660 research outputs found
Ontologies and Information Extraction
This report argues that, even in the simplest cases, IE is an ontology-driven
process. It is not a mere text filtering method based on simple pattern
matching and keywords, because the extracted pieces of texts are interpreted
with respect to a predefined partial domain model. This report shows that
depending on the nature and the depth of the interpretation to be done for
extracting the information, more or less knowledge must be involved. This
report is mainly illustrated in biology, a domain in which there are critical
needs for content-based exploration of the scientific literature and which
becomes a major application domain for IE
The fourth V, as in evolution: How evolutionary linguistics can contribute to data science
The paper explores the importance of closer interaction between data science and evolutionary linguistics, pointing to the potential benefits for both disciplines. In the context of big data, the microblogging social networking service – Twitter – can be treated as a source of empirical input for analyses in the field of language evolution. In an attempt to utilize this kind of disciplinary interplay, I propose a model, which constitutes an adaptation of the Iterated Learning framework, for investigating the glossogenetic evolution of sublanguages.
A Modular Logic Approach for Expressing Web Services in XML Applying Dynamic Rules in XML
RuleML is considered to be a markup language for the semantic web. It allows the enrichment of web ontologies by adding definitions of derived concepts and it enhances interoperability among different systems and tools by publishing rules in an XML format. Moreover the in-creasing demand for interfaces that enhance information sharing has given rise to XML doc-uments that include embedded calls to web services. In this paper we propose a variation of RuleML that is based on modular logic programming. Our approach is based in a two level architecture. In the first level a modular logic language, called M-log, is presented. This lan-guage encompasses several mechanisms for invoking web services. In the second level we ex-ploit the semantics of M-log to present a variation of RuleML with rich modeling capabilities. Formal foundations for this variation are given through direct translation to M-log semantics.Knowledge Management, XML, Modular Logic Programming, E-Services
Natural Language Communication With Virtual Actor
The development of realistic virtual actors in many
applications, from user interface to computer entertainment,
creates expectations on the intelligence of these actors
including their ability to understand natural language. Based
on our research in that area over the past years, we highlight
specific technical aspects in the development of
language-enabled actors. The embodied nature of virtual
agents lead to specific syntactic constructs that are not unlike
sublanguages: these can be used to specify the parsing
component of a natural language interface. However, the
most specific aspects of interacting with virtual actors
consist in mapping the semantic content of users’ input to the
mechanisms that support agents’ behaviours. We suggest
that a generalisation of speech acts can provide principles for
this integration. Both aspects are illustrated by results
obtained during the development of research prototypes.
Mapping relational data model to OWL ontology: knowledge conceptualization in OWL
In this paper, we introduce the issues and solutions of using OWL ontology to model extra restriction on 'Properties' of 'Classes' that are not provided by OWL specifications and to represent associations amongst 'Properties' other than 'Classes'. Two specific types of knowledge that cannot be modeled directly using OWL DL elements are identified and presented. Firstly the data value range constraint for a "DatatypeProperty"; secondly the calculation knowledge representation. Our approach to such issues is to conceptualize the knowledge in OWL and map the conceptualization in an implementation. Examples for each type of the knowledge and their OWL code are provided in detail to demonstrate our approach
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