A Novel Fuzzy Logic Model for Pseudo-Relevance Feedback-Based Query Expansion

Singh, Jagendra; Prasad, Mukesh; Prasad, Om Kumar; Joo, Er Meng; Saxena, Amit; Lin, Chin‐Teng

doi:10.1007/s40815-016-0254-1

Cited by 32 publications

(15 citation statements)

References 25 publications

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“…Recently, fuzzy logic based expansion techniques have also become popular. Singh et al [249,248] used a fuzzy logic-based QE technique, and, the top-retrieved documents (obtained using pseudo-relevance feedback) as data sources. Here, each expansion term (obtained from the top retrieved documents) is given a relevance score using fuzzy rules.…”

Section: External Text Collections and Resourcesmentioning

confidence: 99%

Query expansion techniques for information retrieval: A survey

Azad

Deepak

2019

Information Processing & Management

228

104

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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...

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Section: External Text Collections and Resourcesmentioning

confidence: 99%

Query expansion techniques for information retrieval: A survey

Azad

Deepak

2019

Information Processing & Management

228

104

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“…Expansion query using document analysis is divided into four: clustering based, local context analysis, global analysis, and local analysis. Local analysis is divided into two: relevance feedback and pseudo relevance feedback [12].…”

Section: B Search and Browse Log Analysismentioning

confidence: 99%

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

Maryamah¹,

Arifin²,

Sarno³

et al. 2019

IJIES

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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.

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“…The fuzzy logic algorithm is used to update the model probability [24], so that the model probability is quickly converted to accelerate the response speed of the filtering system. Figure 1 shows the structure of the fuzzy logic algorithm in the model probability update module.…”

Section: Fuzzy Logic Algorithmmentioning

confidence: 99%

“…However, this method requires a certain amount of time, and there is hysteresis, resulting in a decrease in tracking effect. For solving the problem of slow convergence caused by hysteresis, a fuzzy logic (FL) is proposed [24], and combine the FL algorithm with the IMM algorithm, and the corresponding fuzzy rules are formulated, when the model is transformed, the FL algorithm is used to judge whether the probability of the model is 1 or 0 to accelerate the update of the probability, this will speed up the convergence rate, the related references also proves the effectiveness of the combined algorithm [20,25].Although the IMMCKF algorithm and the IMM5CKF algorithm have already achieved good results [14,18], they still cannot effectively solve the problem of low filtering precision and slow convergence in the tracking process, therefore, an interactive multimodel adaptive five-degree cubature Kalman algorithm based on fuzzy logic is proposed in this paper, it uses the maximum likelihood function obtained by the improved A5CKF algorithm in the parallel filtering, updates the probability through the FL algorithm, and finally obtains the result through the output data fusion. Finally, by setting the same simulation model analysis, compared with IMMCKF [14], IMMA5CKF, and IMM5CKF [18], FLIMMA5CKF has better tracking effect and robustness, and the hysteresis is also improved.…”

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Application of Interactive Multiple Model Adaptive Five-Degree Cubature Kalman Algorithm Based on Fuzzy Logic in Target Tracking

2019

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Aiming at the shortcomings of low precision, hysteresis, and poor robustness of the general interactive multimodel algorithm in the "snake-like" maneuver tracking of anti-ship missiles, an interactive multimodel adaptive five-degree cubature Kalman algorithm based on fuzzy logic (FLIMM5ACKF) is proposed. The algorithm mainly includes adaptive five-degree cubature Kalman algorithm (A5CKF) and fuzzy logic algorithm (FL). A5CKF uses the Sage-Husa noise estimation principle to propose a state error covariance adaptive five-degree cubature Kalman algorithm to improve the performance of state estimation. Then, the fuzzy logic algorithm (FL) is added to the model probability update module to control the model probability update module. Finally, by setting the same tracking model simulation analysis, the algorithm has better convergence speed, tracking effect and robustness than the interactive multimodel cubature Kalman algorithm (IMMCKF), the interactive multimodel five-degree cubature Kalman algorithm (IMM5CKF) and the interactive multimodel adaptive five-degree cubature Kalman (IMMA5CKF).Symmetry 2019, 11, 767 2 of 16 third-order spherical-radial cubature rule was proposed by Haykin et al. [13,14]; it is different from UKF, CKF has a strict mathematical formula to prove, it has been shown that CKF has better performance than UKF when the dimension of the system is greater than three [15]. In order to pursue better performance of CKF, 5CKF was proposed in the literature [16][17][18], and the simulation shows that 5CKF performance is better than CKF. For pursuing a better estimation performance of 5CKF filtering algorithm, combined with the improvement of Sage-Husa noise estimation in the literature [19], and the combination with the corresponding filtering algorithm in the literature [20][21][22][23], the adaptive five-degree cubature Kalman algorithm based on error covariance estimation is proposed, and it is used as a filtering algorithm under the IMM framework.In the IMM algorithm, the transition probability between models is performed by the Markov transition matrix. When the target is tracked at a certain time, it works by a specific model, and its probability is approximately 1, other approximations are 0. However, this method requires a certain amount of time, and there is hysteresis, resulting in a decrease in tracking effect. For solving the problem of slow convergence caused by hysteresis, a fuzzy logic (FL) is proposed [24], and combine the FL algorithm with the IMM algorithm, and the corresponding fuzzy rules are formulated, when the model is transformed, the FL algorithm is used to judge whether the probability of the model is 1 or 0 to accelerate the update of the probability, this will speed up the convergence rate, the related references also proves the effectiveness of the combined algorithm [20,25].Although the IMMCKF algorithm and the IMM5CKF algorithm have already achieved good results [14,18], they still cannot effectively solve the problem of low filtering precision and slow convergence in the tr...

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A Novel Fuzzy Logic Model for Pseudo-Relevance Feedback-Based Query Expansion

Cited by 32 publications

References 25 publications

Query expansion techniques for information retrieval: A survey

Query expansion techniques for information retrieval: A survey

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

Application of Interactive Multiple Model Adaptive Five-Degree Cubature Kalman Algorithm Based on Fuzzy Logic in Target Tracking

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