Geospatial Information Retrieval Base on Query Expansion and Semantic Indexing

We propose a new method for query expansion called "term similarity tree model" (TSTM). Term similarity tree is built to represent and estimate similarities between terms. Based on TSTM, we use similarity restriction and overlay restriction to implement query expansion. This method can cluster terms automatically, make the process of query expansion more flexible and controllable, and control noise effectively. In addition, the parameters of TSTM can be adjusted easily to meet the requirements of different types of queries. TREC data is used to test the method. The experiments show that TSTM method outperforms the existing methods in query expansion, such as WordNet-based method, local cooccurrence method and latent semantic indexing (LSI-based) method.

This paper study about a review of the literature on Query Expansion (QE) methods. The main aspects of the review study are the methods, limitations of the QE methods, and datasets used in QE. As the results of this study, we can conclude several methods used on QE i.e., Ontology, Association Rules, Wordnet, Methathesaurus, Synonym mapping, Concept-based, Local Co-occurrence, and Latent Semantic Indexing (LSI). Each of the methods still has limitations. For datasets used in QE, many researchers use public datasets.

We propose a novel method for automatic annotation, indexing and annotation-based retrieval of images. The new method, that we call Markovian Semantic Indexing (MSI), is presented in the context of an online image retrieval system. Assuming such a system, the users' queries are used to construct an Aggregate Markov Chain (AMC) through which the relevance between the keywords seen by the system is defined. The users' queries are also used to automatically annotate the images. A stochastic distance between images, based on their annotation and the keyword relevance captured in the AMC, is then introduced. Geometric interpretations of the proposed distance are provided and its relation to a clustering in the keyword space is investigated. By means of a new measure of Markovian state similarity, the mean first cross passage time (CPT), optimality properties of the proposed distance are proved. Images are modeled as points in a vector space and their similarity is measured with MSI. The new method is shown to possess certain theoretical advantages and also to achieve better Precision versus Recall results when compared to Latent Semantic Indexing (LSI) and probabilistic Latent Semantic Indexing (pLSI) methods in Annotation-Based Image Retrieval (ABIR) tasks.

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.

The paper presents a new multiple feature fusion (MFF) based on latent semantic indexing (LSI) method to achieve an improved image retrieval performance. The proposed method extracts different physical features, which come from not the whole image but its main objects, and constructs a multi-modal semantic space, each dimension of which represents a different feature component of the image. Furthermore, semantic relevance feedback information from the users is also integrated to improve the feedback performance of the system. The experimental results demonstrate the good robustness of LSI-MFF and have shown that this method is especially suitable for mass image database such as web environment.

Dimensionality reduction is an essential task for many large-scale information processing problems such as classifying document sets, searching over Web data sets, etc. It can be used to improve both the efficiency and the effectiveness of classifiers. In this paper, a comparative study is conducted of five Dimension Reduction Techniques in the context of the Arabic text classification problem using an in house Arabic dataset. We evaluated and compared Stemming, Light-Stemming, Document Frequency (DF), TFIDF and Latent Semantic Indexing (LSI)methods to reduce the feature space into an input space of much lower dimension for the neural network classifier. The results showed that the proposed model was able to achieve high categorization effectiveness as measured by Macro-Average F1 measure. Experiments on Arabic datasets indicate that the DF, TFIDF and LSI techniques are favorable in terms of its effectiveness and efficiency when compared with the two other methods.

The present study aims to: a) systematically map the of birth cohort studies from the South Asian region b) examine the major research foci and landmark contributions from these cohorts using reproducible scientometric techniques and c) offer recommendations on establishing new birth cohorts in Pakistan, building upon the strengths, weaknesses and gaps of previous cohorts. Bibliographic records for a total of 260 articles, published during through December 2018, were retrieved from the Web of Science (core database). All data were analysed using Microsoft Excel (2013), Web of Science platform and CiteSpace. A series of network analysis were then run for each time-period using the link reduction method and pathfinder network scaling. The co-cited articles were clustered into their homogeneous research clusters. The clusters were named using the Latent Semantic Indexing (LSI) method that utilized author keywords as source of names for these clusters. The scientometric analyses of original research output from these birth cohorts also paint a pessimistic landscape in Pakistan- where Pakistani sites for birth cohorts contributed only 31 publications; a majority of these utilized the MAL-ED birth cohort data. A majority of original studies were published from birth cohorts in India (156), Bangladesh (63), and Nepal (15). Out of these contributions, 31 studies reported data from multiple countries. The three major birth cohorts include prospective and multi-country MAL-ED birth cohort and The Pakistan Early Childhood Development Scale Up Trial, and a retrospective Maternal and infant nutrition intervention cohort. In addition to these, a few small-scale birth cohorts reported findings pertaining to neonatal sepsis, intrauterine growth retardation and its effects on linear growth of children and environmental enteropathy.

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.

With the development of the web 2.0 communities, information retrieval has been widely applied based on the collaborative tagging system. However, a user issues a query that is often a brief query with only one or two keywords, which leads to a series of problems like inaccurate query words, information overload and information disorientation. The query expansion addresses this issue by reformulating each search query with additional words. By analyzing the limitation of existing query expansion methods in folksonomy, this paper proposes a novel query expansion method, based on user profile and topic model, for search in folksonomy. In detail, topic model is constructed by variational antoencoder with Word2Vec firstly. Then, query expansion is conducted by user profile and topic model. Finally, the proposed method is evaluated by a real dataset. Evaluation results show that the proposed method outperforms the baseline methods.

The method of latent semantic indexing (LSI) is an information retrieval technique using a low-rank singular value decomposition (SVD) of the term-document matrix. Although the LSI method has empirical success, it suffers from the lack of interpretation for the low-rank approximation and, consequently, the lack of controls for accomplishing specific tasks in information retrieval. A method introduced by Dhillon and Modha is an improvement in that direction. It uses centroids of clusters or so called concept decomposition for lowering the rank of the term-document matrix. We focus on improvements of that method using fuzzy k-means algorithm. Also, we compare the precision of information retrieval for the two above methods.

We address the problem of irrelevant results for short queries on Web search engines using latent semantic indexing in the WordSpace model and query expansion. First, we predict the potential concept topics, which are the domains for the search terms. Next, we expand the search terms in each of the predicted domains in parallel. We then submit separate queries, specialized for each domain, to a general-purpose search engine. The user is presented with categorized search results under the predicted domains. We prepared a categorized text collection (corpus) using Web directory listing to build word association models. We compare the results obtained using this corpus with those using Reuters corpus. User evaluations indicate that our approach helps the users avoid having to examine irrelevant Web search results, especially with short queries

Internet is becoming an integral part of everyday life and email has become a powerful tool to exchange ideas and information. Through email users connect socially and commercially. But mass mailing i.e. Spam has become one of the biggest worldwide problem. So there is requirement to develop a spam filter which can detect the spam and can stop the mass mailing. In this paper, Latent Semantic Indexing (LSI) approach is used for spam filtering. Latent semantic indexing (LSI) is an indexing and retrieval method which uses singular value decomposition (SVD) to find the relationships between the terms and unstructured collection of message. LSI is based on the principle that terms that are used in the same contexts tend to have similar meanings. Apart from this spam are obtained more or less abundant replications in a significant numbers. The detection of these duplicates is important because it allows to lighten email box and will improve the efficiency of spam filter. The Duplicate Message Detection Techniques (DMDT) has been used to improve the efficiency and effectiveness of spam filter.

Latent Semantic Indexing (LSI) and Relevance Feedback (RF) have been shown to be extremely useful in information retrieval respectively. But at the context of Web image retrieval, LSI is limited for the Single Value Decomposition (SVD) computation cost to large dataset while RF is confused with the reluctance of interaction for most Web users. In this paper, a Pseudo Relevance Feedback approach based on Local Latent Semantic Indexing (PRF-LLSI) is proposed, which integrating the LSI and RF, and making use of the benefit of them while solving the limitation of them. The Local LSI (LLSI) method performs a low-dimensional SVD on the local region of initial retrieved results. Both keywords and image contents of the Web images are computed by LLSI to re-rank the initial retrieval results automatically. The PRF-LLSI contribute to the following: 1) Local LSI resolves the heavy computation cost of LSI; 2) Pseudo Relevance Feedback doesn’t need the user’s interaction; 3) LLSI combine the textual and visual features, which improves the precision of the system. The experiments are done in our VAST (VisuAl & SemanTic image search) system, and the results show the effectiveness of the proposed method.

We present an application of a novel methodology called Text Influenced Molecular Indexing (TIMI) to mine the information in the scientific literature. TIMI is an extension of two existing methodologies: (1) Latent Semantic Structure Indexing (LaSSI), a method for calculating chemical similarity using two-dimensional topological descriptors, and (2) Latent Semantic Indexing (LSI), a method for generating correlations between textual terms. The singular value decomposition (SVD) of a feature/object matrix is the fundamental mathematical operation underlying LSI, LaSSI, and TIMI and is used in the identification of associations between textual and chemical descriptors. We present the results of our studies with a database containing 11,571 PubMed/MEDLINE abstracts which show the advantages of merging textual and chemical descriptors over using either text or chemistry alone. Our work demonstrates that searching text-only databases limits retrieved documents to those that explicitly mention compounds by name in the text. Similarly, searching chemistry-only databases can only retrieve those documents that have chemical structures in them. TIMI, however, enables search and retrieval of documents with textual, chemical, and/or text- and chemistry-based queries. Thus, the TIMI system offers a powerful new approach to uncovering the contextual scientific knowledge sought by the medical research community.