Showing papers on "Upper ontology 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

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.

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.

Developing an Ontology for Cyber Security Knowledge Graphs

Abstract: In this paper we describe an ontology developed for a cyber security knowledge graph database. This is intended to provide an organized schema that incorporates information from a large variety of structured and unstructured data sources, and includes all relevant concepts within the domain. We compare the resulting ontology with previous efforts, discuss its strengths and limitations, and describe areas for future work.

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.

Toward semantic interoperability in oneM2M architecture

Abstract: The oneM2M standard is a global initiative led jointly by major standards organizations around the world in order to develop a unique architecture for M2M communications. Prior standards, and also oneM2M, while focusing on achieving interoperability at the communication level, do not achieve interoperability at the semantic level. An expressive ontology for IoT called IoT-O is proposed, making best use of already defined ontologies in specific domains such as sensor, observation, service, quantity kind, units, or time. IoT-O also defines some missing concepts relevant for IoT such as thing, node, actuator, and actuation. The extension of the oneM2M standard to support semantic data interoperability based on IoT-O is discussed. Finally, through comprehensive use cases, benefits of the extended standard are demonstrated, ranging from heterogeneous device interoperability to autonomic behavior achieved by automated reasoning.

Ontologies in the context of product lifecycle management: state of the art literature review

Abstract: The use of ontologies in the context of product lifecycle management (PLM) is gaining importance and popularity, while at the same time it generates a lot of controversy in discussions within scientific and engineering communities. Yet, what is ontology? What challenges have been addressed so far? What role does ontology play? Do we really need ontology? These are the core questions this paper seeks to address. We propose to conduct a comprehensive study of the concept of Ontology firstly in its domain of origin, Philosophy, and secondly in information science. Based on the understanding of this concept and an in-depth analysis of the state of the art, seven key roles of ontology are defined. These roles serve as a framework describing the general state of research on the use of ontologies in the context of PLM.

Klink-2: Integrating Multiple Web Sources to Generate Semantic Topic Networks

Abstract: The amount of scholarly data available on the web is steadily increasing, enabling different types of analytics which can provide important insights into the research activity. In order to make sense of and explore this large-scale body of knowledge we need an accurate, comprehensive and up-to-date ontology of research topics. Unfortunately, human crafted classifications do not satisfy these criteria, as they evolve too slowly and tend to be too coarse-grained. Current automated methods for generating ontologies of research areas also present a number of limitations, such as: i they do not consider the rich amount of indirect statistical and semantic relationships, which can help to understand the relation between two topics --- e.g., the fact that two research areas are associated with a similar set of venues or technologies; ii they do not distinguish between different kinds of hierarchical relationships; and iii they are not able to handle effectively ambiguous topics characterized by a noisy set of relationships. In this paper we present Klink-2, a novel approach which improves on our earlier work on automatic generation of semantic topic networks and addresses the aforementioned limitations by taking advantage of a variety of knowledge sources available on the web. In particular, Klink-2 analyses networks of research entities including papers, authors, venues, and technologies to infer three kinds of semantic relationships between topics. It also identifies ambiguous keywords e.g., "ontology" and separates them into the appropriate distinct topics --- e.g., "ontology/philosophy" vs. "ontology/semantic web". Our experimental evaluation shows that the ability of Klink-2 to integrate a high number of data sources and to generate topics with accurate contextual meaning yields significant improvements over other algorithms in terms of both precision and recall.

Aber-OWL: a framework for ontology-based data access in biology.

Abstract: Background Many ontologies have been developed in biology and these ontologies increasingly contain large volumes of formalized knowledge commonly expressed in the Web Ontology Language (OWL). Computational access to the knowledge contained within these ontologies relies on the use of automated reasoning.

Domain Specificity and Intuitive Ontology.

Abstract: Traditionally, psychologists have assumed that people come equipped only with a set of relatively domain-general faculties, such as “memory” and “reasoning,” which are applied in equal fashion to diverse problems. Recent research has begun to suggest that human expertise about the natural and social environment, including what is often called “semantic knowledge”, is best construed as consisting of different domains of competence. Each of these corresponds to recurrent evolutionary problems, is organised along specific principles, is the outcome of a specific developmental pathway and is based on specific neural structures. What we call a “human evolved intuitive ontology” comprises a catalogue of broad domains of information, different sets of principles applied to these different domains as well as different learning rules to acquire more information about those objects. All this is intuitive in the sense that it is not the product of deliberate reflection on what the world is like. This notion of an intuitive ontology as a motley of different domains informed by different principles was first popularised by developmental psychologists (R. Gelman, 1978; R. Gelman & Baillargeon, 1983) who proposed distinctions between physical-mechanical, biological, social and numerical competencies as based on different learning principles (Hirschfeld & Gelman, 1994). In the following decades, this way of slicing up semantic knowledge received considerable support both in developmental and neuro-psychology. For example, patients with focal brain damage were found to display selective impairment of one of these domains of knowledge to the exclusion of others (Caramazza, 1998). Neuroimaging and cognitive neuroscience are now adding to the picture of a

Using ontologies to improve semantic interoperability in health data

Abstract: The present-day health data ecosystem comprises a wide array of complex heterogeneous data sources. A wide range of clinical, health care, social and other clinically relevant information are stored in these data sources. These data exist either as structured data or as free-text. These data are generally individual person-based records, but social care data are generally case based and less formal data sources may be shared by groups. The structured data may be organised in a proprietary way or be coded using one-of-many coding, classification or terminologies that have often evolved in isolation and designed to meet the needs of the context that they have been developed. This has resulted in a wide range of semantic interoperability issues that make the integration of data held on these different systems changing. We present semantic interoperability challenges and describe a classification of these. We propose a four-step process and a toolkit for those wishing to work more ontologically, progressing from the identification and specification of concepts to validating a final ontology. The four steps are: (1) the identification and specification of data sources; (2) the conceptualisation of semantic meaning; (3) defining to what extent routine data can be used as a measure of the process or outcome of care required in a particular study or audit and (4) the formalisation and validation of the final ontology. The toolkit is an extension of a previous schema created to formalise the development of ontologies related to chronic disease management. The extensions are focused on facilitating rapid building of ontologies for time-critical research studies.

Extensions to the core ontology for robotics and automation

Abstract: The working group Ontologies for Robotics and Automation, sponsored by the IEEE Robotics & Automation Society, recently proposed a Core Ontology for Robotics and Automation (CORA). This ontology was developed to provide an unambiguous definition of core notions of robotics and related topics. It is based on SUMO, a top-level ontology of general concepts, and on ISO 8373:2012 standard, developed by the ISO/TC184/SC2 Working Group, which defines-in natural language-important terms in the domain of Robotics and Automation (R&A). In this paper, we introduce a set of ontologies that complement CORA with notions such as industrial design and positioning. We also introduce updates to CORA in order to provide more ontologically sound representations of autonomy and of robot parts. HighlightsWe discuss extensions to a core ontology for the robotics and automation field.The ontology aims to specify the main notions across robotics subdomains.We define robot, robotic system, robotic environment, and related notions.We discuss concepts regarding the notion of design, in industrial contexts.We discuss notions regarding the modes of operation of a robot.We discuss notions regarding the position, orientation and pose of a robot.

Type-2 fuzzy ontology-based opinion mining and information extraction: A proposal to automate the hotel reservation system

Abstract: The volume of traveling websites is rapidly increasing. This makes relevant information extraction more challenging. Several fuzzy ontology-based systems have been proposed to decrease the manual work of a full-text query search engine and opinion mining. However, most search engines are keyword-based, and available full-text search engine systems are still imperfect at extracting precise information using different types of user queries. In opinion mining, travelers do not declare their hotel opinions entirely but express individual feature opinions in reviews. Hotel reviews have numerous uncertainties, and most featured opinions are based on complex linguistic wording (small, big, very good and very bad). Available ontology-based systems cannot extract blurred information from reviews to provide better solutions. To solve these problems, this paper proposes a new extraction and opinion mining system based on a type-2 fuzzy ontology called T2FOBOMIE. The system reformulates the user's full-text query to extract the user requirement and convert it into the format of a proper classical full-text search engine query. The proposed system retrieves targeted hotel reviews and extracts feature opinions from reviews using a fuzzy domain ontology. The fuzzy domain ontology, user information and hotel information are integrated to form a type-2 fuzzy merged ontology for the retrieving of feature polarity and individual hotel polarity. The Protege OWL-2 (Ontology Web Language) tool is used to develop the type-2 fuzzy ontology. A series of experiments were designed and demonstrated that T2FOBOMIE performance is highly productive for analyzing reviews and accurate opinion mining.

Addressing semantic heterogeneity through multiple knowledge base assisted merging of domain-specific ontologies

Abstract: With the development of the Semantic Web (SW), the creation of ontologies to formally conceptualize our understanding of various domains has widely increased in number. However, the conceptual and terminological differences (a.k.a semantic heterogeneity problem) between ontologies form a major limiting factor towards their use/reuse and full adoption in practical settings. A key solution to addressing this problem can be through identifying semantic correspondences between the entities (including concepts, relations, and instances) of heterogeneous ontologies, and consequently achieving interoperability between them. This process is also known as ontology alignment. The output of this process can be further exploited to merge ontologies into a single coherent ontology. Indeed, this is widely regarded as a crucial, yet difficult task, specifically when dealing with heavyweight ontologies that consist of hundreds of thousands of concepts. To address this issue, various ontology merging approaches have been proposed. These approaches can be classified into three categories: single-strategy-based approaches, multiple-strategy-based approaches, and approaches based on exploiting external semantic resources. In this paper, we first discuss the strengths and limitations of each of these approaches, and then present our framework for addressing the semantic heterogeneity problem through merging domain-specific ontologies based on multiple external semantic resources. The novelty of the proposed approach is mainly based on employing knowledge represented by multiple external resources (knowledge bases in our work) to make aggregated decisions on the semantic correspondences between the entities of heterogeneous ontologies. Other important issues that we attempt to tackle in the proposed framework are: (i) Identifying and handling inconsistency of semantic relations between the ontology concepts and, (ii) Handling the issue of missing background knowledge (such as concepts and instances) in the exploited knowledge bases by utilizing an integrated statistical and semantic technique. Additionally, the proposed solution soundly enriches the knowledge bases with missing background knowledge, and thus enables the reuse of the newly obtained knowledge in future ontology merging tasks. To validate our proposal, we tested the framework using the OAEI 2009 benchmark and compared the produced results with state-of-the-art syntactic and semantic based systems. In addition, we utilized the proposed techniques to merge three heavyweight ontologies from the environmental domain.

Ontology in Information Security

Abstract: The past several years we have witnessed that information has become the most precious asset, while protection and security of information is becoming an ever greater challenge due to the large amount of knowledge necessary for organizations to successfully withstand external threats and attacks. This knowledge collected from the domain of information security can be formally described by security ontologies. A large number of researchers during the last decade have dealt with this issue, and in this paper we have tried to identify, analyze and systematize the relevant papers published in scientific journals indexed in selected scientific databases, in period from 2004 to 2014. This paper gives a review of literature in the field of information security ontology and identifies a total of 52 papers systematized in three groups: general security ontologies (12 papers), specific security ontologies (32 papers) and theoretical works (8 papers). The papers were of different quality and level of detail and varied from presentations of simple conceptual ideas to sophisticated frameworks based on ontology.

Ontology Development: A Comparing Study on Tools, Languages and Formalisms

Abstract: This paper reviews and compares some Ontology Development Tools, Formalisms and Languages from those reported in the Literature, with a special attention accorded to the interoperability between them. Additionally, this paper presents the Structure and Basic Features of Tools, Formalisms and languages. The main criterion for comparison of these tools and languages was the user interest and their application in different kind of real world tasks. The primary goal of this study is to introduce several tools and languages to ensure more understanding from their use. Consequently,we can solve the problems of current tools and languages and ensure the easy development of a new generation of tools and languages.

The GeoLink Modular Oceanography Ontology

Abstract: GeoLink is one of the building block projects within EarthCube, a major effort of the National Science Foundation to establish a next-generation knowledge infrastructure for geosciences. As part of this effort, GeoLink aims to improve data retrieval, reuse, and integration of seven geoscience data repositories through the use of ontologies. In this paper, we report on the GeoLink modular ontology, which consists of an interlinked collection of ontology design patterns engineered as the result of a collaborative modeling effort. We explain our design choices, present selected modeling details, and discuss how data integration can be achieved using the patterns while respecting the existing heterogeneity within the participating repositories.

Fields of Sense: A New Realist Ontology

Abstract: Presents a new realist ontology based on the concept of fields of sense. Markus Gabriel presents us with an innovative answer to one of the central questions of philosophy: what is the meaning of 'being' - or, rather, 'existence' - and how does that concept relate to the totality of what there is? This ontology hinges on Gabriel's concept of fields of sense, which shows that he fundamentally opposes the idea that mathematics or the natural sciences could ever replace a richer philosophical understanding of what there is and how we know about it. The first contribution to a speculative epistemology on the basis of an ontology first method and develops a new realist ontology as well as outlining a realist epistemology grounded in ontology.

Ontorat: automatic generation of new ontology terms, annotations, and axioms based on ontology design patterns

Abstract: Background: It is time-consuming to build an ontology with many terms and axioms. Thus it is desired to automate the process of ontology development. Ontology Design Patterns (ODPs) provide a reusable solution to solve a recurrent modeling problem in the context of ontology engineering. Because ontology terms often follow specific ODPs, the Ontology for Biomedical Investigations (OBI) developers proposed a Quick Term Templates (QTTs) process targeted at generating new ontology classes following the same pattern, using term templates in a spreadsheet format. Results: Inspired by the ODPs and QTTs, the Ontorat web application is developed to automatically generate new ontology terms, annotations of terms, and logical axioms based on a specific ODP(s). The inputs of an Ontorat execution include axiom expression settings, an input data file, ID generation settings, and a target ontology (optional). The axiom expression settings can be saved as a predesigned Ontorat setting format text file for reuse. The input data file is generated based on a template file created by a specific ODP (text or Excel format). Ontorat is an efficient tool for ontology expansion. Different use cases are described. For example, Ontorat was applied to automatically generate over 1,000 Japan RIKEN cell line cell terms with both logical axioms and rich annotation axioms in the Cell Line Ontology (CLO). Approximately 800 licensed animal vaccines were represented and annotated in the Vaccine Ontology (VO) by Ontorat. The OBI team used Ontorat to add assay and device terms required by ENCODE project. Ontorat was also used to add missing annotations to all existing Biobank specific terms in the Biobank Ontology. A collection of ODPs and templates with examples are provided on the Ontorat website and can be reused to facilitate ontology development. Conclusions: With ever increasing ontology development and applications, Ontorat provides a timely platform for generating and annotating a large number of ontology terms by following design patterns. Availability: http://ontorat.hegroup.org/

An ontology design pattern for IoT device tagging systems

Abstract: Modeling devices has become a crucial task in the Internet of Things (IoT) and Semantic Web technologies are seen as a promising tool for this purpose. However, as it may be arduous to manipulate semantic models, industrial solutions often re-define non-standard, simplified semantics. This is the case with Project Haystack, a framework to tag devices with labels from a predefined vocabulary in the field of Building Automation. In order to make Project Haystack standard and fully semantic, we wrapped its vocabulary in an ontology. In this paper, we present the general strucure of this ontology, along with a method to turn tag sets into a Semantic Web model and back. The whole results in a reusable ontology design pattern. We aligned our Haystack tagging ontology with the wide-spreaded Semantic Sensor Network upper ontology and we designed a configuration environment for Building Automation systems based on semantic data to illustrate, so as to discuss the added-value of semantics in automation.

Automated concept and relationship extraction for the semi-automated ontology management (SEAM) system.

Abstract: We develop medical-specialty specific ontologies that contain the settled science and common term usage. We leverage current practices in information and relationship extraction to streamline the ontology development process. Our system combines different text types with information and relationship extraction techniques in a low overhead modifiable system. Our SEmi-Automated ontology Maintenance (SEAM) system features a natural language processing pipeline for information extraction. Synonym and hierarchical groups are identified using corpus-based semantics and lexico-syntactic patterns. The semantic vectors we use are term frequency by inverse document frequency and context vectors. Clinical documents contain the terms we want in an ontology. They also contain idiosyncratic usage and are unlikely to contain the linguistic constructs associated with synonym and hierarchy identification. By including both clinical and biomedical texts, SEAM can recommend terms from those appearing in both document types. The set of recommended terms is then used to filter the synonyms and hierarchical relationships extracted from the biomedical corpus. We demonstrate the generality of the system across three use cases: ontologies for acute changes in mental status, Medically Unexplained Syndromes, and echocardiogram summary statements. Across the three uses cases, we held the number of recommended terms relatively constant by changing SEAM’s parameters. Experts seem to find more than 300 recommended terms to be overwhelming. The approval rate of recommended terms increased as the number and specificity of clinical documents in the corpus increased. It was 60% when there were 199 clinical documents that were not specific to the ontology domain and 90% when there were 2879 documents very specific to the target domain. We found that fewer than 100 recommended synonym groups were also preferred. Approval rates for synonym recommendations remained low varying from 43% to 25% as the number of journal articles increased from 19 to 47. Overall the number of recommended hierarchical relationships was very low although approval was good. It varied between 67% and 31%. SEAM produced a concise list of recommended clinical terms, synonyms and hierarchical relationships regardless of medical domain.

Process Ontology Specification for Enhancing the Process Compliance of a Measurement and Evaluation Strategy

Abstract: In this paper, we specify a generic ontology for the process domain considering the related state-of-the-art research literature. As a result, the recently built process ontology contributes to enrich semantically the terms for the (previously developed) measurement and evaluation domain ontology by means of stereotypes. One of the underlying hypothesis in this research is that the generic ontology for process can be seen as a reusable artifact which can be used to enrich semantically not only the measurement and evaluation domain ontology but also to other domains involved in different organizational endeavors. For instance, for the measurement domain, now is explicit that the measurement term has the semantic of task, the measure term has the meaning of outcome, and the metric term has the semantic of method, from the process terminological base standpoint. The augmented conceptual framework, i.e. measurement and evaluation concepts plus process concepts, has also a positive impact on the GOCAME (Goal-Oriented Context-Aware Measurement and Evaluation) strategy capabilities since ensures terminological uniformity, consistency and verifiability to its process and method specifications. In order to illustrate how the augmented conceptual framework impacts on the verifiability of GOCAME process and method specifications in addition to the consistency and comparability of results in measurement and evaluation projects, an ICT (Information and Communications Technology) security and risk evaluation case study is used.

ONLI: An ontology-based system for querying DBpedia using natural language paradigm

Abstract: The Semantic Web has emerged as an extension of the current Web, in which Web content has well-defined meaning through the addition of logic-based metadata. However, current mechanisms for information retrieval from semantic knowledge bases restrict their use to only experienced users. To address this gap, the natural language processing (NLP) is deemed to be very intuitive from a use point of view, due to it hides the formality of a knowledge base as well as the executable query language. This paper presents a novel ontology-based information retrieval system for DBpedia called ONLI (Ontology-based Natural Language Interface). ONLI proposes the use of an ontology model in order to represent both the syntactic question’s structure and the question’s context. This model allows inferring the answer type expected by the user through an established question’s classification. These features allow reducing the search space thus increasing the probability of providing the correct answer. From this perspective, ONLI was evaluated in terms of their ability to find the correct answer into DBpedia’s content, achieving promising results and proving to be very useful to non-experienced users.

Unified Data Model for Wireless Sensor Network

Abstract: The constant evolution of technology in terms of inexpensive and embedded wireless interfaces and powerful chipsets has led to the massive usage and deployment of wireless sensors networks (WSNs). These networks are made of a growing number of small sensing devices and are used in multiple use cases, such as home automation (e.g., smart buildings), energy management and smart grids, crisis management and security, e-Health, entertainment, and so forth. Sensor devices, generally self-organized in clusters and domain dedicated, are provided by an increasing number of manufacturers, which leads to interoperability problems (e.g., heterogeneous interfaces and/or grounding, heterogeneous descriptions, profiles, models, and so forth). Furthermore, the data provided by these WSNs are very heterogeneous because they are coming from sensing nodes with various abilities (e.g., different sensing ranges, formats, coding schemes, and so forth). In this paper, we propose a solution for handling WSNs’ heterogeneity, as well as easing interoperability management. The solution consists of a semantic open data model for sensor and sensor data generic description. This data model, designed for handling any kind of sensors/actuators and measured data (which is still not the case of existing WSNs data models), is fully detailed and formalized in an original ontology format called MyOntoSens and written using Ontology Web Language 2 description logic language. The proposed ontology has been implemented using Protege 4.3, prevalidated with pellet reasoner, and is being standardized (A Body Area Network (BAN) dedicated instance of the proposed “MyOntoSens” ontology is being standardized as a Technical Specification (TS) within the SmartBAN Technical Committee of the ETSI (European Telecommunications Standards Institute) standardization body: ETSI SmartBAN Technical Committee website: portal.etsi.org/portal/server.pt/community/SmartBAN/368v). In addition, this original ontology has been prequalified through a runner’s exercise monitoring application, using in particular SPARQL query language, within a small wireless body area network platform comprising heartbeat, GPS sensors, and Android mobile phones.

A reference ontology for profiling scholar's background knowledge in recommender systems

Abstract: We develop a reference ontology which is adapted to the structure of scholars' knowledge.The reference ontology is developed by merging six Web taxonomies.We employ DBpedia to transform and map scholar's knowledge to the reference ontology.We improve coverage, specificity, and richness of the domain's reference ontology. The profiling of background knowledge is essential in scholar's recommender systems. Existing ontology-based profiling approaches employ a pre-built reference ontology as a backbone structure for representing the scholar's preferences. However, such singular reference ontologies lack sufficient ontological concepts and are unable to represent the hierarchical structure of scholars' knowledge. They rather encompass general-purpose topics of the domain and are inaccurate in representing the scholars' knowledge. This paper proposes a method for integrating of multiple domain taxonomies to build a reference ontology, and exploits this reference ontology for profiling scholars' background knowledge. In our approach, various topics of Computer Science domain from Web taxonomies are selected, transformed by DBpedia, and merged to construct a reference ontology. We demonstrate the effectiveness of our approach by measuring five quality-based metrics as well as application-based evaluation against the developed reference ontology. The empirical results show an improvement over the existing reference ontologies in terms of completeness, richness, and coverage.

Social Information Retrieval Based on Semantic Annotation and Hashing upon the Multiple Ontologies

Abstract: Ontology is the best way for representing the useful information. In this paper, we have planned to develop a model which utilizes multiple ontologies. From those ontologies, based on the mutual information among the concepts the taxonomy is constructed, then the relationship among the concepts is calculated. Thereby the useful information is extracted. There is multiple numbers of ontologies available through the web. But there are various issues to be faced while sharing and reusing the existing ontologies. To resolve the ambiguity which exists, when comparing two concepts are semantically similar, but physically different, an approach is proposed here to index and retrieve the documents from two different ontologies. The ontologies used are WordNet and SWETO ontology. The results are compared based on semantic annotation based on RMS and hashing between the cross ontologies using Rabin Karp fingerprinting algorithm. Also the datasets are trained to yield better results.