Mode of access: World Wide Web.

System requirements: Adobe Acrobat Reader.

Part of: Synthesis digital library of engineering and computer science.

Series from website.

Includes bibliographical references (pages 145-155).

1. Introduction -- 1.1 Ontology-based interpretation of natural language -- 1.2 State of the art in natural language processing -- 1.3 Relation to the semantic web -- 1.4 What you need to know about RDF -- 1.5 What you need to know about soccer --

2. Ontologies -- 2.1 Defining ontologies -- 2.2 Ontologies in first-order logic -- 2.3 Ontologies in description logics -- 2.3.1 OWL DL or SHOIN (D) -- 2.3.2 OWL 2 DL or SROIQ(D) -- 2.4 Further reading --

3. Linguistic formalisms -- 3.1 Form and meaning -- 3.2 Syntactic representations: LTAG -- 3.3 Aligning syntactic representations to an ontology -- 3.4 Semantic representations: DRT -- 3.5 Aligning semantic representations to an ontology -- 3.6 From DRT to DUDES: pairing syntactic and semantic representations -- 3.7 Quantifiers and negation -- 3.8 Further reading --

4. Ontology lexica -- 4.1 The lemon model -- 4.2 Using LexInfo as linguistic ontology -- 4.3 Modeling word classes in lemon -- 4.3.1 Proper nouns -- 4.3.2 Nouns -- 4.3.3 Verbs -- 4.3.4 Adjectives -- 4.3.5 Adverbs -- 4.4 Further reading --

5. Grammar generation -- 5.1 From ontology lexica to grammars -- 5.2 Generating noun entries -- 5.3 Generating verb entries -- 5.4 Generating adjective entries -- 5.5 Implementation -- 5.6 Further reading --

6. Putting everything together -- 6.1 From conceptualizations to meaning representations -- 6.2 Challenges --

7. Ontological reasoning for ambiguity resolution -- 7.1 Ambiguities in the context of ontology-based interpretation -- 7.2 Representing and resolving ambiguities -- 7.2.1 Enumeration -- 7.2.2 Underspecification -- 7.3 Further reading --

8. Temporal interpretation -- 8.1 The time ontology -- 8.1.1 Temporal entities and their ordering -- 8.1.2 Temporal relations -- 8.1.3 The structure of time -- 8.2 Temporal interpretation with respect to the time ontology -- 8.3 Further reading --

9. Ontology-based interpretation for question answering -- 9.1 Question answering over structured data -- 9.2 Querying structured data -- 9.3 From natural language questions to answers -- 9.4 Implementation -- 9.5 Further reading --

10. Conclusion -- Bibliography -- Authors' biographies.

Abstract freely available; full-text restricted to subscribers or individual document purchasers.

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For humans, understanding a natural language sentence or discourse is so effortless that we hardly ever think about it. For machines, however, the task of interpreting natural language, especially grasping meaning beyond the literal content, has proven extremely difficult and requires a large amount of background knowledge. This book focuses on the interpretation of natural language with respect to specific domain knowledge captured in ontologies. the main contribution is an approach that puts ontologies at the center of the interpretation process. This means that ontologies not only provide a formalization of domain knowledge necessary for interpretation but also support and guide the construction of meaning representations. We start with an introduction to ontologies and demonstrate how linguistic information can be attached to them by means of the ontology lexicon model lemon. These lexica then serve as basis for the automatic generation of grammars, which we use to compositionally construct meaning representations that conform with the vocabulary of an underlying ontology. As a result, the level of representational granularity is not driven by language but by the semantic distinctions made in the underlying ontology and thus by distinctions that are relevant in the context of a particular domain. We highlight some of the challenges involved in the construction of ontology-based meaning representations, and show how ontologies can be exploited for ambiguity resolution and the interpretation of temporal expressions. Finally, we present a question answering system that combines all tools and techniques introduced throughout the book in a real-world application, and sketch how the presented approach can scale to larger, multi-domain scenarios in the context of the Semantic Web.

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Title from PDF title page (viewed on April 21, 2014).