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A Meta-model and Ontology for Managing Heterogenous Alignment Resources
Abstract
Since the invention of the Semantic Web concept, the number of ontological and terminological resources on the Web has been increasing. These heterogeneous resources may share entities or contain similar ones. To cope with the diversity of these resources and collect them, resources repositories have been created. Matching entities belonging to different resources is then required to use or combine them, which generates alignment resources. Thus, collecting and managing resulting alignments is useful in order to compose them, compare sets of alignments for the same input resources, enrich existing alignments from each other or enhance their quality. In this paper we introduce our approach for building a repository of heterogeneous alignments. We propose to represent alignments by means of a generic model and we introduce some operators and algorithms to import, compose, intersect and merge alignments. For a concrete application we use ontology alignments and parallel corpora.
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This paper presents the current status of OPUS, a growing language resource of parallel corpora and related tools. The focus in OPUS is to provide freely available data sets in various formats together with basic annotation to be useful for applications in computational linguistics, translation studies and cross-linguistic corpus studies. In this paper, we report about new data sets and their features, additional annotation tools and models provided from the website and essential interfaces and on-line services included in the project.
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We present a sub-sentential alignment system that links linguistically motivated phrases in parallel texts based on lexical correspondences and syntactic similarity. We compare the performance of our sub- sentential alignment system with different symmetrization heuristics that combine the GIZA++ alignments of both translation di- rections. We demonstrate that the aligned linguistically motivated phrases are a use- ful means to extract bilingual terminology and more specifically complex multiword terms.
After years of research on ontology matching, it is reasonable to consider several questions: is the field of ontology matching still making progress? Is this progress significant enough to pursue further research? If so, what are the particularly promising directions? To answer these questions, we review the state of the art of ontology matching and analyze the results of recent ontology matching evaluations. These results show a measurable improvement in the field, the speed of which is albeit slowing down. We conjecture that significant improvements can be obtained only by addressing important challenges for ontology matching. We present such challenges with insights on how to approach them, thereby aiming to direct research into the most promising tracks and to facilitate the progress of the field.
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An adaptable statistical or hybrid MT system relies heavily on the quality of word-level alignments of real-world data. Statistical alignment approaches provide a reasonable initial estimate for word alignment. However, they cannot handle certain types of linguistic phenomena such as long-distance dependencies and structural differences between languages. We address this issue in Multi-Align, a new framework for incremental testing of different alignment algorithms and their combinations. Our design allows users to tune their systems to the properties of a particular genre/domain while still benefiting from general linguistic knowledge associated with a language pair. We demonstrate that a combination of statistical and linguistically-informed alignments can resolve translation divergences during the alignment process.
ACM International Conferences on Web Intelligence (WI) and Intelligent Agent Technology (IAT) 978-1-4799-2902-3
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2013 IEEE/WIC/ACM International Conferences on Web Intelligence (WI) and Intelligent Agent Technology (IAT) 978-1-4799-2902-3/13 $31.00 © 2013 IEEE DOI 10.1109/WI-IAT.2013.175 REFERENCES
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