Concept-based Query Research Articles

Multimodal query-level fusion for efficient multimedia information retrieval

Managing a large volume of multimedia data containing various modalities such as visual, audio, and text reveals the necessity for efficient methods for modeling, processing, storing, and retrieving complex data. In this paper, we propose a fusion-based approach at the query level to improve query retrieval performance of multimedia data. We discuss various flexible query types including the combination of content as well as concept-based queries that provide users with the ability to efficiently perform multimodal querying. We have carried out a number of experiments on a video database to show the efficiency of our approach for various types of queries. Our experimental results show that our query-level fusion approach presents a notable improvement in retrieval performance especially for the concept-based queries.

An intelligent multimedia information system for multimodal content extraction and querying

This paper introduces an intelligent multimedia information system, which exploits machine learning and database technologies. The system extracts semantic contents of videos automatically by using the visual, auditory and textual modalities, then, stores the extracted contents in an appropriate format to retrieve them efficiently in subsequent requests for information. The semantic contents are extracted from these three modalities of data separately. Afterwards, the outputs from these modalities are fused to increase the accuracy of the object extraction process. The semantic contents that are extracted using the information fusion are stored in an intelligent and fuzzy object-oriented database system. In order to answer user queries efficiently, a multidimensional indexing mechanism that combines the extracted high-level semantic information with the low-level video features is developed. The proposed multimedia information system is implemented as a prototype and its performance is evaluated using news video datasets for answering content and concept-based queries considering all these modalities and their fused data. The performance results show that the developed multimedia information system is robust and scalable for large scale multimedia applications.

A framework of query expansion for image retrieval based on knowledge base and concept similarity

We study several semantic concept-based query expansion and re-ranking scheme and compare different ontology-based expansion methods in image search and retrieval. To improve the query expansion efficiency and accuracy, we employ the CYC knowledge base to generate the expansion candidate concepts, while filter and rank the expansion results by calculating concept similarities using the Semantic Relatedness Metrics. Using our knowledge-based query expansion in image retrieval, the efficiency and accuracy has been improved.

A Framework for Ontology Development of Information and Communication Technology Experts Using Thesaurus, Association for Computing Machinery Taxonomy and Domain Experts Approaches

A search for experts is a search for individuals with skills and knowledge in a particular field. Early studies studied profiling of experts in the expert search application by using keyword query. The use of concept-based query that we highlight is able to match experts and expertise to the searched field. Existing expert search systems perform search merely based on the experts’ publication information. There are no relations to other information that can act as support resources to relate them to related fields. The technique to search for expert based on publication information narrows the search scope. There are other aspects that are related to the experts such as education, teachings, supervisions, consultations, conferences, community services and professional affiliations. The use of ontology that is based on concept can also help to expand the search results as well as to link to other relations. The purpose of this research is to design a framework for ontology development of Information, Communication and Technology (ICT) experts, to be used in the development of ICT expert directory. This framework is developed based on the critical evaluation of the literatures focusing on the following areas: experts search systems, experts profiling and ontology in the experts search system. The development of the framework involves extraction processes from web resources, for example, resume, individual websites, organization websites and research centers websites, to build a glossary of word index that characterizes experts. This glossary index becomes the basis for ontology development. In this research, the ontology development integrates the use of thesaurus, Association of Computing Machinery (ACM) taxonomy and ICT domain experts. The ontology that had enriched the glossary index provides the semantics ability in the ICT expert directory. There are three components in the framework; they are extraction of data, development of ontology, and validation of ICT expert ontology by domain experts. The development of this framework can guide the development of concept-based ontology that is relevant and accurate to user’s needs.

Concept Tree Based Information Retrieval Model

This paper proposes a novel concept-based query expansion technique named Markov concept tree model (MCTM), discovering term relationship through the concept tree deduced by term markov network. We address two important issues for query expansion: the selection and the weighting of expansion search terms. In contrast to earlier methods, queries are expanded by adding those terms that are most similar to the concept of the query, rather than selecting terms that are similar to a signal query terms. Utilizing Markov network which is constructed according to the co-occurrence information of the terms in collection, it generate concept tree for each original query term, remove the redundant and irrelevant nodes in concept tree, then adjust the weight of original query and the weight of expansion term based on a pruning algorithm. We use this model for query expansion and evaluate the effectiveness of the model by examining the accuracy and robustness of the expansion methods, Compared with the baseline model, the experiments on standard dataset reveal that this method can achieve a better query quality

Time-Aware Latent Concept Expansion for Microblog Search

Incorporating the temporal property of words into query expansion methods based on relevance feedback has been shown to have a significant positive effect on microblog search.In contrast to such word-based query expansion methods, we propose a concept-based query expansion method based on a temporal relevance model that uses the temporal variation of concepts (e.g., terms and phrases) on microblogs. Our model naturally extends an extremely effective existing concept-based relevance model by tracking the concept frequency over time.Moreover, the proposed model produces important concepts that are frequently used within a particular time periodassociated with a given topic, which better discriminate between relevant and non-relevant microblog documents than words.Our experiments using a corpus of microblog data (Tweets2011 corpus) show that the proposed concept-based query expansion method improves search performance significantly, especially for highly relevant documents.

Adaptive diversification for tag-based social image retrieval

With the great popularity of social photos sharing websites, a tremendous volume of digital images is hosted together with their associated tags. Thus, extensive research efforts have been dedicated to tag-based social image search which enables users to formulate their queries using tags. However, tag queries are often ambiguous and typically short. Diversifying search results is a common solution in the absence of further knowledge about the user’s intention. Such approach aims to retrieve relevant images covering as much of the diverse meanings the query may have. However, not all queries are uniformly ambiguous and hence different diversification strategies might be suggested. In such a context, two new processes are jointly investigated at query pre-processing and post-processing levels. On the one hand, we propose a multi-view concept-based query expansion process, using a predefined list of semantic concepts, which aims to weight concepts from different views or contexts, aggregate the obtained weights and select the most representative ones using a dynamic threshold. On the other hand, we propose a new ranking process called “adaptive diverse relevance ranking” which automatically predicts an effective trade-off between relevance scores and diversity scores according to the query ambiguity level. Thorough experiments using 12 ambiguous queries over the NUS-WIDE dataset show the effectiveness of our approach versus classical uniform diversification approaches.

Web Search using Improved Concept Based Query Refinement

The information extracted from Web pages can be used for effective query expansion. The aspect needed to improve accuracy of web search engines is the inclusion of metadata, not only to analyze Web content, but also to interpret. With the Web of today being unstructured and semantically heterogeneous, keyword-based queries are likely to miss important results. . Using data mining methods, our system derives dependency rules and applies them to concept-based queries. This paper presents a novel approach for query expansion that applies dependence rules mined from a large Web World, combining several existing techniques for data extraction and mining, to integrate the system into COMPACT, our prototype implementation of a concept-based search engine.

Improved concept-based query expansion using Wikipedia

The query formulation has always been a challenge for the users. In this paper, we propose a novel interactive query expansion methodology that identifies and presents the potential directions generalised concepts for the given query enabling the user to explore the interested topic further. The methodology proposed is concept-based direction CoD finder which relies on the external knowledge repository for finding the directions. Wikipedia, the most important non-profit crowdsourcing project, is considered as the external knowledge repository for CoD finder methodology. CoD finder identifies the concepts for the given query and derives the generalised direction for each of the concepts, based on the content of the Wikipedia article and the categories it belongs to. The CoD finder methodology has been evaluated in the crowdsourcing marketplace - Amazon's Mechanical Turk - for measuring the quality of the identified potential directions. The evaluation result shows that the potential directions identified by the CoD finder methodology produces better precision and recall for the given queries.

Biotea: RDFizing PubMed Central in support for the paper as an interface to the Web of Data.

BackgroundThe World Wide Web has become a dissemination platform for scientific and non-scientific publications. However, most of the information remains locked up in discrete documents that are not always interconnected or machine-readable. The connectivity tissue provided by RDF technology has not yet been widely used to support the generation of self-describing, machine-readable documents.ResultsIn this paper, we present our approach to the generation of self-describing machine-readable scholarly documents. We understand the scientific document as an entry point and interface to the Web of Data. We have semantically processed the full-text, open-access subset of PubMed Central. Our RDF model and resulting dataset make extensive use of existing ontologies and semantic enrichment services. We expose our model, services, prototype, and datasets at http://biotea.idiginfo.org/ConclusionsThe semantic processing of biomedical literature presented in this paper embeds documents within the Web of Data and facilitates the execution of concept-based queries against the entire digital library. Our approach delivers a flexible and adaptable set of tools for metadata enrichment and semantic processing of biomedical documents. Our model delivers a semantically rich and highly interconnected dataset with self-describing content so that software can make effective use of it.

Leveraging visual concepts and query performance prediction for semantic-theme-based video retrieval

In this paper, we present a novel approach that utilizes noisy shot-level visual concept detection to improve text-based video retrieval. As opposed to most of the related work in the field, we consider entire videos as the retrieval units and focus on queries that address a general subject matter (semantic theme) of a video. Retrieval is performed using a coherence-based query performance prediction framework. In this framework, we make use of video representations derived from the visual concepts detected in videos to select the best possible search result given the query, video collection, available search mechanisms and the resources for query modification. In addition to investigating the potential of this approach to outperform typical text-based video retrieval baselines, we also explore the possibility to achieve further improvement in retrieval performance through combining our concept-based query performance indicators with the indicators utilizing the spoken content of the videos. The proposed retrieval approach is data driven, requires no prior training and relies exclusively on the analyses of the video collection and different results lists returned for the given query text. The experiments are performed on the MediaEval 2010 datasets and demonstrate the effectiveness of our approach.

Balancing the Trade-Offs Between Diversity and Precision for Web Image Search Using Concept-Based Query Expansion

Even though Web image search queries are often ambiguous, traditional search engines retrieve and present results solely based on relevance ranking, where only the most common and popular interpretations of the query are considered. Rather than assuming that all users are interested in the most common meaning of the query, a more sensible approach may be to produce a diversified set of images that cover the various aspects of the query, under the expectation that at least one of these interpretations will match the searcher’s needs. However, such a promotion of diversity in the search results has the side-effect of decreasing the precision of the most common sense. In this paper, we evaluate these competing factors in the context of a method for explicitly diversifying image search results via concept-based query expansion using Wikipedia. Experiments with controlling the degree of diversification illustrate this trade-off between diversity and precision for both ambiguous and more specific queries. As a result of these experiments, an automatic method for tuning the diversification parameter is proposed based on the degree of ambiguity of the original query.

Federated ontology-based queries over cancer data

BackgroundPersonalised medicine provides patients with treatments that are specific to their genetic profiles. It requires efficient data sharing of disparate data types across a variety of scientific disciplines, such as molecular biology, pathology, radiology and clinical practice. Personalised medicine aims to offer the safest and most effective therapeutic strategy based on the gene variations of each subject. In particular, this is valid in oncology, where knowledge about genetic mutations has already led to new therapies. Current molecular biology techniques (microarrays, proteomics, epigenetic technology and improved DNA sequencing technology) enable better characterisation of cancer tumours. The vast amounts of data, however, coupled with the use of different terms - or semantic heterogeneity - in each discipline makes the retrieval and integration of information difficult.ResultsExisting software infrastructures for data-sharing in the cancer domain, such as caGrid, support access to distributed information. caGrid follows a service-oriented model-driven architecture. Each data source in caGrid is associated with metadata at increasing levels of abstraction, including syntactic, structural, reference and domain metadata. The domain metadata consists of ontology-based annotations associated with the structural information of each data source. However, caGrid's current querying functionality is given at the structural metadata level, without capitalising on the ontology-based annotations. This paper presents the design of and theoretical foundations for distributed ontology-based queries over cancer research data. Concept-based queries are reformulated to the target query language, where join conditions between multiple data sources are found by exploiting the semantic annotations. The system has been implemented, as a proof of concept, over the caGrid infrastructure. The approach is applicable to other model-driven architectures. A graphical user interface has been developed, supporting ontology-based queries over caGrid data sources. An extensive evaluation of the query reformulation technique is included.ConclusionsTo support personalised medicine in oncology, it is crucial to retrieve and integrate molecular, pathology, radiology and clinical data in an efficient manner. The semantic heterogeneity of the data makes this a challenging task. Ontologies provide a formal framework to support querying and integration. This paper provides an ontology-based solution for querying distributed databases over service-oriented, model-driven infrastructures.

Concept-based query language approach to enterprise information systems

In enterprise information systems (EISs) it is necessary to model, integrate and compute very diverse data. In advanced EISs the stored data often are based both on structured (e.g. relational) and semi-structured (e.g. XML) data models. In addition, the ad hoc information needs of end-users may require the manipulation of data-oriented (structural), behavioural and deductive aspects of data. Contemporary languages capable of treating this kind of diversity suit only persons with good programming skills. In this paper we present a concept-oriented query language approach to manipulate this diversity so that the programming skill requirements are considerably reduced. In our query language, the features which need technical knowledge are hidden in application-specific concepts and structures. Therefore, users need not be aware of the underlying technology. Application-specific concepts and structures are represented by the modelling primitives of the extended RDOOM (relational deductive object-oriented modelling) which contains primitives for all crucial real world relationships (is-a relationship, part-of relationship, association), XML documents and views. Our query language also supports intensional and extensional–intensional queries, in addition to conventional extensional queries. In its query formulation, the end-user combines available application-specific concepts and structures through shared variables.

Proof of concept

In this thesis we investigate the possibility to integrate domain-specific knowledge into biomedical information retrieval (IR). Recent decades have shown a fast growing interest in biomedical research, reflected by an exponential growth in scientific literature. An important problem for biomedical IR is dealing with the complex and inconsistent terminology encountered in biomedical publications. Dealing with the terminology problem requires domain knowledge stored in terminological resources: controlled indexing vocabularies and thesauri. The integration of this knowledge is, however, far from trivial. The first research theme investigates heuristics for obtaining word-based representations from biomedical text for robust retrieval. We investigated the effect of choices in document preprocessing heuristics on retrieval effectiveness. Document preprocessing heuristics such as stop word removal, stemming, and breakpoint identification and normalization were shown to strongly affect retrieval performance. An effective combination of heuristics was identified to obtain a word-based representation from text for the remainder of this thesis. The second research theme deals with concept-based retrieval. We compared a word-based to a concept-based representation and determined to what extent a manual concept-based representation can be automatically obtained from text. Retrieval based on only concepts was demonstrated to be significantly less effective than word-based retrieval. This deteriorated performance could be explained by errors in the classification process, limitations of the concept vocabularies and limited exhaustiveness of the concept-based document representations. Retrieval based on a combination of word-based and automatically obtained concept-based query representations did significantly improve word-only retrieval. In the third and last research theme we propose a cross-lingual framework for monolingual biomedical IR. In this framework, the integration of a concept-based representation is viewed as a cross-lingual matching problem involving a word-based and concept-based representation language. This framework gives us the opportunity to adopt a large set of established crosslingual information retrieval methods and techniques for this domain. Experiments with basic term-to-term translation models demonstrate that this approach can significantly improve word-based retrieval. Directions for future work are using these concepts for communication between user and retrieval system, extending upon the translation models and extending CLIR-enhanced concept-based retrieval outside the biomedical domain. Available online from http://purl.utwente.nl/publications/72481.

Concept-based query expansion for retrieving gene related publications from MEDLINE

BackgroundAdvances in biotechnology and in high-throughput methods for gene analysis have contributed to an exponential increase in the number of scientific publications in these fields of study. While much of the data and results described in these articles are entered and annotated in the various existing biomedical databases, the scientific literature is still the major source of information. There is, therefore, a growing need for text mining and information retrieval tools to help researchers find the relevant articles for their study. To tackle this, several tools have been proposed to provide alternative solutions for specific user requests.ResultsThis paper presents QuExT, a new PubMed-based document retrieval and prioritization tool that, from a given list of genes, searches for the most relevant results from the literature. QuExT follows a concept-oriented query expansion methodology to find documents containing concepts related to the genes in the user input, such as protein and pathway names. The retrieved documents are ranked according to user-definable weights assigned to each concept class. By changing these weights, users can modify the ranking of the results in order to focus on documents dealing with a specific concept. The method's performance was evaluated using data from the 2004 TREC genomics track, producing a mean average precision of 0.425, with an average of 4.8 and 31.3 relevant documents within the top 10 and 100 retrieved abstracts, respectively.ConclusionsQuExT implements a concept-based query expansion scheme that leverages gene-related information available on a variety of biological resources. The main advantage of the system is to give the user control over the ranking of the results by means of a simple weighting scheme. Using this approach, researchers can effortlessly explore the literature regarding a group of genes and focus on the different aspects relating to these genes.

Issues in the Design of a Pilot Concept-Based Query Interface for the Neuroinformatics Information Framework

This paper describes a pilot query interface that has been constructed to help us explore a "concept-based" approach for searching the Neuroscience Information Framework (NIF). The query interface is concept-based in the sense that the search terms submitted through the interface are selected from a standardized vocabulary of terms (concepts) that are structured in the form of an ontology. The NIF contains three primary resources: the NIF Resource Registry, the NIF Document Archive, and the NIF Database Mediator. These NIF resources are very different in their nature and therefore pose challenges when designing a single interface from which searches can be automatically launched against all three resources simultaneously. The paper first discusses briefly several background issues involving the use of standardized biomedical vocabularies in biomedical information retrieval, and then presents a detailed example that illustrates how the pilot concept-based query interface operates. The paper concludes by discussing certain lessons learned in the development of the current version of the interface.

The Neuroscience Information Framework: A Data and Knowledge Environment for Neuroscience

With support from the Institutes and Centers forming the NIH Blueprint for Neuroscience Research, we have designed and implemented a new initiative for integrating access to and use of Web-based neuroscience resources: the Neuroscience Information Framework. The Framework arises from the expressed need of the neuroscience community for neuroinformatic tools and resources to aid scientific inquiry, builds upon prior development of neuroinformatics by the Human Brain Project and others, and directly derives from the Society for Neuroscience's Neuroscience Database Gateway. Partnered with the Society, its Neuroinformatics Committee, and volunteer consultant-collaborators, our multi-site consortium has developed: (1) a comprehensive, dynamic, inventory of Web-accessible neuroscience resources, (2) an extended and integrated terminology describing resources and contents, and (3) a framework accepting and aiding concept-based queries. Evolving instantiations of the Framework may be viewed at http://nif.nih.gov , http://neurogateway.org , and other sites as they come on line.

An effective indexing model to manage versioned objects in a digital library

Digital libraries offer diverse information resources in digital format. These resources may include any digital collection of books, journals, papers, proceedings and so on. The major challenge for the library is to provide access to all these resources in an integrated and user-friendly way. It is essential to provide an effective method for the users to enable them to make concept-based queries on these resources. In order to help the information users, we propose an index-based model, ATVLIB. This model has a search engine that uses data structures such as signature pattern, hash table and B+ trees. Extensive study shows that the proposed model has better performance than the existing method.

An intelligent approach to handling imperfect information in concept-based natural language queries

Missing information, imprecision, inconsistency, vagueness, uncertainty, and ignorance abound in information systems. Such imperfection is a fact of life in database systems. Although these problems are widely studied in relational database systems, this is not the case in conceptual query systems. And yet, concept-based query languages have been proposed and some are already commercial products. It is therefore imperative to study these problems in concept-based query languages, with a view to prescribing formal approaches to dealing with the problems. In this article, we have done just that for a concept-based natural language query system that we developed. A methodology for handling and resolving each type of imperfection is developed. The proposed approaches are automated as much as possible, with the user mainly serving an assistive function.