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Search Results: 1 - 10 of 1600 matches for " ontology "
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Ontology of African Ritual  [PDF]
Francis Etim
Advances in Applied Sociology (AASoci) , 2019, DOI: 10.4236/aasoci.2019.91001
Abstract: African rituals, like other phenomena of African cultural heritage are usually faced with criticisms of being either superstitious, fetish, mundane or simply irrational. These criticisms, often based on certain logical criteria, have categorized the African mode of thinking as illogical, unreasonable and non-rational. Given the proclivity of the African mode of thinking of fusing the epistemological into the metaphysical, such criticism could either be excused or be regarded as a misinterpretation, misrepresentation and non-sequitur. However, the issue at stake calls for a deep examination of some of these phenomena to establish their reasonableness as veritable reality among Africans with serious existential import. One of such phenomena is ritual, which is actually pervasive as far as African existential reality is concerned. This paper examines African ritual to establish its reasonableness by establishing its ontology. The paper argues that based on African ontology, African rituals cannot be judged on the principles of Western scientific rationality but rather should be seen as a non-rational action like other human phenomenon like love or possessing rationality internal to the metaphysical beliefs that underpin the African worldview.
Node-Match Tool
Asankhaya Sharma
PeerJ , 2015, DOI: 10.7287/peerj.preprints.104v1
Abstract: We present a tool Node-Match that can do ontology matching using node ranking over graphical representation of the ontologies. Simple ontology matching algorithms are based on lexical measures that only consider text similarity. Taking the shape and structure of the ontology into account while matching can lead to better results [1, 2, 3]. Node-Match illustrates the difference between the two approaches. It is based on S-Match [4] an open source toolkit for lightweight ontology matching and alignment. S-Match supports a rich GUI for designing lightweight ontologies and several ontology alignment algorithms. We use the Department Ontology from the running example in [1] to show the difference between difference ontology matching algorithms.
Using an Ontology to Help Reason about the Information Content of Data  [PDF]
Shuang Zhu, Junkang Feng
Journal of Software Engineering and Applications (JSEA) , 2010, DOI: 10.4236/jsea.2010.37073
Abstract: We explore how an ontology may be used with a database to support reasoning about the “information content” of data whereby to reveal hidden information that would otherwise not derivable by using conventional database query languages. Our basic ideas rest with “ontology” and the notions of “information content”. A public ontology, if available, would be the best choice for reliable domain knowledge. To enable an ontology to work with a database would involve, among others, certain mechanism thereby the two systems can form a coherent whole. This is achieved by means of the notion of “information content inclusion relation”, IIR for short. We present what an IIR is, and how IIR can be identified from both an ontology and a database, and then reasoning about them.
A COMPREHENSIVE HYBRID LCGA CONCEPTUAL FRAMEWORK FOR ONTOLOGY MAPPING.
N.VANJULAVALLI,DR.A.KOVALAN
International Journal of Engineering Science and Technology , 2012,
Abstract: The emerging of semantic web has urged the users to access a huge collection of in formations extensively. The increase in use of the web has motivated the researchers to design various web enabled system like ontologies. Ontologies are used to interoperate across heterogeneous systems and semantic web applications. Ontologymapping is the key to solve the heterogeneous nature of the web. Mapping these heterogeneous ontologies is one of the key challenges in the field of ontological research. This paper reveals the fact about ontologies and ontology mappings. This categorizes the mapping process and explains the approaches and the existing work on it. It then concludes by proposing a new model for ontology mapping based on the machine learning model. The future challenges with ontology mapping are discussed by stating the limitations that prevails now.
Metadata Extended Model Based On Geological Domain Ontology  [PDF]
Ying HUANG, Mingqiang GUO, Xiangang LUO, Zhong XIE
Journal of Geographic Information System (JGIS) , 2009, DOI: 10.4236/jgis.2009.11003
Abstract: The current metadata modeling techniques can not meet the needs of knowledge conception expression, knowledge organization, and metadata semantic consistency in geological domain. This paper introduces ontology and integrates this theory to geological domain metadata modeling. It adopts the first order logic equivalent algorithm and defines the metadata extended model as a quaternion group which is consists of geological term set, geological term definition set, attribute definition set and instance set. It also provides the formal description of each set. Finally the five steps for building geological domain metadata extended model are given. The result presents that this model not only provides the content standards for geological domain knowledge representation and knowledge organization, but also provides the basis for geological domain multi-source data and historical data integration and application in semantic consistency.
A Process for Extracting Non-Taxonomic Relationships of Ontologies from Text  [PDF]
Ivo Serra, Rosario Girardi
Intelligent Information Management (IIM) , 2011, DOI: 10.4236/iim.2011.34014
Abstract: Manual construction of ontologies by domain experts and knowledge engineers is an expensive and time consuming task so, automatic and/or semiautomatic approaches are needed. Ontology learning looks for identifying ontology elements like non-taxonomic relationships from information sources. These relationships correspond to slots in a frame-based ontology. This article proposes an initial process for semiautomatic extraction of non-taxonomic relationships of ontologies from textual sources. It uses Natural Language Processing (NLP) techniques to identify good candidates of non-taxonomic relationships and a data mining technique to suggest their possible best level in the ontology hierarchy. Once the extraction of these relationships is essentially a retrieval task, the metrics of this field like recall, precision and f-measure are used to perform evaluation.
An Appraisal of Man’s Essence in Bantu Ontology  [PDF]
Celestine Chukwuemeka Mbaegbu
Open Journal of Philosophy (OJPP) , 2015, DOI: 10.4236/ojpp.2015.54027
Abstract: With the Socratic injunction: “man know thyself”; the West began a formal search for the nature of man. What is man? What is his essence in real life; what exactly makes him what he is? These questions, apart from dividing philosophers in the West into different warring camps, also portray man as incapable of self knowledge; hence man is described as a paradox. This seemingly insoluble problem among Western philosophers is grounded on their conception of reality as static and dichotomised. In Africa with the understanding of reality as one unitary whole, though distinct and yet complementary, penetrating and interacting with each other, the dualism disappears and there is what we call the “harmony of African conceptions”. In this article, using comparative analysis, the essence of man is critically examined within a particular African culture, namely, Bantu ontology and with their conception of reality as dynamic, a conception in contradistinction to the Western static conception of reality The conclusion is that a new definition of man emerges, a definition which is one of the essential characteristics of who is an African?
BINLI: An Ontology-Based Natural Language Interface for Multidimensional Data Analysis  [PDF]
José Saias, Paulo Quaresma, Pedro Salgueiro, Tiago Santos
Intelligent Information Management (IIM) , 2012, DOI: 10.4236/iim.2012.45033
Abstract: Current technology facilitates access to the vast amount of information that is produced every day. Both individuals and companies are active consumers of data from the Web and other sources, and these data guide decision making. Due to the huge volume of data to be processed in a business context, managers rely on decision support systems to facilitate data analysis. OLAP tools are Business Intelligence solutions for multidimensional analysis of data, allowing the user to control the perspective and the degree of detail in each dimension of the analysis. A conventional OLAP system is configured to a set of analysis scenarios associated with multidimensional data cubes in the repository. To handle a more spontaneous query, not supported in these provided scenarios, one must have specialized technical skills in data analytics. This makes it very difficult for average users to be autonomous in analyzing their data, as they will always need the assistance of specialists. This article describes an ontology-based natural language interface whose goal is to simplify and make more flexible and intuitive the interaction between users and OLAP solutions. Instead of programming an MDX query, the user can freely write a question in his own human language. The system interprets this question by combining the requested information elements, and generates an answer from the OLAP repository.
The Application of Ontology in Semantic Discovery for GeoData Web Service  [PDF]
Mingwu Guo
Communications and Network (CN) , 2013, DOI: 10.4236/cn.2013.53B2121
Abstract:

GeoData Web service is an important way to achieve the integration and sharing of heterogeneous geospatial data at present. However, due to the complexity of GeoData and no sematic supporting Webservice discovery, it is very hard for data users to accurately find the GeoData WebService they really want. In order to make it easy for users to quickly and accurately find the GeoData Web Service they want in semantic level, this article firstly, constructs MetaData Ontololy, and uses MetaData Ontology to describe the related semantic information for GeoData Web Service. Then it comes up with a new way of computing the degree of semantic similarity among concepts based on Ontology. Finally, it realizes the automatic discovery for GeoData Web Service based on semantic matching. The experiment result shows that the way in this article can dramatically improve the accuracy and intelligence of GeoData Web Service discovery.

Web Semantic and Ontology  [PDF]
Elodie Marie Gontier
Advances in Internet of Things (AIT) , 2015, DOI: 10.4236/ait.2015.52003
Abstract: Ontologies have become a popular research topic in many communities. In fact, ontology is a main component of this research; therefore, the definition, structure and the main operations and applications of ontology are provided. Web content consists mainly of distributed hypertext and hypermedia, and is accessed via a combination of keyword based search and link navigation. Hence, the ontology can provide a common vocabulary, and a grammar for publishing data, and can supply a semantic description of data which can be used to preserve the ontologies and keep them ready for inference. This paper provides basic concepts of semantic web, and defines the structure and the main applications of ontology.
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