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Metadata Mining Metadata Mining Masoud Makrehchi Supervisor: Prof. Mohamed Kamel May 2004

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Metadata Mining. Masoud Makrehchi Supervisor: Prof. Mohamed Kamel May 2004. Outlines. Metadata Mining Proposed Approach Experimental Results Future Research. Metadata. Metadata Definition: data about data, for example a library catalogue Metadata Application - PowerPoint PPT Presentation

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Page 1: Metadata Mining

Metadata Mining Metadata Mining

Masoud MakrehchiSupervisor:

Prof. Mohamed Kamel

May 2004

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Outlines

• Metadata Mining• Proposed Approach• Experimental Results• Future Research

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Metadata

• Metadata Definition: – data about data, for example a library catalogue

• Metadata Application– cataloging (Item and Collections) – resource discovery – e-commerce and digital signatures– Intelligent Software Agents – content rating – intellectual property rights (IP rights)– Semantic Web – Learning Objects (LO): LOM standards such as IEEE

LOM, DC, SCORM, CANCORE

Metadata Mining

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Metadata

Metadata Mining

Map legend

LO metadata

Library catalogueRDF

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Metadata

Metadata Mining

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Metadata Specifications

Metadata Mining

Content-basedData Mining

Context-basedData Mining

Conceptual data architecture

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Metadata Specifications

• More structured (usually semi-structured)• Low dimensional (~400,000 vs. 15,791

terms)• More homogenous than raw data• Less noisy (stopwords)• Less time-varying

Metadata Mining

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Metadata Specifications

Metadata Mining

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Motivations

• No access to the raw data• The raw data is inconvenient for

mining (heterogeneous formats and non-text format)

• Diversity of metadata standards, the need to merge different metadata repositories

• Attempt to use background knowledge in mining process

Metadata Mining

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• Either alternative or complementary approach, depends on having access to the raw data

• Metadata enrichment (keyword extraction & automatic title generation), towards automatic metadata generation

• Semi-structured data mining, such as XML,RDF, OWL and other semantic tagged scripts

• Semi-automatic Ontology extraction• information integration based on metadata

(LOs aggregation and integration)

Applications

Metadata Mining

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Outlines

• Metadata Mining• Proposed Approach• Experimental Results• Future Research

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• There is a corpora or a metadata collection, usually labeled

• No natural Language Processing (NLP), and no computational linguistic

• No semantic analysis and processing• Metadata as a text document, no graph,

tree or other data structures• No dictionaries, thesauruses, and any

other global vocabularies• Using only frequency-based measures

Assumptions

Proposed Approach

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• Mining metadata as a text document using classical document mining techniques by considering the background knowledge which is represented by proposed data model and using fuzzy approach to capture expert knowledge

Problem Statement

Proposed Approach

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• Metadata as a text document (semi-structured format)

• Using only statistical measures– frequency-based measures (the location

and the order of words don’t matter)

Metadata Representation

Proposed Approach

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Metadata Representation

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Metadata Representation

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Metadata Representation

Title’s objective semantic

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Metadata Representation

Whole document’s semantic about title

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Metadata Representation

Title’s subjective semantic

Title’s objective semantic

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• The meaning of a metadata is broken into a set of objective semantics which can be easily extracted using a parser (to extract contents and attributes of every tag)

• Adopting document vector space model (to use all classical document mining techniques)

Metadata Representation

Metadata representation model =a set of objective semantics

+ Salton’s vector space model

Multiple-partition document vector space model

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Metadata Representation

Concepts about the domain

(discourse)

Expert

Document

Background knowledge

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Metadata Representation

Concepts about the domain

(discourse)

Expert

Background knowledge- subjective

title

abstract

subject

author

Background knowledge- objective

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Metadata as a text document

The mining process is translated into a classical document mining.

Metadata mining has no advantages because the background knowledge has been ignored.

Metadata Representation

Proposed Approach

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Metadata Representation

Metadata a a Multiple-partition Text document

The mining process is translated into a classical document mining

Considering the background knowledge

Proposed Approach

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Metadata Representation

• Vector Space Model- fuzzy representation

di

Vocabulary

Proposed Approach

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Metadata Representation

• Multi-Partition Vector Space Model

di

Vocabulary

To model the objective semantic(a potion of background knowledge)

Proposed Approach

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Metadata Representation

• Multi-Partition Vector Space Model

Proposed Approach

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Metadata Representation

• Converting to standard vector space model

Proposed Approach

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Metadata Representation

• Weight of each partition– To be determined by expert, for

example:• Wabstract=1.0,

• Wtitile=1.5.

Proposed Approach

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Metadata Representation

• Membership degree of each term in every partition– By expert (considering the vocabulary),– Statistical (considering the collection)

• Absolute frequency-based measures (like tfidf),

• Relative frequency-based (Geometric) measures (location of each term in the partition).

Proposed Approach

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• Types of Frequency Measures – Within partition– Within document: by document-term

frequency (like tf)– Within class: by class-term frequency

(like term significance)– Within collection: by collection-term

frequency (like mean of term significances)

Frequency Measures

Proposed Approach

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Class-Term Matrix

• Document-Term Matrix – 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.

Proposed Approach

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Class-Term Matrix

• Class-Term Matrix– The matrix is large. (tens of classes and

millions of terms in the vocabulary),– The matrix is less sparse,– The matrix is dual with respect to terms

and classes.

Proposed Approach

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Class-Term Matrix

Significance factor:Representing the relationship between a term and a class

Proposed Approach

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Concept Terminology

Terminology• All terms which occur in a class (or concept)• A fuzzy set of all terms in the vocabulary

Proposed Approach

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Term Definition

Definition• All concepts (classes) which the term belongs to• A fuzzy set of all concepts (classes)

Proposed Approach

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Term Relationships

Proposed Approach

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Fuzzy Similarity

For example: “sum” and “product” will be similar, if they are partially (fuzzily) co-occurring.

Similarity is a bi-directional relationship.

Proposed Approach

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Fuzzy Inclusion

For example: “web” will include “world”, if “world” occurs wherever “web” appears.

Inclusion is one-directional relationship.

Proposed Approach

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Fuzzy Document Model

• Fuzzy document representation– a fuzzy set of all terms in the vocabulary– obtaining Keyword set for the document;

either a threshold on the fuzzy set or term categorization

Proposed Approach

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• Term Categorization: Categorizing all terms into three main groups– Features: Most frequent terms within a class– Keywords: More frequent terms within some

documents belonging to a given class– Stopwords: More frequent terms in all classes

Term Categories

Document

Collection

Class

features

keywords

stopwords

Proposed Approach

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• Stopwords: not contributing 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

Domain-specific Stopwords

Proposed Approach

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Class-Collection Map

Introducing Class-Collection Map to visualize the location of each category

Features

Keywords

Stopwords

Within-collection Frequency

Within

-cla

ss F

requ

ency

Proposed Approach

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Class-Collection Map

Introducing Class-Collection Map to visualize the location of each category

Proposed Approach

Features

Keywords

Stopwords

Within-collection Frequency

Within

-cla

ss F

requ

ency

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Class-Collection Map

Introducing Class-Collection Map to visualize the location of each category

Proposed Approach

Features

Keywords

Stopwords

Within-collection Frequency

Within

-cla

ss F

requ

ency

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• A model for metadata representation: Multiple- partition document vector space model

• Class-term model, representing the relationship between classes and the vocabulary

• New fuzzy representations for documents, terms and concepts (word definition and terminology)

Contributions

Proposed Approach

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• Class-collection map to visualize the distribution of terms

• Representing the keyword set of a document by a fuzzy model

• New definitions for fuzzy similarity, fuzzy inclusion, and fuzzy term-relation based on fuzzy term definition

• A framework for extracting domain-specific stopwords

Contributions

Proposed Approach

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Outlines

• Problem Statement: Metadata Mining• Proposed Approach• Experimental Results• Future Research

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Data Set

• 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.

Preliminary Results

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Data Set

Preliminary Results

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Preliminary Results

Stopwords

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Preliminary Results

Stopwords

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Results

Preliminary Results

title = Convergence Results for the {EM} Approach to Mixtures of ExpertsArchitectures,Abstract: The Expectation-Maximization (EM) algorithm is an iterativeapproach to maximum likelihood parameter estimation. Jordan and Jacobs(1993) recently proposed an EM algorithm for the mixture of expertsarchitecture of Jacobs, Jordan, Nowlan and Hinton (1991) and the hierarchicalmixture of experts architecture of Jordan and Jacobs (1992). They showedempirically that the EM algorithm for these architectures yields significantlyfaster convergence than gradient ascent. In the current paper we

keywords: ascent, em, expert, faster, hinton, jacob, jordan, nowlan

title = Text Classification from Labeled and Unlabeled Documents using EM,

Abstract: This paper shows that the accuracy of learned text classifierscan be improved by augmenting a small number of labeled training documentswith a large pool of unlabeled documents. This is important because in manytext classification problems obtaining training labels is expensive, whilelarge quantities of unlabeled documents are readily available. We introduce analgorithm for learning from labeled and unlabeled documents based on thecombination of Expectation-Maximization (EM) and a naive

keywords: accuraci, em, label, pool, quantiti, readili, unlabel, valuabl

~400,000

15,971

12,044

5,605

226 Features4,872 Keywords,507 Stopwords

using metadata insteadof the document

stemming

multi-partitionvector space model

Fuzzy-based termclustering

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Outlines

• Problem Statement: Metadata Mining• Proposed Approach• Experimental Results• Future Research

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• Evaluate the metadata representation model

• Test and evaluate the proposed fuzzy document and term representations in metadata mining problems such as dimensionality reduction, stopword and keyword extraction and term association

• Evaluate the performance of the multiple-classifier architecture in metadata classification problem

Research Plan

Future Research

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Thank You!