Information Retrieval Model Research Articles

Semantic concept-enriched dependence model for medical information retrieval

ObjectiveIn medical information retrieval research, semantic resources have been mostly used by expanding the original query terms or estimating the concept importance weight. However, implicit term-dependency information contained in semantic concept terms has been overlooked or at least underused in most previous studies. In this study, we incorporate a semantic concept-based term-dependence feature into a formal retrieval model to improve its ranking performance. DesignStandardized medical concept terms used by medical professionals were assumed to have implicit dependency within the same concept. We hypothesized that, by elaborately revising the ranking algorithms to favor documents that preserve those implicit dependencies, the ranking performance could be improved. The implicit dependence features are harvested from the original query using MetaMap. These semantic concept-based dependence features were incorporated into a semantic concept-enriched dependence model (SCDM). We designed four different variants of the model, with each variant having distinct characteristics in the feature formulation method. MeasurementsWe performed leave-one-out cross validations on both a clinical document corpus (TREC Medical records track) and a medical literature corpus (OHSUMED), which are representative test collections in medical information retrieval research. ResultsOur semantic concept-enriched dependence model consistently outperformed other state-of-the-art retrieval methods. Analysis shows that the performance gain has occurred independently of the concept’s explicit importance in the query. ConclusionBy capturing implicit knowledge with regard to the query term relationships and incorporating them into a ranking model, we could build a more robust and effective retrieval model, independent of the concept importance.

Utilizing term proximity for blog post retrieval

Term proximity is effective for many information retrieval (IR) research fields yet remains unexplored in blogosphere IR. The blogosphere is characterized by large amounts of noise, including incohesive, off‐topic content and spam. Consequently, the classical bag‐of‐words unigram IR models are not reliable enough to provide robust and effective retrieval performance. In this article, we propose to boost the blog postretrieval performance by employing term proximity information. We investigate a variety of popular and state‐of‐the‐art proximity‐based statistical IR models, including a proximity‐based counting model, the Markov random field (MRF) model, and the divergence from randomness (DFR) multinomial model. Extensive experimentation on the standard TREC Blog06 test dataset demonstrates that the introduction of term proximity information is indeed beneficial to retrieval from the blogosphere. Results also indicate the superiority of the unordered bi‐gram model with the sequential‐dependence phrases over other variants of the proximity‐based models. Finally, inspired by the effectiveness of proximity models, we extend our study by exploring the proximity evidence between uery terms and opinionated terms. The consequent opinionated proximity model shows promising performance in the experiments.

Intelligent Information Retrieval System Based on Muti-Agent Model

The intelligent information retrieval model discussed in this paper is constructed by multi-agent. Currently, BDI cognitive theory is accepted widely by the scholars of this field, but the related researches mainly focus on the theoretical derivation and presentation of symbols, lack of model facing practical application. In this article, a dynamic rule-based reasoning model is proposed. The model based on BDI theory is an expression of agent intelligence. The basic logical reasoning of the BDI theory is extended in this article. The author not only introduces several functions to study the dynamic changes of agents mental state, but also give a detailed description of how to use the theory of production rules to express BDI-based reasoning. This article also studies and designs the agent communication mechanism in MAS. Finally, the intelligent information retrieval system is designed and implemented with the idea of AOP.

Information Retrieval Models: Foundations and Relationships

Information Retrieval (IR) models are a core component of IR research and IR systems. The past decade brought a consolidation of the family of IR models, which by 2000 consisted of relatively isolated

Mining pure high-order word associations via information geometry for information retrieval

The classical bag-of-word models for information retrieval (IR) fail to capture contextual associations between words. In this article, we propose to investigate pure high-order dependence among a number of words forming an unseparable semantic entity, that is, the high-order dependence that cannot be reduced to the random coincidence of lower-order dependencies. We believe that identifying these pure high-order dependence patterns would lead to a better representation of documents and novel retrieval models. Specifically, two formal definitions of pure dependence—unconditional pure dependence (UPD) and conditional pure dependence (CPD)—are defined. The exact decision on UPD and CPD, however, is NP-hard in general. We hence derive and prove the sufficient criteria that entail UPD and CPD, within the well-principled information geometry (IG) framework, leading to a more feasible UPD/CPD identification procedure. We further develop novel methods for extracting word patterns with pure high-order dependence. Our methods are applied to and extensively evaluated on three typical IR tasks: text classification and text retrieval without and with query expansion.

Probabilistic models in IR and their relationships

A solid research path towards new information retrieval models is to further develop the theory behind existing models. A profound understanding of these models is therefore essential. In this paper, we revisit probability ranking principle (PRP)-based models, probability of relevance (PR) models, and language models, finding conceptual differences in their definition and interrelationships. The probabilistic model of the PRP has not been explicitly defined previously, but doing so leads to the formulation of two actual principles with different objectives. First, the belief probability ranking principle (BPRP), which considers uncertain relevance between known documents and the current query, and second, the popularity probability ranking principle (PPRP), which considers the probability of relevance of documents among multiple queries with the same features. Our analysis shows how some of the discussed PR models implement the BPRP or the PPRP while others do not. However, for some models the parameter estimation is challenging. Finally, language models are often presented as related to PR models. However, we find that language models differ from PR models in every aspect of a probabilistic model and the effectiveness of language models cannot be explained by the PRP.

Recovering from a decade: a systematic mapping of information retrieval approaches to software traceability

Engineers in large-scale software development have to manage large amounts of information, spread across many artifacts. Several researchers have proposed expressing retrieval of trace links among artifacts, i.e. trace recovery, as an Information Retrieval (IR) problem. The objective of this study is to produce a map of work on IR-based trace recovery, with a particular focus on previous evaluations and strength of evidence. We conducted a systematic mapping of IR-based trace recovery. Of the 79 publications classified, a majority applied algebraic IR models. While a set of studies on students indicate that IR-based trace recovery tools support certain work tasks, most previous studies do not go beyond reporting precision and recall of candidate trace links from evaluations using datasets containing less than 500 artifacts. Our review identified a need of industrial case studies. Furthermore, we conclude that the overall quality of reporting should be improved regarding both context and tool details, measures reported, and use of IR terminology. Finally, based on our empirical findings, we present suggestions on how to advance research on IR-based trace recovery.

Studying the clustering paradox and scalability of search in highly distributed environments

With the ubiquitous production, distribution and consumption of information, today's digital environments such as the Web are increasingly large and decentralized. It is hardly possible to obtain central control over information collections and systems in these environments. Searching for information in these information spaces has brought about problems beyond traditional boundaries of information retrieval (IR) research. This article addresses one important aspect of scalability challenges facing information retrieval models and investigates a decentralized, organic view of information systems pertaining to search in large-scale networks. Drawing on observations from earlier studies, we conduct a series of experiments on decentralized searches in large-scale networked information spaces. Results show that how distributed systems interconnect is crucial to retrieval performance and scalability of searching. Particularly, in various experimental settings and retrieval tasks, we find a consistent phenomenon, namely, the Clustering Paradox , in which the level of network clustering (semantic overlay) imposes a scalability limit. Scalable searches are well supported by a specific, balanced level of network clustering emerging from local system interconnectivity. Departure from that level, either stronger or weaker clustering, leads to search performance degradation, which is dramatic in large-scale networks.

Combining compound and single terms under language model framework

Most existing Information Retrieval model including probabilistic and vector space models are based on the term independence hypothesis. To go beyond this assumption and thereby capture the semantics of document and query more accurately, several works have incorporated phrases or other syntactic information in IR, such attempts have shown slight benefit, at best. Particularly in language modeling approaches this extension is achieved through the use of the bigram or n-gram models. However, in these models all bigrams/n-grams are considered and weighted uniformly. In this paper we introduce a new approach to select and weight relevant n-grams associated with a document. Experimental results on three TREC test collections showed an improvement over three strongest state-of-the-art model baselines, which are the original unigram language model, the Markov Random Field model, and the positional language model.

Study of E-learning Information Retrieval Model based on Ontology

Research work in the field of E-learning is represented by a broad area of applications ranged from web learning to virtual courses. Web-based courses offer obvious advantages for learners by making access to educational resource very fast and relevance, at any time or place. The objective of this paper is to create an ontology based domain specific representation for e-learning domain. This paper analyzes the drawbacks of traditional keyword based search engines and proposes the need for semantic based intelligent information retrieval systems. This paper presents ontology based information retrieval (IR) for e-learning domain which is developed using Protege tool. This discusses about the Elearning information retrieval model which consists Resource collection module, Semantic procession module, Resource collection module, resource integration system. General Terms Computer Science, Information Retrieval.

Scalable self-organizing structured P2P information retrieval model based on equivalence classes

This paper proposes a new autonomous self-organizing content-based node clustering peer to peer Information Retrieval (P2PIR) model. This model uses incremental transitive document-to-document similarity technique to build Local Equivalence Classes (LECes) of documents on a source node. Locality Sensitive Hashing (LSH) scheme is applied to map a representative of each LEC into a set of keys which will be published to hosting node (s). Similar LECes on different nodes form Universal Equivalence Classes (UECes), which indicate the connectivity between these nodes. The same LSH scheme is used to submit queries to subset of nodes that most likely have relevant information. The proposed model has been implemented. The obtained results indicate efficiency in building connectivity between similar nodes, and correctly allocate and retrieve relevant answers to high percentage of queries. The system was tested for different network sizes and proved to be scalable as efficiency downgraded gracefully as the network size grows exponentially.

A Formal Model of Information Retrieval Based on User Sensitivities

Search engines are a very important web applications used by millions of users around the world on a daily basis to search the Web. Finding relevant information in this growing space is challenging, and is complicated by the diversity and needs of the community of Web users. Indeed, the Web is one, but the needs of users are multiple and different. Thus, information relevancy is not only related to the formulated query, but also to the user who is formulating this query. For example, user sensitivities may enhance information relevancy. In this paper, we are proposing to derive a formal model of user sensitivities integration into search engines.

New application of genetic algorithm in optimization of structural weights

It has been widely recognized that the performance of a multi-agent system (MAS) is highly affected by its organization. A large scale MAS may have billions of possible ways of organization, depending on the number of agents, the roles, and the relationships among these agents. These characteristics make it impractical to find an optimal choice of organization using exhaustive search methods. In this report, we propose a genetic algorithm aided optimization scheme for designing hierarchical structures of multi-agent systems. We introduce a novel algorithm, called the hierarchical genetic algorithm, in which hierarchical crossover with a repair strategy and mutation of small perturbation are used. The phenotypic hierarchical structure space is translated to the genome-like array representation space, which makes the algorithm genetic-operator-literate. A case study with 10 scenarios of a hierarchical information retrieval model is provided. Our experiments have shown that competitive baseline structures which lead to the optimal organization in terms of utility can be found by the proposed algorithm during the evolutionary search. Compared with the traditional genetic operators, the newly introduced operators produced better organizations of higher utility more consistently in a variety of test cases. The proposed algorithm extends the search processes of the state-of-the-art multi-agent organization design methodologies, and is more computationally efficient in a large search space.

Information retrieval with query hypergraphs

Current information retrieval models are optimized for retrieval with short keyword queries. In contrast, in this dissertation we focus on longer, verbose queries with more complex structure that are becoming more common in both mobile and web search. To this end, we propose an expressive query representation formalism based on query hypergraphs. Unlike the existing query representations, query hypergraphs model the dependencies between arbitrary concepts in the query, rather than dependencies between single query terms. Query hypergraphs are parameterized by importance weights, which are assigned to concepts and concept dependencies in the query hypergraph, based on their contribution to the overall retrieval effectiveness. Query hypergraphs are not limited to modeling the explicit query structure. Accordingly, we develop two methods for query expansion using query hypergraphs. In these methods, the expansion concepts in the query hypergraph may come either from the retrieval corpus alone or from a combination of multiple information sources such as Wikipedia or the anchor text extracted from a large-scale web corpus. We empirically demonstrate that query hypergraphs are consistently and significantly more effective than many of the current state-of-the-art retrieval methods, as demonstrated by the experiments on newswire and web corpora. Query hypergraphs improve the retrieval performance for all query types, and, in particular, they exhibit the highest effectiveness gains for verbose queries.

Analyzing the Order Factor of Query Matrix in Private Information Retrieval Protocol for Outsourced Databases

In this paper, we present analysis of non-concurrent model of fast single-database Private Information Retrieval (PIR) scheme for maintaining data privacy with data confidentiality using encryption algorithm like AES for Outsourced Database Service (ODBS) Model. In this model encryption method maintains data confidentiality and a utility that help to create PIR reply which maintains data privacy. Single-database PIR schemes are generally unusable because of its expensiveness from computational point of view. This model suggests use of Graphics Processing Unit (GPU) commodity to process database. This model will help to achieve practical implementation of single-database PIR scheme which uses encryption algorithm to maintain data confidentiality as secondary goal and it serves to create database reply to maintain data privacy as primary goal. This paper shows some real time results that proves need of concurrency in PIR algorithms. Our paper describes the use of lattice based single-database PIR protocol with GPU as a processing unit to achieve high speed-up and hence, the performance of the system.

OGIR: an ontology‐based grid information retrieval framework

PurposeA grid information retrieval model has benefits for sharing resources and processing mass information, but cannot handle conceptual heterogeneity without integration of semantic information. The purpose of this research is to propose a concept‐based retrieval mechanism to catch the user's query intentions in a grid environment. This research re‐ranks documents over distributed data sources and evaluates performance based on the user judgment and processing time.Design/methodology/approachThis research uses the ontology lookup service to build the concept set in the ontology and captures the user's query intentions as a means of query expansion for searching. The Globus toolkit is used to implement the grid service. The modification of the collection retrieval inference (CORI) algorithm is used for re‐ranking documents over distributed data sources.FindingsThe experiments demonstrate that this proposed approach successfully describes the user's query intentions evaluated by user judgment. For processing time, building a grid information retrieval model is a suitable strategy for the ontology‐based retrieval model.Originality/valueMost current semantic grid models focus on construction of the semantic grid, and do not consider re‐ranking search results from distributed data sources. The significance of evaluation from the user's viewpoint is also ignored. This research proposes a method that captures the user's query intentions and re‐ranks documents in a grid based on the CORI algorithm. This proposed ontology‐based retrieval mechanism calculates the global relevance score of all documents in a grid and displays those documents with higher relevance to users.

Bridging memory-based collaborative filtering and text retrieval

When speaking of information retrieval, we often mean text retrieval. But there exist many other forms of information retrieval applications. A typical example is collaborative filtering that suggests interesting items to a user by taking into account other users’ preferences or tastes. Due to the uniqueness of the problem, it has been modeled and studied differently in the past, mainly drawing from the preference prediction and machine learning view point. A few attempts have yet been made to bring back collaborative filtering to information (text) retrieval modeling and subsequently new interesting collaborative filtering techniques have been thus derived. In this paper, we show that from the algorithmic view point, there is an even closer relationship between collaborative filtering and text retrieval. Specifically, major collaborative filtering algorithms, such as the memory-based, essentially calculate the dot product between the user vector (as the query vector in text retrieval) and the item rating vector (as the document vector in text retrieval). Thus, if we properly structure user preference data and employ the target user’s ratings as query input, major text retrieval algorithms and systems can be directly used without any modification. In this regard, we propose a unified formulation under a common notational framework for memory-based collaborative filtering, and a technique to use any text retrieval weighting function with collaborative filtering preference data. Besides confirming the rationale of the framework, our preliminary experimental results have also demonstrated the effectiveness of the approach in using text retrieval models and systems to perform item ranking tasks in collaborative filtering.

Information Retrieval System Assigning Context to Documents by Relevance Feedback

In this paper we have proposed user feedback driven Information retrieval model. The proposed model assigns weights to the retrieved documents based on its context. The documents are re-ranked based on the user profile and his feedback. Proposed Information retrieval system uses vector space model and expert system. Need for user profile and relevance of information while searching and extracting information, from information retrieval system is highlighted.

Development of a text search engine for medicinal chemistry patents

Division of Medical Information Sciences (SIMED), University Hospitals of Geneva and University of Geneva, Geneva, Switzerland Bibliomics and Text-Mining Group (BiTeM), Information Science Department, University of Applied Sciences, Geneva, Switzerland Novartis Institute for BioMedical Research – Text Mining Services (NIBR-IT/TMS), Novartis Pharma AG, Basel, Switzerland e-mail: emilie.pasche@hcuge.ch (corresponding), julien.gobeill@hesge.ch, fatma.oezdemirzaech@novartis.com, therese.vachon@novartis.com, christian.lovis@hcuge.ch, patrick.ruch@hesge.ch

Diversifying Search Results through Pattern-Based Subtopic Modeling

Traditional information retrieval models do not necessarily provide users with optimal search experience because the top ranked documents may contain excessively redundant information. Therefore, satisfying search results should be not only relevant to the query but also diversified to cover different subtopics of the query. In this paper, the authors propose a novel pattern-based framework to diversify search results, where each pattern is a set of semantically related terms covering the same subtopic. They first apply a maximal frequent pattern mining algorithm to extract the patterns from retrieval results of the query. The authors then propose to model a subtopic with either a single pattern or a group of similar patterns. A profile-based clustering method is adapted to group similar patterns based on their context information. The search results are then diversified using the extracted subtopics. Experimental results show that the proposed pattern-based methods are effective to diversify the search results.