Ontology Learning

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Ontology Learning

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Contents

• Introduction Ontologies, Ontology learning
• Technical description
• Ontology learning in the Semantic Information
  description
• Ontology Learning Process
• Ontology Learning - Architecture
• Ontology Learning data sources
• Methods used in ontology learning
• Tools of ontology learning
• Uses of ontology learning

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Ontologies

• Provide a formal, explicit specification of a
  shared conceptualization of a domain that can be
  communicated between people and heterogeneous and
  widely spreads application systems.
• They have been developed in Artificial
  Intelligent and Machine Learning to facilitate
  knowledge sharing and reuse.
• Unlike knowledge bases ontologies have all in
  one
• formal or machine readable representation
• full and explicitly described vocabulary
• full model of some domain
• consensus knowledge common understanding of a
  domain
• easy to share and reuse

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Ontology learning - General

• Machine learning of ontologies
• Main task to automatically learn complicated
  domain ontologies
• Explores techniques for applying knowledge
  discovery techniques to different data sources (
  html documents, dictionaries, free text, legacy
  ontologies etc.) in order to support the task of
  engineering and maintaining ontologies

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• Introduction Ontologies, Ontology learning
• Technical description
• Ontology learning in the Semantic Information
  descritpion
• Ontology Learning Process
• Ontology Learning - Architecture
• Ontology Learning data sources
• Methods used in ontology learning
• Tools of ontology learning
• Uses of ontology learning

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Ontology learning Technical description

• The manual building of ontologies is a tedious
  task, which can easily result in a knowledge
  acquisition bottleneck. In addition, human expert
  modeling by hand is biased, error prone and
  expensive
• Fully automatic machine knowledge acquisition
  remains in the distant future
• Most systems are semi-automatic and require human
  (expert) intervention and balanced cooperative
  modeling for constructing ontologies

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• Introduction Ontologies, Ontology learning
• Technical description
• Ontology learning in the Semantic Information
  descritpion
• Ontology Learning Process
• Ontology Learning - Architecture
• Ontology Learning data sources
• Methods used in ontology learning
• Tools of ontology learning
• Uses of ontology learning

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Semantic Information Integration
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Ontology Engineering
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• Introduction Ontologies, Ontology learning
• Technical description
• Ontology learning in the Semantic Information
  descritpion
• Ontology Learning Process
• Ontology Learning - Architecture
• Ontology Learning data sources
• Methods used in ontology learning
• Tools of ontology learning
• Uses of ontology learning

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Ontology learning Process (1/2)
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Ontology learning Process (2/2)

• Stages analysis
• Merging existing structures or defining mapping
  rules between these structures allows importing
  and reusing existing ontologies
• Ontology extraction models major parts of the
  target ontology, with learning support fed from
  various input sources
• The target ontologys rough outline, which
  results from import, reuse and extraction is
  pruned to better fit the ontology to its primary
  purpose
• Ontology refinement profits from the pruned
  ontology but completes the ontology at a fine
  granularity (in contrast to extraction)
• The target application serves as a measure for
  validating the resulting ontology
• The ontology engineer can begin this cycle again-
  for example, to include new domains in the
  constructing ontology or to maintain and update
  its scope

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• Introduction Ontologies, Ontology learning
• Technical description
• Ontology learning in the Semantic Information
  descritpion
• Ontology Learning Process
• Ontology Learning - Architecture
• Ontology Learning data sources
• Methods used in ontology learning
• Tools of ontology learning
• Uses of ontology learning

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Ontology learning Architecture (1/5)
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Ontology learning Architecture (2/5)

• Ontology Engineering Workbench A sophisticated
  means for manual modeling and refining of the
  final ontology. The ontology engineer can browse
  the resulting ontology from the ontology learning
  process and decide to follow, delete or modify
  the proposals as the task requires.

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Ontology learning Architecture (3/5)

• Management component The ontology engineer uses
  the management component to select input data
  that is relevant resources such as HTML and XML
  documents, DTDs, databases or existing ontologies
  that the discovery process can further exploit.
  Then, using the management component the engineer
  chooses of a set of resource-processing methods
  available in the resource-processing component
  and from a set of algorithms available in the
  algorithm library.

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Ontology learning Architecture (4/5)

• Resource processing Component Depending on the
  available data the engineer can choose various
  strategies for resource processing
• Index and reduce HTML documents to free text
• Transform semi-structured documents such as
  dictionaries into predefined relational structure
  
• Handle semi-structured and structured schema data
  by following different strategies for import
• Process free natural text
• After first preprocessing data according to one
  of
• these or similar strategies the resource
  processing
• module transforms the data into an algorithm
  specific
• relational representation.

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Ontology learning Architecture (5/5)

• Algorithm library A collection of various
  algorithms that work on the ontology definition
  and the preprocess input data. Although specific
  algorithms can vary greatly from one type of
  input to the next, a considerable overlap exists
  for underlying learning approaches such as
  associations rules, formal concept analysis or
  clustering.

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Contents

• Introduction Ontologies, Ontology learning
• Technical description
• Ontology learning in the Semantic Information
  descritpion
• Ontology Learning Process
• Ontology Learning - Architecture
• Ontology Learning data sources
• Methods used in ontology learning
• Tools of ontology learning
• Uses of ontology learning

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Ontology Learning from Natural Language

• Natural language texts exhibit morphological,
  syntactic, semantic, pragmatic and conceptual
  constraints that interact in order to convey a
  particular meaning to the reader. Thus, the text
  transports information to the reader and the
  reader embeds this information into his
  background knowledge
• Through the understanding of the text, data is
  associated with conceptual structures and new
  conceptual structures are learned from the
  interacting constraints given through language
• Tools that learn ontologies from natural language
  exploit the interacting constraints on the
  various language levels (from morphology to
  pragmatics and background knowledge) in order to
  discover new concepts and stipulate relationships
  between concepts

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Ontology Learning from Semi-structured Data

• HTML data, XML data, XML DTDs, XML-Schemata and
  their likes add - more or less expressive -
  semantic information to documents
• A number of approaches understand ontologies as a
  common generalizing level that may communicate
  between the various data types and data
  descriptions. Ontologies play a major role for
  allowing semantic access to these vast resources
  of semi-structured data
• Learning of ontologies from these data and data
  descriptions may considerably enforce the
  application of ontologies and, thus, facilitate
  the access to these data

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Ontology Learning from Structured Data

• The learning of ontologies from metadata, such as
  database schemata, in order to derive a common
  high-level abstraction of underlying data
  descriptions can be an important precondition for
  data warehousing or intelligent information
  agents

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• Introduction Ontologies, Ontology learning
• Technical description
• Ontology learning in the Semantic Information
  descritpion
• Ontology Learning Process
• Ontology Learning - Architecture
• Ontology Learning data sources
• Methods used in ontology learning
• Tools of ontology learning
• Uses of ontology learning

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Methods for learning ontologies (1/8)

• Clustering
• The elaboration of any clustering method involves
  the definition of two main elements- a distance
  metrics and a classification algorithm
• A workbench that supports the development of
  conceptual clustering methods for the (semi-)
  automatic construction of ontologies of a
  conceptual hierarchy type from parsed corpora is
  the MoK workbench

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Methods for learning ontologies (2/8)

• Clustering
• Ontologies are organized as multiple hierarchies
  that form an acyclic graph where nodes are term
  categories described by intention and links
  represent inclusion.
• Learning though hierarchical classification of a
  set of objects can be performed in two main ways
  top down, by incremental specialization of
  classes and bottom-up by incremental
  generalization

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Methods for learning ontologies (3/8)

• Information Extraction Rules

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Methods for learning ontologies (4/8)

• Information Extraction Rules
• We start with
• An initial hand crafted seed ontology of
  reasonable quality which contains already the
  relevant types of relationships between ontology
  concepts in the given domain
• An initial set of documents which exemplarily
  represent (informally) substantial parts of the
  knowledge represented in the seed ontology

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Methods for learning ontologies (5/8)

• Information Extraction Rules
• Compared to other ontology learning approaches
  this technique is not restricted to learning
  taxonomy relationships, but arbitary
  relationships in an application domain.
• A project that uses this technique is the FRODO
  project.

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Methods for learning ontologies (6/8)

• Association Rules
• Association-rule-learning algorithms are used for
  prototypical applications of data mining and for
  finding associations that occur between items in
  order to construct ontologies (extraction stage)
• Classes are expressed by the expert as a free
  text conclusion to a rule. Relations between
  these classes may be discovered from existing
  knowledge bases and a model of the classes is
  constructed (ontology) based on user-selected
  patterns in the class relations
• This approach is useful for solving
  classification problems by creating
  classification taxonomies (ontologies) from rules

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Methods for learning ontologies (7/8)

• Association Rules Example
• A classification knowledge based system with
  experimental results based on medical data
  (Suryanto Compton Australia)
• Ripple Down Rules (RDR) were used to describe
  classes and their attributes
• ?Satisfactory lipid profile previous raised LDL
  noted ?
• (LDL lt 3.4)AND(Triglyceride is
  NORMAL)AND(Max(LDL)gt3.4)OR
• ((LDL is NORMAL)AND(Triglyceride is
  NORMAL)AND(Max(LDL) is HIGH)
• Experts were allowed to modify or add conclusions
  in order to correct errors
• The conclusions of the rules formed the classes
  of the classification ontology

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Methods for learning ontologies (8/8)

• Association Rules Example
• Ontology learning methodology used
• Firstly, class relations between rules were
  discovered. There were three basic relations
  subsumption/ intersection, mutual exclusivity and
  similarity
• Secondly, more compound relations which appeared
  interesting using the three basic relations were
  specified
• Finally, instances of these compound relations or
  patterns were extracted and the class model was
  assembled
• Problems that occurred
• Very similar conclusions were sometimes
  identified as mutually exclusive in cases where
  there different values for the same attribute
• The method did not consider any other information
  about the classes themselves

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• Introduction Ontologies, Ontology learning
• Technical description
• Ontology learning in the Semantic Information
  descritpion
• Ontology Learning Process
• Ontology Learning - Architecture
• Ontology Learning data sources
• Methods used in ontology learning
• Tools of ontology learning
• Uses of ontology learning

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Ontology learning tools ASIUM (1/8)

• Acronym for "Acquisition of Semantic knowledge
  Using Machine learning method"
• The main aim of Asium is to help the expert in
  the acquisition of semantic knowledge from texts
  and to generalize the knowledge of the corpus
• Asium provides the expert with an interface which
  will first help him or her to explore the texts
  and then to learn knowledge which are not in the
  texts
• During the learning step, Asium helps the expert
  to acquire semantic knowledge from the texts,
  like subcategorization frames and an ontology.
  The ontology represents an acyclic graph of the
  concepts of the studied domain. The
  subcategorization frames represent the use of the
  verbs in these texts

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Ontology learning tools ASIUM (2/8)

• Methodology

The input for Asium are syntactically parsed
texts from a specific domain. It then extracts
these triplets verb, preposition/function (if
there is no preposition), lemmatized head noun of
the complement. Next, using factorization, Asium
will group together all the head nouns occurring
with the same couple verb, preposition/function.
These lists of nouns are called basic clusters.
They are linked with the couples
verb,preposition/ function they are coming from.
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Ontology learning tools ASIUM (3/8)

• Methodology

Asium then computes the similarity among all the
basic clusters together. The nearest ones will be
aggregated and this aggregation is suggested to
the expert for creating a new concept. The expert
defines a minimum threshold for gathering
clusters into concepts. Any learned concepts can
contain noise (e.g. mistakes in the parsing), any
sub-concepts the expert wants to identify or
over-generalization due to aggre- gations may
occur,so the experts contribution is necessary.
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Ontology learning tools ASIUM (4/8)

• Methodology

After this, Asium will have learned the first
level of the ontology. Asium computes similarity
again but among all the clusters the old and the
new ones in order to learn the next level of the
ontology. The cooperative process runs until
there are no more possible aggregations. The
output of the learning process is an ontology and
subcategorization frames. The ontology represents
an acyclic graph of the concepts of the studied
domain. The subcategorization frames represent
the use of the verbs in these texts.
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Ontology learning tools ASIUM (5/8)

• Methodology
• The advantages of this method are twofold
• First, the similarity measure identifies all
  concepts of the domain and the expert can
  validate or split them. Next the learning process
  is, for one part, based on these new concepts and
  suggests more relevant and more general concepts.
• Second, the similarity measure will offer the
  expert aggregations between already validated
  concepts and new basic clusters in order to get
  more knowledge from the corpus.

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Ontology learning tools ASIUM (6/8)

• The interface

This window allows the expert to validate the
concepts learned by Asium.
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Ontology learning tools ASIUM (7/8)

• The interface

This window displays the list of all the examples
covered for the learned concept. This display
allows the expert to visualize all the sentences
which will be allowed if this class is validated.
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Ontology learning tools ASIUM (8/8)

• The interface

This window displays the ontology like it
actually is in memory i.e. learned concepts and
concepts to be proposed for a level (each blue
circle represents a class).
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Ontology learning tools TEXT-TO-ONTO (1/8)

• It develops a semi-automatic ontology learning
  from text
• It tries to overcome the knowledge acquisition
  bottleneck
• It is based on a general architecture for
  discovering conceptual structures and engineering
  ontologies from text

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Ontology learning tools TEXT-TO-ONTO (2/8)
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Ontology learning tools TEXT-TO-ONTO (3/8)

• Architecture

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Ontology learning tools TEXT-TO-ONTO (4/8)

• Architecture - Main components
• Text Processing Management Component
• The ontology engineer uses that component to
  select domain texts exploited in the further
  discovery process.Can choose among a set of text
  (pre-) processing methods available on the Text
  Processing Server and among a set of algorithms
  available at the Learning Discovering
  component.The former module returns text that is
  annotated by XML and XML-tagged is fed to the
  Learning Discovering component

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Ontology learning tools TEXT-TO-ONTO (5/8)

• Architecture - Main components
• Text Processing Server
• It contains a shallow text processor based on the
  core system SMES. SMES is a system that performs
  syntactic analysis on natural language documents
• It organized in modules, such as tokenizer,
  morphological and lexical processing and chunk
  parsing that use lexical resources to produce a
  mixed syntactic/semantic information
• The results are stored in annotations using
  XML-tagged text

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Ontology learning tools TEXT-TO-ONTO (6/8)

• Architecture - Main components
• Lexical DB Domain Lexicon
• SMES accesses a lexical database with more than
  120.000 stem entries and more than 12.000
  subcategorization frames that are used for
  lexical analysis and chunk parsing
• The domain-specific part of the lexicon
  associates word stems with concepts available in
  the concept taxonomy and links syntactic
  information with semantic knowledge that may be
  further refined in the ontology

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Ontology learning tools TEXT-TO-ONTO (7/8)

• Architecture - Main components
• Learning Discovering component
• Uses various discovering methods on the annotated
  texts e.g. term extraction methods for concept
  acquisition.

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Ontology learning tools TEXT-TO-ONTO (8/8)

• Architecture - Main components
• Ontology Engineering Enviroment-ONTOEDIT
• Supports the ontology engineer in
  semi-automatically adding newly discovered
  conceptual structures to the ontology
• Internally stores modeled ontologies using an XML
  serialization

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• Introduction Ontologies, Ontology learning
• Technical description
• Ontology learning in the Semantic Information
  descritpion
• Ontology Learning Process
• Ontology Learning - Architecture
• Ontology Learning data sources
• Methods used in ontology learning
• Tools of ontology learning
• Uses of ontology learning

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Uses of ontology learning Knowledge sharing
(1/2)

• Identifying candidate relations between
  expressive, diverse ontologies using concept
  cluster integration in multi-agent systems
• Agents with diverse ontologies should be able to
  share knowledge by automated learning methods and
  agent communication strategies
• Agents that do not know the relationships of
  their concepts to each other need to be able to
  teach each other these relationships (ontology
  learning)

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Uses of ontology learning Knowledge sharing
(2/2)

• Concept
  representation and
  learning on each
  agent
• Process an agent sends a query to another agent
  and receives a response with new concepts. A new
  category is created from these concepts. The
  agent re-learns the ontology rules and if the new
  concept relation rules are verified, they are
  stored in the agent.

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Uses of ontology learning Interest matching
(1/2)

• Designing a general algorithm for interest
  matching is a major challenge in building online
  community and agent-based communication networks.
• These algorithms can be applied in user
  categorization for an online community . Users
  behavior can be analyzed and matched against
  other users to provide collaborative
  categorization and recommendation services to
  tailor and enhance the online experience.
• The process of finding similar users based on
  data from logged behavior in called interest
  matching.

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Uses of ontology learning Interest matching
(2/2)

• User interests can be described
  by ontologies as
  weighed tree- hierarchies
  of concepts
• Each node has a weight attribute to represent the
  importance of the concept
• These weights can be explored to calculate
  similarities between users
• Learning process a standard ontology is used and
  the websites the user visits can be classified
  and entered into the standard ontology to
  personalize it if a user frequents websites of
  a category (instance of a class) it is likely he
  is interested in other instances of the class

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Uses of ontology learning Web Directory
Classification

• Ontologies and ontology learning can be used to
  create information extraction tools for
  collecting general information from the free text
  of web pages and classifying them in categories
• The goal is to collect indicator terms from the
  web pages that may assist the classification
  process. This terms can be derived from directory
  headings of a web page as well as its content.
• The indicator terms along with a collection of
  interpretation rules can result in a hierarchy
  (ontology) of web pages.

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Uses of ontology learning E-mail classification
(1/2)

• KMi Planet
• A web-based news server for communication of
  stories between member in Knowledge Media
  Institute
• Main goal To classify an incoming story, obtain
  the relevant objects within the story, deduce the
  relationships between them and to populate the
  ontology
• Integrate a template-driven information
  extraction engine with an ontology engine to
  supply the necessary semantic content

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Uses of ontology learning E-mail classification
(2/2)

• KMi Planet
• There are three tools
• PlanetOnto
• MyPlanet
• an IE tool
• PlanetOnto supports some activities.One of them
  is Ontology editing.In that point ontology
  learning is concerned.
• A tool called WebOnto provides Web-based
  visualisation, browsing and editing support for
  the ontology. The Operational Conceptual
  Modelling Language, OCML, is a language designed
  for knowledge modeling. WebOnto uses OCML and
  allows the creation of classes and instances in
  the ontology, along with easier development and
  maintenance of the knowledge models

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Bibliography

• M.Sintek, M. Junker, Ludger van Est, A. Abecker,
  Using Information Extraction Rules for Extending
  Domain Ontologies, German Research Center for
  Artificial Intelligence (DFKI)
• M.Vargas-Vera, J.Domingue, Y.Kalfoglou, E.Motta,
  S.Buckingham Shum, Template-Driven Information
  Extraction for Populating Ontologies, Knowledge
  Media Institute (UK)
• G.Bisson, C.Nedellec, Designing clustering
  methods for ontology building, University of
  Paris
• A.Maedche, S.Staab, The TEXT-TO-ONTO Ontology
  Learning Environment, University of Karlsruhe
• A.Maedche, S.Staab, Ontology Learning for the
  Semantic Web, University of Karlsruhe
• H.Suryanto,P.Compton, Learning classification
  taxonomies from a classification knowledge based
  system, University of New South Wales (Australia)
• Proceedings of the First Workshop on Ontology
  Learning OL'2000Berlin, Germany, August 25, 2000
• Proceedings of the Second Workshop on Ontology
  Learning OL'2001Seattle, USA, August 4, 2001
• ASIUM web page http//www.lri.fr/faure/Demonstrat
  ion.UK/Presentation_Demo.html

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