Showing papers on "Ontology (information science) published in 2015"

Gene Ontology Consortium: going forward

Abstract: The Gene Ontology (GO; http://wwwgeneontologyorg) is a community-based bioinformatics resource that supplies information about gene product function using ontologies to represent biological knowledge Here we describe improvements and expansions to several branches of the ontology, as well as updates that have allowed us to more efficiently disseminate the GO and capture feedback from the research community The Gene Ontology Consortium (GOC) has expanded areas of the ontology such as cilia-related terms, cell-cycle terms and multicellular organism processes We have also implemented new tools for generating ontology terms based on a set of logical rules making use of templates, and we have made efforts to increase our use of logical definitions The GOC has a new and improved web site summarizing new developments and documentation, serving as a portal to GO data Users can perform GO enrichment analysis, and search the GO for terms, annotations to gene products, and associated metadata across multiple species using the all-new AmiGO 2 browser We encourage and welcome the input of the research community in all biological areas in our continued effort to improve the Gene Ontology

The protégé project: a look back and a look forward

Abstract: Few funded research projects in computer science go on for decades. It’s even questionable whether any project should be sustained that long. Nevertheless, the Protege project at Stanford University began in the 1980s and is still going strong, helping developers to construct reusable ontologies and to build knowledge-based systems. The recognition of our work at the International Semantic Web Conference in October 2014 with the “Ten Years” Award was a great honor. The award has provided an opportunity f o r reflection—both on the Protege project itself and on the need for computational infrastructure in the AI community. Protege has become the most widely used software for building and maintaining ontologies. It is by no means the only solution, and there are known problems with Protege, but we appreciate that the system is extremely popular. Although his study is now a bit dated, Cardoso (2007) surveyed the Semantic Web community and found that two-thirds of his respondents used Protege. To date, more than 250,000 people have registered to use the software. Many Fortune 500 companies use Protege to build their ontologies. Important government projects, such as the development of the National Cancer Institute Thesaurus (Noy et al., 2008; Figure 1) and the World Health Organization’s International Classification of Diseases (ICD-11; Tudorache et al., 2010) depend on the software. Figure 1 The Protege 5 Desktop System. The figure shows the most recent version of the Protege system used to edit the National Cancer Institute Thesaurus. In the Figure, the user has selected the class Antigen Gene. The visualization ... Protege currently exists in a variety of frameworks. A desktop system (Protege 5) supports many advanced features to enable the construction and management of OWL ontologies (see Figure 1). A Web-based system (WebProtege) offers distributed access over the Internet using any Web browser and, by design, is much simpler to use for many ontology-engineering tasks (Figure 2). The Web-based version has become extremely popular, and it recently exceeded the desktop-client in its degree of usage. It is extremely handy to be able to point a Web browser to an appropriate server and to begin editing. Like a Google doc, a WebProtege ontology can be easily shared with a distributed group of users who can engage in collaborative authoring activities from wherever they happen to be logged in. The development environment used by the World Health Organization to manage ICD-11 is based on WebProtege (Tudorache et al., 2010). Figure 2 WebProtege. The Web-based version of Protege offers users and their collaborators the opportunity to share and edit ontologies online, much like a Google doc. Here we see the Ontology for Parasite Lifecycle (OPL), an ontology ... Older versions of the Protege desktop system have included support for editing ontologies represented in a frame language (namely, in the OKBC framework developed by the DARPA Knowledge Sharing Initiative in the 1990s; Neches et al., 1991). Comparable functionality has not yet been migrated to current versions of Protege, however. All versions of Protege may be downloaded from the project’s Web site (http://protege.stanford.edu). They are available under an open-source license. The paper for which the Protege team won the “Ten Years” Award (Knublauch et al., 2004) describes the first Protege system to support the World Wide Web Consortium’s recommended Web Ontology Language (OWL). We had been tracking the emerging OWL specification, and Holger Knublauch, then a post-doctoral fellow in our laboratory, worked with the rest of the team to extend Protege to create what was, at the time, the only ontology-development platform that could accommodate nearly the complete OWL specification. Protege’s support for OWL has been enhanced over the years, particularly through a very successful collaboration with Alan Rector’s CO-ODE project at the University of Manchester, and recent versions of Protege fully support the latest OWL 2.0 specification. When people think of Protege, they think of an editor for ontologies, and they think of OWL. Protege did not start out this way, however. Gennari et al. (2003) traced the history of the first 15 years of the Protege project, documenting the many shifts in perspective as Protege progressed from a student project for a Ph.D. dissertation that focused on problems of building knowledge-based systems (Musen, 1989) to a major open-source platform supported by a huge community of diverse users (Musen, 2005).

Building Ontologies with Basic Formal Ontology

Abstract: In the era of "big data," science is increasingly information driven, and the potential for computers to store, manage, and integrate massive amounts of data has given rise to such new disciplinary fields as biomedical informatics. Applied ontology offers a strategy for the organization of scientific information in computer-tractable form, drawing on concepts not only from computer and information science but also from linguistics, logic, and philosophy. This book provides an introduction to the field of applied ontology that is of particular relevance to biomedicine, covering theoretical components of ontologies, best practices for ontology design, and examples of biomedical ontologies in use.After defining an ontology as a representation of the types of entities in a given domain, the book distinguishes between different kinds of ontologies and taxonomies, and shows how applied ontology draws on more traditional ideas from metaphysics. It presents the core features of the Basic Formal Ontology (BFO), now used by over one hundred ontology projects around the world, and offers examples of domain ontologies that utilize BFO. The book also describes Web Ontology Language (OWL), a common framework for Semantic Web technologies. Throughout, the book provides concrete recommendations for the design and construction of domain ontologies.

Disease Ontology 2015 update: an expanded and updated database of human diseases for linking biomedical knowledge through disease data

Abstract: The current version of the Human Disease Ontology (DO) (http://www.disease-ontology.org) database expands the utility of the ontology for the examination and comparison of genetic variation, phenotype, protein, drug and epitope data through the lens of human disease. DO is a biomedical resource of standardized common and rare disease concepts with stable identifiers organized by disease etiology. The content of DO has had 192 revisions since 2012, including the addition of 760 terms. Thirty-two percent of all terms now include definitions. DO has expanded the number and diversity of research communities and community members by 50+ during the past two years. These community members actively submit term requests, coordinate biomedical resource disease representation and provide expert curation guidance. Since the DO 2012 NAR paper, there have been hundreds of term requests and a steady increase in the number of DO listserv members, twitter followers and DO website usage. DO is moving to a multi-editor model utilizing Protege to curate DO in web ontology language. This will enable closer collaboration with the Human Phenotype Ontology, EBI's Ontology Working Group, Mouse Genome Informatics and the Monarch Initiative among others, and enhance DO's current asserted view and multiple inferred views through reasoning.

Text Understanding from Scratch

Abstract: This article demontrates that we can apply deep learning to text understanding from character-level inputs all the way up to abstract text concepts, using temporal convolutional networks (ConvNets). We apply ConvNets to various large-scale datasets, including ontology classification, sentiment analysis, and text categorization. We show that temporal ConvNets can achieve astonishing performance without the knowledge of words, phrases, sentences and any other syntactic or semantic structures with regards to a human language. Evidence shows that our models can work for both English and Chinese.

The role of ontologies in biological and biomedical research: a functional perspective

Abstract: Ontologies are widely used in biological and biomedical research. Their success lies in their combination of four main features present in almost all ontologies: provision of standard identifiers for classes and relations that represent the phenomena within a domain; provision of a vocabulary for a domain; provision of metadata that describes the intended meaning of the classes and relations in ontologies; and the provision of machine-readable axioms and definitions that enable computational access to some aspects of the meaning of classes and relations. While each of these features enables applications that facilitate data integration, data access and analysis, a great potential lies in the possibility of combining these four features to support integrative analysis and interpretation of multimodal data. Here, we provide a functional perspective on ontologies in biology and biomedicine, focusing on what ontologies can do and describing how they can be used in support of integrative research. We also outline perspectives for using ontologies in data-driven science, in particular their application in structured data mining and machine learning applications.

A semantic approach for text clustering using WordNet and lexical chains

Abstract: A modified WordNet based similarity measure for word sense disambiguation.Lexical chains as text representation for ideally cover the theme of texts.Extracted core semantics are sufficient to reduce dimensionality of feature set.The proposed scheme is able to correctly estimate the true number of clusters.The topic labels have good indicator of recognizing and understanding the clusters. Traditional clustering algorithms do not consider the semantic relationships among words so that cannot accurately represent the meaning of documents. To overcome this problem, introducing semantic information from ontology such as WordNet has been widely used to improve the quality of text clustering. However, there still exist several challenges, such as synonym and polysemy, high dimensionality, extracting core semantics from texts, and assigning appropriate description for the generated clusters. In this paper, we report our attempt towards integrating WordNet with lexical chains to alleviate these problems. The proposed approach exploits ontology hierarchical structure and relations to provide a more accurate assessment of the similarity between terms for word sense disambiguation. Furthermore, we introduce lexical chains to extract a set of semantically related words from texts, which can represent the semantic content of the texts. Although lexical chains have been extensively used in text summarization, their potential impact on text clustering problem has not been fully investigated. Our integrated way can identify the theme of documents based on the disambiguated core features extracted, and in parallel downsize the dimensions of feature space. The experimental results using the proposed framework on reuters-21578 show that clustering performance improves significantly compared to several classical methods.

Building new computational models to support health behavior change and maintenance: new opportunities in behavioral research.

Abstract: Adverse and suboptimal health behaviors and habits are responsible for approximately 40 % of preventable deaths, in addition to their unfavorable effects on quality of life and economics. Our current understanding of human behavior is largely based on static “snapshots” of human behavior, rather than ongoing, dynamic feedback loops of behavior in response to ever-changing biological, social, personal, and environmental states. This paper first discusses how new technologies (i.e., mobile sensors, smartphones, ubiquitous computing, and cloud-enabled processing/computing) and emerging systems modeling techniques enable the development of new, dynamic, and empirical models of human behavior that could facilitate just-in-time adaptive, scalable interventions. The paper then describes concrete steps to the creation of robust dynamic mathematical models of behavior including: (1) establishing “gold standard” measures, (2) the creation of a behavioral ontology for shared language and understanding tools that both enable dynamic theorizing across disciplines, (3) the development of data sharing resources, and (4) facilitating improved sharing of mathematical models and tools to support rapid aggregation of the models. We conclude with the discussion of what might be incorporated into a “knowledge commons,” which could help to bring together these disparate activities into a unified system and structure for organizing knowledge about behavior.

Created in Close Interaction with the Industry: The Smart Appliances REFerence (SAREF) Ontology

Abstract: Around two thirds of the energy consumed by buildings can be traced back to the residential sectors and thus household appliances. Today, most appliances are highly intelligent and networked devices, in principle being able to form complete energy consuming, producing, and managing systems. Reducing the use of energy has therefore become a matter of managing and optimizing the energy utilization at a system level. These systems are technically very heterogeneous, and standardized interfaces on a sensor and device level are therefore needed. Many of the required standards already exist, but a common architecture does not, resulting in a too fragmented and powerless market. To enable semantic interoperability for smart appliances we therefore developed SAREF, the Smart Appliance REFerence ontology. In this paper we present SAREF and describe our experience in creating this ontology in close interaction with the industry, pointing out the lessons learned and identifying topics for follow-up actions. © Springer International Publishing Switzerland 2015.

RecomMetz: A context-aware knowledge-based mobile recommender system for movie showtimes

Abstract: Recommender systems are used to provide filtered information from a large amount of elements. They provide personalized recommendations on products or services to users. The recommendations are intended to provide interesting elements to users. Recommender systems can be developed using different techniques and algorithms where the selection of these techniques depends on the area in which they will be applied. This paper proposes a recommender system in the leisure domain, specifically in the movie showtimes domain. The system proposed is called RecomMetz, and it is a context-aware mobile recommender system based on Semantic Web technologies. In detail, a domain ontology primarily serving a semantic similarity metric adjusted to the concept of “packages of single items” was developed in this research. In addition, location, crowd and time were considered as three different kinds of contextual information in RecomMetz. In a nutshell, RecomMetz has unique features: (1) the items to be recommended have a composite structure (movie theater + movie + showtime), (2) the integration of the time and crowd factors into a context-aware model, (3) the implementation of an ontology-based context modeling approach and (4) the development of a multi-platform native mobile user interface intended to leverage the hardware capabilities (sensors) of mobile devices. The evaluation results show the efficiency and effectiveness of the recommendation mechanism implemented by RecomMetz in both a cold-start scenario and a no cold-start scenario.

Sentiment analysis using common-sense and context information

Abstract: Sentiment analysis research has been increasing tremendously in recent times due to the wide range of business and social applications. Sentiment analysis from unstructured natural language text has recently received considerable attention from the research community. In this paper, we propose a novel sentiment analysis model based on common-sense knowledge extracted from ConceptNet based ontology and context information. ConceptNet based ontology is used to determine the domain specific concepts which in turn produced the domain specific important features. Further, the polarities of the extracted concepts are determined using the contextual polarity lexicon which we developed by considering the context information of a word. Finally, semantic orientations of domain specific features of the review document are aggregated based on the importance of a feature with respect to the domain. The importance of the feature is determined by the depth of the feature in the ontology. Experimental results show the effectiveness of the proposed methods.

Semantic data mining: A survey of ontology-based approaches

Abstract: Semantic Data Mining refers to the data mining tasks that systematically incorporate domain knowledge, especially formal semantics, into the process. In the past, many research efforts have attested the benefits of incorporating domain knowledge in data mining. At the same time, the proliferation of knowledge engineering has enriched the family of domain knowledge, especially formal semantics and Semantic Web ontologies. Ontology is an explicit specification of conceptualization and a formal way to define the semantics of knowledge and data. The formal structure of ontology makes it a nature way to encode domain knowledge for the data mining use. In this survey paper, we introduce general concepts of semantic data mining. We investigate why ontology has the potential to help semantic data mining and how formal semantics in ontologies can be incorporated into the data mining process. We provide detail discussions for the advances and state of art of ontology-based approaches and an introduction of approaches that are based on other form of knowledge representations.

Ontology evolution: a process-centric survey

Abstract: Ontology evolution aims at maintaining an ontology up to date with respect to changes in the domain that it models or novel requirements of information systems that it enables. The recent industrial adoption of Semantic Web techniques, which rely on ontologies, has led to the increased importance of the ontology evolution research. Typical approaches to ontology evolution are designed as multiple-stage processes combining techniques from a variety of fields (e.g., natural language processing and reasoning). However, the few existing surveys on this topic lack an in-depth analysis of the various stages of the ontology evolution process. This survey extends the literature by adopting a process-centric view of ontology evolution. Accordingly, we first provide an overall process model synthesized from an overview of the existing models in the literature. Then we survey the major approaches to each of the steps in this process and conclude on future challenges for techniques aiming to solve that particular stage.

The NeOn Methodology framework: A scenario-based methodology for ontology development

Abstract: This paper describes a scenario-based methodology called the NeOn Methodology framework. The aim of this framework is to speed up the construction of ontologies and ontology networks by reusing available knowledge resources (ontologies, non-ontological resources and ontology design patterns). The methodology is founded on four pillars: (1) a glossary of processes and activities; (2) a set of nine scenarios for building ontologies and ontology networks; (3) two ontology life-cycle models; and (4) a set of prescriptive methodological guidelines for performing specific processes and activities. The paper also shows how to apply this methodology framework to develop an ontology network within the multimedia domain. Finally, it presents an empirical evaluation of the NeOn Methodology framework.

An ontology-based adaptive personalized e-learning system, assisted by software agents on cloud storage

Abstract: DL Query is used to extract information from content stored in the ontology.The Felder Silverman model is used to determine learning styles of learner's.JADE agents monitor learner's behavior to provide adaptive learning.Deployments on cloud enable scope for expanding the content stored on an ontology.The proposed system supports the vision of?Semantic web education learning (SWEL). E-learning and online education have made great strides in the recent past. It has moved from a knowledge transfer model to a highly intellect, swift and interactive proposition capable of advanced decision-making abilities. Two challenges have been observed during the exploration of recent developments in e-learning. Firstly, to incorporate e-learning systems effectively in the evolving semantic web environment and secondly, to realize adaptive personalization according to the learner's changing behavior. An ontology-driven system has proposed to implement the Felder-Silverman learning style model in addition to the learning contents, to validate its integration with the semantic web environment. Software agents are employed to monitor the learner's actual learning style and modify them accordingly. The learner's learning style and their modifications are made within the proposed e-learning system. Cloud storage is used as the primary back-end in order to maintain the ontology, databases and other required server resources. To verify the system, comparisons are made between the information presented and adaptive learning styles of the learner along with actions of agents according to learners' behavior. Finally, various conclusions are drawn by exploring the learner's behavior in an adaptive environment for the proposed e-learning system.

Ontologically Grounded Multi-sense Representation Learning for Semantic Vector Space Models

Abstract: Words are polysemous. However, most approaches to representation learning for lexical semantics assign a single vector to every surface word type. Meanwhile, lexical ontologies such as WordNet provide a source of complementary knowledge to distributional information, including a word sense inventory. In this paper we propose two novel and general approaches for generating sense-specific word embeddings that are grounded in an ontology. The first applies graph smoothing as a postprocessing step to tease the vectors of different senses apart, and is applicable to any vector space model. The second adapts predictive maximum likelihood models that learn word embeddings with latent variables representing senses grounded in an specified ontology. Empirical results on lexical semantic tasks show that our approaches effectively captures information from both the ontology and distributional statistics. Moreover, in most cases our sense-specific models outperform other models we compare against.

Concept-Level Sentiment Analysis with Dependency-Based Semantic Parsing: A Novel Approach

Abstract: Sentiment analysis from unstructured natural language text has recently received considerable attention from the research community. In the frame of biologically inspired machine learning approaches, finding good feature sets is particularly challenging yet very important. In this paper, we focus on this fundamental issue of the sentiment analysis task. Specifically, we employ concepts as features and present a concept extraction algorithm based on a novel concept parser scheme to extract semantic features that exploit semantic relationships between words in natural language text. Additional conceptual information of a concept is obtained using the ConceptNet ontology: Concepts extracted from text are sent as queries to ConceptNet to extract their semantics. We select important concepts and eliminate redundant concepts using the Minimum Redundancy and Maximum Relevance feature selection technique. All selected concepts are then used to build a machine learning model that classifies a given document as positive or negative. We evaluate our concept extraction approach using a benchmark movie review dataset provided by Cornell University and product review datasets on books, DVDs, and electronics. Comparative experimental results show that our proposed approach to sentiment analysis outperforms existing state-of-the-art methods.

Characterizing Task-Based Human–Robot Collaboration Safety in Manufacturing

Abstract: A new methodology for describing the safety of human–robot collaborations is presented. Taking a task-based perspective, a risk assessment of a collaborative robot system safety can be evaluated offline during the initial design stages. This risk assessment factors in such elements as tooling, the nature and duration of expected contacts, and any amortized transfer of pressures and forces onto a human operator. Risk assessments of example tasks are provided for illustrative purposes.

Visual Affect Around the World: A Large-scale Multilingual Visual Sentiment Ontology

Abstract: Every culture and language is unique. Our work expressly focuses on the uniqueness of culture and language in relation to human affect, specifically sentiment and emotion semantics, and how they manifest in social multimedia. We develop sets of sentiment- and emotion-polarized visual concepts by adapting semantic structures called adjective-noun pairs, originally introduced by Borth et al. (2013), but in a multilingual context. We propose a new language-dependent method for automatic discovery of these adjective-noun constructs. We show how this pipeline can be applied on a social multimedia platform for the creation of a large-scale multilingual visual sentiment concept ontology (MVSO). Unlike the flat structure in Borth et al. (2013), our unified ontology is organized hierarchically by multilingual clusters of visually detectable nouns and subclusters of emotionally biased versions of these nouns. In addition, we present an image-based prediction task to show how generalizable language-specific models are in a multilingual context. A new, publicly available dataset of >15.6K sentiment-biased visual concepts across 12 languages with language-specific detector banks, >7.36M images and their metadata is also released.

A Survey on Ontology Evaluation Methods

Abstract: Ontologies nowadays have become widely used for knowledge representation, and are considered as foundation for Semantic Web. However with their wide spread usage, a question of their evaluation increased even more. This paper addresses the issue of finding an efficient ontology evaluation method by presenting the existing ontology evaluation techniques, while discussing their advantages and drawbacks. The presented ontology evaluation techniques can be grouped into four categories: gold standard-based, corpus-based, task-based and criteria based approaches.

RoboEarth Semantic Mapping: A Cloud Enabled Knowledge-Based Approach

Abstract: The vision of the RoboEarth project is to design a knowledge-based system to provide web and cloud services that can transform a simple robot into an intelligent one. In this work, we describe the RoboEarth semantic mapping system. The semantic map is composed of: 1) an ontology to code the concepts and relations in maps and objects and 2) a SLAM map providing the scene geometry and the object locations with respect to the robot. We propose to ground the terminological knowledge in the robot perceptions by means of the SLAM map of objects. RoboEarth boosts mapping by providing: 1) a subdatabase of object models relevant for the task at hand, obtained by semantic reasoning, which improves recognition by reducing computation and the false positive rate; 2) the sharing of semantic maps between robots; and 3) software as a service to externalize in the cloud the more intensive mapping computations, while meeting the mandatory hard real time constraints of the robot. To demonstrate the RoboEarth cloud mapping system, we investigate two action recipes that embody semantic map building in a simple mobile robot. The first recipe enables semantic map building for a novel environment while exploiting available prior information about the environment. The second recipe searches for a novel object, with the efficiency boosted thanks to the reasoning on a semantically annotated map. Our experimental results demonstrate that, by using RoboEarth cloud services, a simple robot can reliably and efficiently build the semantic maps needed to perform its quotidian tasks. In addition, we show the synergetic relation of the SLAM map of objects that grounds the terminological knowledge coded in the ontology.

Optique: Zooming in on Big Data

Abstract: Optique overcomes problems in current ontology-based data access systems pertaining to installation overhead, usability, scalability, and scope by integrating a user-oriented query interface, semi-automated managing methods, new query rewriting techniques, and temporal and streaming data processing in one platform.

A Semantic IoT Early Warning System for Natural Environment Crisis Management

Abstract: An early warning system (EWS) is a core type of data driven Internet of Things (IoTs) system used for environment disaster risk and effect management. The potential benefits of using a semantic-type EWS include easier sensor and data source plug-and-play, simpler, richer, and more dynamic metadata-driven data analysis and easier service interoperability and orchestration. The challenges faced during practical deployments of semantic EWSs are the need for scalable time-sensitive data exchange and processing (especially involving heterogeneous data sources) and the need for resilience to changing ICT resource constraints in crisis zones. We present a novel IoT EWS system framework that addresses these challenges, based upon a multisemantic representation model. We use lightweight semantics for metadata to enhance rich sensor data acquisition. We use heavyweight semantics for top level W3C Web Ontology Language ontology models describing multileveled knowledge-bases and semantically driven decision support and workflow orchestration. This approach is validated through determining both system related metrics and a case study involving an advanced prototype system of the semantic EWS, integrated with a deployed EWS infrastructure.

A new Ontology Lookup Service at EMBL-EBI.

Abstract: The Ontology Lookup Service (OLS) hosted at the EMBL European Bioinformatics Institute has been providing ontology search and visualisation services for over ten years. In this time the range and diversity of ontologies has changed dramatically. One of the major shifts has been the increasing use of the W3C Web Ontology Language (OWL) for representing biomedical ontologies. The OLS has been reengineered in order to accommodate the OWL standard and provide a wider range of ontology-based services to the community.

Semantic web methodologies, best practices and ontology engineering applied to Internet of Things

Abstract: We discuss in this paper, semantic web methodologies, best practices and recommendations beyond the IERC Cluster Semantic Interoperability Best Practices and Recommendations (IERC AC4). The semantic web community designed best practices and methodologies which are unknown from the IoT community. In this paper, we synthesize and highlight the most relevant work regarding ontology methodologies, engineering, best practices and tools that could be applied to Internet of Things (IoT). To the best of our knowledge, this is the first work aiming at bridging such methodologies to the IoT community and go beyond the IERC AC4 cluster. This research is being applied to three uses cases: (1) the M3 framework assisting IoT developers in designing interoperable ontology-based IoT applications, (2) the FIESTA-IoT EU project encouraging semantic interoperability within IoT, and (3) a collaborative publication of legacy ontologies.