Information Retrieval Model Research Articles

OMIR: Ontology-Based Multimedia Information Retrieval System for Web Usage Mining

ABSTRACTInformation extraction relevant to the user queries is the challenging task in the ontology environment due to data varieties such as image, video, and text. The utilization of appropriate semantic entities enables the content-based search on annotated text. Recently, the automatic extraction of textual content in the audio-visual content is an advanced research area in a multimedia (MM) environment. The annotation of the video includes several tags and comments. This paper proposes the Collaborative Tagging (CT) model based on the Block Acquiring Page Segmentation (BAPS) method to retrieve the tag-based information. The information extraction in this model includes the Ontology-Based Information Extraction (OBIE) based on the single ontology utilization. The semantic annotation phase in the proposed work inserts the metadata with limited machine-readable terms. The insertion process is split into two major processes such as database uploading to server and extraction of images/web pages based on the results of semantic phase. Novel weight-based novel clustering algorithms are introduced to extract knowledge from MM contents. The ranking based on the weight value in the semantic annotation phase supports the image/web page retrieval process effectively. The comparative analysis of the proposed BAPS-CT with the existing information retrieval (IR) models regarding the average precision rate, time cost, and storage space rate assures the effectiveness of BAPS-CT in OMIR.

A passage retrieval method based on probabilistic information retrieval model and UMLS concepts in biomedical question answering

Background and ObjectivePassage retrieval, the identification of top-ranked passages that may contain the answer for a given biomedical question, is a crucial component for any biomedical question answering (QA) system. Passage retrieval in open-domain QA is a longstanding challenge widely studied over the last decades. However, it still requires further efforts in biomedical QA. In this paper, we present a new biomedical passage retrieval method based on Stanford CoreNLP sentence/passage length, probabilistic information retrieval (IR) model and UMLS concepts. MethodsIn the proposed method, we first use our document retrieval system based on PubMed search engine and UMLS similarity to retrieve relevant documents to a given biomedical question. We then take the abstracts from the retrieved documents and use Stanford CoreNLP for sentence splitter to make a set of sentences, i.e., candidate passages. Using stemmed words and UMLS concepts as features for the BM25 model, we finally compute the similarity scores between the biomedical question and each of the candidate passages and keep the N top-ranked ones. ResultsExperimental evaluations performed on large standard datasets, provided by the BioASQ challenge, show that the proposed method achieves good performances compared with the current state-of-the-art methods. The proposed method significantly outperforms the current state-of-the-art methods by an average of 6.84% in terms of mean average precision (MAP). ConclusionWe have proposed an efficient passage retrieval method which can be used to retrieve relevant passages in biomedical QA systems with high mean average precision.

Learning Ranking Functions For Information Retrieval Using Layered Multi-Population Genetic Programming

Ranking plays a key role in many applications, such as document retrieval, recommendation, question answering, and machine translation. In practice, a ranking function (or model) is exploited to determine the rank-order relations between objects, with respect to a particular criterion. In this paper, a layered multipopulation genetic programming based method, known as RankMGP, is proposed to learn ranking functions for document retrieval by incorporating various types of retrieval models into a singular one with high effectiveness. RankMGP represents a potential solution (i.e., a ranking function) as an individual in a population of genetic programming and aims to directly optimize information retrieval evaluation measures in the evolution process. Overall, RankMGP consists of a set of layers and a sequential workflow running through the layers. In one layer, multiple populations evolve independently to generate a set of the best individuals. When the evolution process is completed, a new training dataset is created using the best individuals and the input training set of the layer. Then, the populations in the next layer evolve with the new training dataset. In the final layer, the best individual is obtained as the output ranking function. The proposed method is evaluated using the LETOR datasets and is found to be superior to classical information retrieval models, such as Okapi BM25. It is also statistically competitive with the state-of-the-art methods, including Ranking SVM, ListNet, AdaRank and RankBoost.

Psychologically inspired visual information storage and retrieval modeling for multiclass image classification

The computer vision research aims to enable the computers to recognize visual images as easily as human. Studies have shown that human could segregate target from its surrounding environment, which is intimately associated with the brain memory mechanism. However, it is not quite clear about how the visual images are stored and retrieved in the human brain. In this paper, we propose a psychologically visual information storage and retrieval model (PVISRM) based on sparse coding and probabilistic decision theory. First, the dense scale invariant feature transform (SIFT) algorithm is applied to extract the features of visual images and then the extracted features are represented by sparse coding. In the storage procedure, each component of the feature vector is correctly copied with certain probability generated by an exponential distribution. For retrieval, the likelihood ratio between the probe image feature vector and that of each studied image is calculated based on probabilistic theory. Then the category likelihood ratio between the probe image and each category is obtained by adding the ratio values of all images belonging to the same category. Finally, the Bayesian decision rule for image classification is presented. Experimental results show that the proposed PVISRM model can obtain good classification performance and outperforms the SVM approach.

A Decision Support System for Plant Optimization in Urban Areas with Diversified Solar Radiation

Sunshine is an important factor which limits the choice of urban plant species, especially in environments with high-density buildings. In practice, plant selection and configuration is a key step of landscape architecture, which has relied on an experience-based qualitative approach. However, the rationality and efficiency of this need to be improved. To maintain the diversity of plant species and to ensure their ecological adaptability (solar radiation) in the context of sustainable development, we developed the Urban Plants Decision Support System (UP-DSS) for assisting plant selection in urban areas with diversified solar radiation. Our methodology mainly consists of the solar radiation model and calibration, the urban plant database, and information retrieval model. The structure of UP-DSS is also presented at the end of the methodology section, which is based on the platform of Geographic Information Systems (GIS) and Microsoft Excel. An application of UP-DSS is demonstrated in a residential area of Wuhan, China. The results show that UP-DSS can provide a very scientific and stable tool for the adaptive planning of shade-tolerant plants and photoperiod-sensitive plants, meanwhile, it also provides a specific plant species and the appropriate types of plant community for user decision-making according to different sunshine radiation conditions and the designer’s preferences.

A study on query terms proximity embedding for information retrieval

Information retrieval is applied widely to models and algorithms in wireless networks for cyber-physical systems. Query terms proximity has proved that it is a very useful information to improve the performance of information retrieval systems. Query terms proximity cannot retrieve documents independently, and it must be incorporated into original information retrieval models. This article proposes the concept of query term proximity embedding, which is a new method to incorporate query term proximity into original information retrieval models. Moreover, term-field-convolutions frequency framework, which is an implementation of query term proximity embedding, is proposed in this article, and experimental results show that this framework can improve the performance effectively compared with traditional proximity retrieval models.

Study of applications of content based information Retrieval models in healthcare

Study of applications of content based information Retrieval models in healthcare

Study of applications of content based information Retrieval models in healthcare

Study of applications of content based information Retrieval models in healthcare

Leveraging word embeddings and medical entity extraction for biomedical dataset retrieval using unstructured texts.

The recent movement towards open data in the biomedical domain has generated a large number of datasets that are publicly accessible. The Big Data to Knowledge data indexing project, biomedical and healthCAre Data Discovery Index Ecosystem (bioCADDIE), has gathered these datasets in a one-stop portal aiming at facilitating their reuse for accelerating scientific advances. However, as the number of biomedical datasets stored and indexed increases, it becomes more and more challenging to retrieve the relevant datasets according to researchers’ queries. In this article, we propose an information retrieval (IR) system to tackle this problem and implement it for the bioCADDIE Dataset Retrieval Challenge. The system leverages the unstructured texts of each dataset including the title and description for the dataset, and utilizes a state-of-the-art IR model, medical named entity extraction techniques, query expansion with deep learning-based word embeddings and a re-ranking strategy to enhance the retrieval performance. In empirical experiments, we compared the proposed system with 11 baseline systems using the bioCADDIE Dataset Retrieval Challenge datasets. The experimental results show that the proposed system outperforms other systems in terms of inference Average Precision and inference normalized Discounted Cumulative Gain, implying that the proposed system is a viable option for biomedical dataset retrieval. Database URL: https://github.com/yanshanwang/biocaddie2016mayodata

Combining IR and LDA Topic Modeling for Filtering Microblogs

Twitter is a networking micro-blogging service where users post millions of short messages every day. Building multilingual corpora from these microblogs contents can be useful to perform several computational tasks such as opinion mining. However, Twitter data gathering involves the problem of irrelevant included data. Recent literary works have proved that topic models such as Latent Dirichlet Allocation (LDA) are not consistent when applied to short texts like tweets. In order to prune the irrelevant tweets, we investigate in this paper a novel method to improve topics learned from Twitter content without modifying the basic machinery of LDA. This latter is based on a pooling process which combines Information retrieval (IR) approach and LDA.This is achieved through an aggregation strategy based on IR task to retrieve similar tweets in a same cluster. The result of tweet pooling is then used as an input for a basic LDA to overcome the sparsity problem of Twitter content. Empirical results highlight that tweets aggregation based on IR and LDA leads to an interesting improvement in a variety of measures for topic coherence, in comparison to unmodified LDA baseline and a variety of pooling schemes.

A Survey of Information Retrieval Techniques

The explosive growth of resources stored in various forms and transmitted over the internet has necessitated researches into information retrieval technologies. The major information retrieval mechanisms commonly employed include vector space model, Boolean model, Fuzzy Set model, and probabilistic retrieval model. These models are used to find similarities between the query and the documents to retrieve documents that reflect the query. These approaches are based on key-word, which uses lists of keywords to describe the information content. In this paper, a survey of these models is provided in order to understand their working mechanisms and shortcomings. This understanding is vital as it facilitates the choice of an information retrieval technique, based on the underlying requirements. The results of this survey revealed that the current information retrieval models fall short of the expectations in one way or the other. As such, they are not ideal for high precision information retrieval applications.

Combining heterogeneous sources in an interactive multimedia content retrieval model

Interactive multimodal information retrieval systems (IMIR) increase the capabilities of traditional search systems, by adding the ability to retrieve information of different types (modes) and from different sources. This article describes a formal model for interactive multimodal information retrieval. This model includes formal and widespread definitions of each component of an IMIR system. A use case that focuses on information retrieval regarding sports validates the model, by developing a prototype that implements a subset of the features of the model. Adaptive techniques applied to the retrieval functionality of IMIR systems have been defined by analysing past interactions using decision trees, neural networks, and clustering techniques. This model includes a strategy for selecting sources and combining the results obtained from every source. After modifying the strategy of the prototype for selecting sources, the system is re-evaluated using classification techniques. This evaluation compares the normalised discounted cumulative gain (NDCG) measure obtained using two different approaches: the multimodal system using a baseline strategy based on predefined rules as a source selection strategy, and the same multimodal system with the functionality adapted by past user interactions. In the adapted system, a final value of 81,54% was obtained for the NDCG.

Learning a Robust Relevance Model for Search Using Kernel Methods

This paper points out that many search relevance models in information retrieval, such as the Vector Space Model, BM25 and Language Models for Information Retrieval, can be viewed as a similarity f...

A Part-Of-Speech term weighting scheme for biomedical information retrieval

In the era of digitalization, information retrieval (IR), which retrieves and ranks documents from large collections according to users’ search queries, has been popularly applied in the biomedical domain. Building patient cohorts using electronic health records (EHRs) and searching literature for topics of interest are some IR use cases. Meanwhile, natural language processing (NLP), such as tokenization or Part-Of-Speech (POS) tagging, has been developed for processing clinical documents or biomedical literature. We hypothesize that NLP can be incorporated into IR to strengthen the conventional IR models. In this study, we propose two NLP-empowered IR models, POS-BoW and POS-MRF, which incorporate automatic POS-based term weighting schemes into bag-of-word (BoW) and Markov Random Field (MRF) IR models, respectively. In the proposed models, the POS-based term weights are iteratively calculated by utilizing a cyclic coordinate method where golden section line search algorithm is applied along each coordinate to optimize the objective function defined by mean average precision (MAP). In the empirical experiments, we used the data sets from the Medical Records track in Text REtrieval Conference (TREC) 2011 and 2012 and the Genomics track in TREC 2004. The evaluation on TREC 2011 and 2012 Medical Records tracks shows that, for the POS-BoW models, the mean improvement rates for IR evaluation metrics, MAP, bpref, and P@10, are 10.88%, 4.54%, and 3.82%, compared to the BoW models; and for the POS-MRF models, these rates are 13.59%, 8.20%, and 8.78%, compared to the MRF models. Additionally, we experimentally verify that the proposed weighting approach is superior to the simple heuristic and frequency based weighting approaches, and validate our POS category selection. Using the optimal weights calculated in this experiment, we tested the proposed models on the TREC 2004 Genomics track and obtained average of 8.63% and 10.04% improvement rates for POS-BoW and POS-MRF, respectively. These significant improvements verify the effectiveness of leveraging POS tagging for biomedical IR tasks.

Practical and privacy-preserving information retrieval from a database table

We study the problem of privately performing database queries (i.e., keyword searches and conjunctions over them), where a server provides its own database for a client's query-based access. We propose a cryptographic model for the study of such protocols, by expanding previous well-studied models of keyword search and private information retrieval to incorporate a more practical data model: a time-varying, multi-attribute and multiple-occurrence database table. Our first result is a 2-party private database retrieval protocol. This is the first protocol that preserves query and data privacy on such a practical data model. Like all previous work in private information retrieval and keyword search, this protocol still satisfies server time complexity linear in the database size. Our main result is a private database retrieval protocol in a 3-party model where encrypted data is outsourced to a third party (i.e., a cloud server), satisfying highly desirable privacy and efficiency properties; most notably: (1) no unintended information is leaked to clients or servers, and only minimal 'access pattern' information is leaked to the third party; (2) for each query, all parties run in time only logarithmic in the number of database records; (3) the protocol's runtime is practical for real- life applications, as shown in our implementation where we achieve response time that is only a small constant slower than commercial non-private protocols like MySQL. This is the first protocol that achieves privacy of database and query content with practical performance. Finally, we show a second private database retrieval protocol in the 3-party model for which we can show that no unintended information is leaked to an adversary corrupting both clients and third parties, at an only constant additional performance overhead cost.

Towards a probabilistic model for supporting collaborative information access

In information retrieval research, models and systems traditionally assume that a single person is querying and reviewing the results. However, several empirical studies of professional practice identified collaboration during IR as everyday work patterns in order to solve a shared information need and to benefit from the diverse expertise and experience of the team members. Moreover, most IR systems that are employed in professional work routines are designed for individual use and prototype collaborative systems are too limited to support use in todays work practice. To bridge this gap, this papers develops and formalizes a decision theoretic approach towards supporting a team of people that explicitly set out together to resolve a shared information need. We develop a formal cost model for collaborative IR that considers the trade-off between estimated relevance of a document as well as estimated document redundancy. From this cost model, we use a decision theoretic approach to derive the notion of activity suggestions, that is, a formal optimum criterion that describes optimum collaboration strategies in IR as the solution of an integer linear program. Those collaboration strategies are suggested to team members with the aim to facilitate the collaborative performance of information retrieval tasks. We demonstrate the application of our model by means of search result division in two collaborative search tasks. In the conducted experiments, we study the effects of different domain knowledge and resulting relevance assessments of team members in four different conditions. The gathered results indicate that our approach can improve the retrieval effectiveness of teams in recall-oriented tasks.

Logics, Lattices and Probability: The Missing Links to Information Retrieval

Logic-based information retrieval (IR) models represent the retrieval decision as an implication d→q between a document d and a query q, where d and q are logical sentences. However, d→q is generally a binary decision, thus we need a measurement to estimate the degree to which d implies q, denoted U(d→q). Most of the existing logic-based IR models either do not precisely define the implication d→q or use non-classical definitions. Some models also define the uncertainty U in informal ways. More importantly, they use two non-related frameworks to define d→q and its uncertainty U, even though the two notions are intrinsically related. The goal of this study is to propose a new logic-based IR model, which overcome these shortcomings. To this end, we first propose to replace the implication d→q by the validity of material implication ⊨d⊃q. Second, we redefine and adapt the mathematical relationship between logics, lattices and probability. Our new IR model presents a possible formalism for van Rijsbergen's intuition about replacing U(d→q) by P(q∣d).

When time meets information retrieval: Past proposals, current plans and future trends

With the advent of Web search and the large amount of data published on the Web sphere, a tremendous amount of documents become strongly time-dependent. In this respect, the time dimension has been extensively exploited as a highly important relevance criterion to improve the retrieval effectiveness of document ranking models. Thus, a compelling research interest is going on the temporal information retrieval realm, which gives rise to several temporal search applications. In this article, we intend to provide a scrutinizing overview of time-aware information retrieval models. We specifically put the focus on the use of timeliness and its impact on the global value of relevance as well as on the retrieval effectiveness. First, we attempt to motivate the importance of temporal signals, whenever combined with other relevance features, in accounting for document relevance. Then, we review the relevant studies standing at the crossroads of both information retrieval and time according to three common information retrieval aspects: the query level, the document content level and the document ranking model level. We organize the related temporal-based approaches around specific information retrieval tasks and regarding the task at hand, we emphasize the importance of results presentation and particularly timelines to the end user. We also report a set of relevant research trends and avenues that can be explored in the future.

Exploration of Quantum Interference in Document Relevance Judgement Discrepancy

Quantum theory has been applied in a number of fields outside physics, e.g., cognitive science and information retrieval (IR). Recently, it has been shown that quantum theory can subsume various key IR models into a single mathematical formalism of Hilbert vector spaces. While a series of quantum-inspired IR models has been proposed, limited effort has been devoted to verify the existence of the quantum-like phenomenon in real users’ information retrieval processes, from a real user study perspective. In this paper, we aim to explore and model the quantum interference in users’ relevance judgement about documents, caused by the presentation order of documents. A user study in the context of IR tasks have been carried out. The existence of the quantum interference is tested by the violation of the law of total probability and the validity of the order effect. Our main findings are: (1) there is an apparent judging discrepancy across different users and document presentation orders, and empirical data have violated the law of total probability; (2) most search trials recorded in the user study show the existence of the order effect, and the incompatible decision perspectives in the quantum question (QQ) model are valid in some trials. We further explain the judgement discrepancy in more depth, in terms of four effects (comparison, unfamiliarity, attraction and repulsion) and also analyse the dynamics of document relevance judgement in terms of the evolution of the information need subspace.

A Quantum Query Expansion Approach for Session Search

Recently, Quantum Theory (QT) has been employed to advance the theory of Information Retrieval (IR). Various analogies between QT and IR have been established. Among them, a typical one is applying the idea of photon polarization in IR tasks, e.g., for document ranking and query expansion. In this paper, we aim to further extend this work by constructing a new superposed state of each document in the information need space, based on which we can incorporate the quantum interference idea in query expansion. We then apply the new quantum query expansion model to session search, which is a typical Web search task. Empirical evaluation on the large-scale Clueweb12 dataset has shown that the proposed model is effective in the session search tasks, demonstrating the potential of developing novel and effective IR models based on intuitions and formalisms of QT.