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The tokens are:
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[There] [are] [deliverables] [.]
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The tagger nds the parts of speech for each of the tokens. In other words it nds out what kind of a word each of the tokens is; whether it is a noun, an adjective, a verb, etc.
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[There]: existential quanti er [are]: verb 3rd person singular present [deliverables]: noun plural
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DETERMINISTIC ONTOLOGY AUTHORING
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The morphological analyser gives the roots of all the words.
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[There]: root there [are]: root be [deliverables]: root deliverable
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The morphological analyser allows these general types of sentences which announce existence of classes without the need for using arti cial singular expressions, that is There is deliverable. The JAPE transducers take the above annotated sentence and look for and mark noun patterns which are likely candidates for CLASS, INSTANCE, and other ontological objects. They also look for speci c patterns to extract the information, such as:
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There are .
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which triggers the creation of one or more new classes in the ontology. The Controlled Language IE (CLIE) application employs a deterministic approach, so that each sentence can be parsed in one way only. Allowed sentences are unambiguous, so each sentence type is translated to one type of statement. If parsing fails it will result in a warning and no ontological output. In certain cases where the input is invalid, the system will try a less strict analysis mode in order to suggest how such repair may be effected. For example the text:
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There are projects. There are workpackages, tasks and deliverables. SEKT is a project. MUSING, Knowledge Web , and Presto Space are projects. Projects have workpackages. Workpackages can have tasks. WP1, WP2, WP3, WP4, WP5 and WP6 are workpackages. SEKT has WP1. MUSING has WP2, WP3 and WP4. Knowledge Web has WP5 and WP6.
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generates the ontology shown in Figure 3.5, which is then saved as OWL: The use of linguistic analysis allows for small variations in the surface form used to name objects (for instance the use of plurals where it feels appropriate from a linguistic point of view) without affecting the capability of the system of identifying different references for the same entity. For example the sentence There are animals will create a new ontology class with the name Animal and the sentence Cat is a type of animal will create a new class with the name Cat as a subclass of the Animal class. The Animal class is referred to in two different ways: one capitalised and in plural form and another lower case and singular. There is also support for listing items; so a sentence like There are
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SEMANTIC ANNOTATION AND HUMAN LANGUAGE TECHNOLOGY
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Figure 3.5 Ontology generation from natural language.
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projects, work packages and deliverables will lead to the creation of three new classes: Project , Work_Package and Deliverable . The names of entities are normalised rst letters are capitalised, spaces are replaced with underscores and the head word in the case of noun phrases is shown un-in ected. If this is undesirable, names can be included in single quotes which will cause them to be used as they appear in the text. CLIE can be used in one of two modes to create a new ontology or to add information to an existing one. Extending an existing ontology requires that names of concepts, instance and properties in the text are rst checked against those already in the ontology and only added if necessary. The domain and range restrictions of properties are also checked to ensure consistency.
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3.7. CONCLUSION
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This chapter motivated the need for Semantic Web enabled Human Language Technology tools and discussed existing systems and outstanding challenges in this area. It introduced the idea of a language loop and showed how HLT can be used to bridge the gap between the current web of language and the Semantic Web. A number of practical semantic-based applications using HLT-based automatic annotation have already been developed successfully in areas such as market intelligence, nancial analysis, media monitoring, etc. For instance, Ontotext have now released a product for recruitment intelligence, which automatically discovers and consolidates jobs vacancies by crawling company web pages. The results from the web mining process are stored in an ontology and conceptual search provides users with information about the latest vacancies by industry sector, region,
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