Improving retrieval feedback with multiple term-ranking function combination

The existing query expansion (QE) methods cannot find the most users-requested source code version at times due to the over-expansion resulting from noises. To solve this problem, we propose a QE method based on evolving contexts (EC) that are added/deleted terms and their dependent terms during code evolution. On expanding a query, we appended the added terms as relevant terms, and excluded the deleted terms as noisy terms. We also developed a QE-integrating framework based on the Support Vector Machine (SVM) Ranking, called QESR, to simultaneously integrate multiple QE methods. Our experiment shows that QESR outperforms the state-of-the-art QE methods CodeHow and Query Expansion based on Crowd Knowledge (QECK) by 13%–16% in terms of precision when the first query result is inspected.

Queries to search engines on the Web are usually short. They do not provide sufficient information for an effective selection of relevant documents. Previous research has proposed the utilization of query expansion to deal with this problem. However, expansion terms are usually determined on term co-occurrences within documents. In this study, we propose a new method for query expansion based on user interactions recorded in user logs. The central idea is to extract correlations between query terms and document terms by analyzing user logs. These correlations are then used to select high-quality expansion terms for new queries. Compared to previous query expansion methods, ours takes advantage of the user judgments implied in user logs. The experimental results show that the log-based query expansion method can produce much better results than both the classical search method and the other query expansion methods.

Proksch has proved the changed terms of source code negatively affect code search quality. However, current query expansion (QE) methods always ignore it. In this paper we propose a novel QE method based on the semantics of change sequences (QESC). It not only captures which changes occurred by ext racting change sequences from Github commits, but also understands why changes occurred by learning sequence semantics with Deep Belief Network (DBN). Thus it could extract relevant terms to expand or irrelevant terms to exclude from the changes semantically similar to a query. Our experimental results show QESC outperforms the existing QE methods by 15–23% in terms of precision on inspecting the first query result.

The techniques of information retrieval eliminate the difficulty of finding specific information in electronic medical records (EMR), and the methods of query expansion (QE) improve the recall of EMR retrieval. However, most existing QE methods of EMR retrieval can’t get high-quality expansion terms and corresponding weights for Chinese EMR retrieval because of low-quality sources and improper calculation methods. In this paper, we propose a novel algorithm of expansion term selection and weight assignment for QE of Chinese EMR retrieval based on the clinical needs and unique characteristics of Chinese medical terms. The algorithm first selects expansion terms from a high quality Chinese medical knowledge graph and standard medical term sets, which can ensure the quality of expansion terms. Then it assigns weights to the selected expansion terms based on semantic similarities and manually designed expansion categories that reflect the opinions of medical experts. Experiment results show that our algorithm gets higher-quality expansion terms and more rational weights compared with four benchmark algorithms, using Precision at 10, Recall, Mean Average Precision and Binary Preference-based Measure as evaluation metrics, which implies that our algorithm can significantly improve the effectiveness of QE.

This paper describes approaches to improving the perfor- mance of one of the most common and increasingly important aspects of the Human Genome Project (HGP) — large-volume, batch comparison of DNA sequence data. This basic comparison operation, usually carried out by the well-known BLAST program on one subject sequence against the internationally-available databases of over 3 million target sequences, is already used hundreds of thousands of times each day by researchers around the world. At present, it is still used primarily in single query, or small batch query mode. As the entire sequence of the human genome nears completion, the area of functional genomics, and the use of micro- arrays of sets of genes, is coming to the fore. These developments will demand ever more efficient means of BLASTing sets of data that will make single processor implementation on powerful workstations infea- sible. We describe the three primary parallel components to BLAST. The first is at the sequence-to-sequence comparison level. The second parallelizes a single query across a partitioned and distributed database. And finally, the set of queries themselves are partitioned across a set of servers with replicated or partitioned databases. The three methods may be employed alone or in concert. Our current implementation is described which parallelizes batch requests, and our plans for implementation of the other levels is also described. The results will ultimately be applied to hardware assistance for this soon-to-be primitive computer operation.

A novel location privacy-preserving scheme based on l-queries for continuous LBS

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.

BackgroundIlizarov method has become one of primary methods for treating bone defects. Currently, there is growing trend in the application of modified Ilizarov methods (e.g., applying unilateral external fixators or with flap tissue) and its combined methods (e.g., Ilizarov method with antibiotic spacer or internal fixation) to manage bone defects. However, there is a lack of studies with systematical evaluation of the clinical effects of these evolving methods. This study aimed to conduct a systematic review and meta-analysis for overall evaluating the clinical effects on long bone defects of lower extremity in Ilizarov methods and its combined methods.MethodsStudies were identified in three electronic databases (Pubmed, Embase and Cochrane Library) from the earliest indexing year through November 01, 2022, and relevant data were extracted subsequently. The total number of participants, number of participants with bone unions, bone result or functional result, and related complications including pin infection, pin loosening, pain, refracture, limb discrepancy, malalignment, joint stiffness, recurrent infection, and amputation were extracted in this study. Then, union rate (defined as the proportion of patients who achieved bone unions) and specific complication incidence rate (defined as the proportion of patients who experienced specific complication) were pooled estimated respectively. Relative risk (RR) was used for comparing the clinical effects among various Ilizarov technique.ResultsSixty-eight case series studies, 29 comparative studies, and 3 randomized clinical trials were finally included. The union rate of Ilizarov methods was 99.29% (95% CI: 98.67% ~ 99.86%) in tibial defects and 98.81% (95% CI: 98.81% ~ 100.00%) in femoral defects. The union rate of Ilizarov method with antibiotic spacer and intramedullary nail in tibial defects was 99.58% (95% CI: 98.05% ~ 100.00%) and 95.02% (95% CI: 87.28% ~ 100.00%), respectively. Compared to the Ilizarov methods, the union rate of the Ilizarov method with antibiotic spacer in tibial defects increased slightly (RR = 1.02, 95% CI: 1.01 ~ 1.04). Meanwhile, compared to Ilizarov methods, we found lower excellent rate in bone result in Ilizarov method with antibiotic spacer, with the moderate to high heterogeneity. Compared to the Ilizarov method, lower rate of pin infection, higher rate of recurrent infection and amputation were observed in Ilizarov method with intramedullary nail, however, the findings about the comparison of pin infection and recurrent infection between the two groups were presented with high degree of statistical heterogeneity.ConclusionOur study confirmed the reliable treatment of Ilizarov methods and its combined technique on long bone defects, and founded there were significant differences on some complications rate between Ilizarov methods and its combined technique. However, the findings need to be confirmed by further studies.

In this paper we propose the schema of 3-layer domain ontology. The entity relation, entity-attribute pair and textual description are distributed in layers respectively. Based on the 3-layer ontology we proposed 4 query expansion methods: top-down expansion, synonyms expansion, bottom-up expansion and backtracking expansion. A set of experiment1 is carried out to prove the expansion methods. Different result shows the expansion methods is effective as a whole, and we analysis the shortage of our methods.

Manifold ranking has been successfully applied in query-oriented multi-document summarization. It not only makes use of the relationships among the sentences, but also the relationships between the given query and the sentences. However, the information of original query is often insufficient. So we present a query expansion method, which is combined in the manifold ranking to resolve this problem. Our method not only utilizes the information of the query term itself and the knowledge base WordNet to expand it by synonyms, but also uses the information of the document set itself to expand the query in various ways (mean expansion, variance expansion and TextRank expansion). Compared with the previous query expansion methods, our method combines multiple query expansion methods to better represent query information, and at the same time, it makes a useful attempt on manifold ranking. In addition, we use the degree of word overlap and the proximity between words to calculate the similarity between sentences. We performed experiments on the datasets of DUC 2006 and DUC2007, and the evaluation results show that the proposed query expansion method can significantly improve the system performance and make our system comparable to the state-of-the-art systems.

Query expansion methods have been proven to be effective to improve the average performance of patent retrieval, and most of query expansion methods use single source of information for query expansion term selection. In this paper, we propose a method which exploits external resources for improving patent retrieval. Google search engine and Derwent World Patents Index were used as external resources to enhance the performance of query expansion methods. LambdaRank was employed to improve patent retrieval performance by combining different query expansion methods with different text fields weighting strategies of different resources. Experiments on TREC data sets showed that our combination of multiple information sources for query formulation was more effective than using any single source to improve patent retrieval performance.

Query Expansion (QE) is widely applied to improve the retrieval performance of ad-hoc search, using different techniques and several data sources to find expansion terms. In Information Retrieval literature, selecting expansion terms remains a challenging task that relies on the extraction of term relationships. In this paper, we propose a new learning to rank-based query expansion model. The main idea behind is that, given a query and the set of its related ARs, our model ranks these ARs according to their relevance score regarding to this query and then selects the most suitable ones to be used in the QE process. Experiments are conducted on three test collections, namely: CLEF2003, TREC-Robust and TREC-Microblog, including long, hard and short queries. Results showed that the retrieval performance can be significantly improved when the ARs ranking method is used compared to other state of the art expansion models, especially for hard and long queries.

Accepted: 24.11.2015 In this paper, a hybrid approach is developed for geospatial information retrieval which combines query expansion (QE) and latent semantic indexing (LSI) methods. The hybrid method uses the advantages of Query Expansion and Semantic indexing methods for improving search results. LSI establishes relations depending on the similarities between queries and the documents where QE helps by adding extra similar terms to the queries. The dataset is populated using data extracted from Wikipedia and USGS (The United States Geological Survey). Automation is programmed to get all the information from the web and to extract the meaningful words. The significant terms are the name of the places, directions, and nearby locations. The dataset includes mountains and places with their latitudes, longitudes, locations and directions. The extracted data is also taken into Protege for semantically querying. The approach is applied to geographical data (popular search items on search engines), and by querying the data with the combination of LSI and QE more related results are handled than the results of methods when they are applied alone. Many queries are run, and the results are listed and compared. At the end of the study it is understood that the hybrid method considerably improves the search results.

We present a novel, hybrid approach for clustering text databases. We use a genetic algorithm to generate and evolve a set of single word search queries in Apache Lucene format. Clusters are formed as the set of documents matching a search query. The queries are optimized to maximize the number of documents returned and to minimize the overlap between clusters (documents returned by more than one query in a set). Optionally, the number of clusters can be specified in advance, which will normally result in an improvement in performance. Not all documents in a collection are returned by any of the search queries in a set, so once the search query evolution is completed a second stage is performed whereby a KNN algorithm is applied to assign all unassigned documents to their nearest cluster. We describe the method and compare effectiveness with other well-known existing systems on 8 different text datasets. We note that search query format has the qualitative benefits of being interpretable and providing an explanation of cluster construction.

This chapter puts forward a new approach to address a general purpose Content-Based Image Retrieval(CBIR) task. Six spatial color moments extracted from visually significant locations of an image are used as features to characterize an image. It utilizes information given by a set of queries, as opposed to a single image query. A set of similar images is posed as a query and Mahalanobis distance is used to evaluate the similarity between query images and target images of the database. Given a query set, the mean and covariance for computing Mahalanobis distance is obtained from the same. Unlike conventional CBIR methods in which images are retrieved based on considering similarities between the query image and the database images through a sequential search, a clustering technique using K-means algorithm is first used to create meaningful groups in the database. As clusters are created by considering similarities between images in the database, the image retrieval search space is reduced if clusters near to the query are searched. The effectiveness of the proposed algorithm is demonstrated with increased accuracy and reduced retrieval time.KeywordsImage matchingMahalanobis distanceclusteringprecisionrecall