An information retrieval system for Malayalam using query expansion technique

Document searching using queries that can understand the context can affect the intent and purpose of the user's desire when searching documents. Many studies have been conducted on understanding the context of the query, but differences in terms of language can lead to different methods of context understanding; therefore, methods implemented in the previous studies need to be improved. In this paper, we proposed a query expansion method based on BabelNet search and Word Embedding (BabelNet Embedding). Query expansion method focuses on developing queries based on semantic relationships on queries to understand the context of the query. Candidate queries were developed by finding synonyms, measuring similarity using WordNet, Word Embedding on all articles of Wikipedia, and BableNet Embedding on articles Wikipedia Online. We compared our proposed method with the existing semantic query expansion. Our result provided better result in retrieving relevant document with accuracy of 89% in searching Arabic documents.

show abstract

“…A number of studies that used the related term expansion method are Wordnet [17][18][19], synonym [20], synonym for finding relevant tweet [21].…”

Section: Related Term Expansion Is a Query Expansionmentioning

confidence: 99%

Query Expansion Based on Wikipedia Word Embedding and BabelNet Method for Searching Arabic Documents

Maryamah¹,

Arifin²,

Sarno³

et al. 2019

IJIES

View full text Add to dashboard Cite

show abstract

“…Research [12] similar to research conducted [10], query expansion is done by mapping words and searching for synonyms of words entered by the user. The query entered by the user is expanded, the relevant word is searched for and reweighting is done.…”

Section: Related Workmentioning

confidence: 99%

Query Expansion in Information Retrieval using Frequent Pattern (FP) Growth Algorithm for Frequent Itemset Search and Association Rules Mining

Afuan¹,

Ashari²,

Suyanto³

2019

ijacsa

View full text Add to dashboard Cite

Documents on the Internet have increased in number exponentially; this has resulted in users having difficulty finding documents or information needed. Special techniques are needed to retrieve documents that are relevant to user queries. One technique that can be used is Information Retrieval (IR). IR is the process of finding data (generally documents) in the form of text that matches the information needed from a collection of documents stored on a computer. Problems that often appear on IRs are incorrect user queries; this is caused by user limitations in representing their needs in the query. Researchers have proposed various solutions to overcome these limitations, one of which is to use the Expansion Query (QE). Various methods that have been applied to QE include Ontology, Latent Semantic Indexing (LSI), Local Co-Occurrence, Relevance Feedback, Concept Based, WordNet / Synonym Mapping. However, these methods still have limitations, one of them in terms of displaying the connection or relevance of the appearance of words or phrases in the document collection. To overcome this limitation, in this study we have proposed an approach to QE using the FP-Growth algorithm for the search for frequent itemset and Association Rules (AR) on QE. In this study, we applied the use of AR to QE to display the relevance of the appearance of a word or term with another word or term in the collection of documents, where the term produced is used to perform QE on user queries. The main contribution in this study is the use of Association rules with FP-Growth in the collection of documents to look for the connection of the emergence of words, which is then used to expand the original query of users on IR. For the evaluation of QE performance, we use recall, precision, and f-measure. Based on the research that has been done, it can be concluded that the use of AR on QE can improve the relevance of the documents produced. This is indicated by the average recall, precision, and f-measure values produced at 94.44%, 89.98%, and 92.07%. After comparing the IR process without QE with IR using QE, an increase in recall value was 25.65%, precision was 1.93%, and F-Measure was 15.78%.

show abstract

An information retrieval system for Malayalam using query expansion technique

Cited by 2 publications

References 7 publications

Query Expansion Based on Wikipedia Word Embedding and BabelNet Method for Searching Arabic Documents

Query Expansion Based on Wikipedia Word Embedding and BabelNet Method for Searching Arabic Documents

Query Expansion in Information Retrieval using Frequent Pattern (FP) Growth Algorithm for Frequent Itemset Search and Association Rules Mining

Contact Info

Product

Resources

About