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Lexical Resources Lexicalized Ontologies
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Lab
Barsalou[1]
• We shall assume that concepts are people’s psychological
representations of categories (e.g., apple, chair); whereas
meanings are people’s understandings of words and other
linguistic expressions (e.g., ”apple”, ”large chair”).
• We shall argue that concepts and meanings differ
substantially. Although they are related in important ways,
the relationship is one of complementarity, not equivalence.
Ontology and the Lexicon Shu-Kai Hsieh
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Lexical Resources Lexicalized Ontologies
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Lab
FrameNet
FrameNet
• Word evokes the frame.
• Instead of words, FN works with lexical units (LUs), each of
these being a pairing of a word with a sense.
Example (of FN work)
Let’s work through the Revenge frame following Fillmore (pp26-):
(https://framenet.icsi.berkeley.edu/fndrupal/sites/default/files/FNintroCJF.ppt); The
glossary also helps (https://framenet.icsi.berkeley.edu/fndrupal/glossary)
Ontology and the Lexicon Shu-Kai Hsieh
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Lexical Resources Lexicalized Ontologies
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Lab
FrameNet
FrameNet Relations
source: FrameNet (II) book
Inheritance An IS-A relation. The child frame is a subtype of the
parent frame, and each FE in the parent is bound to
a corresponding FE in the child. An example is the
Revenge frame which inherits from the Rewards and
punishments frame.
Subframe The child frame is a subevent of a complex event
represented by the parent, e.g. the Criminal
process frame has subframes of Arrest,
Arraignment, Trial, and Sentencing.
Ontology and the Lexicon Shu-Kai Hsieh
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Lexical Resources Lexicalized Ontologies
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Lab
FrameNet
FrameNet Relations
source: FrameNet (II) book
Using The child frame presupposes the parent frame as
background, e.g the Speed frame ”uses” (or
presupposes) the Motion frame; however, not all
parent FEs need to be bound to child FEs.
Perspective on The child frame provides a particular perspective
on an un-perspectivized parent frame. A pair of
examples consists of the Hiring and Get_a_job
frames, which perspectivize the Employment_start
frame from the Employer’s and the Employee’s point
of view, respectively.
Ontology and the Lexicon Shu-Kai Hsieh
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Lexical Resources Lexicalized Ontologies
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Lab
FrameNet
Lexical Resources: Comparison and Alignment
• WN: a syset comprises only synonyms of the same part of
speech; FN: a frame may include different parts of speech,
and words with contradictory definitions (such as antonyms
related to the same idea).
• Statistical measure that shows where WordNet and FrameNet
agree well on the meanings of words and phrases, and where
they do not [2].
Ontology and the Lexicon Shu-Kai Hsieh
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Lexical Resources Lexicalized Ontologies
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Lab
Multilingual Wordnets
• (EuroWordnet) The development of a multilingual database
with WordNets for several European languages, with 10,000
up to 50,000 synsets. (Dutch, German, French, Spanish,
Italian, Czech, Estonian).
• Inter-Lingual-Index, which are mainly based on EWN
synsets, serves as unstructured fund of concepts that provide
an efficient mapping across the languages;
• Various types of equivalence relations are distinguished to
link synsets with index records.
• Some cross-linguistic issues identified: different lexicalization,
differencs in synonymy and homonymy, etc.
Ontology and the Lexicon Shu-Kai Hsieh
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Lexical Resources Lexicalized Ontologies
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Lab
Wiki Taxonomy
Wiki: its role
• A good review of current state-of-arts can be found in [3].
• Resolving the Knowledge acquisition bottleneck: The
creation of very large knowledge bases has been made possible
by the availability of collaboratively-curated online resources
such as Wikipedia and Wiktionary.
• structured, semi-structured, unstructured resources.
• what are the advantages and disadvantages, respectively?
Ontology and the Lexicon Shu-Kai Hsieh
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Lexical Resources Lexicalized Ontologies
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Lab
Wiki Taxonomy
Wiki as semi-structured content for Ontologies
• Transforming Wikipedia into machine-readable knowledge
• Acquiring related terms: thesaurus extraction
• Relation extraction
• Leitmotif: generating semantics by exploiting the shallow
structure found in Wikipedia.
• Building and enriching ontologies from Wikipedia: YAGO,
WikiNet and BabelNet.
Ontology and the Lexicon Shu-Kai Hsieh
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Lexical Resources Lexicalized Ontologies
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Lab
Wiki Taxonomy
• WikiTaxonomy (Ponzetto and Strube, 2007; Ponzetto and
Strube, 2011)(100k is-a relations)
• WikiNet: (Nas- tase et al., 2010; Nastase and Strube, 2013)
is a project which heuristically exploits different aspects of
Wikipedia to obtain a multilingual concept network by deriving
not only is-a relations, but also other types of relations.
• MENTA (de Melo and Weikum, 2010), creates one of the
largest multilingual lexical knowledge bases by interconnecting
more than 13M articles in 271 languages.
Ontology and the Lexicon Shu-Kai Hsieh