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E-raamat: New Trends of Research in Ontologies and Lexical Resources: Ideas, Projects, Systems

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New Trends of Research in Ontologies and Lexical Resources: Ideas, Projects, SystemsSuurem pilt
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In order to exchange knowledge, humans need to share a common lexicon of words as well as

to access the world models underlying that lexicon. What is a natural process for a human turns out to be an extremely hard task for a machine: computers can’t represent knowledge as effectively as humans do, which hampers, for example, meaning disambiguation and communication. Applied ontologies and NLP have been developed to face these challenges. Integrating ontologies with (possibly multilingual) lexical resources is an essential requirement to make human language understandable by machines, and also to enable interoperability and computability across information systems and, ultimately, in the Web.

This book explores recent advances in the integration of ontologies and lexical resources, including questions such as building the required infrastructure (e.g., the Semantic Web) and different formalisms, methods and platforms for eliciting, analyzing and encoding knowledge contents (e.g., multimedia, emotions, events, etc.). The contributors look towards next-generation technologies, shifting the focus from the state of the art to the future of Ontologies and Lexical Resources. This work will be of interest to research scientists, graduate students, and professionals in the fields of knowledge engineering, computational linguistics, and semantic technologies.



Surveying new directions of research and development in the interdisciplinary framework where ontologies and lexical resources intersect, this book deals with the complex relation between lexicons (in different languages) and the underlying ontological model.

Arvustused

From the reviews:

This exquisite collection of really trendsetting research is captivating reading for any student, scholar, or engineer interested in the growing field of semantic technologies and the semantic web. The book is recommended as mandatory reading for all serious NLP and semantic web students and experts. (Mariana Damova, Computing Reviews, September, 2013)

1 Introduction
1(6)
Alessandro Oltramari
Piek Vossen
Lu Qin
Eduard Hovy
Part I Achieving the Interoperability of Linguistic Resources in the Semantic Web
2 Towards Open Data for Linguistics: Linguistic Linked Data
7(20)
Christian Chiarcos
John McCrae
Philipp Cimiano
Christiane Fellbaum
2.1 Motivation and Overview
8(2)
2.2 Modelling Linguistic Resources as Linked Data
10(6)
2.2.1 Modelling Lexical-Semantic Resources: WordNet
12(2)
2.2.2 Modelling Annotated Corpora: MASC
14(2)
2.3 Benefits of Linked Data for Linguistics
16(5)
2.3.1 Structural Interoperability
17(1)
2.3.2 Linking and Federation
18(1)
2.3.3 Conceptual Interoperability
19(1)
2.3.4 Ecosystem
20(1)
2.3.5 Dynamic Import
20(1)
2.4 Community Efforts Towards Lexical Linked Data
21(2)
2.4.1 The Open Linguistics Working Group
21(1)
2.4.2 W3C Ontology-Lexica Community Group
22(1)
2.5 Summary
23(4)
References
24(3)
3 Establishing Interoperability Between Linguistic and Terminological Ontologies
27(16)
Wim Peters
3.1 Introduction
27(2)
3.2 Linguistic Knowledge
29(2)
3.3 Networking Linguistic Ontologies
31(2)
3.4 Related Work
33(1)
3.5 LingNet
34(4)
3.5.1 The LingNet Model
34(2)
3.5.2 LingNet Implementation
36(2)
3.6 Discussion
38(2)
3.7 Conclusion and Future Work
40(3)
References
41(2)
4 On the Role of Senses in the Ontology-Lexicon
43(22)
Philipp Cimiano
John McCrae
Paul Buitelaar
Elena Montiel-Ponsoda
4.1 Introduction
43(3)
4.2 Senses: Universal or Context-Specific?
46(2)
4.3 Senses in the Ontology-Lexicon Interface
48(4)
4.3.1 Senses as Reification
49(1)
4.3.2 Sense as Subset of Uses
50(1)
4.3.3 Sense as a Subconcept
50(2)
4.3.4 The Three Facets
52(1)
4.4 Systematic Polysemy in the Ontology-Lexicon Interface
52(3)
4.5 Senses in the Ontology-Lexicon Model Lemon
55(5)
4.5.1 Sense Properties
56(1)
4.5.2 Contexts and Conditions
57(2)
4.5.3 Sense Relations
59(1)
4.6 Conclusions
60(5)
References
61(4)
Part II Event Analysis from Text and Multimedia
5 KYOTO: A Knowledge-Rich Approach to the Interoperable Mining of Events from Text
65(26)
Piek Vossen
Eneko Agirre
German Rigau
Aitor Soroa
5.1 Introduction
65(1)
5.2 Packaging of Events
66(3)
5.3 KYOTO Overview
69(3)
5.4 Ontological and Lexical Background Knowledge
72(4)
5.4.1 Ontology
73(1)
5.4.2 Wordnet to Ontology Mappings
74(2)
5.5 Off-Line Reasoning and Ontological Tagging
76(1)
5.6 Event Extraction
77(3)
5.7 Experimental Results
80(8)
5.7.1 In-Depth Evaluation
80(3)
5.7.2 Large Scale Evaluation
83(4)
5.7.3 Transferring to Another Language
87(1)
5.8 Conclusion
88(3)
References
89(2)
6 Anchoring Background Knowledge to Rich Multimedia Contexts in the KnowledgeStore
91(22)
R. Cattoni
F. Corcoglioniti
C. Girardi
B. Magnini
L. Serafini
R. Zanoli
6.1 Introduction
92(2)
6.2 State of the Art
94(2)
6.3 The KnowledgeStore Approach
96(4)
6.3.1 Representation Layers
96(3)
6.3.2 Content Processing
99(1)
6.4 System Implementation
100(5)
6.4.1 KnowledgeStore Core
100(1)
6.4.2 Resource Preprocessing
101(1)
6.4.3 Mention Extraction
102(1)
6.4.4 Coreference Resolution
102(2)
6.4.5 Mention-Entity Linking
104(1)
6.4.6 Entity Creation and Enrichment
105(1)
6.5 Experiments and Results
105(5)
6.5.1 KnowledgeStore Population
106(1)
6.5.2 Entity-Based Search
107(1)
6.5.3 Contextualized Semantic Enrichment
108(2)
6.6 Conclusions and Future Work
110(3)
References
111(2)
7 Lexical Mediation for Ontology-Based Annotation of Multimedia
113(22)
Mario Cataldi
Rossana Damiano
Vincenzo Lombardo
Antonio Pizzo
7.1 Introduction
113(2)
7.2 Related Work
115(2)
7.3 Case Study: Annotating Stories in Video
117(4)
7.4 Accessing Large Scale Commonsense Knowledge Through a Lexical Interface
121(6)
7.4.1 The Architecture of CADMOS
121(2)
7.4.2 The Meaning Negotiation Process
123(4)
7.5 Annotation Test and Discussion
127(4)
7.5.1 Experimental Setting
127(2)
7.5.2 Results and Discussion
129(2)
7.6 Conclusion
131(4)
References
132(3)
8 Knowledge in Action: Integrating Cognitive Architectures and Ontologies
135(22)
Alessandro Oltramari
Christian Lebiere
8.1 Introduction
135(2)
8.2 Knowledge Mechanisms Meet Contents in Visual Intelligence
137(4)
8.2.1 Mechanisms: Cognitive Architectures as Modules of Knowledge Production
137(1)
8.2.2 Contents: Ontologies as Declarative Knowledge Resources
138(1)
8.2.3 Human Visual Intelligence
139(2)
8.3 Making Sense of Visual Data
141(9)
8.3.1 HOMinE: Model and Implementation
142(4)
8.3.2 The Cognitive Engine
146(1)
8.3.3 Recognition Task
147(2)
8.3.4 Description Task
149(1)
8.4 Evaluation
150(2)
8.5 Conclusions and Future Work
152(5)
References
152(5)
Part III Enhancing NLP with Ontologies
9 Use of Ontology, Lexicon and Fact Repository for Reference Resolution in Ontological Semantics
157(30)
Marjorie McShane
Sergei Nirenburg
9.1 Introduction
157(2)
9.2 Our View of Reference Resolution Versus Others
159(2)
9.3 The OntoAgent Environment and Its Resources
161(5)
9.3.1 Comparing OntoAgent Static Knowledge Resources with Others
164(1)
9.3.2 The OntoSem Text Analyzer
165(1)
9.4 The Reference Resolution Algorithm
166(15)
9.4.1 Stage 1: Proper Name Analysis During Preprocessing
166(1)
9.4.2 Stage 2: Detection of Potentially Missing Elements in the Syntactic Parse
167(1)
9.4.3 Stage 3: Reference Processing During Basic Semantic Analysis
168(4)
9.4.4 Stage 4: Running Lexically Recorded Meaning Procedures
172(1)
9.4.5 Stage 5: Dedicated Reference Resolution Module
172(9)
9.5 Final Thoughts: Semantics in Reference Resolution
181(6)
References
183(4)
10 Ontology-Based Semantic Interpretation via Grammar Constraints
187(22)
Smaranda Muresan
10.1 Introduction
187(1)
10.2 Lexicalized Well-Founded Grammar
188(6)
10.2.1 Semantic Molecule: A Syntactic-Semantic Representation
189(2)
10.2.2 Semantic Composition and Interpretation as Grammar Constraints
191(1)
10.2.3 LWFG Learning Model
192(2)
10.3 Ontology-Based Semantic Interpretation
194(5)
10.3.1 Levels of Representation
194(2)
10.3.2 The Local Ontology-Based Semantic Interpreter
196(2)
10.3.3 Global Semantic Interpreter
198(1)
10.4 Knowledge Acquisition and Querying Experiments
199(4)
10.4.1 Acquisition of Terminological Knowledge from Consumer Health Definitions
200(2)
10.4.2 Natural Language Querying
202(1)
10.5 Ambiguity Handling
203(2)
10.6 Conclusions
205(4)
References
205(4)
11 How Ontology Based Information Retrieval Systems May Benefit from Lexical Text Analysis
209(26)
Sylvie Ranwez
Benjamin Duthil
Mohameth Francois Sy
Jacky Montmain
Patrick Augereau
Vincent Ranwez
11.1 Introduction
210(1)
11.2 Related Work
211(8)
11.2.1 Conceptual Versus Keyword-Based IRSs
212(1)
11.2.2 Hybrid Ontology Based Information Retrieval System
213(5)
11.2.3 Concept Identification Through Lexical Analysis
218(1)
11.3 Concept Identification Through Lexical Analysis: The "Synopsis" Approach
219(4)
11.3.1 Concept Characterization
220(2)
11.3.2 Thematic Extraction
222(1)
11.4 Human Accessibility Enhanced at the Crossroads of Ontology and Lexicology
223(3)
11.4.1 An Example of Concept-Based IRS: OBIRS
223(2)
11.4.2 Ontology and Lexical Resource Interfacing Within Hybrid IRSs
225(1)
11.5 Evaluation: User Feedback on a Real Case Study
226(1)
11.6 Conclusion and Perspectives
227(8)
References
228(7)
Part IV Sentiment Analysis Thorugh Lexicon and Ontologies
12 Detecting Implicit Emotion Expressions from Text Using Ontological Resources and Lexical Learning
235(22)
Alexandra Balahur
Jesus M. Hermida
Hristo Tanev
12.1 Introduction
235(2)
12.2 Related Work
237(2)
12.2.1 Appraisal Theories
237(1)
12.2.2 Affect Detection and Classification in Natural Language Processing
237(1)
12.2.3 Knowledge Bases for NLP Applications
238(1)
12.2.4 Lexical Learning
238(1)
12.2.5 Linking Ontologies with Lexical Resources
239(1)
12.3 The EmotiNet Knowledge Base
239(5)
12.3.1 Self-Reported Affect and the ISEAR Data Set
240(1)
12.3.2 Building the EmotiNet Knowledge Base
240(2)
12.3.3 Preliminary Extensions of EmotiNet
242(2)
12.4 Further Extensions of EmotiNet with Lexical and Ontological Resources
244(4)
12.4.1 Extending EmotiNet with Additional Emotion-Triggering Situations
244(1)
12.4.2 Extending EmotiNet Using Ontopopulis
245(3)
12.5 Evaluation
248(3)
12.6 Discussion, Conclusions and Future Work
251(6)
References
253(4)
13 The Agile Cliche: Using Flexible Stereotypes as Building Blocks in the Construction of an Affective Lexicon
257(20)
Tony Veale
13.1 Introduction
257(2)
13.2 Related Work and Ideas
259(2)
13.3 Finding Stereotypes on the Web
261(5)
13.3.1 Web-derived Models of Typical Behavior
263(2)
13.3.2 Mutual Reinforcement Among Properties
265(1)
13.4 Estimating Lexical Affect
266(3)
13.5 In the Mood for Affective Search
269(1)
13.6 Empirical Evaluation
270(3)
13.6.1 Bottom Level: Properties and Behaviors of Stereotypes
270(1)
13.6.2 Top Level: Stereotypical Concepts
271(1)
13.6.3 Separating Words by Affect: Two Views
272(1)
13.7 Conclusions
273(4)
References
274(3)
Index 277
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