2011
DOI: 10.1007/978-3-642-24606-7_12
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Multilingual Ontologies for Cross-Language Information Extraction and Semantic Search

Abstract: Abstract. Valuable local information is often available on the web, but encoded in a foreign language that non-local users do not understand. Can we create a system to allow a user to query in language L1 for facts in a web page written in language L2? We propose a suite of multilingual extraction ontologies as a solution to this problem. We ground extraction ontologies in each language of interest, and we map both the data and the metadata among the language-specific extraction ontologies. The mappings are th… Show more

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Cited by 15 publications
(11 citation statements)
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“…OntoES (ML-OntoES) is the initial proposal for an extraction system that supports extraction from two languages, namely, English and Japanese. 18 This system is based on Ontos extraction engine, which is capable of extracting information from semistructured data sources. It uses an object-oriented model to store the ontology, with the extraction rules embedded in the ontology itself.…”
Section: Literature Reviewmentioning
confidence: 99%
See 1 more Smart Citation
“…OntoES (ML-OntoES) is the initial proposal for an extraction system that supports extraction from two languages, namely, English and Japanese. 18 This system is based on Ontos extraction engine, which is capable of extracting information from semistructured data sources. It uses an object-oriented model to store the ontology, with the extraction rules embedded in the ontology itself.…”
Section: Literature Reviewmentioning
confidence: 99%
“…For structured content such as tables, table headers are mapped to concepts or concept attributes, and rows are mapped as instances of concepts or values of attributes. 18 For unstructured content like paragraph text, rule-based, pattern matching, and ML approaches are commonly followed. 39 We classify relations between entities into two main categories based on the complexity of the relationships: simple and complex.…”
Section: Semantic Recognition Layermentioning
confidence: 99%
“…Hence the epistemological and linguistic problem: Can we create a system to allow a user to query in language L 1 for facts in a web page written in language L 2 ? We propose a suite of multilingual extraction ontologies as a solution to this problem [13]. We ground extraction ontologies in each language of interest, and we map both the data and the metadata via the language-specific extraction ontologies through a central, language-agnostic ontology, reifying our ontological commitment to cross-linguistic information extraction in a particular domain.…”
Section: Multilingual Extraction Ontologiesmentioning
confidence: 99%
“…Furthermore, we adopt a multifaceted engineering approach for cross-language mappings, and while recognizing the equivalency problem, we allow for various types of correspondence beyond one-to-one mappings (Embley et al 2011c). We note, however, that the technologies for our system at present originate from the conceptual-modeling and data-extraction communities rather than from natural language processing and computational linguistics, though we foresee being able to orient our work more toward the nexus of all of these areas.…”
Section: Introductionmentioning
confidence: 99%
“…What distinguishes our approach is the narrow, domain-specific, user-definable nature of our ontologies and their construction, as well as the role of these ontologies at the center of a larger infrastructure (Embley et al 2011c). Our ontologies tend to be less elaborate than others and hence less rich in the types of context required for successful treatment by statistical translation methods.…”
Section: Introductionmentioning
confidence: 99%