2,244 research outputs found
Ontology-based Navigation of Bibliographic Metadata
This paper describes the work done within the Food and
Agriculture Organization of the United Nations (FAO) on providing an
ontology-based navigation for the Food, Nutrition and Agriculture
(FNA) Journal. The aim of the revised navigation was to provide more
efficient and effective browsing of the Food and Nutrition Publications
using a knowledge model to guide the user with concepts and
relationships relevant to a specific subject area. With this approach, data
from two different bibliographical databases was merged, unified and
presented to the user with improved services. A preliminary metadata
merge was needed to combine all the information into one system in
order to produce a metadata-ontology. Resource Description
Framework Schema (RDFS) was chosen to exploit semantic
relationships, e.g. the possibilities of browsing the data in different
ways (by keywords, categories, authors, etc.), and the creation of a
multilingual concept-based advanced search
Visual knowledge representation of conceptual semantic networks
This article presents methods of using visual analysis to visually represent large amounts of massive, dynamic, ambiguous data allocated in a repository of learning objects. These methods are based on the semantic representation of these resources. We use a graphical model represented as a semantic graph. The formalization of the semantic graph has been intuitively built to solve a real problem which is browsing and searching for lectures in a vast repository of colleges/courses located at Western Kentucky University1. This study combines Formal Concept Analysis (FCA) with Semantic Factoring to decompose complex, vast concepts into their primitives in order to develop knowledge representation for the HyperManyMedia2 platform. Also, we argue that the most important factor in building the semantic representation is defining the hierarchical structure and the relationships among concepts and subconcepts. In addition, we investigate the association between concepts using Concept Analysis to generate a lattice graph. Our domain is considered as a graph, which represents the integrated ontology of the HyperManyMedia platform. This approach has been implemented and used by online students at WKU3
Towards a Universal Wordnet by Learning from Combined Evidenc
Lexical databases are invaluable sources of knowledge about words and their meanings, with numerous applications in areas like NLP, IR, and AI. We propose a methodology for the automatic construction of a large-scale multilingual lexical database where words of many languages are hierarchically organized in terms of their meanings and their semantic relations to other words. This resource is bootstrapped from WordNet, a well-known English-language resource. Our approach extends WordNet with around 1.5 million meaning links for 800,000 words in over 200 languages, drawing on evidence extracted from a variety of resources including existing (monolingual) wordnets, (mostly bilingual) translation dictionaries, and parallel corpora. Graph-based scoring functions and statistical learning techniques are used to iteratively integrate this information and build an output graph. Experiments show that this wordnet has a high level of precision and coverage, and that it can be useful in applied tasks such as cross-lingual text classification
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What can be done with the Semantic Web? An overview of Watson-based applications
Thanks to the huge efforts deployed in the community for creating, building and generating semantic information for the Semantic Web, large amounts of machine processable knowledge are now openly available. Watson is an infrastructure component for the Semantic Web, a gateway that provides the necessary functions to support applications in using the Semantic Web. In this paper, we describe a number of applications relying on Watson, with the purpose of demonstrating what can be achieved with the Semantic Web nowadays and what sort of new, smart and useful features can be derived from the exploitation of this large, distributed and heterogeneous base of semantic information
ASPICO: Advanced Scientific Portal for International Cooperation on Digital Cultural Conten
* This research is partially supported by a grant (bourse Lavoisier) from the French Ministry of Foreign Affairs (Ministère des Affaires Etrangères).In this paper, we present the development of an advanced open source multi-lingual cooperative portal
system (ASPICO) dedicated to semantic management, and to cooperative exchange for research and education
purpose on digital cultural projects. Advantages of using ASPICO include greater flexibility for digital resource
management, generic and systematic ontology-based metadata management, and better semantic access and
delivery based on an innovative Information Modeling for Adaptive Management (IMAM)
Towards Building a Knowledge Base of Monetary Transactions from a News Collection
We address the problem of extracting structured representations of economic
events from a large corpus of news articles, using a combination of natural
language processing and machine learning techniques. The developed techniques
allow for semi-automatic population of a financial knowledge base, which, in
turn, may be used to support a range of data mining and exploration tasks. The
key challenge we face in this domain is that the same event is often reported
multiple times, with varying correctness of details. We address this challenge
by first collecting all information pertinent to a given event from the entire
corpus, then considering all possible representations of the event, and
finally, using a supervised learning method, to rank these representations by
the associated confidence scores. A main innovative element of our approach is
that it jointly extracts and stores all attributes of the event as a single
representation (quintuple). Using a purpose-built test set we demonstrate that
our supervised learning approach can achieve 25% improvement in F1-score over
baseline methods that consider the earliest, the latest or the most frequent
reporting of the event.Comment: Proceedings of the 17th ACM/IEEE-CS Joint Conference on Digital
Libraries (JCDL '17), 201
Intelligent multimedia indexing and retrieval through multi-source information extraction and merging
This paper reports work on automated meta-data\ud
creation for multimedia content. The approach results\ud
in the generation of a conceptual index of\ud
the content which may then be searched via semantic\ud
categories instead of keywords. The novelty\ud
of the work is to exploit multiple sources of\ud
information relating to video content (in this case\ud
the rich range of sources covering important sports\ud
events). News, commentaries and web reports covering\ud
international football games in multiple languages\ud
and multiple modalities is analysed and the\ud
resultant data merged. This merging process leads\ud
to increased accuracy relative to individual sources
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