Multilingual Ontology Research Articles

The African wildlife ontology tutorial ontologies

BackgroundMost tutorial ontologies focus on illustrating one aspect of ontology development, notably language features and automated reasoners, but ignore ontology development factors, such as emergent modelling guidelines and ontological principles. Yet, novices replicate examples from the exercise they carry out. Not providing good examples holistically causes the propagation of sub-optimal ontology development, which may negatively affect the quality of a real domain ontology.ResultsWe identified 22 requirements that a good tutorial ontology should satisfy regarding subject domain, logics and reasoning, and engineering aspects. We developed a set of ontologies about African Wildlife to serve as tutorial ontologies. A majority of the requirements have been met with the set of African Wildlife Ontology tutorial ontologies, which are introduced in this paper. The African Wildlife Ontology is mature and has been used yearly in an ontology engineering course or tutorial since 2010 and is included in a recent ontology engineering textbook with relevant examples and exercises.ConclusionThe African Wildlife Ontology provides a wide range of options concerning examples and exercises for ontology engineering well beyond illustrating just language features and automated reasoning. It assists in demonstrating tasks concerning ontology quality, such as alignment to a foundational ontology and satisfying competency questions, versioning, and multilingual ontologies.

Two Ways for the Automatic Generation of Application Ontologies by Using BalkaNet

This article presents two methods for the automatic generation of application ontologies from the multilingual BalkaNet WordNets Web ontology language (OWL) representation. Both proposed methods are applied on the BalkaNet WordNets ontology for the Serbian language (SerWN). The first one uses only the SerWN, both for generating class hierarchy and instances of classes, while the other method combines the SerWN with a domain ontology. The first method was used to automatically generate the FoodOntology, whereas the second method to generate the ontology of rhetorical figures tropes. Preliminary evaluation results corroborate the soundness of the approach. Since BN consists of individual WNs for five Balkan languages and Czech, the methodology presented in this article can also be used for all these languages. The first method can also be used for other domains.

How can wikipedia be used to support the process of automatically building multilingual domain modules? a case study.

The goal of the research here presented is to identify which aspects of Wikipedia can be exploited to support the process of automatically building Multilingual Domain Modules from textbooks. First, we have defined a representation formalism for Multilingual Domain Modules that is essential for Technology Supported Learning Systems which aim to serve a globalized society. To our knowledge, no attempt has been made at achieving domain models that consider multiple languages. Our approach combines Multilingual Educational Ontologies with Learning Objects in different languages. Wikipedia is a valuable resource to accomplish this purpose. In this scenario, we have developed LiDom Builder, a framework that uses Wikipedia as an additional knowledge base for the automatic generation of Multilingual Domain Modules from textbooks. The framework includes domain-independent term extraction methods to identify which topics of Wikipedia are related to the domain to be learnt and, also, extracts their equivalents in other languages. In order to complete the Educational Ontology, we have defined a method to extract pedagogical relationships from Wikipedia and other general-purpose knowledge bases. From this task, we highlight the extraction of relationship that will allow the sequencing of the topics in Technology Supported Learning Systems. In addition, LiDom Builder takes advantage of the structured contents of Wikipedia to identify text fragments that can be used for educational purposes, classifies them and generates their corresponding Learning Objects. The interlanguage links between topics of Wikipedia are used to create Learning Objects in other languages.

Action Type induction from multilingual lexical features

This paper presents a vector representation and a clustering of action concepts based on lexical features extracted from IMAGACT, a multilingual and multimodal ontology of actions in which concepts are represented through video prototypes. We computed vectors for 1,010 action concepts, where the dimensions correspond to verbs in 10 languages. Finally, an unsupervised clustering method has been applied on these data in order to discover action classes based on typological closeness. Those clusters are not language-specific or language-biased, and thus constitute an inter-linguistic classification of action domain.

Extracting safety information from multi-lingual accident reports using an ontology-based approach

This paper describes an approach to extract meaning from multi-lingual free-text safety incident reports. A sample of 5065 safety incident reports from the Swiss Federal Office of Transport were used in the study. Each report was written in either German, French or Italian natural language. An interactive learning approach between a human and computer software was undertaken to identify key terms in the text that are relevant to discovering meaning. A multi-lingual ontology was created to join meaningful semantic patterns and identify specific classes of safety incident on the railway, including injuries occurring whilst passengers were boarding or alighting from vehicles, falling down stairs, struck by closing doors, or struck by objects such as suitcases. A graph database was used to query the text records via the ontology and identify reports of incidents in each class, regardless of the language used in the report. Fluent speakers of each language – German, French and Italian – reviewed the results to confirm true positive results and detect false positives. The performance of the process varied across languages and incident types, however the overall true positive rate was determined by the fluent speakers to be 98.5%.

Ontology-based Tamil–English cross-lingual information retrieval system

Cross-lingual information retrieval (CLIR) systems facilitate users to query for information in one language and retrieve relevant documents in another language. In general, CLIR systems translate query in source language to target language and retrieve documents in target language based on the keywords present in the translated query. However, the presence of ambiguity in source and translated queries reduces the performance of the system. Ontology can be used to address this problem. The current approaches to ontology-based CLIR systems use manually constructed multilingual ontology, which is expensive. However, many methods exist to automatically construct ontology for any domain in English but not in other languages like Tamil. We propose a methodology for Tamil–English CLIR system by translating the Tamil query to English and retrieve pages in English to address these issues. Our approach uses a word sense disambiguation module to resolve the ambiguity in Tamil query. An automatically constructed ontology in English is used to address the ambiguity of English query. We have developed a morphological analyser for Tamil language, Tamil–English bilingual dictionary and named entity database to translate a Tamil query to English. The translated query is reformulated using ontology and the reformulated queries are given to a search engine to retrieve English documents from the Internet. We have evaluated our methodology for agriculture domain and the evaluation results show that our approach outperforms other approaches in terms of precision.

Cross-Lingual Ontology Enrichment Based on Multi-Agent Architecture

The proliferation of ontologies and multilingual data available on the Web has motivated many researchers to contribute to multilingual and cross-lingual ontology enrichment. Cross-lingual ontology enrichment greatly facilitates ontology learning from multilingual text/ontologies in order to support collaborative ontology engineering process. This article proposes a cross-lingual ontology enrichment (CLOE) approach based on a multi-agent architecture in order to enrich ontologies from a multilingual text or ontology. This has several advantages: 1) an ontology is used to enrich another one, written in a different natural language, and 2) several ontologies could be enriched at the same time using a single chunk of text (Simultaneous Ontology Enrichment). A prototype for the proposed approach has been implemented in order to enrich several ontologies using English, Arabic and German text. Evaluation results are promising and showing that CLOE performs well in comparison with four state-of-the-art approaches.

Ontology mediation method for building multilingual ontologies

Ontology is an emerging technology in building knowledge based information retrieval systems. It is used to conceptualize the information in human understandable manner. Over the years, much importance was given to the systematic development of ontologies. As a result many methods and strategies to build ontology in different domains came up in the field of ontological engineering. These methods contained different steps to plan, design and build ontology efficiently. Most of these methods provided a generic structure to construct ontology. The development of ontology has cut across different domains and applications and this has arisen a need to develop methods to build ontologies in various applications. Nowadays, multilingual ontology applications are developed in various domains to have Knowledge based information retrieval according to the language preferred by user. Multilingual ontology allows sharing of information and data in different languages and thus facilitates multi-language users. A method to develop multilingual ontologies is in need now. In this paper, we propose an ontology mediation method to develop multilingual ontology applications. It explains the steps involved in the development process. A six step method is followed which is done iteratively to develop multilingual ontologies. Merging and mapping techniques are also used in mediation of multilingual ontologies. New algorithms are proposed for merging and mapping multilingual ontologies. The proposed method is experimented by developing multilingual ontology application for Thirukkural, the famous Tamil literature. The experimental results show that multilingual ontologies can be developed using the proposed method and semantic knowledge can be retrieved from the multilingual ontology.

METHODS, APPROACHES, PRINCIPLES, GUIDELINES AND APPLICATIONS ON MULTILINGUAL ONTOLOGIES: A SURVEY

Ontologies are used in various domains to provide meaningful knowledge from data. The data can be in different natural languages. Ontologies with a lexical layer in different natural languages are needed essential to access the multilingual data by the users. Multilingual ontologies are needed to overcome this defect. Most of the multilingual ontologies are built up in English language and translated into the desired native languages. Many approaches are proposed to develop multilingual ontology applications. The aim of this article is to review the various approaches, methods for developing multilingual ontologies. The principles and guidelines for developing multilingual lexical layer are discussed. The different techniques adopted in the use and developments of multilingual ontologies are enumerated. The multilingual applications are compared using several dimensions.

Development and testing of the media monitoring tool MedISys for early identification and reporting of existing and emerging plant health threats

Media monitoring for emerging risks has become an essential tool in public health. This approach also has the potential to deliver early warning of emerging risks to plant health. The European Food Safety Authority (EFSA) has launched a project in collaboration with the Joint Research Centre (JRC) of the European Commission to make use of the Medical Information System (MedISys) media monitoring tool for monitoring plant health threats. This paper provides a summary of the project, which is taking place in partnership with the University of Lleida and the Institut de Recerca I Tecnologia Agroalimentàries. The four specific objectives of the project are: (1) to collate new and appropriate media and information sources, (2) to develop a multilingual ontology for the global identification of emerging new plant health threats, (3) to develop strategies to monitor (re‐)emerging plant health threats at global and regional scales, and (4) to test approaches for reporting the identified warnings to the relevant EFSA units and experts through the MedISys interface, including mapping and georeferencing. The plan and structure of the project are presented with examples of preliminary results.

Health, Food and User's Profile Ontologies for Personalized Information Retrieval

As we aim to retrieve personalized information to user's queries related to food, health and nutrition domains such as “Is apple good for people with heart diseases?”, “How much honey can be taken by a diabetic patient?”, “What are the health benefits of eating pineapple?” and “What are the fruits that contain the daily need quantity of calcium?” The information retrieval system needs to integrate ontologies from different domains such as food, nutrition, health (diseases, body parts, body functions) and recipe in order to answer such kind of queries. In addition, to support multilingual queries, the system and ontologies require aggregation of information from multi-level ontologies. Also, to achieve high relevancy and coverage we need to use ontologies that have comprehensive and rich vocabularies. Moreover, to make effective use for the annotation, ontologies concept names should be unique and self-contained. The main focus of this paper is to integrate ontologies from food, health and nutrition domains to help the personalized information systems to retrieve food and heath recommendations based on the user's health conditions and food preferences. Such ontologies that satisfy these requirements do not explicitly exist. Therefore, we were challenged to develop these ontologies by creating, integrating and reusing some of the existing ontologies to meet our requirements.

Collaborative multilingual knowledge management based on controlled natural language

User interfaces are a critical aspect of semantic knowledge representation systems, as users have to understand and use a formal representation language to model a particular domain of interest, which is known to be a difficult task. Things are even more challenging in a multilingual setting, where users speaking different languages have to create a multilingual ontology. To address these problems, we introduce a semantic wiki system that is based on controlled natural language to provide an intuitive yet formal interface. We use a well-defined subset of Attempto Controlled English (ACE) implemented in Grammatical Framework. Our wiki system offers precise bidirectional automatic translations between ACE and language fragments of a number of other natural languages, making the wiki content accessible multilingually. Because ACE has a partial but deterministic mapping to the Web Ontology Language, our wiki engine can offer automatic reasoning and question answering over the wiki content. Users speaking different languages can therefore build, query, and view the same knowledge base in an intuitive and user-friendly interface based on the respective natural language. We present the results of a user evaluation where participants using different languages were asked to write and assess statements about European geography in our wiki environment. Our results show that users reach a high level of consensus, which is not negatively affected by the presence of automatic translation.

Cross-language information retrieval

AbstractThe article reviews the motivation behind the development of cross-language information retrieval (CLIR) – a relatively new area of information retrieval in multilingual textual databases, defines its objectives and position among the research disciplines dealing with various aspects of processing electronic texts. A short historical overview is followed by a description of the most important methodologies (document translation and query translation) and language resources used in connection with them. Regarding the resources, attention is focused on the two- and multilingual ontologies (thesauri, transfer lexicons and similarity thesauri), corpora, their construction and use with the CLIR experiments. The article primarily aims at illustrating the various methodological approaches, while the functioning of particular systems is less prominent. There are no references to the condition of CLIR in Slovenia or to the existence of language resources suitable for processing Slovenian texts in CLIR systems, since this topic calls for a separate review.

Geo-Enrichment and Semantic Enhancement of Metadata Sets to Augment Discovery in Geoportals

Geoportals are established to function as main gateways to find, evaluate, and start “using” geographic information. Still, current geoportal implementations face problems in optimizing the discovery process due to semantic heterogeneity issues, which leads to low recall and low precision in performing text-based searches. Therefore, we propose an enhanced semantic discovery approach that supports multilingualism and information domain context. Thus, we present workflow that enriches existing structured metadata with synonyms, toponyms, and translated terms derived from user-defined keywords based on multilingual thesauri and ontologies. To make the results easier and understandable, we also provide automated translation capabilities for the resource metadata to support the user in conceiving the thematic content of the descriptive metadata, even if it has been documented using a language the user is not familiar with. In addition, to text-enable spatial filtering capabilities, we add additional location name keywords to metadata sets. These are based on the existing bounding box and shall tweak discovery scores when performing single text line queries. In order to improve the user’s search experience, we tailor faceted search strategies presenting an enhanced query interface for geo-metadata discovery that are transparently leveraging the underlying thesauri and ontologies.

Reuse of termino-ontological resources and text corpora for building a multilingual domain ontology: An application to Alzheimer’s disease

Ontologies are useful tools for sharing and exchanging knowledge. However ontology construction is complex and often time consuming. In this paper, we present a method for building a bilingual domain ontology from textual and termino-ontological resources intended for semantic annotation and information retrieval of textual documents. This method combines two approaches: ontology learning from texts and the reuse of existing terminological resources. It consists of four steps: (i) term extraction from domain specific corpora (in French and English) using textual analysis tools, (ii) clustering of terms into concepts organized according to the UMLS Metathesaurus, (iii) ontology enrichment through the alignment of French and English terms using parallel corpora and the integration of new concepts, (iv) refinement and validation of results by domain experts. These validated results are formalized into a domain ontology dedicated to Alzheimer’s disease and related syndromes which is available online (http://lesim.isped.u-bordeaux2.fr/SemBiP/ressources/ontoAD.owl). The latter currently includes 5765 concepts linked by 7499 taxonomic relationships and 10,889 non-taxonomic relationships. Among these results, 439 concepts absent from the UMLS were created and 608 new synonymous French terms were added. The proposed method is sufficiently flexible to be applied to other domains.

Populating a multilingual ontology of proper names from open sources

Even if proper names play a central role in natural language processing (NLP) applications they are still under-represented in lexicons, annotated corpora, and other resources dedicated to text processing. One of the main challenges is both the prevalence and the dynamicity of proper names. At the same time, large and regularly-updated knowledge sources containing partially-structured data, such as Wikipedia or GeoNames, are publicly available and contain large numbers of proper names. We present a method for a semi-automatic enrichment of Prolexbase, an existing multilingual ontology of proper names dedicated to natural language processing, with data extracted from these open sources in three languages: Polish, English and French. Fine-grained data extraction and integration procedures allow the user to enrich previous contents of Prolexbase with new incoming data. All data are manually validated and available under an open licence.

Using Linguistic Resource for Cross-Lingual Ontology Alignment

In the Semantic Web register, ontology alignment process can be seen as a cornerstone solution for the data heterogeneousness by allowing their interoperability. However, the most of the existing alignment methods assume that all ontologies to be aligned are described by identical languages. Indeed, very few approaches paid attention to the thriving challenge of multilingual ontology alignment. This paper introduces a new alignment method for multilingual ontologies. The proposed method implements a strategy of a direct alignment based on an external resource. Results obtained after extensive carried experiments are very encouraging and highlight many useful insights about the new proposed method.

Analyzing multilingual knowledge innovation in patents

In the process of analyzing knowledge innovation, it is necessary to identify the existing boundaries of knowledge so as to determine whether knowledge is new – outside these boundaries. For a patent to be granted, all aspects of the patent request must be studied to determine the patent innovation. Knowledge innovation for patent requests depends on analyzing current state of the art in multiple languages. Currently the process is usually limited to the languages and search terms the patent seeker knows. The paper describes a model for representing the patent request by a set of concepts related to a multilingual knowledge ontology. The search for patent knowledge is based on Fuzzy Logic Decision Support and allows a multilingual search. The model was analyzed using a twofold approach: a total of 104,296 patents from the United States Patent and Trademark Office were used to analyze the patent extraction process, and patents from the Korean, US, and Chinese patent offices were used in the analysis of the multilingual decision process. The results display high recall and precision and suggest that increasing the number of languages used only has minor effects on the model results.

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.

Symbiosis of thesaurus, domain expert and reference sources in designing a framework for the construction of a multilingual ontology for Islamic Portal

This paper discusses the conceptual and vocabulary problems users faced when searching the web and subsequently shows how a well structured thesaurus can be used as knowledge base for an interface that can assist user with search topic clarification. Recent research justified that thesaurus is useful in building ontology to help standardize terminologies and more importantly help to save time in building ontology that fully depend on domain expert. Several initiatives has been undertaken in web'ifying thesaurus with the idea of converting thesaurus or controlled vocabularies to web standards such as Web Ontology (OWL). The aim of this research is to explore how thesaurus can be integrated into ontology development. Our research method emphasizes on web technologies and the development of ontology using thesaurus, domain expert and reference sources such as Index Islamicus, encyclopedia, biographies etc. as the basis for implementing novel mechanism for retrieving Islamic web in 3 different languages simultaneously. As a result, we developed a framework for the development of Islamic ontology that advocates the symbiosis of thesaurus, domain expert and several reference sources in Islam as the basis for the construction of a multilingual ontology for our Islamic portal. In recent years, there were a variety of difficulties that users have to face in searching for information through Internet. Past research had shown that in performing searches, users use query terms that consist of everyday language, technical terms (with or without knowledge of underlying concepts) and various explanatory model, all influenced by psychosocial and cultural variations. (1).Very often users failed to retrieve relevant information that meet their needs and this could be due to the conceptual and vocabulary problems that users faced when searching the web. The first part of this paper elucidates queries illustrating these problems and how a well structured thesaurus can be used as knowledge base for an interface that can assist user with search topic clarification. We then explain how thesaurus helps users to identify the right appropriate concepts to use when searching for information through Internet. However, if we want to create a knowledge-rich description of, for example an (image of an) art object, medical, business, crime etc. such as required by the semantic web, thesauri turn out to provide only part of the knowledge needed. The second part of this paper discussed various controlled vocabularies, classification schemes and thesauri that can serve as some building blocks of the web. These vocabularies have been developed over decades and can be used in the development of robust web services and Semantic Web technologies. Thus the third part of this paper, addresses initiatives undertaken to 'web'ifying' thesaurus with the idea of converting thesaurus/controlled vocabularies to web standards. Since the aim of this research is to develop ontology using symbiotic approach involving thesaurus, then fourth and fifth part of this paper discuss the concept of ontology and thesaurus. Part six analysed the