Automatic multi-label subject indexing in a multilingual environment

This paper presents an approach to automatically subject index fulltext documents with multiple labels based on binary support vector machines(SVM). The aim was to test the applicability of SVMs with a real world dataset. We have also explored the feasibility of incorporating multilingual background knowledge, as represented in thesauri or ontologies, into our text document representation for indexing purposes. The test set for our evaluations has been compiled from an extensive document base maintained by the Food and Agriculture Organization (FAO) of the United Nations (UN). Empirical results show that SVMs are a good method for automatic multi- label classification of documents in multiple languages

Pruning-based identification of domain ontologies

We present a novel approach of extracting a domain ontology from large-scale thesauri. Concepts are identified to be relevant for a domain based on their frequent occurrence in domain texts. The approach allows to bootstrap the ontology engineering process from given legacy thesauri and identifies an initial domain ontology that may easily be refined by experts in a later stage. We present a thorough evaluation of the results obtained in building a biosecurity ontology for the UN FAO AOS project

Comparing human and automatic thesaurus mapping approaches in the agricultural domain

Knowledge organization systems (KOS), like thesauri and other controlled vocabularies, are used to provide subject access to information systems across the web. Due to the heterogeneity of these systems, mapping between vocabularies becomes crucial for retrieving relevant information. However, mapping thesauri is a laborious task, and thus big efforts are being made to automate the mapping process. This paper examines two mapping approaches involving the agricultural thesaurus AGROVOC, one machine-created and one human created. We are addressing the basic question "What are the pros and cons of human and automatic mapping and how can they complement each other?" By pointing out the difficulties in specific cases or groups of cases and grouping the sample into simple and difficult types of mappings, we show the limitations of current automatic methods and come up with some basic recommendations on what approach to use when.Comment: 10 pages, Int'l Conf. on Dublin Core and Metadata Applications 200

From thesauri to Ontologies: A short case study in the food safety area in how ontologies are more powerful than thesauri From thesauri to RDFS to OWL

This short case study will show on the basis of a simple example taken from the Food Safety area, how ontologies differ from thesauri. The example will start with showing an extract from the AGROVOC1 thesaurus and exploring the information that can be extracted from here. We will then develop this example further in order to show growing functionality and expressive power first in RDFS and finally in OWL ontologies

The AGROVOC concept server: rationale, goals and usage

The main objective of the AGROVOC Concept Server (CS)is to create a collaborative reference platform and a one-stop shop for a pool of commonly used concepts related to agriculture, containing terms, definitions and relationships between terms in multiple languages derived from various sources. This paper aims to address the issues

From AGROVOC to the Agricultural Ontology Service / Concept Server

This paper illustrates the conversion from a traditional thesaurus in agriculture (AGROVOC) to a new system, the Agricultural Ontology Service Concept Server (AOS/CS). The Concept Server will serve as a multilingual repository of concepts in the agricultural domain providing ontological relationships and a rich, semantically sound terminology. The Food and Agriculture Organization recently developed the underlying model for this new system in the Web ontology language OWL. In this paper, we describe the purpose of this conversion and the use of OWL and highlight in particular the core features of the developed OWL model. We go on to explain how it evolves and differs from the traditional thesaurus approach

From AGROVOC to the Agricultural Ontology Service/Concept Server. An OWL model for creating ontologies in the agricultural domain

This paper illustrates the conversion from a traditional thesaurus in agriculture (AGROVOC) to a new system, the Agricultural Ontology Service Concept Server (AOS/CS). The Concept Server will serve as a multilingual repository of concepts in the agricultural domain providing ontological relationships and a rich, semantically sound terminology. The Food and Agriculture Organization recently developed the underlying model for this new system in the Web ontology language OWL. In this paper, we describe the purpose of this conversion and the use of OWL and highlight in particular the core features of the developed OWL model. We go on to explain how it evolves and differs from the traditional thesaurus approach

Automatically Categorizing Metadata Databases into a Categorization Scheme on a Large Scale Web Site

In order to provide thematic access to the large scale web site of the Food and Agriculture Organization (FAO), we have adapted a scheme used in bibliographical databases to allow subject browsing on FAO’s web site. This requires categorizing existing database records across the organizations web sites into the new scheme. An algorithm to automatically categorize these metadata records based on their subject descriptors has been devised

A Comprehensive Framework for Building Multilingual Domain Ontologies: Creating a Prototype Biosecurity Ontology

This paper presents our ongoing work in establishing a multilingual domain ontology for a biosecurity portal. As a prototypical approach, this project is embedded into the bigger context of the Agricultural Ontology Service (AOS) project of the Food and Agriculture Organization (FAO) of the UN. The AOS will act as a reference tool for ontology creation assistance and herewith enable the transfer of the agricultural domain towards the Semantic Web. The paper focuses on introducing a comprehensive, reusable framework for the process of semi-automatically supported ontology evolvement, which aims to be used in follow-up projects and can eventually be applied to any other domain. Within the multinational context of the FAO, multilingual aspects play a crucial role and therefore an extendable layered ontology modelling approach will be described within the framework. The paper will present the project milestones achieved so far: the creation of a core ontology, the semiautomatic extension of this ontology using a heuristic toolset, and the representation of the resulting ontology in a multilingual web portal. The reader will be provided with a practical example for the creation of a specific domain ontology, which can be applied to any possible domain. Future projects, including automatic text classification, and ontology facilitated search opportunities, will be addressed at the end of the paper