Improve Information Retrieval Research Articles

Improve information retrieval and e-learning using mobile agent based on semantic web technology

Improve information retrieval and e-learning using mobile agent based on semantic web technology

基于Agent的智能化元搜索引擎个性化机制

Information overload is a common issue in “big data” environments, because of which such environments require precise and intelligent retrieval tools. In this paper, we propose the design and implementation of a personalization function for an intelligent, agent-based meta-search engine that improves information retrieval efficiency in “big data” environments. We focus on individualized methods, along with the relevant theoretical and technological requirements for the implementation of a personalization function in an intelligent, agent-based meta-search engine, provide query analyses, and describe the interest mining and scheduling processes of the search engine. We then detail the recognition mechanism of complex inquires based on a dynamic learning process, the interest mining mechanism based on the general view model of user interest, which involves dynamic updates, and the strategy for search engine scheduling based on a concept lattice and user logs. Experiments showed that the personalization function for intelligent, agent-based meta search engines proposed here accurately identified complex queries, effectively learned users search interests, improved the relevance of the search results, and intelligently scheduled member search engine. It also helped improve the efficiency of information retrieval by users, thus enhancing their searching experiences.

Assessment of learning to rank methods for query expansion

Pseudo relevance feedback, as an effective query expansion method, can significantly improve information retrieval performance. However, the method may negatively impact the retrieval performance when some irrelevant terms are used in the expanded query. Therefore, it is necessary to refine the expansion terms. Learning to rank methods have proven effective in information retrieval to solve ranking problems by ranking the most relevant documents at the top of the returned list, but few attempts have been made to employ learning to rank methods for term refinement in pseudo relevance feedback. This article proposes a novel framework to explore the feasibility of using learning to rank to optimize pseudo relevance feedback by means of reranking the candidate expansion terms. We investigate some learning approaches to choose the candidate terms and introduce some state‐of‐the‐art learning to rank methods to refine the expansion terms. In addition, we propose two term labeling strategies and examine the usefulness of various term features to optimize the framework. Experimental results with three TREC collections show that our framework can effectively improve retrieval performance.

Information literacy and peer-to-peer infrastructures: An autopoietic perspective

This article argues that an autopoietic perspective of human communities would allow to understand societies as self-organized systems and thus promote information literacy as a facilitator of social development. Peer-to-peer (P2P) social dynamics generate public information available worldwide in digital repositories, websites and bibliographic resources. However, processing such amount of data is not achievable by a single central-controlled system. We claim that distributed and heterogeneous networks of coordinated mechanisms, composed by both specialized human and artificial agents, are needed to improve information retrieval, knowledge inference and decision-making, but also to produce social value, goods and services. Handling these issues implies the collective construction of global semantic networks but also the active labor of knowledge producers and consumers. We conclude that information literacy is as much important as any technical implementation and, therefore, may lead to networks of Commons-oriented communities which would utilize P2P infrastructures.

Credible Mechanism for More Reliable Search Engine Results

the number of websites on the Internet is growing randomly, thanks to HTML language. Consequently, a diversity of information is available on the Web, however, sometimes the content of it may be neither valuable nor trusted. This leads to a problem of a credibility of the existing information on these Websites. This paper investigates aspects affecting on the Websites credibility and then uses them along with dominant meaning of the query for improving information retrieval capabilities and to effectively manage contents. It presents a design and development of a credible mechanism that searches Web search engine and then ranks sites according to its reliability. Our experiments show that the credibility terms on the Websites can affect the ranking of the Web search engine and greatly improves retrieval effectiveness.

Fuzzy Ontology Based Multi-Modal Semantic Information Retrieval

Lack of today's technology of information retrieval system is the semantic between the information resources. The semantic can be expressed for a resource using the underlying technology of ontology. So the main objective of this paper is to design an information retrieval system for both text and image data using the concept of ontology. The main focus of this paper is to improve information retrieval for sports events using Ontologies. As it is complex problem, it is addressed into the following sub-problems in this paper. (1) Integrating domain knowledge and images using fuzzy ontology and Retrieving the required Muti-modal information using fuzzy rule set. (2) Providing image semantic by constructing visual codebook for affine covariant-Semantic segmented patches. (3) Analysing the discriminative power of each visual word using the probabilistic latent semantic and quantizing them using the Chi-Square test. In this work, the domain of Sports is considered for creating both Low-level visual ontology for certain sport event images and also for building a high-level domain ontology from the information on web. These two Ontologies are integrated using Fuzzy concepts.

Evaluating distance-based clustering for user (browse and click) sessions in a domain-specific collection

We seek to improve information retrieval in a domain-specific collection by clustering user sessions from a click log and then classifying later user sessions in real time. As a preliminary step, w...

English

The rapid development in Crowdsourcing offers new ideas to the traditional methods for improving Information Retrieval systems in design, training and evaluation. Recently, Crowdsourcing has been used as a source of relevant and non- relevant judgements. Crowdsourcing is an outsourcing a task to crowd/people where all the steps from design to implementation are included. The popularity of Crowdsourcing is increasing rapidly because of the large number of crowd involvement. The purpose is to apply wisdom of crowd strategy in academia. For this purpose we provide student feedback system, in which feedbacks can be both relevant and non-relevant. After that feedbacks are pre-process. The feedbacks are then clustered for the classification of feedbacks using similarity based technique. Our system takes related feedbacks. We considered two approaches k-means and hierarchical algorithm to implement a text mining methodology. We have used hierarchical clustering to classify student feedbacks and compare our work with the k- means approach for clustering and evaluated our approach with standard metrics precision, recall and F1-Score.

From Record-Bound to Boundless: FRBR, Linked Data, and New Possibilities for Serials Cataloging

As resources have become ever more complicated in a digital world, it is evident that cataloging practices and the metadata standards we use to guide these practices are becoming more constraining. Recent developments within the library community can have a significant impact on serials cataloging and may help improve information retrieval for the end user. While the Functional Requirements for Bibliographic Records (FRBR) holds some promise for improving the representation of serials, linked data principles may further transform the way in which resources and the relationships between them are captured and presented to our users. By taking description out of the current record constraints, serials librarians will better be able to express the relationships between multiple versions of the same publication, and how a particular journal has changed over time. The linked data model also opens up many opportunities for the provision of value-added content to bibliographic descriptions.

Event extraction from corefering mentions to enrich semantic web metadata

Since an enormous amount of information is available on the web and much time is needed to find and read the desired information, users require precise information from the documents. More specifically, most users are interested in precise information about events occurring in different parts of the world, such as when and where the event has occurred and who was involved in the event. The task of finding such information is called event extraction. Event extraction from text documents is an important task in the field of natural language processing, as it helps improve information retrieval and summarisation, as well as enriching the semantic web with event-based metadata. This paper provides a method to extract event arguments from corefering sentences of a particular event instance using rule learning based on rough sets. It also provides a metadata adoption strategy to exploit the results in the semantic web environment as metadata.

AN ACCURACY-ENHANCED STEMMING ALGORITHM FOR ARABIC INFORMATION RETRIEVAL

This paper provides a method for indexing and retrieving Arabic texts, based on natural language processing. Our approach exploits the notion of template in word stemming and replaces the words by their stems. This technique has proven to be effective since it has returned significant relevant retrieval results by decreasing silence during the retrieval phase. Series of experiments have been conducted to test the performance of the proposed algorithm ESAIR (Enhanced Stemmer for Arabic Information Retrieval). The results obtained indicate that the algorithm extracts the exact root with an accuracy rate up to 96% and hence, improving information retrieval.

How can i find a doggy book?: Children's cognitive processes of physical characteristics of books

How can i find a doggy book?: Children's cognitive processes of physical characteristics of books

An Efficient Method for Noisy Annotation Data Modeling

Probabilistic topic models are used for analyzing and extracting content-related annotations from noisy annotated discrete data like WebPages on WWW and these WebPages are stored using social bookmarking services with the help of social bookmarking services, reason behind this process most of time users can attach annotations freely, some annotations do not describe the semantics of the content, therefore they are noisy, simply they are not content related. The extraction of content-related annotations can be used as a prepossessing step in machine learning. Prepossessing step in machine learning is like text classification and image recognition, and can improve information retrieval performance. The proposed model is a generative model for content and annotations, where annotations are assumed to be originated either from topics that generated the content or from a general distribution unrelated to the content. We demonstrate the effectiveness of the proposed method with the help of synthetic data and real social annotation data for text and images.

Cost and benefit estimation of experts' mediation in an enterprise search

The success of an enterprise information retrieval system is determined by interactions among three key entities: the search engine employed; the service provider who delivers, modifies, and maintains the engine; and the users of the service within the organization. Evaluations of an enterprise search have predominately focused on the effectiveness and efficiency of the engine, with very little analysis of user involvement in the process, and none on the role of service providers. We propose and evaluate a model of costs and benefits to a service provider when investing in enhancements to the ranking of documents returned by their search engine. We demonstrate the model through a case study to analyze the potential impact of using domain experts to provide enhanced mediated search results. By demonstrating how to quantify the cost and benefit of an improved information retrieval system to the service provider, our case study shows that using the relevance assessments of domain experts to rerank original search results can significantly improve the accuracy of ranked lists. Moreover, the service provider gains substantial return on investment and a higher search success rate by investing in the relevance assessments of domain experts. Our cost and benefit analysis results are contrasted with standard modes of effectiveness analysis, including quantitative (using measures such as precision) and qualitative (through user preference surveys) approaches. Modeling costs and benefits explicitly can provide useful insights that the other approaches do not convey.

Developing an ontological explosion knowledge base for business continuity planning purposes

Industrial accidents are among the most known challenges to business continuity. Many organisations have lost their reputation following devastating accidents. To manage the risks of such accidents, it is necessary to accumulate sufficient knowledge regarding their roots, causes and preventive techniques. The required knowledge might be obtained through various approaches, including databases. Unfortunately, many databases are hampered by (among other things) static data presentations, a lack of semantic features, and the inability to present accident knowledge as discrete domains. This paper proposes the use of Protégé software to develop a knowledge base for the domain of explosion accidents. Such a structure has a higher capability to improve information retrieval compared with common accident databases. To accomplish this goal, a knowledge management process model was followed. The ontological explosion knowledge base (EKB) was built for further applications, including process accident knowledge retrieval and risk management. The paper will show how the EKB has a semantic feature that enables users to overcome some of the search constraints of existing accident databases.

Research on Intelligent Information Retrieval Based on Semantic Web Technology

According to the rapid development of the Internet and the increase of the network resources, it was very important to search the information resources for meeting the need of large ground of users. The key words were used as the indexes for retrieval with traditional method. But it couldnt reflect the real purposes and intentions of the users precisely. An improved information retrieval method based on the semantic web technology was proposed for carrying out the information retrieval work precisely and effectively. The mew retrieval system was not just recognizing the words simply, but it could comprehend the semantic ingredients of the words and sentences. The semantic web retrieval system was constructed eventually. Comparing to the traditional keywords retrieval method, the similarity degree was calculated in the simulation, simulation result shows that the retrieval precision ratio of new method increases by 15%, then the intelligent, efficient and accurate information retrieval system is realized successfully.

Modeling Noisy Annotated Data with Application to Social Annotation

We propose a probabilistic topic model for analyzing and extracting content-related annotations from noisy annotated discrete data such as webpages stored using social bookmarking services. With these services, because users can attach annotations freely, some annotations do not describe the semantics of the content, thus they are noisy, i.e., not content related. The extraction of content-related annotations can be used as a prepossessing step in machine learning tasks such as text classification and image recognition, or can improve information retrieval performance. The proposed model is a generative model for content and annotations, in which the annotations are assumed to originate either from topics that generated the content or from a general distribution unrelated to the content. We demonstrate the effectiveness of the proposed method by using synthetic data and real social annotation data for text and images.

A system for information retrieval in a medical digital library based on modular ontologies and query reformulation

Ontologies have proven to be useful in the area of Information Retrieval and the biomedical informatics community has acknowledged, in recent years, their utility. However, building and updating manually ontologies is a long and tedious task. This paper proposes a system that allows any search engine to develop its semantic layer by applying ontology learning techniques on Web snippets and applies it to a well-known medical digital library, PubMed. The new system (SemPubMed) automatically builds new ontology fragments related to the user's query and then it reformulates queries using the new concepts in order to improve information retrieval. Our system has endured a twofold evaluations. On the one hand, we have evaluated the quality of the modular ontologies built by the system. On the other hand, we have studied how the semantic reformulation of the queries has led to an improvement of the quality of the results given by PubMed, both in terms of precision and recall. Obtained results show that adding semantic layer to PubMed enables an improvement of query reformulation and predicted ranking score.

Improving information retrieval with multiple health terminologies in a quality-controlled gateway.

BackgroundThe Catalog and Index of French-language Health Internet resources (CISMeF) is a quality-controlled health gateway, primarily for Web resources in French (n=89,751). Recently, we achieved a major improvement in the structure of the catalogue by setting-up multiple terminologies, based on twelve health terminologies available in French, to overcome the potential weakness of the MeSH thesaurus, which is the main and pivotal terminology we use for indexing and retrieval since 1995. The main aim of this study was to estimate the added-value of exploiting several terminologies and their semantic relationships to improve Web resource indexing and retrieval in CISMeF, in order to provide additional health resources which meet the users’ expectations.MethodsTwelve terminologies were integrated into the CISMeF information system to set up multiple-terminologies indexing and retrieval. The same sets of thirty queries were run: (i) by exploiting the hierarchical structure of the MeSH, and (ii) by exploiting the additional twelve terminologies and their semantic links. The two search modes were evaluated and compared.ResultsThe overall coverage of the multiple-terminologies search mode was improved by comparison to the coverage of using the MeSH (16,283 vs. 14,159) (+15%). These additional findings were estimated at 56.6% relevant results, 24.7% intermediate results and 18.7% irrelevant.ConclusionThe multiple-terminologies approach improved information retrieval. These results suggest that integrating additional health terminologies was able to improve recall. Since performing the study, 21 other terminologies have been added which should enable us to make broader studies in multiple-terminologies information retrieval.

Utilization of ontology look-up services in information retrieval for biomedical literature.

With the vast amount of biomedical data we face the necessity to improve information retrieval processes in biomedical domain. The use of biomedical ontologies facilitated the combination of various data sources (e.g. scientific literature, clinical data repository) by increasing the quality of information retrieval and reducing the maintenance efforts. In this context, we developed Ontology Look-up services (OLS), based on NEWT and MeSH vocabularies. Our services were involved in some information retrieval tasks such as gene/disease normalization. The implementation of OLS services significantly accelerated the extraction of particular biomedical facts by structuring and enriching the data context. The results of precision in normalization tasks were boosted on about 20%.