An IR-Based Approach Utilizing Query Expansion for Plagiarism Detection in MEDLINE

Nawab, Rao Muhammad Adeel; Stevenson, Mark; Clough, Paul

doi:10.1109/tcbb.2016.2542803

Cited by 15 publications

(7 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some other recent applications of QE are plagiarism detection [203], event search [89,15,43], text classification [269], patent retrieval [180,181,268], dynamic process in IoT [122,123], classification of e-commerce [128], biomedical IR [1], enterprise search [174], code search [205], parallel computing in IR [179] and twitter search [151,304]. Table 7 summarizes some of the prominent and recent applications of QE in literature based on the above discussion.…”

Section: Other Applicationsmentioning

confidence: 99%

Query expansion techniques for information retrieval: A survey

Azad

Deepak

2019

Information Processing & Management

228

104

View full text Add to dashboard Cite

With the ever increasing size of the web, relevant information extraction on the Internet with a query formed by a few keywords has become a big challenge. Query Expansion (QE) plays a crucial role in improving searches on the Internet. Here, the user's initial query is reformulated by adding additional meaningful terms with similar significance. QE -as part of information retrieval (IR) -has long attracted researchers' attention. It has become very influential in the field of personalized social document, question answering, cross-language IR, information filtering and multimedia IR. Research in QE has gained further prominence because of IR dedicated conferences such as TREC (Text Information Retrieval Conference) and CLEF (Conference and Labs of the Evaluation Forum). This paper surveys QE techniques in IR from 1960 to 2017 with respect to core techniques, data sources used, weighting and ranking methodologies, user participation and applications -bringing out similarities and differences.There is a huge amount of data available on the Internet, and it is growing exponentially. This unconstrained information-growth has not been accompanied by a corresponding technical advancement in the approaches for extracting relevant information [191]. Often, a web-search does not yield relevant results. There are multiple reasons for this. First, the keywords submitted by the user can be related to multiple topics; as a result, the search results are not focused on the topic of interest. Second, the query can be too short to capture appropriately what the user is looking for. This can happen just as a matter of habit (e.g., the average size of a web search is 2.4 words [257,255]). Third, the user is often not sure about what he is looking for until he sees the results. Even if the user knows what he is searching for, he does not know how to formulate an appropriate query (navigational queries are exceptions to this [51]). QE plays an important part in fetching relevant results in the above cases. Most web queries fall under the following three fundamental categories [51, 139] :-Informational Queries: Queries that cover a broad topic (e.g., India or journals) for which there may be thousands of relevant results. -Navigational Queries: Queries that are looking for specific website or URL (e.g., ISRO).-Transactional Queries: Queries that demonstrate the user's intent to execute a specific activity (e.g., downloading papers or buying books).2 Hiteshwar Kumar Azad, Akshay DeepakCurrently, user-queries are mostly processed using indexes and ontologies, which work on exact matches and are hidden from the users. This leads to the problem of term mismatch: user queries and search index are not based on the same set of terms. This is also known as the vocabulary problem [99]; it results from a combination of synonymy and polysemy. Synonymy refers to multiple words with common meaning, e.g., "buy" and "purchase". Polysemy refers to words with multiple meanings, e.g., "mouse" (a computer device or an animal). Synonymous and polysemou...

show abstract

Section: Other Applicationsmentioning

confidence: 99%

Query expansion techniques for information retrieval: A survey

Azad

Deepak

2019

Information Processing & Management

228

104

View full text Add to dashboard Cite

show abstract

“…These articles often described NLP pipelines that process words and phrases, and then “assign UMLS CUIs” or create an index with UMLS CUIs. For example, Nawab et al 18 used UMLS for query expansion to detect plagiarism in MEDLINE: “Input terms are mapped to UMLS CUIs using MetaMap. The UMLS Metathesaurus MRCONSO table is then consulted to identify synonymous terms for each CUI and these are used for query expansion.” Nawab’s information retrieval-based approach using UMLS outperformed the Kullback-Leibler distance approach on the plagiarism detection retrieval tasks.…”

Section: Resultsmentioning

confidence: 99%

UMLS users and uses: a current overview

Amos

Anderson

Brody

et al. 2020

Journal of the American Medical Informatics Association

View full text Add to dashboard Cite

The US National Library of Medicine regularly collects summary data on direct use of Unified Medical Language System (UMLS) resources. The summary data sources include UMLS user registration data, required annual reports submitted by registered users, and statistics on downloads and application programming interface calls. In 2019, the National Library of Medicine analyzed the summary data on 2018 UMLS use. The library also conducted a scoping review of the literature to provide additional intelligence about the research uses of UMLS as input to a planned 2020 review of UMLS production methods and priorities. 5043 direct users of UMLS data and tools downloaded 4402 copies of the UMLS resources and issued 66 130 951 UMLS application programming interface requests in 2018. The annual reports and the scoping review results agree that the primary UMLS uses are to process and interpret text and facilitate mapping or linking between terminologies. These uses align with the original stated purpose of the UMLS.

show abstract

“…As with the LSI approach, relevance feedback has not been able to display the connectedness and meaning of words. Research conducted [10] proposed QE using ULMS Metathesaurus. User words or queries are mapped into UMLS CUIs by using Meta Map, and then the MRCONSO Metathesaurus table identifies the synonyms of the words and those words used for expansion queries.…”

Section: Related Workmentioning

confidence: 99%

“…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 IR-Based Approach Utilizing Query Expansion for Plagiarism Detection in MEDLINE

Cited by 15 publications

References 33 publications

Query expansion techniques for information retrieval: A survey

Query expansion techniques for information retrieval: A survey

UMLS users and uses: a current overview

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

Contact Info

Product

Resources

About