Showing papers on "Ontology-based data integration published in 2016"

The Cell Ontology 2016: enhanced content, modularization, and ontology interoperability

Abstract: The Cell Ontology (CL) is an OBO Foundry candidate ontology covering the domain of canonical, natural biological cell types. Since its inception in 2005, the CL has undergone multiple rounds of revision and expansion, most notably in its representation of hematopoietic cells. For in vivo cells, the CL focuses on vertebrates but provides general classes that can be used for other metazoans, which can be subtyped in species-specific ontologies. Recent work on the CL has focused on extending the representation of various cell types, and developing new modules in the CL itself, and in related ontologies in coordination with the CL. For example, the Kidney and Urinary Pathway Ontology was used as a template to populate the CL with additional cell types. In addition, subtypes of the class ‘cell in vitro’ have received improved definitions and labels to provide for modularity with the representation of cells in the Cell Line Ontology and Reagent Ontology. Recent changes in the ontology development methodology for CL include a switch from OBO to OWL for the primary encoding of the ontology, and an increasing reliance on logical definitions for improved reasoning. The CL is now mandated as a metadata standard for large functional genomics and transcriptomics projects, and is used extensively for annotation, querying, and analyses of cell type specific data in sequencing consortia such as FANTOM5 and ENCODE, as well as for the NIAID ImmPort database and the Cell Image Library. The CL is also a vital component used in the modular construction of other biomedical ontologies—for example, the Gene Ontology and the cross-species anatomy ontology, Uberon, use CL to support the consistent representation of cell types across different levels of anatomical granularity, such as tissues and organs. The ongoing improvements to the CL make it a valuable resource to both the OBO Foundry community and the wider scientific community, and we continue to experience increased interest in the CL both among developers and within the user community.

The environment ontology in 2016: bridging domains with increased scope, semantic density, and interoperation

Abstract: The Environment Ontology (ENVO; http://www.environmentontology.org/ ), first described in 2013, is a resource and research target for the semantically controlled description of environmental entities. The ontology's initial aim was the representation of the biomes, environmental features, and environmental materials pertinent to genomic and microbiome-related investigations. However, the need for environmental semantics is common to a multitude of fields, and ENVO's use has steadily grown since its initial description. We have thus expanded, enhanced, and generalised the ontology to support its increasingly diverse applications. We have updated our development suite to promote expressivity, consistency, and speed: we now develop ENVO in the Web Ontology Language (OWL) and employ templating methods to accelerate class creation. We have also taken steps to better align ENVO with the Open Biological and Biomedical Ontologies (OBO) Foundry principles and interoperate with existing OBO ontologies. Further, we applied text-mining approaches to extract habitat information from the Encyclopedia of Life and automatically create experimental habitat classes within ENVO. Relative to its state in 2013, ENVO's content, scope, and implementation have been enhanced and much of its existing content revised for improved semantic representation. ENVO now offers representations of habitats, environmental processes, anthropogenic environments, and entities relevant to environmental health initiatives and the global Sustainable Development Agenda for 2030. Several branches of ENVO have been used to incubate and seed new ontologies in previously unrepresented domains such as food and agronomy. The current release version of the ontology, in OWL format, is available at http://purl.obolibrary.org/obo/envo.owl . ENVO has been shaped into an ontology which bridges multiple domains including biomedicine, natural and anthropogenic ecology, ‘omics, and socioeconomic development. Through continued interactions with our users and partners, particularly those performing data archiving and sythesis, we anticipate that ENVO’s growth will accelerate in 2017. As always, we invite further contributions and collaboration to advance the semantic representation of the environment, ranging from geographic features and environmental materials, across habitats and ecosystems, to everyday objects in household settings.

Unified IoT ontology to enable interoperability and federation of testbeds

Abstract: After a thorough analysis of existing Internet of Things (IoT) related ontologies, in this paper we propose a solution that aims to achieve semantic interoperability among heterogeneous testbeds. Our model is framed within the EU H2020's FIESTA-IoT project, that aims to seamlessly support the federation of testbeds through the usage of semantic-based technologies. Our proposed model (ontology) takes inspiration from the well-known Noy et al. methodology for reusing and interconnecting existing ontologies. To build the ontology, we leverage a number of core concepts from various mainstream ontologies and taxonomies, such as Semantic Sensor Network (SSN), M3-lite (a lite version of M3 and also an outcome of this study), WGS84, IoT-lite, Time, and DUL. In addition, we also introduce a set of tools that aims to help external testbeds adapt their respective datasets to the developed ontology.

Gene Ontology semantic similarity tools: survey on features and challenges for biological knowledge discovery

Abstract: Gene Ontology (GO) semantic similarity tools enable retrieval of semantic similarity scores, which incorporate biological knowledge embedded in the GO structure for comparing or classifying different proteins or list of proteins based on their GO annotations. This facilitates a better understanding of biological phenomena underlying the corresponding experiment and enables the identification of processes pertinent to different biological conditions. Currently, about 14 tools are available, which may play an important role in improving protein analyses at the functional level using different GO semantic similarity measures. Here we survey these tools to provide a comprehensive view of the challenges and advances made in this area to avoid redundant effort in developing features that already exist, or implementing ideas already proven to be obsolete in the context of GO. This helps researchers, tool developers, as well as end users, understand the underlying semantic similarity measures implemented through knowledge of pertinent features of, and issues related to, a particular tool. This should empower users to make appropriate choices for their biological applications and ensure effective knowledge discovery based on GO annotations.

Ontology Engineering with Ontology Design Patterns: Foundations and Applications

Abstract: The use of ontologies for data and knowledge organization has become ubiquitousin many dataintensive and knowledgedriven application areas, in science, industry,and the humanities. At the same time, ontology engineering best practices continueto evolve. In particular, modular ontology modeling based on ontology designpatterns is establishing itself as an approach for creating versatile and extendableontologies for data management and integration. This book is the very first comprehensive treatment of Ontology Engineering withOntology Design Patterns. It contains both advanced and introductory materialaccessible for readers with only a minimal background in ontology modeling. Someintroductory material is written in the style of tutorials, and specific chapters aredevoted to examples and to applications. Other chapters convey the state of the artin research regarding ontology design patterns. The editors and the contributing authors include the leading contributors to thedevelopment of ontologydesignpatterndriven ontology engineering.

Research on heterogeneous data integration model of group enterprise based on cluster computing

Abstract: Cluster, consisting of a group of computers, is to act as a whole system to provide users with computer resources. Each computer is a node of this cluster. Cluster computer refers to a system consisting of a complete set of computers connected to each other. With the rapid development of computer technology, cluster computing technique with high performance---cost ratio has been widely applied in distributed parallel computing. For the large-scale close data in group enterprise, a heterogeneous data integration model was built under cluster environment based on cluster computing, XML technology and ontology theory. Such model could provide users unified and transparent access interfaces. Based on cluster computing, the work has solved the heterogeneous data integration problems by means of Ontology and XML technology. Furthermore, good application effect has been achieved compared with traditional data integration model. Furthermore, it was proved that this model improved the computing capacity of system, with high performance---cost ratio. Thus, it is hoped to provide support for decision-making of enterprise managers.

Overview and Analysis of Ontology Studies Supporting Development of the Construction Industry

Abstract: Being information-intensive, the construction industry has the feature of multiagents, including multiparticipants from different disciplines, multiprocesses with a long-span timeline, and multidocuments generated by various systems. The multistakeholder context of the construction industry creates problems such as poor information interoperability and low productivity arising from difficulties in information reuse. Many researchers have explored the use of ontology to address these issues. This study aims to review ontology research to explore its trends, gaps, and opportunities in the construction industry. A systematic process employing three-phase search method, objective analysis and subjective analysis, helps to provide enough potential articles related to construction ontology research, and to reduce arbitrariness and subjectivity involved in research topic analysis. As a result, three main research topics aligned with the ontology development lifecycle were derived as follows: information ...

Smart Data Integration by Goal Driven Ontology Learning

Abstract: The smart data integration approach is proposed to compose data and knowledge of the different nature, origin, formats and standards. This approach is based on the selective goal driven ontology learning. The automated planning paradigm in a combination with a value of the perfect information approach is proposed to be used for evaluating the knowledge correspondence with the learning goal for the data integration domain. The information model of a document is represented as a supplement to the Partially Observable Markov Decision Process (POMDP) strategy of a domain. It helps to estimate the document a pertinence as the increment of the strategy expected utility. A statistical method for identifying the semantic relations in the natural language texts for their linguistic characteristics is developed. It helps to extract the Ontology Web Language (OWL) predicates from the natural language text using data about sub semantic links. A set of methods and means based on ontology learning was developed to support the smart data integration process. A technology uses the Natural Language Processing software Link Grammar Parser, WordNet Application Programming Interface (API) as well as the OWL API.

An Approach to Ontology Integration for Ontology Reuse

Abstract: In the last years, the large availability of data and schema models formalized through different languages has demanded effective and efficient methodologies to reuse such models. One of the most challenging problem consists in integrating different models in a global conceptualization of a specific knowledge or application domain. This is a hard task to accomplish due to ambiguities, inconsistencies and heterogeneities, at different levels, that could stand in the way. The ability to effectively and efficiently perform knowledge reuse is a crucial factor in knowledge management systems, and it also represents a potential solution to the problem of standardization of information and a viaticum towards the realization of the Semantic web. In this paper, an approach to ontology reuse based on heterogeneous matching techniques will be presented, in particular, we will show how the process of ontology construction will be improved and simplified, by automatizing the selection and the reuse of existing data models. The proposed approach will be applied to the food domain, specifically to the food production.

Frame-Based Ontology Population with PIKES

Abstract: We present an approach for ontology population from natural language English texts that extracts RDF triples according to FrameBase, a Semantic Web ontology derived from FrameNet. Processing is decoupled in two independently-tunable phases. First, text is processed by several NLP tasks, including Semantic Role Labeling (SRL), whose results are integrated in an RDF graph of mentions , i.e., snippets of text denoting some entity/fact. Then, the mention graph is processed with SPARQL-like rules using a specifically created mapping resource from NomBank/PropBank/FrameNet annotations to FrameBase concepts, producing a knowledge graph whose content is linked to DBpedia and organized around semantic frames , i.e., prototypical descriptions of events and situations. A single RDF/OWL representation is used where each triple is related to the mentions/tools it comes from. We implemented the approach in PIKES, an open source tool that combines two complementary SRL systems and provides a working online demo. We evaluated PIKES on a manually annotated gold standard, assessing precision/recall in (i) populating FrameBase ontology, and (ii) extracting semantic frames modeled after standard predicate models, for comparison with state-of-the-art tools for the Semantic Web. We also evaluated (iii) sampled precision and execution times on a large corpus of 110 K Wikipedia-like pages.

SEON: A Software Engineering Ontology Network

Abstract: Software Engineering SE is a wide domain, where ontologies are useful instruments for dealing with Knowledge Management KM related problems When SE ontologies are built and used in isolation, some problems remain, in particular those related to knowledge integration The goal of this paper is to provide an integrated solution for better dealing with KM-related problems in SE by means of a Software Engineering Ontology Network SEON SEON is designed with mechanisms for easing the development and integration of SE domain ontologies The current version of SEON includes core ontologies for software and software processes, as well as domain ontologies for the main technical software engineering subdomains, namely requirements, design, coding and testing We discuss the development of SEON and some of its envisioned applications related to KM

OAM: An Ontology Application Management Framework for Simplifying Ontology-Based Semantic Web Application Development

Abstract: Although the Semantic Web data standards are established, ontology-based applications built on the standards are relatively limited. This is partly due to high learning curve and efforts demanded in building ontology-based Semantic Web applications. In this paper, we describe an ontology application management (OAM) framework that aims to simplify creation and adoption of ontology-based application that is based on the Semantic Web technology. OAM introduces an intermediate layer between user application and programming and development environment in order to support ontology-based data publishing and access, abstraction and interoperability. The framework focuses on providing reusable and configurable data and application templates, which allow the users to create the applications without programming skill required. Three forms of templates are introduced: database to ontology mapping configuration, recommendation rule and application templates. We describe two case studies that adopted the framework: activity recognition in smart home domain and thalassemia clinical support system, and how the framework was used in simplifying development in both projects. In addition, we provide some performance evaluation results to show that, by limiting expressiveness of the rule language, a specialized form of recommendation processor can be developed for more efficient performance. Some advantages and limitations of the application framework in ontology-based applications are also discussed.

A multi-agent system to support evidence based medicine and clinical decision making via data sharing and data privacy

Abstract: Evidence based medicine is the modern standard for clinical decision making where the use of medical evidence combined with clinical expertise and research is leveraged for clinical decisions. Supporting evidence based medicine (EBM) and clinical decision making (CDM) requires access to accurate predictive models and a multi-dimensional patient view that is aggregated from multiple sources in a multitude of configurations. Data sharing in healthcare remains a challenge due to widespread privacy concerns. Despite abundant research in privacy preserving data mining, healthcare organizations are unwilling to release their medical data on account of the Health Insurance Portability and Accountability Act (HIPAA) requires protecting the confidentiality and security of healthcare data. Further, sensitive data spanning multiple organizations result in not only the data syntax and semantic heterogeneity but also diverse privacy requirements, posing additional challenges to data sharing and integration. In overcoming these challenges, a multi-agent approach is a viable alternative. Despite its potential for addressing the aforementioned issues, little research has been conducted in integrating a multi-agent architecture with privacy preserving data mining in big healthcare data spanning multiple organizations. This research proposes a multi-agent architecture coupled with privacy preserving techniques to facilitate data sharing while preserving privacy. Results indicate that our design artifact is capable of overcoming the aforementioned challenges, thereby facilitating data sharing for knowledge discovery in healthcare and supporting evidence based medicine and clinical decision making via improving predictive models. The paper presents a design artifact, MAPP4MD, to facilitate data integration and sharing while preserving privacy in healthcare.MAPP4MD is a multi-agent system for healthcare data integration and privacy protection.Data integration is ontology, lexical, and instance basedPrivacy method selection is based on ontology and semantic web rule languageEvaluation is based on the k-Anonymity standard

Ontology algorithm using singular value decomposition and applied in multidisciplinary

Abstract: As a popular data management and computation tool, ontology is widely used in material science, biological genetics, chemistry, biology and pharmaceuticals. It can be regarded as a dictionary or a database, and the key of various ontology applications is similarity measuring between concepts in ontology. In this paper, we propose a new ontology learning algorithm for ontology similarity measuring and ontology mapping by means of singular value decomposition method and deterministic sampling iteration. Then, the new ontology learning is applied in plant science, gene biology, bionics and physics ontologies. The data results show the high efficiency of our singular value decomposition based ontology learning algorithm for ontology similarity measuring and ontology mapping.

An Ontology to Semantically Declare and Describe Functions

Abstract: Applications built on top of the Semantic Web are emerging as a novel solution in different areas, such as decision making and route planning. However, to connect results of these solutions – i.e., the semantically annotated data – with real-world applications, this semantic data needs to be connected to actionable events. A lot of work has been done (both semantically as non-semantically) to describe and define Web services, but there is still a gap on a more abstract level, i.e., describing interfaces independent of the technology used. In this paper, we present a data model, specification, and ontology to semantically declare and describe functions independently of the used technology. This way, we can declare and use actionable events in semantic applications, without restricting ourselves to programming language-dependent implementations. The ontology allows for extensions, and is proposed as a possible solution for semantic applications in various domains.

Data integration for research and innovation policy: an Ontology-Based Data Management approach

Abstract: This paper proposes an Ontology-Based Data Management (OBDM) approach to coordinate, integrate and maintain the data needed for Science, Technology and Innovation (STI) policy development. The OBDM approach is a form of integration of information in which the global schema of data is substituted by the conceptual model of the domain, formally specified through an ontology. Implemented in Sapientia, the ontology of multi-dimensional research assessment, it offers a transparent platform as the base for the assessment process; it enables one to define and specify in an unambiguous way the indicators on which the evaluation is based, and to track their evolution over time; also it allows to the analysis of the effects of the actual use of the indicators on the behavior of scholars, and spot opportunistic behaviors; and it provides a monitoring system to track over time the changes in the established evaluation criteria and their consequences for the research system. It is argued that easier access to and a more transparent view of scientific-scholarly outcomes help to improve the understanding of basic science and the communication of research outcomes to the wider public. An OBDM approach could successfully contribute to solve some of the key issues in the integration of heterogeneous data for STI policies.

FOCA: A Methodology for Ontology Evaluation.

Abstract: Modeling an ontology is a hard and time-consuming task. Although methodologies are useful for ontologists to create good ontologies, they do not help with the task of evaluating the quality of the ontology to be reused. For these reasons, it is imperative to evaluate the quality of the ontology after constructing it or before reusing it. Few studies usually present only a set of criteria and questions, but no guidelines to evaluate the ontology. The effort to evaluate an ontology is very high as there is a huge dependence on the evaluator's expertise to understand the criteria and questions in depth. Moreover, the evaluation is still very subjective. This study presents a novel methodology for ontology evaluation, taking into account three fundamental principles: i) it is based on the Goal, Question, Metric approach for empirical evaluation; ii) the goals of the methodologies are based on the roles of knowledge representations combined with specific evaluation criteria; iii) each ontology is evaluated according to the type of ontology. The methodology was empirically evaluated using different ontologists and ontologies of the same domain. The main contributions of this study are: i) defining a step-by-step approach to evaluate the quality of an ontology; ii) proposing an evaluation based on the roles of knowledge representations; iii) the explicit difference of the evaluation according to the type of the ontology iii) a questionnaire to evaluate the ontologies; iv) a statistical model that automatically calculates the quality of the ontologies.

Extracting Cross-Ontology Weighted Association Rules from Gene Ontology Annotations

Abstract: Gene Ontology (GO) is a structured repository of concepts (GO Terms) that are associated to one or more gene products through a process referred to as annotation. The analysis of annotated data is an important opportunity for bioinformatics. There are different approaches of analysis, among those, the use of association rules (AR) which provides useful knowledge, discovering biologically relevant associations between terms of GO, not previously known. In a previous work, we introduced GO-WAR (Gene Ontology-based Weighted Association Rules), a methodology for extracting weighted association rules from ontology-based annotated datasets. We here adapt the GO-WAR algorithm to mine cross-ontology association rules, i.e., rules that involve GO terms present in the three sub-ontologies of GO. We conduct a deep performance evaluation of GO-WAR by mining publicly available GO annotated datasets, showing how GO-WAR outperforms current state of the art approaches.

FCA based ontology development for data integration

Abstract: A formal and semi-automated method is proposed to support ontology integration.The method is designed to deal with data exhibiting implicit and ambiguous information.Case studies have been carried out on several non-trivial industrial datasets.Resultant ontologies better fit and respect underlying knowledge structure of the domain. Data is a valuable asset to our society. Effective use of data can enhance productivity of business and create economic benefit to customers. However with data growing at unprecedented rates, organisations are struggling to take full advantage of available data. One main reason for this is that data is usually originated from disparate sources. This can result in data heterogeneity, and prevent data from being digested easily. Among other techniques developed, ontology based approaches is one promising method for overcoming heterogeneity and improving data interoperability. This paper contributes a formal and semi-automated approach for ontology development based on Formal Concept Analysis (FCA), with the aim to integrate data that exhibits implicit and ambiguous information. A case study has been carried out on several non-trivial industrial datasets, and our experimental results demonstrate that proposed method offers an effective mechanism that enables organisations to interrogate and curate heterogeneous data, and to create the knowledge that meets the need of business. Display Omitted

An ontology-based data integration approach for web analytics in e-commerce

Abstract: A semantic approach to represent and consolidate web analytic data is proposed.An OWL ontology for web analytics in e-commerce is designed and proposed.The proposed approach is validated with tracking data of 15 real-world e-shops.Obtained semantized data successfully train advanced data mining algorithms.We provide actual e-shops with tools to enhance their commercial activities. Web analytics has emerged as one of the most important activities in e-commerce, since it allows companies and e-merchants to track the behavior of customers when visiting their web sites. There exist a series of tools for web analytics that are used not only for tracking and measuring web traffic, but also for analyzing the commercial activity. However, most of these tools focus on low level web attributes and metrics, making other sophisticated functionalities and analyses only available for commercial (non-free) versions.In this context, the SME-Ecompass European initiative aims at providing e-commerce SMEs with accessible tools for high level web analytics. These software facilities should use different sources of data coming from digital footprints allocated in e-shops, to fuse them together in a coherent way, and to make them available for advanced data mining procedures. This motivated us to propose in this work an ontology-based approach to collect, integrate and store web analytics data, from many sources of popular and commercial digital footprints. As article's main impact, we obtain enriched and semantically annotated data that is used to properly train an intelligent system, involving data mining procedures, for the analysis of customer behavior in real e-commerce sites. In concrete, for the validation of our semantic approach, we have captured and integrated data from Google Analytics and Piwik digital footprints allocated in 15 e-shops of different commercial sectors and countries (UK, Spain, Greece and Germany), throughout several months of activity. The obtained results show different perspectives in customer's behavior analysis that go one step beyond the most popular web analytics tools in the current market.

LOTED2: An ontology of European public procurement notices

Abstract: This paper describes the construction of the LOTED2 ontology for the representation of European public procurement notices. LOTED2 follows initiatives around the creation of linked data-compliant representations of information regarding tender notices in Europe, but focusing on placing such representations within their legal context. It is therefore considered a legal ontology, as it supports the identification of legal concepts and more generally, legal reasoning. Unlike many other legal ontologies however, LOTED2 is designed to support the creation of Semantic Web applications. The methodology applied for building LOTED2 therefore seeks to find a compromise between the accurate representation of legal concepts and the usability of the ontology as a knowledge model for Semantic Web applications, while creating connections to other relevant ontologies in the domain.

OmniSearch: a semantic search system based on the Ontology for MIcroRNA Target (OMIT) for microRNA-target gene interaction data.

Abstract: As a special class of non-coding RNAs (ncRNAs), microRNAs (miRNAs) perform important roles in numerous biological and pathological processes. The realization of miRNA functions depends largely on how miRNAs regulate specific target genes. It is therefore critical to identify, analyze, and cross-reference miRNA-target interactions to better explore and delineate miRNA functions. Semantic technologies can help in this regard. We previously developed a miRNA domain-specific application ontology, Ontology for MIcroRNA Target (OMIT), whose goal was to serve as a foundation for semantic annotation, data integration, and semantic search in the miRNA field. In this paper we describe our continuing effort to develop the OMIT, and demonstrate its use within a semantic search system, OmniSearch, designed to facilitate knowledge capture of miRNA-target interaction data. Important changes in the current version OMIT are summarized as: (1) following a modularized ontology design (with 2559 terms imported from the NCRO ontology); (2) encoding all 1884 human miRNAs (vs. 300 in previous versions); and (3) setting up a GitHub project site along with an issue tracker for more effective community collaboration on the ontology development. The OMIT ontology is free and open to all users, accessible at: http://purl.obolibrary.org/obo/omit.owl. The OmniSearch system is also free and open to all users, accessible at: http://omnisearch.soc.southalabama.edu/index.php/Software.

The Plant Ontology: A Tool for Plant Genomics.

Abstract: The use of controlled, structured vocabularies (ontologies) has become a critical tool for scientists in the post-genomic era of massive datasets. Adoption and integration of common vocabularies and annotation practices enables cross-species comparative analyses and increases data sharing and reusability. The Plant Ontology (PO; http://www.plantontology.org/ ) describes plant anatomy, morphology, and the stages of plant development, and offers a database of plant genomics annotations associated to the PO terms. The scope of the PO has grown from its original design covering only rice, maize, and Arabidopsis, and now includes terms to describe all green plants from angiosperms to green algae.This chapter introduces how the PO and other related ontologies are constructed and organized, including languages and software used for ontology development, and provides an overview of the key features. Detailed instructions illustrate how to search and browse the PO database and access the associated annotation data. Users are encouraged to provide input on the ontology through the online term request form and contribute datasets for integration in the PO database.

OntoMetrics: Putting Metrics into Use for Ontology Evaluation

Abstract: Automatically calculated metrics are needed in order to evaluate ontology quality. Otherwise, major resources are required in order to manually assess certain Quality Criteria of an ontology. While there is rule based support for the detection modelling errors and the violation of ontology modelling guidelines, there is a lack of support for calculating ontology metrics. However, metrics can serve as indicators for possible quality problems that are not covered rulebased ontology evaluation. Many metrics have been proposed that correlate for example with ontology characteristics like Readability, Adaptability, and Reusability. However, there is a lack of tool support. OntoMetrics provides free access to metric definition and calculation. Furthermore it fosters the development of knowledge regarding the application of Ontology Metrics. This paper provides theoretical background and usage scenarios for the OntoMetrics on-line platform.

Folksonomy-Based Visual Ontology Construction and Its Applications

Abstract: An ontology hierarchically encodes concepts and concept relationships, and has a variety of applications such as semantic understanding and information retrieval. Previous work for building ontologies has primarily relied on labor-intensive human contributions or focused on text-based extraction. In this paper, we consider the problem of automatically constructing a folksonomy-based visual ontology (FBVO) from the user-generated annotated images. A systematic framework is proposed consisting of three stages as concept discovery, concept relationship extraction, and concept hierarchy construction. The noisy issues of the user-generated tags are carefully addressed to guarantee the quality of derived FBVO. The constructed FBVO finally consists of 139 825 concept nodes and millions of concept relationships by mining more than 2.4 million Flickr images. Experimental evaluations show that the derived FBVO is of high quality and consistent with human perception. We further demonstrate the utility of the derived FBVO in applications of complex visual recognition and exploratory image search.