associated query terms. Embedding vectors are evaluated via a query expansion technique for ad-hoc retrieval task over two benchmark corpora viz. TREC-CDS 2014 collection with 733,138 PubMed articles and OHSUMED corpus having 348,566 articles collected from a Medline database. The results show that the proposed technique has significantly outperformed other state-of-the-art retrieval techniques

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“…Traditional search applications that rely on keyword search face problems such as natural language ambiguity and reference reconciliation. As a result, keyword-based retrieval systems produce many irrelevant results [33]. Query expansion has been applied to include synonyms of words used in queries, improving the retrieval.…”

Section: Knowledge Retrieval Systems For Al-quranmentioning

confidence: 99%

“…In term of evaluation for knowledge retrieval systems, Precision and Recall are widely used as they measure the relevancy of the retrieved information [6], [33], [36]. F-score has also been used as it is the harmonic mean of Precision and Recall, which would show the retrieval accuracy of the systems [6].…”

Section: Knowledge Retrieval Systems For Al-quranmentioning

confidence: 99%

Semantic Graph Knowledge Representation for Al-Quran Verses Based on Word Dependencies

Khazani

Mohamed

Sembok

et al. 2021

MJCS

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Semantic approaches present an efficient, detailed and easily understandable representation of knowledge from documents. Al-Quran contains a vast amount of knowledge that needs appropriate knowledge extraction. A semantic based approach can help in designing an efficient and explainable knowledge representation model for Al-Quran. This research aims to propose a semantic-graph knowledge representation model for verses of Al-Quran based on word dependencies. These features are used in the proposed knowledge representation model allowing the semantic graph matching to improve Al-Quran search applications' accuracy. The proposed knowledge representation model is essentially a formalism for generating a semantic graph representation of Quranic verses, which can be applied for knowledge base construction for other applications such as information retrieval system. A set of rules called Semantic Dependency Triple Rules are defined to be mapped into the semantic graph representing the verse's logic. The rules translate word dependencies and other NLP metadata into a triple form that holds logical information. The proposed model has been tested with English translation of Al-Quran on a document retrieval prototype The basic system has been enhanced with anaphoric pronouns correction, which has shown improvement in retrieval performance. The results have been compared with a closely related system and evaluated on the accuracy of the document retrieval in Precision, Recall and F-score measurements. The proposed model has achieved 65%, 60% and 62.4% for the measurements, respectively. It has also improved the overall accuracy of previous system by 43.8%.

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Semantic Ambiguous Query Formulation Using Statistical Linguistics Technique

Cited by 5 publications

References 17 publications

A Method for Improving Reasoning and Realization Problem Solving in Descriptive Logic- Based and Ontology-Based Reasoners

A Method for Improving Reasoning and Realization Problem Solving in Descriptive Logic- Based and Ontology-Based Reasoners

Leveraging Neural Network Phrase Embedding Model for Query Reformulation in Ad-Hoc Biomedical Information Retrieval

Semantic Graph Knowledge Representation for Al-Quran Verses Based on Word Dependencies

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