Exploring criteria for successful query expansion in the genomic domain

System performance assessment and comparison are fundamental for large-scale image search engine development. This article documents a set of comprehensive empirical studies to explore the effects of multiple query evidences on large-scale social image search. The search performance based on the social tags, different kinds of visual features and their combinations are systematically studied and analyzed. To quantify the visual query complexity, a novel quantitative metric is proposed and applied to assess the influences of different visual queries based on their complexity levels. Besides, we also study the effects of automatic text query expansion with social tags using a pseudo relevance feedback method on the retrieval performance. Our analysis of experimental results shows a few key research findings: (1) social tag-based retrieval methods can achieve much better results than content-based retrieval methods; (2) a combination of textual and visual features can significantly and consistently improve the search performance; (3) the complexity of image queries has a strong correlation with retrieval results’ quality—more complex queries lead to poorer search effectiveness; and (4) query expansion based on social tags frequently causes search topic drift and consequently leads to performance degradation.

A digital library is a type of information retrieval (IR) system. The existing information retrieval methodologies generally have problems on keyword-searching. We proposed a model to solve the problem by using concept-based approach (ontology) and metadata case base. This model consists of identifying domain concepts in user’s query and applying expansion to them. The system aims at contributing to an improved relevance of results retrieved from digital libraries by proposing a conceptual query expansion for intelligent concept-based retrieval. We need to import the concept of ontology, making use of its advantage of abundant semantics and standard concept. Domain specific ontology can be used to improve information retrieval from traditional level based on keyword to the lay based on knowledge (or concept) and change the process of retrieval from traditional keyword matching to semantics matching. One approach is query expansion techniques using domain ontology and the other would be introducing a case based similarity measure for metadata information retrieval using Case Based Reasoning (CBR) approach. Results show improvements over classic method, query expansion using general purpose ontology and a number of other approaches.

Essie: A Concept-based Search Engine for Structured Biomedical Text

Content-based multimedia information retrieval is never a trivial task even with state-of-the-art approaches. Its mandatory challenge, called “semantic gap,” requires much more understanding of the way human perceive things (i.e., visual and auditory information). Computer scientists have spent thousands of hours seeking optimal solutions, only ended up falling in the bound of this gap for both visual and spoken contexts. While an over-the-gap approach is unreachable, we insist on assembling current viable techniques from both contexts, aligned with a domain concept base (i.e., an ontology), to construct an info service for the retrieval of agricultural multimedia information. The development process spans over three packages: (1) building a Vietnamese agricultural thesaurus; (2) crafting a visual-auditory intertwined search engine; and (3) system deployment as an info service. We spring our the thesaurus in 2 sub-boughs: the aquaculture ontology consists of 3455 concepts and 5396 terms, with 28 relationships, covering about 2200 fish species and their related terms; and the plant production ontology comprises of 3437 concepts and 6874 terms, with 5 relationships, covering farming, plant production, pests, etc. These ontologies serve as a global linkage between keywords, visual, and spoken features, as well as providing the reinforcement for the system performances (e.g., through query expansion, knowledge indexing…). On the other hand, constructing a visual-auditory intertwined search engine is a bit trickier. Automatic transcriptions of audio channels are marked as the anchor points for the collection of visual features. These features, in turn, got clustered based on the referenced thesauri, and ultimately tracking out missing info induced by the speech recognizer’s word error rates. This compensation technique bought us back 14 % of loss recall and an increase of 9 % accuracy over the baseline system. Finally, wrapping the retrieval system as an info service guarantees its practical deployment, asour target audiences are the majority of farmers in developing countries who are unable to reach modern farming information and knowledge.

In view of the need for a highly distributed and federated architecture, a robust query expansion in a specific domain has great impact on the performance of information retrieval. We aim to determine robust expansion terms using different weighting techniques and finding out the most k-top relevant terms. For this, first, we consider each individual ontology and user query keywords to determine the Basic Expansion Terms (BET) using a number of semantic measures namely Density Measure (DM), Betweenness Measure (BM), and Semantic Similarity Measure (SSM). Second, we specify New Expansion Terms (NET) by Ontology Alignment (OA). Third, we weight expanded terms using a combination of these semantic measures. Fourth, we use a Specific Interval(SI) to determine a set of Robust Expansion Terms (RET). Finally, we compare the result of our novel weighting approach with existing expansion approaches and show the effectiveness of our robust expansion in federated architecture.

The application of lexical simplification techniques in this study aims to reduce vocabulary mismatch between user queries and document collections. Two distinct strategies —complex word substitution (Stg1) and query expansion with simplified alternative (Stg2)— are systematically evaluated across six benchmark datasets (FiQA, ArguAna, NFCorpus, SCIDOCS, SciFact, and TREC-COVID) using nDCG@10 and MAP utilizing dense and sparse neural retrieval models. Our experiments reveal that Stg1 consistently degrades retrieval performance, with nDCG@10 and MAP declines often exceeding 0.10 and 0.03, respectively. In contrast, Stg2 preserves nearly all baseline effectiveness, nDCG@10 drops by fewer than 0.02 points, MAP by fewer than 0.01, and in some instances even surpasses the baseline. Risk-sensitive evaluation further confirms that Stg2 incurs minimal or no per-query risk and exhibits promising results, whereas Stg1 uniformly harms effectiveness. These findings indicate that appending simplified term variants alongside original query terms offers a fair and robust means of addressing vocabulary mismatch without sacrificing precision or recall. It is recommended that future IR pipelines incorporate carefully tailored query simplification models to balance user query intent and high retrieval quality.

With the rapid development of biomedicine, the number of biomedical articles has increased accordingly, which presents a great challenge for biologists trying to keep up with the latest research. Information retrieval seeks to meet this challenge by searching among a large number of articles based on given queries and providing the most relevant ones to fulfill information needs. As an effective information retrieval technique, query expansion has some room for improvement to achieve the desired performance when directly applied for biomedical information retrieval because there exist many domain-related terms both in users' queries and in related articles. To solve this problem, we propose a biomedical query expansion framework based on learning-to-rank methods, in which we refine candidate expansion terms by training term-ranking models to select the most relevant terms. To train the term-ranking models, we first propose a pseudo-relevance feedback method based on MeSH to select candidate expansion terms and then represent the candidate terms as feature vectors by defining both the corpus-based term features and the resource-based term features. Experimental results obtained for TREC genomics datasets show that our method can capture more relevant terms to expand the original query and effectively improve biomedical information retrieval performance.

Pseudo-relevance feedback (PRF) improves search quality by expanding the query using terms from high-ranking documents from an initial retrieval. Although PRF can often result in large gains in effectiveness, running two queries is time consuming, limiting its applicability. We describe a PRF method that uses corpus pre-processing to achieve query-time speeds that are near those of the original queries. Specifically, Relevance Modeling, a language modeling based PRF method, can be recast to benefit substantially from finding pairwise document relationships in advance. Using the resulting Fast Relevance Model (fastRM), we substantially reduce the online retrieval time and still benefit from expansion. We further explore methods for reducing the preprocessing time and storage requirements of the approach, allowing us to achieve up to a 10% increase in MAP over unexpanded retrieval,vwhile only requiring 1% of the time of standard expansion.

Pseudo-relevance feedback (PRF) has been proven to be an effective query expansion strategy to improve retrieval performance. Several PRF methods have so far been proposed for many retrieval models. Recent theoretical studies of PRF methods show that most of the PRF methods do not satisfy all necessary constraints. Among all, the log-logistic model has been shown to be an effective method that satisfies most of the PRF constraints. In this paper, we first introduce two new PRF constraints. We further analyze the log-logistic feedback model and show that it does not satisfy these two constraints as well as the previously proposed "relevance effect" constraint. We then modify the log-logistic formulation to satisfy all these constraints. Experiments on three TREC newswire and web collections demonstrate that the proposed modification significantly outperforms the original log-logistic model, in all collections.

In the domain of bioinformatics, extracting a relation such as protein-protein interations from a large database of text documents is a challenging task. One major issue with biomedical information extraction is how to efficiently digest the sheer size of unstructured biomedical data corpus. Often, among these huge biomedical data, only a small fraction of the documents contain information that is relevant to the extraction task. We propose a novel query expansion algorithm to automatically discover the characteristics of documents that are useful for extraction of a target relation. Our technique introduces a hybrid query re-weighting algorithm combining the modified Robertson Sparck-Jones query ranking algorithm with a keyphrase extraction algorithm. Our technique also adopts a novel query translation technique that incorporates POS categories to query translation. We conduct a series of experiments and report the experimental results. The results show that our technique is able to retrieve more documents that contain protein-protein pairs from MEDLINE as iteration increases. Our technique is also compared with SLIPPER, a supervised rule-based query expansion technique. The results show that our technique outperforms SLIPPER from 17.90% to 29.98 better in four iterations.

Physicians often seek scientific evidence regarding how to best care for their patients, while making clinical decisions These scientific evidence are available in the form of published medical articles, reports and clinical trials. Considering the volume of already existing medical literature and the pace at which medical research is growing, getting the most relevant information will be a tedious task. In this paper, we describe an empirical approach to fetch relevant medical articles from the PubMed (about 733,328 articles of 45.2 gigabytes) collection, based on a given query. Our IR system comprises of three parts: inverted indexing using Lucene, lexical query expansion to increase recall with MetaMap and reranking aimed at optimizing the system. Word sense of ambiguous terms are introduced to limit the negative effects that synonymy-based query expansion may have on precision. The subsequent ranked list was then re-ranked with learning to rank algorithms. We evaluated our system using 30 medical queries and the results show that our system can handle various medical queries effectively and efficiently. Also, the final results demonstrate that the ensemble approach performs better than the Lucene baseline by boosting the ranking of articles that are near the top of several single ranked lists.

This paper presents an ontology-based digital library system, whose capability goes far beyond simple key word based query. There are two important components in our system: query expansion and ontology extraction. In the proposed system, we integrate ldquoWorldNetrdquo as the query expansion module to broaden the query scope, which leads to better recall rate for retrieval. A novel ontology inference algorithm was also proposed. The proposed ontology inference algorithm automatically constructs the ontology of the knowledge base for specific query domain. With this domain specific ontology based knowledge representation, our query system is capable of human-like general semantic reasoning to disambiguate the ambiguous words and improve the precision of retrieval. We evaluate the proposed system on a large set of documents from 10 different subject domains. Experimental results demonstrate the effectiveness of our proposed system. Our system achieves on average 5% improvement compared with the traditional key word based search method.

For information retrieval systems, the quality of searching is most important. There are many techniques to improve the quality of search, the query expansion (QE) is considered in order to improve the search result in terms of recall and user satisfaction. This paper proposed the query expansion technique using keyword-based query. These semantic terms are added to the original query in order to reformulate query before sending to the searching process. The experiments were tested on twitter data collection which class is annotated by human. The results show that the retrieval effectiveness is considerably higher than using only original query. Compared to the base-line system, this method provides higher performance in terms of recall and precision.

The article describes the method of dynamically designing a search engine based on the analysis of user search queries and automated user's document generation. The advanced method of dynamic search with the account of individual features of users is offered, which allows to increase the cost of search. The service for designing own search engine is created. It replaces the algorithm of ranking on the algorithm for sorting search engines by category using the pseudo relevance feedback method. The information is filled in with a system of successive objects, which is a list. Objects contain key information, these keys allow to set up work with information and increase the relevance of the search. The service contains graphical tools for updating, storing and deleting information in the search engine. An analysis of the configuration of the search service is performed to increase the end-user search cost. There is a conclusion of the results, which indicates the shortcomings of the search engine developer of this system and generated documentation on its use based on the settings for the end user. The sorting system analyzes the end-user request and provides the information it finds, sorted by relevancy. The developed method provides acceleration of the algorithm of the search engine and the quality of the information found for the user.

Clinical decision support (CDS) search is performed to retrieve key medical literature that can assist the practice of medical experts by offering appropriate medical information relevant to the medical case in hand. In this paper, we present a novel CDS search framework designed for passage retrieval from biomedical textbooks in order to support clinical decision-making using laboratory test results. The framework utilizes two unique characteristics of the textual reports derived from the test results, which are syntax variation and negation information. The proposed framework consists of three components: domain ontology, index repository, and query processing engine. We first created a domain ontology to resolve syntax variation by applying the ontology to detect medical concepts from the test results with language translation. We then preprocessed and performed indexing of biomedical textbooks recommended by clinicians for passage retrieval. We finally built the query-processing engine tailored for CDS, including translation, concept detection, query expansion, pseudo-relevance feedback at the local and global levels, and ranking with differential weighting of negation information. To evaluate the effectiveness of the proposed framework, we followed the standard information retrieval evaluation procedure. An evaluation dataset was created, including 28,581 textual reports for 30 laboratory test results and 56,228 passages from widely used biomedical textbooks, recommended by clinicians. Overall, our proposed passage retrieval framework, GPRF-NEG, outperforms the baseline by 36.2, 100.5, and 69.7 percent for MRR, R-precision, and Precision at 5, respectively. Our study results indicate that the proposed CDS search framework specifically designed for passage retrieval of biomedical literature represents a practically viable choice for clinicians as it supports their decision-making processes by providing relevant passages extracted from the sources that they prefer to refer to, with improved performances.