Multilingual Ontology Research Articles

MultiFarm: A Benchmark for Multilingual Ontology Matching

In this paper we present the MultiFarm dataset, which has been designed as a benchmark for multilingual ontology matching. The MultiFarm dataset is composed of a set of ontologies translated in different languages and the corresponding alignments between these ontologies. It is based on the OntoFarm dataset, which has been used successfully for several years in the Ontology Alignment Evaluation Initiative (OAEI). By translating the ontologies of the OntoFarm dataset into eight different languages – Chinese, Czech, Dutch, French, German, Portuguese, Russian, and Spanish - we created a comprehensive set of realistic test cases. Based on these test cases, it is possible to evaluate and compare the performance of matching approaches with a special focus on multilingualism.

Designing a cross-language comparison-shopping agent

This research pertains to the design and development of a shopbot called WebShopper+. This shopbot is intended to help customers find and compare e-tailers that market their wares using different languages. WebShopper+ is built with a multilingual ontology to overcome the language barriers that arise with global e-commerce. This research proposes a semi-automatic method of constructing a multilingual ontology by using the formal concept analysis and association analysis. It also proposes an automatic method for the categorization of product data into predefined classes, with the aim of alleviating administrators' task load. Additionally, a semantic search mechanism based on concept similarity is designed to assist customers in finding more desirable products. The experimental results show that these methods perform well and the shopbot can help customers find real bargains on the Web and to find products that cannot be bought locally.

Exploiting multi-agent platform for indirect alignment between multilingual ontologies: A case study on tourism business

The existing ontology alignment methods have been trying to automatically obtain semantic correspondences between ontologies. However, they assume that all elements of source ontologies are written by identical languages. In this paper, we propose a multi-agent system for building indirect alignment between multilingual ontologies. Thereby, a novel architecture to reuse and compose alignments between ontologies has been designed. For a case study, we have implemented an information retrieval system for searching for tourism information by using JADE API. This system has been tested with multilingual ontologies written by English, Korean and Swedish languages.

Enriching ontologies with multilingual information

AbstractThis paper presents a novel approach to ontology localization with the objective of obtaining multilingual ontologies. Within the ontology development process, ontology localization has been defined as the activity of adapting an ontology to a concrete linguistic and cultural community. Depending on the ontology layers – terminological and/or conceptual – involved in the ontology localization activity, three heterogeneous multilingual ontology metamodels have been identified, of which we propose one of them. Our proposal consists in associating the ontology metamodel to an external model for representing and structuring lexical and terminological data in different natural languages. Our model has been called Linguistic Information Repository (LIR). The main advantages of this modelling modality rely on its flexibility by allowing (1) the enrichment of any ontology element with as much linguistic information as needed by the final application, and (2) the establishment of links among linguistic elements within and across different natural languages. The LIR model has been designed as an ontology of linguistic elements and is currently available in Web Ontology Language (OWL). The set of lexical and terminological data that it provides to ontology elements enables the localization of any ontology to a certain linguistic and cultural universe. The LIR has been evaluated against the multilingual requirements of the Food and Agriculture Organization of the United Nations in the framework of the NeOn project. It has proven to solve multilingual representation problems related to the establishment of well-defined relations among lexicalizations within and across languages, as well as conceptualization mismatches among different languages. Finally, we present an extension to the Ontology Metadata Vocabulary, the so-called LexOMV, with the aim of reporting on multilinguality at the ontology metadata level. By adding this contribution to the LIR model, we account for multilinguality at the three levels of an ontology: data level, knowledge representation level and metadata level.

Cross Language Information Retrieval using Multilingual Ontology as Translation and Query Expansion Base (pp. 13-16)

Abstract —This paper reports an experiment to evaluate a Cross Language Information Retrieval (CLIR) system that uses a multilingual ontology to improve query translation in the travel domain. The ontology-based approach significantly outperformed the Machine Readable Dictionary translation baseline using Mean Average Precision as a metric in a user-centered experiment. Index terms —Ontology, multilingual, cross language information retrieval. I. I NTRODUCTION HE growing requirement on the Internet for users to access information expressed in language other than their own has led to Cross Language Information Retrieval (CLIR) becoming established as a major topic in IR. One approach to CLIR uses different translation approaches to translate queries to documents and indexes in other languages. As queries submitted to search engines suffer lack of context, translation approaches have great problems with resolving query ambiguity. In our approach, we built a multilingual ontology to be used as a translation base for CLIR. In this paper we evaluate our proposed query translation methodology and compare it with a base line system that uses a Machine Readable Dictionary (MRD) as translation base in a user-centered experiment. II. B

A multilingual ontology framework for R&D project management systems

R&D project management systems are developed to facilitate the efficient management of R&D projects in government-funding agencies and universities. They should support R&D information sharing among researchers and research administrators from different nationalities using different languages. This paper presents an ontology-based methodology to the development of R&D project management systems with multilingual supports. In particular, a multilingual ontology is proposed, which consists of domain model, application model, user model and linguistic model. The methodology is used in the development of an R&D project management system for the Innovation and Technology Commission of the Hong Kong SAR Government. The system supports three languages and facilitates R&D information sharing among users with different culture backgrounds and usage preferences.

Adaptive Ontology Use for Crisis Knowledge Representation

In a crisis the problem of the lack of a shared platform or similar communication methods among the collaborators usually arises within a few hours. While a crisis requires rapid response of emergency management factors, ontology is generally represented in a static manner. Therefore, an adaptive ontology for crisis knowledge representation is needed to assist in coordinating relief efforts in different crisis situations. The article describes a method of ontology modeling that modifies the ontology in real time during a crisis according to the crisis surroundings. The method is based on modeling a basic predefined multilingual ontology while allowing the expansion of the ontology according to the crisis circumstances and the addition of other languages within the crisis time limitations. An example of ontology use based on a sample Katrina crisis blog is presented. Motivation for multilingual ontology use is supplied by the Boxing Day Tsunami crisis.

Developing a Shopbot with Multilingual Ontology for Global E-Commerce

Language is an enormous barrier to global ecommerce. Internet users favor visiting or shopping on Web sites presented in their native language. This research proposes a shopbot with a multilingual ontology to overcome this language barrier. The shopbot, called WebShopper, collects product data from online vendors over the Web and enables customers to execute semantic searches using different languages. An empirical study is conducted to evaluate this shopbot. The result shows that customers are able to reach more products and find real bargains using WebShopper, and the proposed semantic search can improve search results and user satisfaction.

Building and Using Geospatial Ontology in the BioCaster Surveillance System

AbstractThis abstract presents an approach to building a geospatial ontology from Wikipedia and using it in BioCaster, a system for detecting and tracking infectious disease outbreaks from online news. Motivated by the need to interpret the geospatial dynamics of events we built a database containing the names of countries and major cities from Wikipedia. We started by automatically extracting country and dependent territory names and sub-country (subdivision and dependent area) names in the form of ISO 3166-1 and ISO 3166-2, respectively. Then, we re-created the part-whole relation between countries and sub-countries by verifying links from countries to their sub-countries. Verification was done by manual checking. The building process is semi-automatically implemented with automatically extracting locations and verification with human-aid. In addition,we extracted absolute longitudes/latitudes of each location for the use in Google Map and Google Earth applications. Finally we combined the geospatial hierarchy from Wikipedia with the BioCaster ontology (BCO). The preliminary results show a geospatial ontology with two administrative levels: 243 countries and 4,025 sub-countries. The geospatial ontology was integrated into the extant BCO, a multilingual public health ontology focusing on infectious diseases and was available at "http://biocaster.nii.ac.jp":http://biocaster.nii.ac.jp.The geospatial ontology was used to develop an algorithm for detecting locations of outbreaks that occur in news stories. Firstly, locations in news stories are automatically tagged with a named entity recognizer based on a support vector machine trained on 1,000 manually annotated texts. Secondly, we mapped location names from the text to identifiers in the geospatial ontology at the country and sub-country levels. Grounding proceeded as follows: First, we ranked pairs of disease-location by frequency in a set of collected articles which shared similar date stamps. We then chose the top disease-location pairs to re-map into each news story. The re-mapping process is done by regular expression matching. In order to infer country names where this information was missing from the text we manually constructed a ranked list of sub-country and country pairs based on population size.Data collected in a 10 week period (Dec 20, 2007 to Feb 20, 2008) showed that the system detected 7,412 English articles, covering 110 countries and 360 sub-countries, of which 58.00% Africa, 18.23% Asia, 11.37% South America, 5.30 % North America, 3.40% Middle East, 2.86% Europe and 0.34% Ocean. Relevant articles came predominantly from a few sources such as Google News, the European Media Monitor and ProMED-mail. Among disease/country outbreaks successfully detected during this period were ebola in Uganda (Bundibugyo, Kampala, Mbarara), yellow fever in Brazil (Goias, Sao Paulo), avian influenza in Indonesia (Jakarta, Banten), and cholera in Vietnam (Ha Noi, Ha Tay).The results were plotted on a publicly available Google Map and indicate that our geospatial ontology met our requirements. In the future, we plan to extend the ontology into deeper levels like districts and sub-districts (wards, towns, villages). Evaluation and comparison of our geospatial ontology to other available resources like GAZ and dbpedia will also be considered.

A multilingual knowledge management system: A case study of FAO and WAICENT

This paper provides a case study of a multilingual knowledge management system for a large organization. In so doing we elicit what it means for a system to be “multilingual” and how that changes some previous research on knowledge management. Some researchers have viewed multilingual as meaning a multilingual user interface. However, that is only a small part of the story. In this case we find multilingual also refers to a broad range of “multilingual,” including multilingual knowledge resources, multilingual feedback from users, multilingual search, multilingual ontologies and other concerns.

A multilingual ontology for infectious disease surveillance: rationale, design and challenges.

A lack of surveillance system infrastructure in the Asia-Pacific region is seen as hindering the global control of rapidly spreading infectious diseases such as the recent avian H5N1 epidemic. As part of improving surveillance in the region, the BioCaster project aims to develop a system based on text mining for automatically monitoring Internet news and other online sources in several regional languages. At the heart of the system is an application ontology which serves the dual purpose of enabling advanced searches on the mined facts and of allowing the system to make intelligent inferences for assessing the priority of events. However, it became clear early on in the project that existing classification schemes did not have the necessary language coverage or semantic specificity for our needs. In this article we present an overview of our needs and explore in detail the rationale and methods for developing a new conceptual structure and multilingual terminological resource that focusses on priority pathogens and the diseases they cause. The ontology is made freely available as an online database and downloadable OWL file.

Aligning word senses using bilingual corpora

The growing importance of multilingual information retrieval and machine translation has made multilingual ontologies extremely valuable resources. Since the construction of an ontology from scratch is a very expensive and time-consuming undertaking, it is attractive to consider ways of automatically aligning monolingual ontologies, which already exist for many of the world's major languages. Previous research exploited similarity in the structure of the ontologies to align, or manually created bilingual resources. These approaches cannot be used to align ontologies with vastly different structures and can only be applied to much studied language pairs for which expensive resources are already available. In this paper, we propose a novel approach to align the ontologies at the node level: Given a concept represented by a particular word sense in one ontology, our task is to find the best corresponding word sense in the second language ontology. To this end, we present a language-independent, corpus-based method that borrows from techniques used in information retrieval and machine translation. We show its efficiency by applying it to two very different ontologies in very different languages: the Mandarin Chinese HowNet and the American English WordNet. Moreover, we propose a methodology to measure bilingual corpora comparability and show that our method is robust enough to use noisy nonparallel bilingual corpora efficiently, when clean parallel corpora are not available.