1,345 research outputs found

    Semantically intelligent semi-automated ontology integration

    Get PDF
    An ontology is a way of information categorization and storage. Web Ontologies provide help in retrieving the required and precise information over the web. However, the problem of heterogeneity between ontologies may occur in the use of multiple ontologies of the same domain. The integration of ontologies provides a solution for the heterogeneity problem. Ontology integration is a solution to problem of interoperability in the knowledge based systems. Ontology integration provides a mechanism to find the semantic association between a pair of reference ontologies based on their concepts. Many researchers have been working on the problem of ontology integration; however, multiple issues related to ontology integration are still not addressed. This dissertation involves the investigation of the ontology integration problem and proposes a layer based enhanced framework as a solution to the problem. The comparison between concepts of reference ontologies is based on their semantics along with their syntax in the concept matching process of ontology integration. The semantic relationship of a concept with other concepts between ontologies and the provision of user confirmation (only for the problematic cases) are also taken into account in this process. The proposed framework is implemented and validated by providing a comparison of the proposed concept matching technique with the existing techniques. The test case scenarios are provided in order to compare and analyse the proposed framework in the analysis phase. The results of the experiments completed demonstrate the efficacy and success of the proposed framework

    Designing Statistical Language Learners: Experiments on Noun Compounds

    Full text link
    The goal of this thesis is to advance the exploration of the statistical language learning design space. In pursuit of that goal, the thesis makes two main theoretical contributions: (i) it identifies a new class of designs by specifying an architecture for natural language analysis in which probabilities are given to semantic forms rather than to more superficial linguistic elements; and (ii) it explores the development of a mathematical theory to predict the expected accuracy of statistical language learning systems in terms of the volume of data used to train them. The theoretical work is illustrated by applying statistical language learning designs to the analysis of noun compounds. Both syntactic and semantic analysis of noun compounds are attempted using the proposed architecture. Empirical comparisons demonstrate that the proposed syntactic model is significantly better than those previously suggested, approaching the performance of human judges on the same task, and that the proposed semantic model, the first statistical approach to this problem, exhibits significantly better accuracy than the baseline strategy. These results suggest that the new class of designs identified is a promising one. The experiments also serve to highlight the need for a widely applicable theory of data requirements.Comment: PhD thesis (Macquarie University, Sydney; December 1995), LaTeX source, xii+214 page

    A computational approach to Zulu verb morphology within the context of lexical semantics

    Get PDF
    The central research question that is addressed in this article is: How can ZulMorph, a finite state morphological analyser for Zulu, be employed to add value to Zulu lexical semantics with specific reference to Zulu verbs? The verb is the most complex word category in Zulu. Due to the agglutinative nature of Zulu morphology, limited information can be computationally extracted from running Zulu text without the support of  sufficiently reliable computational mor-phological analysis by means of which the  essential meanings of, amongst others, verbs can be exposed. In this article we describe a corpus-based approach to adding the English meaning to Zulu extended verb roots, thereby enhancing ZulMorph as a lexical knowledge base.Keywords: Zulu Verb Morphology, Verb Extensions, Lexical Semantics, Computational Morphological Analysis, Zulmorph, Zulu Lexical Knowl-Edge Base, Bitext

    Utilising semantic technologies for intelligent indexing and retrieval of digital images

    Get PDF
    The proliferation of digital media has led to a huge interest in classifying and indexing media objects for generic search and usage. In particular, we are witnessing colossal growth in digital image repositories that are difficult to navigate using free-text search mechanisms, which often return inaccurate matches as they in principle rely on statistical analysis of query keyword recurrence in the image annotation or surrounding text. In this paper we present a semantically-enabled image annotation and retrieval engine that is designed to satisfy the requirements of the commercial image collections market in terms of both accuracy and efficiency of the retrieval process. Our search engine relies on methodically structured ontologies for image annotation, thus allowing for more intelligent reasoning about the image content and subsequently obtaining a more accurate set of results and a richer set of alternatives matchmaking the original query. We also show how our well-analysed and designed domain ontology contributes to the implicit expansion of user queries as well as the exploitation of lexical databases for explicit semantic-based query expansion

    Designing an XML Lexicon Architecture for Arabic Machine Translation Based on Role and Reference Grammar

    Get PDF
    Role and Reference Grammar (RRG) is a functional theory of grammar. The main features of Role and Reference Grammar are the use of lexical decomposition, based upon predicate semantics, an analysis of clause structure and the use of a set of thematic roles organized into a hierarchy in which the highestranking roles are Actor (for the most active participant) and Undergoer. The theory allows a sentence in a specific language to be described in terms of its logical structure and grammatical procedures. The lexicon in RRG takes the position that lexical entries for verbs should contain unique information only, with as much information as possible derived from general lexical rules. We use the RRG theory to motivate the architecture of the lexicon. The lexicon is designed to reflect the word categories in the Arabic language with as much information as possible derived from general lexical rules. The lexicon stores the Arabic words in categories; each category is stored in an XML format datasource file. In order to be able to analyse Arabic by computer we must first extract the lexical properties of the Arabic words. Our system (UniArab) uses the lexicon to construct a logical structure for Arabic input sentences, also represented in XML, which is then used for generating the target language translation. We show the structure of the UniArab lexicon, discuss how it is used in the system, and show the user interface used for adding to the lexicon

    Can humain association norm evaluate latent semantic analysis?

    Get PDF
    This paper presents the comparison of word association norm created by a psycholinguistic experiment to association lists generated by algorithms operating on text corpora. We compare lists generated by Church and Hanks algorithm and lists generated by LSA algorithm. An argument is presented on how those automatically generated lists reflect real semantic relations
    corecore