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Maintaining Information Integration OntologiesMaintaining Information Integration Ontologies
Alexandros Valarakos, Georgios Paliouras, Georgios Paliouras, Vangelis Karkaletsis, Georgios Sigletos, Georgios Vouros
Software & Knowledge Engineering Lab
Inst. of Informatics & TelecommunicationsNCSR “Demokritos”
http://www.iit.demokritos.gr/skel
DCAG, Ulm, December 6, 2003
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Structure of the talkStructure of the talk
• Information integration in CROSSMARC• Semi-automated ontology enrichment• Clustering “synonyms”• Conclusions
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CROSSMARC ObjectivesCROSSMARC Objectives
• crawl the Web for interesting Web pages,• extract information from pages of different sites without
a standardized format (structured, semi-structured, free text),
• process Web pages written in several languages,• be customized semi-automatically to new domains and
languages,• deliver integrated information according to personalized
profiles.
Develop technology for Information Integration that can:
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CROSSMARC ArchitectureCROSSMARC Architecture
Ontology
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CROSSMARC OntologyCROSSMARC Ontology
• Meta-conceptual layer• Embodies domain-independent semantics
• Conceptual layer• Contains relevant concepts of each domain
• Instance layer• Contains relevant individuals of each domain
• The lexical layer • Language dependent realizations of domain
information
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CROSSMARC OntologyCROSSMARC Ontology
…<description>Laptops</description> <features> <feature id="OF-d0e5"> <description>Processor</description> <attribute type="basic" id="OA-d0e7"> <description>Processor Name</description> <discrete_set type="open"> <value id="OV-d0e1041"> <description>Intel Pentium 3</description> </value> …
<node idref="OV-d0e1041"> <synonym>Intel Pentium III</synonym> <synonym>Pentium III</synonym> <synonym>P3</synonym> <synonym>PIII</synonym></node>
Lexicon
Ontology
<node idref="OA-d0e7">
<synonym>Όνομα Επεξεργαστή</synonym>
</node>
Greek Lexicon
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Structure of the talkStructure of the talk
• Information integration in CROSSMARC• Semi-automated ontology enrichment• Clustering “synonyms”• Conclusions
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Ontology EnrichmentOntology Enrichment
An ontology captures knowledge in a static way, as it is a snapshot of knowledge from a particular point of view that governs a certain domain of interest in a specific time-period.
Evolving nature of Evolving nature of ontologyontology OntologyOntology MaintenanceMaintenance
OntologyOntology EnrichmentEnrichment
part of
Instances
Conceptualization
T-box A-box
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Ontology EnrichmentOntology Enrichment
• Highly evolving domain (e.g. laptop descriptions)– New Instances characterize new concepts.
e.g. ‘Pentium 2’ is an instance that denotes a new concept if it doesn’t exist in the ontology.
– New surface appearance of an instance.
e.g. ‘PIII’ is a different surface appearance of ‘Intel Pentium 3’
• We concentrate on instances (knowledge of the domain of interest).
• The poor performance of many Information Integration systems is due to their incapability to handle the evolving nature of the domain they cover.
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Ontology EnrichmentOntology Enrichment
Multi-Lingual Domain Ontology
Additional annotations
Validation
Ontology Enrichment / Population
Domain Expert
Annotating Corpus Using Domain Ontology
Information extraction
machine learning
Corpus
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Results: Annotation phase onlyResults: Annotation phase only
0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
0,8
0,9
1
25% 50% 75%
% of Ontology
RE
CA
LL
Union
Ontology
HMM
0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
0,8
0,9
1
75% 50% 25%
% of Ontology
PR
EC
ISIO
N Union
Ontology
HMM
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Results: Full enrichment cycleResults: Full enrichment cycle
Initial Instances
Target Instances Iter-0 Iter-1 Iter-2
processorName 6 15 3 4 2
cdromSpeed 5 8 3 - -
screenResolution 3 7 2 1 1
Ram 4 8 3 0 -
Processor Speed 6 12 6 - -
HDD 4 8 3 0 -
Initial
InstancesTarget
Instances Iter-0 Iter-1 Iter-2
processorName 8 15 4 3 -
cdromSpeed 6 8 2 -
screenResolution 5 7 2 -
RAM 6 8 2 -
Processor Speed 9 12 2 0
HDD 6 8 2 -
Initial Instances
Target Instances
Iter-0 Iter-1 Iter-2
Processor Name 3 15 3 4 3
Cdrom Speed 2 8 3 3 -
Screen Resolution 2 7 0 - -
RAM 2 8 5 0 -
Processor Speed 4 12 7 0 -
HDD 2 8 5 0 -
25% of the initial ontology
50% of the initial ontology
75% of the initial ontology
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Structure of the talkStructure of the talk
• Information integration in CROSSMARC• Semi-automated ontology enrichment• Clustering “synonyms”• Conclusions
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Enrichment with synonymsEnrichment with synonyms
• The number of instances for validation increases with the size of the corpus and the ontology.
• So far, only enrichment with instances that participate in the ‘instance of’ relationship has been supported.
• There is a need for supporting the enrichment of the ‘synonymy’ relationship (in different languages and domains).
ONTOLOGY LEARNING
We approach this problem using …
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Enrichment with synonymsEnrichment with synonyms
• Discover automatically different surface appearances of an instance (CROSSMARC synonymy relationship).
Synonym : ‘Intel pentium 3’ - ‘Intel pIII’
Orthographical : ‘Intel p3’ - ‘intell p3’
Lexicographical : ‘Hewlett Packard’ - ‘HP’
• Issues to be handled:
Combination : ‘Intell Pentium 3’ - ‘P III’
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Compression-based ClusteringCompression-based Clustering
• COCL (COmpression-based CLustering): a model based algorithm that discovers typographic similarities between strings (sequences of elements-letters) over an alphabet (ASCII characters) employing a new score function CCDiff.
• CCDiff is defined as the difference in the code length of a cluster (i.e., of its instances), when adding a candidate string. Huffman trees are used as models of the clusters.
• COCL iteratively computes the CCDiff of each new string from each cluster implementing a hill-climbing search. The new string is added to the closest cluster, or a new cluster is created (threshold on CCDiff ).
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Compression-based ClusteringCompression-based Clustering
Given CLUSTERS and candidate INSTANCESwhile INSTANCES do for each instance in INSTANCES compute CCDiff for every cluster in CLUSTERS
end for each select instance from INSTANCES that maximizes the difference between its two smallest CCDiff’s if min(CCDiff) of instance > threshold create new cluster assign instance to new cluster remove instance from INSTANCES calculate code model for the new cluster add new cluster to CLUSTERS
else assign instance to cluster of min(CCDiff) remove instance from INSTANCES recalculate code model for the cluster
end while
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Results - EvaluationResults - Evaluation
• Concept Generation Scenario
Instances kept (%) Correct Accuracy (%)
90 3 100
80 11 100
70 15 100
60 19 100
50 23 95,6
40 29 96,5
30 34 94,1
• Instance Matching Scenario
We hide incrementally one cluster at a time and measure the ability of the algorithm to discover the hidden clusters
Cluster’s Name Cluster’s Type Instances
Amd Processor Name 19
Intel Processor Name 8
Hewlett-Packard Manufacturer Name 3
Fujitsu-Siemens Manufacturer Name 5
Windows 98 Operating System 10
Windows 2000 Operating System 3
Dataset characteristics
Recall : 100%
Precision : 75%
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Structure of the talkStructure of the talk
• Information integration in CROSSMARC• Semi-automated ontology enrichment• Clustering “synonyms”• Conclusions
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ConclusionsConclusions
• CROSSMARC is a complete multi-lingual information integration system.
• Ontology Maintenance is crucial in evolving domains.• Ontology Enrichment helps the adaptation of the
system to new domains saving time and effort.• Machine-learning based information extraction can
assist the discovery of new instances.• Compression-based clustering discovers string
similarities that support the enrichment with different surface appearances of an instance (“synonyms”).
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ReferencesReferences
1) B. Hachey, C. Grover, V. Karkaletsis, A. Valarakos, M. T. Pazienza, M. Vindigni, E. Cartier, J. Coch, Use of Ontologies for Cross-lingual Information Management in the Web, In Proceedings of the Ontologies and Information Extraction International Workshop held as part of the EUROLAN 2003, Romania, July 28 - August 8, 2003
2) M. T. Pazienza, A. Stellato, M. Vindigni, A. Valarakos, V. Karkaletsis, Ontology Integration in a Multilingual e-Retail System, In Proceedings of the HCI International Conference, Volume 4, pp. 785-789, Heraklion, Crete, Greece, June 22-27 2003.
3) A. Valarakos, G. Sigletos, V. Karkaletsis, G. Paliouras, A Methodology for Semantically Annotating a Corpus Using a Domain Ontology and Machine Learning, In RANLP, 2003
4) A. Valarakos, G. Sigletos, V. Karkaletsis, G. Paliouras, G. Vouros, A Methodology for Enriching a Multi-Lingual Domain Ontology using Machine Learning, In Proceedings of the 6th ICGL workshop on Text Processing for Modern Greek: from Symbolic to Statistical Approaches, held as part of the 6th International Conference in Greek Linguistics, Rethymno, Crete, 20 September, 2003.
5) A. Valarakos, G. Paliouras, V. Karkaletsis, G. Vouros, A Name-Matching Algorithm for Ontology Enrichment, In Proceedings of the Hellenic Artificial Intelligence Conference (SETN’04), Samos, May, 2004.