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Learning Object Metadata Mining. Masoud Makrehchi Supervisor: Prof. Mohamed Kamel. Outlines. Metadata Mining Metadata Representation Model Class-Term Matrix Case Study Conclusion Remarks. Metadata Mining. Metadata Definition Data about data, for example a library catalogue - PowerPoint PPT Presentation
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Learning Object Metadata Mining
Masoud MakrehchiSupervisor:
Prof. Mohamed Kamel
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Outlines
• Metadata Mining• Metadata Representation Model• Class-Term Matrix• Case Study• Conclusion Remarks
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Metadata Mining• Metadata Definition
– Data about data, for example a library catalogue • Metadata Application:
– Cataloging (Item and Collections) – Resource Discovery – Electronic Commerce and Digital Signatures– Intelligent Software Agents – Content Rating – Intellectual Property Rights – Semantic Web– Learning Objects
• LOM Standards: IEEE LOM, DC, SCORM, CANCORE
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Metadata Mining
• Definition– extraction of implicit, previously
unknown, and potentially useful information from metadata.
• Methods– classification, clustering, summarization,
mining association rules, ontology extraction, information integration, keyword extraction, automatic title generation.
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Metadata Mining
• Why metadata mining?– No access to the data itself, lack of raw
data,– The data is not convenient for mining
(heterogeneous formats and non-text format)
– Diversity of metadata standards, and need to merge different metadata repositories,
– Ontology extraction is much easier in metadata level.
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Metadata Mining
Content-basedData Mining
Context-basedData Mining
Conceptual data architecture
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Metadata Mining
• Applications– Metadata mining instead of raw data
mining,– Metadata enrichment (keyword
extraction)– (Semi)-automatic Ontology extraction,– RDF, OWL and other semantic tagged
script mining,– Information integration (LOs aggregation
and integration),
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Metadata Mining
• Statistical methods based on word frequency analysis,
• Syntactic methods based on linguistic parsing and pattern matching,
• Structural methods studying the outline of the document,
• Conceptual (semantic) methods on the use of knowledge base to interpret the meaning.
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Metadata Mining
• We don’t use – Natural Language Processing (NLP),– Semantic analysis and processing, – Graph, tree and other sophisticate data
structures and models,– Dictionaries, thesauruses, and any other
global vocabularies (only a simple Porter stemmer).
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Outlines
• Metadata Mining• Metadata Representation Model• Class-Term Matrix• Case Study• Conclusion Remarks
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Metadata Representation Model
• We treat metadata as a text document (semi-structured format),
• The only measures are– statistical measures (like frequency)– geometric features (like location of a
specific term, the order of words in a term or phrase)
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Metadata Representation Model
• Vector Space Model
T
di
Vocabulary
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Metadata Representation Model
• Multi-Partition Vector Space Model
T
di
Vocabulary
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Metadata Representation Model
• Multi-Partition Vector Space Model
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Metadata Representation Model
• Converting to standard vector model
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Metadata Representation Model
• Weight of each partition– To be determined by expert, for
example: Wabstract=1.0, Wtitile=1.5.
• Membership degree of each term in every partition– By expert,– Frequency based measures (tfidf), – Geometric measures (location of each
term in the partition).
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Outlines
• Metadata Mining• Metadata Representation Model• Class-Term Matrix• Case Study• Conclusion Remarks
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Class-Term Matrix
• Document-Term Matrix (Collection X Vocabulary)– The matrix is very large. (thousands of
documents in the collection and millions of terms in the vocabulary),
– The matrix is sparse. Usually only small number of elements in the matrix are non zero (zipf's law),
– The matrix is dual with respect to terms and documents.
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Class-Term Matrix
• Class-Term Matrix (Class X Vocabulary)– The matrix is large. (tens of classes and
millions of terms in the vocabulary),– The matrix is less sparse,– The matrix is still dual with respect to
terms and classes.
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Class-Term Matrix
Class-term Frequency
Term significance measure
Normalized term significance measure
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Class-Term Matrix
... Sig(q,m)
text
...
...
Term m
Sig(1,m)te
xt ... Sig(2,m)
...
text
...
text
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ss q
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ss 1
Term 1
Sig(1,1)
Term 2
Sig(1,2)
Cla
ss 2
Sig(2,1) Sig(2,2)
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Class-Term Matrix
... Sig(q,m)
text
...
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Term m
Sig(1,m)te
xt ... Sig(2,m)
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text
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text
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ss q
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ss 1
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Term 2
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Cla
ss 2
Sig(2,1) Sig(2,2)
•Terminology•All terms which occur in a class (or concept)•A fuzzy set of all terms in the vocabulary
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Class-Term Matrix
... Sig(q,m)
text
...
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Term m
Sig(1,m)te
xt ... Sig(2,m)
...
text
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Cla
ss q
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ss 1
Term 1
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Term 2
Sig(1,2)
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ss 2
Sig(2,1) Sig(2,2)
•Definition•All concepts (classes) which the term belongs to•A fuzzy set of all concepts (classes)
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Outlines
• Metadata Mining• Metadata Representation Model• Class-Term Matrix• Case Study• Conclusion Remarks
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Case Study
• Data set– There is no available LO metadata
repository– Citeseer computer science directory
(http://citeseer.ist.psu.edu/directory.html)– ~400,000 terms (vocabulary size) – 17 classes– 2,912 documents– Instead of data (in PDF or PS), we collected
BibTeX data (kind of metadata or catalogue) and abstracts of the articles.
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Case Study
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Case Study
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Case Study
• Types of Frequency Measures – Within document: by document-term
frequency (like tfidf)– Within class: by class-term frequency
(like term significance)– Within collection: by collection-term
frequency (like mean of term significances)
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Case Study
• Term Clustering: Categorizing all terms into three main groups– Features: More frequent terms within a class– Keywords: More frequent terms within some
documents belonging to a given class– Stopwords: More frequent terms in all classes
• Introducing Class-Collection Map– To visualize the location of each category
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Case Study
Features
Keywords
Stopwords
Within-collection Frequency
Within
-cla
ss F
requen
cy
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Case Study
Features
Keywords
Stopwords
Within-collection Frequency
Within
-cla
ss F
requen
cy
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Case Study
Features
Keywords
Stopwords
Within-collection Frequency
Within
-cla
ss F
requen
cy
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Case Study
• Extraction of Stopwords (doesn’t contribute to the meaning of the document)– General stopwords (a, an, the, in, …)– Domain-specific stopwords
• Politics: Government, State,• Medicine: Patient, • Education: Learner, Instructor,• Social sciences: Society,• Anthropology: Human.
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Case Study
• Why we need to remove domain specific stopwords?– Dimensionality reduction,– Accurate feature selection (drawbacks of
information gain in selecting noise as feature)
– Based on stopwords, we can find and separate phrases (based on our definition, a phrase is a set of words between two stopwords).
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Mak
rehc
hi &
Kam
el
41 of 46
Case Study
• Dimensionality reduction process~400,000
15,971 stemming 12,044
Multi-partition document Vector space model
5,605Fuzzy-based term
clustering
507 stopwords4,872 keywords
226features
Using metadata
Mak
rehc
hi &
Kam
el
42 of 46
Outlines
• Metadata Mining• Metadata Representation Model• Class-Term Matrix• Case Study• Conclusion Remarks
Mak
rehc
hi &
Kam
el
43 of 46
Conclusion Remarks
• Most statistic-based data mining methods do not use domain knowledge
• Metadata (semi-structured data) mining uses domain knowledge embedded in tags and partitions.
• We introduced multi-partition document vector space model.
• We mine class-term matrix in addition to document-term matrix.
Mak
rehc
hi &
Kam
el
44 of 46
Conclusion Remarks
• Based on the visualization model (class-collection map) and a fuzzy inference, we can cluster vocabulary for each class and extract three essential categories;– Features: to classify unknown documents, – Keywords: for indexing and access to
specific document in IR applications,– Stopwords: for dimensionality reduction
and noise removal.
Mak
rehc
hi &
Kam
el
45 of 46
Conclusion Remarks
• Based on class-term matrix, we defined– Terminologies as fuzzy sets of all terms
in the vocabulary– Definitions as fuzzy sets of all concepts
Mak
rehc
hi &
Kam
el
46 of 46
Conclusion Remarks
• Future Works– Collecting LO metadata and constructing
a LO metadata repository,– A keyword recall method to test and
validate extracted keywords,– Implementing an average classifier (KNN
or Fuzzy classifier) to test and validate selected features,
– Applying multi-classifier architecture on metadata mining problem.
