Semantic Ontology Research Articles

Integration of Process Model and CIM to Represent Events and Chronology in Power System Processes

The conventional power system domain includes various utility business processes that are carried out by multiple market participants. In the conventional setup, these processes are typically captured through various local representations understandable by the concerned actors in their respective subdomains. The heterogeneity in process representations can result in problems of traceability, transparency, time-trackability, and interoperability across actors and software systems. To orchestrate the utility business processes, the IEC has recently started an activity entitled “IEC 62325-451-2:2014” for representing the “energy scheduling business process” of the European market in a standardized way. Along similar lines, this paper presents a process ontology along with a systematic methodology for building process models of end-to-end process operations of power utility. The process ontology consists of power system process modeling artifacts such as process, participant, activity, gateway, event, and event-based subprocess. For representing events and chronology of task execution, the common information model (CIM) can be adopted, which is expected to act complementary to process ontology semantics. An energy scheduling application is considered as a case study, wherein the proposed process ontology artifacts and the CIM ontology are used for building process models for an “energy scheduling business process” in the Indian power grid context.

CAD assembly model retrieval based on multi-source semantics information and weighted bipartite graph

Content-based CAD assembly model retrieval focusing on similarity measure of bottom-level geometry and topology information can hardly meet design demand of engineering designers in the product design reuse. To search quickly and effectively the CAD assembly models with specific functions by using requirements of mechanical product design, inspire designers’ design thought and product design innovation, an assembly retrieval approach based on multi-source semantics information and weighted bipartite graph is proposed. The approach concerns the high-level semantics information rather than geometry and topology information of assembly model. First, the semantic ontology for mechanical assembly model is constructed considering multi-source semantics information including part semantics, assembly constraint semantics and functional semantics. Function region extraction algorithm is given for identifying functional structures in assembly model, and functional semantics of assembly model is annotated from the aspects of assembly structure granularity and part shape granularity. Subsequently, semantic similarity between concepts is evaluated comprehensively with WordNet and domain ontology, and a semantic matching model is built based on weighted bipartite graph (WBG) for solving the many-to-many semantics matching problem of semantic retrieval for CAD assembly models. Finally, a semantic-based assembly retrieval prototype system is developed and the experiment cases verify effectiveness and feasibility of the proposed assembly retrieval approach.

Knowledge Extraction System from Unstructured Documents

In this paper, we design and implement a knowledge extraction system from unstructured documents (unstructured documents are documents where the information is in natural language, and require natural language processing techniques for processing) in HTML format. Basically, the system allows to transform the content of a text that is in natural language, into structured and organized knowledge, semantically described (a Semantic Ontology). Therefore, it is proposed to generate semantic knowledge based on the extraction of entities and relationships, where entities are anything about which something can be said, and relations the interactions between entities. From the generated semantic knowledge model, it is possible to infer new knowledge, such as lexicons, taxonomies and specialized terminological bases. The system can be used by any semantic processing application, in its processes of enriching its information and knowledge

OpenBiodiv-O: ontology of the OpenBiodiv knowledge management system

BackgroundThe biodiversity domain, and in particular biological taxonomy, is moving in the direction of semantization of its research outputs. The present work introduces OpenBiodiv-O, the ontology that serves as the basis of the OpenBiodiv Knowledge Management System. Our intent is to provide an ontology that fills the gaps between ontologies for biodiversity resources, such as DarwinCore-based ontologies, and semantic publishing ontologies, such as the SPAR Ontologies. We bridge this gap by providing an ontology focusing on biological taxonomy.ResultsOpenBiodiv-O introduces classes, properties, and axioms in the domains of scholarly biodiversity publishing and biological taxonomy and aligns them with several important domain ontologies (FaBiO, DoCO, DwC, Darwin-SW, NOMEN, ENVO). By doing so, it bridges the ontological gap across scholarly biodiversity publishing and biological taxonomy and allows for the creation of a Linked Open Dataset (LOD) of biodiversity information (a biodiversity knowledge graph) and enables the creation of the OpenBiodiv Knowledge Management System.A key feature of the ontology is that it is an ontology of the scientific process of biological taxonomy and not of any particular state of knowledge. This feature allows it to express a multiplicity of scientific opinions. The resulting OpenBiodiv knowledge system may gain a high level of trust in the scientific community as it does not force a scientific opinion on its users (e.g. practicing taxonomists, library researchers, etc.), but rather provides the tools for experts to encode different views as science progresses.ConclusionsOpenBiodiv-O provides a conceptual model of the structure of a biodiversity publication and the development of related taxonomic concepts. It also serves as the basis for the OpenBiodiv Knowledge Management System.

A LOCATION AND DIVERSITY-AWARE OPTIMIZED NEWS FEED SYSTEM FOR ANDROID USERS

A location aware news feed system generates news feeds for a mobile user based on her spatial preference and non-spatial preference. Existing LANF systems simply send the most relevant geo-tagged messages to their users. Diversity is a very important feature for location-aware news feeds because it helps users discover new places and activities. In this paper, D-MobiFeed; a new LANF system is proposed which enables a user to specify the minimum number of message categories ( h ) for the messages in a news feed. In D-MobiFeed, our objective is to efficiently schedule news feeds for a mobile user at his/her current and predicted locations, such that (i) each news feed contains messages belonging to at least h different categories, and (ii) their total relevance to the user is maximized. New feed system uses Maximum Likelihood Algorithm to predict future location of user and most relevant messages are retrieve using semantic ontology.

Software Experience for an Ontology-Based Approach for the Definition of Alarms in Geographical Sensor Systems

This paper presents a system based on ontologies for the definition of alarms in sensor systems. Although we consider that the ontology and the system are interesting themselves, we have a special impact on their experience with the software tools used and the technology related to the Semantic Web. This approach is intended to illustrate the process for those readers not familiar with this field and who can consider the use of ontologies for their next developments. An ontology has been designed in the OWL language, complemented with Semantic Web Rule Language rules and search elements in the Semantic Query-enhanced Web Rule Language. The set has offered satisfactory results in simulation. Other interesting contributions of this paper are: a survey of the available literature in the field of the use of Semantic Web technologies and ontologies for the detection of events from data obtained from sensors and a study on the tools and vocabularies to be used to create a system that interfaces with ontologies and an educational method—reporting our experience—to help university students understand this topic.

Ontology-Based Education/Industry Collaboration System

As the main supplier of the workforce to the industry, higher education is increasingly criticized for not being abreast with the digital revolution and being disconnected from the industry. Competency-based education was developed to address this issue and bridge the gap between what the university is producing and the requirements of the industry. Hence, tools need to be developed that assists in the analysis process. This paper focuses on proposing a system that models the competencies required by occupations in the industry and higher education curricula and assists in matching profiles from the two domains. The different concepts in the domain are modeled as a semantic web ontology, and an inference engine performs the profile matching. In addition to the profile matching, the system calculates a score for the matching degree using the analytic hierarchy process (AHP) method.

A Semantic-Middleware-Supported Receding Horizon Optimal Power Flow in Energy Grids

Energy management in electric grids with multiple energy sources, generators, storage devices, and interacting loads along with their complex behaviors requires grid wide control. Communication infrastructure that aggregates information from heterogeneous devices in the electric grid making the applications completely independent of physical connectivity is essential for building in the context of control applications. This investigation presents a semantic middleware that is used to implement a receding-horizon-based optimal power flow (OPF) in smart grids. The presence of renewable energy sources, storage systems, and loads dispersed all along the grid necessitates the use of grid wide control and a communication infrastructure to support it. To this extent, the proposed middleware will serve as the basis for representing various components of the power grid. It is enriched with intelligence by semantic annotation and ontologies that provide situation awareness and context discovery. The middleware deployment is demonstrated by implementing the receding horizon OPF in a network in Steinkjer, Norway. Our results demonstrate the advantages of both the middleware and the algorithm. Furthermore, the results prove the added flexibility obtained in the grid due to the addition of renewable energy and storage systems. The significant advantage of the proposed approach is that the real-time monitoring infrastructure is used for improving the flexibility, reliability, and efficiency of the grid.

A Model of Socially Connected Web Objects for IoT Applications

The Internet of Things (IoT) is evolving with the connected objects at an unprecedented rate, bringing about enormous opportunities for the future IoT applications as well as challenges. One of the major challenges is to handle the complexity generated by the interconnection of billions of objects. However, Social Internet of Things (SIoT), emerging from the conglomeration of IoT and social networks, has realized an efficient way to facilitate the development of complex future IoT applications. Nevertheless, to fully utilize the benefits of SIoT, a platform that can provide efficient services using social relations among heterogeneous objects is highly required. The web objects enabled IoT environment promotes SIoT features by enabling virtualization using virtual objects and supporting the modularity with microservices. To realize SIoT services, this article proposes an architecture that provides a foundation for the development of lightweight microservices based on socially connected web objects. To efficiently discover web objects and reduce the complexity of service provisioning processes, a social relationship model is presented. To realize the interoperable service operations, a semantic ontology model has been developed. Finally, to evaluate the proposed design, a prototype has been implemented based on a use case scenario.

MDDA: A Model-Driven Avionics Data Architecture

Large-scale complex avionics software system which is developed by different team together may exist message semantic ambiguity, lack of information and other issues during the system integration. This paper analyzes the key architecture attributes of avionics software system architecture, i.e. portability and interoperability, and proposes a Model-Driven Avionics Data Architecture (MDDA) based on the standardized software component interoperation message transmission interface. MDDA models the message transformed between avionics software components through the standardization of message transmission interface in the platform-independent ontology semantics level, measurement semantics level and platform-related message data type level respectively. OCL is used to constrain and verify the data model, and codes of platform-related message data type model are generated to ensure that the data used in interoperate is semantically and grammatically consistent. In order to realize the reuse of the basic model elements in the ontology and measurement semantic model, this paper further puts forward the management mechanism of these basic model elements. According to MDDA this paper designs and implements a distributed MDDA environment system (MDDAES), which is based on cloud infrastructure and aimed multi-team coordination, including four tools. Finally, requirements of practical avionics project are extracted and a verification case is developed. The verification shows that MDDAES is feasible and effective.

Bridging the gap: Toward a cognitive semantic analysis of the Lithuanian superlexical prefix be-

AbstractThe present article explores the Lithuanian aspectual prefix be- from a cognitive perspective while building on the generative syntactic approach to be- as a superlexical prefix. The prefix be- is peculiar in that, while being able to stack on lower, the so-called lexical, prefixes, it is used to form a number of constructions, most notably those with continuative, prospective, or modality readings. In addition, it forms two separable compound prefixes, the continuative tebe- and discontinuative nebe-. Building on previous cognitive examinations of the English progressive and Slavic prefixation and incorporating the Ontological Semantic inventory of modalities, the prefix is approached in light of the Figure/ Ground distinction as well as the notion of epistemicity. The article focuses on the relationship holding between the prefix and modality-shaded meanings.

Development, dissemination, and applications of a new terminological resource, the Q-Code taxonomy for professional aspects of general practice/family medicine

Background: While documentation of clinical aspects of General Practice/Family Medicine (GP/FM) is assured by the International Classification of Primary Care (ICPC), there is no taxonomy for the professional aspects (context and management) of GP/FM.Objectives: To present the development, dissemination, applications, and resulting face validity of the Q-Codes taxonomy specifically designed to describe contextual features of GP/FM, proposed as an extension to the ICPC.Development: The Q-Codes taxonomy was developed from Lamberts’ seminal idea for indexing contextual content (1987) by a multi-disciplinary team of knowledge engineers, linguists and general practitioners, through a qualitative and iterative analysis of 1702 abstracts from six GP/FM conferences using Atlas.ti software. A total of 182 concepts, called Q-Codes, representing professional aspects of GP/FM were identified and organized in a taxonomy.Dissemination: The taxonomy is published as an online terminological resource, using semantic web techniques and web ontology language (OWL) (http://www.hetop.eu/Q). Each Q-Code is identified with a unique resource identifier (URI), and provided with preferred terms, and scope notes in ten languages (Portuguese, Spanish, English, French, Dutch, Korean, Vietnamese, Turkish, Georgian, German) and search filters for MEDLINE and web searches.Applications: This taxonomy has already been used to support queries in bibliographic databases (e.g., MEDLINE), to facilitate indexing of grey literature in GP/FM as congress abstracts, master theses, websites and as an educational tool in vocational teaching,Conclusions: The rapidly growing list of practical applications provides face-validity for the usefulness of this freely available new terminological resource.

Type Theories and Lexical Networks: using Serious Games as the basis for Multi-Sorted Typed Systems

In this paper, we show how a rich lexico-semantic network which has been built using serious games, JeuxDeMots, can help us in grounding our semantic ontologies as well as different sorts of information in doing formal semantics using rich or modern type theories (type theories within the tradition of Martin Löf). We discuss the domain of base types, adjectival and verbal types, hyperonymy/hyponymy relations as well as more advanced issues like homophony and polysemy. We show how one can take advantage of this wealth in a formal compositional semantics framework. This is a way to sidestep the problem of deciding how your type ontology should look like once you have made a move to a many sorted type system. Furthermore, we show how this kind of information can be extracted from JeuxdeMots and inserted into a proof-assistant like Coq in order to perform reasoning tasks using modern type theoretic semantics.

A Semantic Web Approach to Simplifying Trigger-Action Programming in the IoT

End-user programming environments for the IoT such as IFTTT rely on a multitude of low-level trigger-action rules that categorize devices and services by technology or brand. EUPont is a Semantic Web ontology that enables users to meet their needs with fewer, higher-level rules that can be adapted to different contextual situations and as-yet-unknown IoT devices and services.

Bridging the Gap between the Social and Semantic Web: Extracting domain-specific ontology from folksonomy

Folksonomies have become very popular as means to organize large sets of resources shared over the Social Web. The bottom-up nature of folksonomies has proved to be an interesting alternative to the current effort at semantic web ontologies since folksonomies provide a rich terminology generated by large user-communities. Besides, ontologies extracted from folksonomies can represent the intelligence collective of social communities. Such ontologies also represent a core element of a new feature of the Web, the Internet of Things. Many research studies have captured semantics in folksonomies, some of which have developed ontologies from folksonomy. However, the formal specific-domain ontology consisting of domain-dependent relations has not been researched yet. This paper introduces an algorithm for deriving a domain-specific ontology from folksonomy tags. The proposed algorithm starts by collecting a domain-specific terminology; next, discovering a pre-defined set of conceptual relationships among the domain terminologies. The evaluation of the algorithm, using a dataset extracted from BibSonomy, demonstrated that the algorithm could effectively learn domain ontologies consisting of domain concepts linked by meaningful and high accurate relationships. Furthermore, the proposed algorithm can help reduce common issues related to tag ambiguity and synonymous tags.

Ontology based heterogeneous materials database integration and semantic query

Materials digital data, high throughput experiments and high throughput computations are regarded as three key pillars of materials genome initiatives. With the fast growth of materials data, the integration and sharing of data is very urgent, that has gradually become a hot topic of materials informatics. Due to the lack of semantic description, it is difficult to integrate data deeply in semantic level when adopting the conventional heterogeneous database integration approaches such as federal database or data warehouse. In this paper, a semantic integration method is proposed to create the semantic ontology by extracting the database schema semi-automatically. Other heterogeneous databases are integrated to the ontology by means of relational algebra and the rooted graph. Based on integrated ontology, semantic query can be done using SPARQL. During the experiments, two world famous First Principle Computational databases, OQMD and Materials Project are used as the integration targets, which show the availability and effectiveness of our method.

A systematic literature review of the use of Semantic Web technologies in formal education

AbstractThis paper presents a systematic literature review of research focused on use of Semantic Web technologies in formal educational contexts. Through systematic search, the review has identified 199 research articles, which are examined with the intention of identifying prevalent themes within the body of research within the field of formal educational use of Semantic Web technologies. The themes identified by the review are: (1) Semantic Web ontologies; (2) Efficient distribution, accessibility, retrieval, reuse and combination of educational resources; (3) Linked Data; (4) Semantic Web enhanced virtual learning environments and personalization of learning environments; (5) Semantic Web learning objects; (6) Evaluation, feedback and assessment; (7) Semantic Web services; (8) Pedagogical tools for teachers and students. Furthermore, this review seeks to examine how these themes and the use of Semantic Web technologies in formal education reflects on the ongoing discussion of how pedagogy and technology should interact. The outset of this discussion is a previous study by Dirckinck‐Holmfeld, which establishes that there is a tendency to emphasize technology over pedagogy in educational design and development.

The ACORN-SAT linked climate dataset

In 2012 the Australian Bureau of Meteorology published a dataset, ACORN-SAT, containing the homogenised daily temperature observations of 112 locations throughout Australia for the last 100 years. The dataset employs the latest analysis techniques and takes advantage of newly digitised observational data to monitor climate variability and change in Australia. The observations in ACORN-SAT were initially published only as comma separated values, whereas the metadata was published in a PDF report. In 2013 we converted the metadata and the observation data into RDF and published the result as Linked Open Data, accessible online via a pilot government linked data service built on the Linked Data API. In this article we describe the process of transforming the original tabular data into a Linked Sensor Data Cube (in: Proc. of the 5th International Workshop on Semantic Sensor Networks, SSN12, CEUR-WS.org, 2012, pp. 1-16) based on the W3C Semantic Sensor Network ontology (Web Semantics: Science, Services and Agents on the World Wide Web 17 (2012), 25-32) and the W3C RDF Data Cube vocabulary (The RDF Data Cube Vocabulary, W3C Recommendation, 16 January 2014). We further discuss how the dataset has since been used and interlinked with near-real time weather observations for the 112 sensing locations of the ACORN-SAT that are published by the Bureau of Meteorology. Both the original ACORN-SAT dataset and the weather observation data are accessible online at lab.environment.data.gov.au.

Ontology-Based Big Data Management

Big data management is no longer an issue for large enterprises only; it has also become a challenge for small and middle-sized enterprises, too. Today, enterprises have to handle business data and processes of increasing complexity that are almost entirely electronic in nature, regardless of enterprises’ size. Enterprises’ information systems need functions based on specific technologies to be able to reduce and interpret the complexity of business data and processes. This paper pursues the question: how can state-of-the-art information systems be improved by the use of semantic technologies, and particularly ontologies? For this purpose, three use cases of information systems that could be improved are described, and approaches based on semantic technologies and ontologies are proposed. The selected use cases relate to data integration, data quality, and business process integration.

The Control of Human Factors in Catastrophic Financial Systems Risk using Ontologies

This paper examines a possible response to local and global socio-economic instability created as a consequence of the global financial crisis starting in 2007 to 2009 which still reverberates. The crisis was at least in part, precipitated by systemic failures in financial regulatory systems, including those systems supposedly designed to monitor for dangerous events. This paper outlines a study which tries to address the predominant failure to appropriately incorporate human factors in systemic financial systems and utilise important features of advanced control systems such as semantic technologies and ontology. The study will ultimately develop a risk management ontology which addresses interoperability issues in the management of risk in the financial sector.