the value of usage scenarios for thesaurus alignment in cultural heritage context

The Value of Usage Scenarios for Thesaurus Alignment in Cultural Heritage Context

Antoine Isaac, Claus Zinn, Henk Matthezing, Lourens van der Meij, Stefan Schlobach, Shenghui Wang

Cultural Heritage on the Semantic Web WorkshopOct. 12th, 2007

OAEI 2007: Results from the Library Track

Introduction

• One important problem in CH

• Heterogeneity of description resources• Thesauri (at large)• Classification schemes, subject heading lists …

• Hampers access across collections


Introduction

Ontology alignment can help• Semantic links between ontology elements

• o1:Cat owl:equivalentClass o2:Chat• Using automatic tools

• E.g. exploiting labels, structure


Introduction

• Problem: not much on alignment applications• Need further research on context-specific

alignment• Generation• Deployment• Evaluation

• Important context dimension: application scenarios


Agenda

• Introduction• Dutch National Library Scenarios for

Alignment• Book Re-indexing• Scenario-specific Evaluation


KB and Thesaurus Alignement

• National Library of the Netherlands (KB)• 2 main collections• Each described (indexed) by its own thesaurus• Problem: maintenance optimized wrt. redundancy/size?

ScientificCollection

Depot

1.4Mbooks

1Mbooks

GTT Brinkman

250Kbooks


Usage Scenarios for Thesaurus Alignment at KB• Concept-based search

• Retrieving GTT-indexed books using Brinkman concepts• Re-indexing

• Indexing GTT-indexed books with Brinkman concepts• Integration of one Thesaurus into the other

• Inserting GTT elements into the Brinkman thesaurus• Thesaurus Merging

• Building a new thesaurus fro GTT and Brinkman• Free-text search

• matching user search terms to both GTT or Brinkman concepts• Navigation:

• browse the 2 collections through a merged version of the thesauri


Agenda




The Book Re-indexing Scenario

• Scenario: re-indexing of GTT-indexed books by Brinkman concepts


Depot

1.4Mbooks

1Mbooks

GTT Brinkman


The Book Re-indexing Scenario

• If one of the thesauri is dropped, legacy data has to be indexed according to the other voc.• Automatically• Semi-automatically, users presented with candidate

annotations


Scenario Requirements

• Mapping sets of GTT concepts to sets of Brinkman• alignreindex: {g1,…,gm} →{b1,…,bn}• Option where users select based on probabilities

• Candidates concepts are given weights (e.g. [0;1])• alignreindex’: {g1,…,gm} →{(b1,w1),…,(bn,wn)}

• Generated index should be generally small• 99.2% of depot books indexed with no more than 3

Brinkman concepts


Semantic Interpretation of Re-indexing Function

1-1 case: g1->b1• b1 is semantically equivalent to g1

• OK• b1 is more general than g1

• Loss of information• Possible if b1 is the most specific subsumer of g1’s

meanings• Indexing specificity rule


Semantic Interpretation of Re-indexing Function

Generic cases: combinations of concepts• Considerations on semantic links are the same• Combination matters

Indexing is post-coordinated• {“Geography”; “the Netherlands”} in GTT-> book about geography of the Netherlands

• Different granularities/indexing points of view• Brinkman has “Netherlands; Geography”


Problem of Alignment Deployment

Results of existing tools may need re-interpretation

• Unclear semantics of mapping links• "=","<"• weights

• Single concepts involved in mappings


Example of Alignment Deployment Approach• Starting from similarity measures over both

thesauri• sim(X,Y)=n

• Aggregation strategy: simple Ranking• For a concept, take the top k similar concepts

• Gather GTT concepts and Brinkman ones

• Re-indexing function specified by conditions for firing rules• E.g., if the book indexing contains the left part of the rule


Agenda




Evaluation Design

• We do not assess the rules• We assess their application on book indexing

• 2 classical aspects:• Correctness (cf. precision)• Completeness (cf. recall)


Evaluation Design: Different Variants and Settings

• Fully automatic evaluation• Using the set of dually indexed books as gold standard

• Manual evaluation 1• Human expert assesses candidate indices• Unsupervised setting: margin of error should be very

low• Supervised setting: less strict, but size also matter

• Manual evaluation 2• Same as 1, but a first index has been produced by the

expert• Distance between the two indices is assessed• Eventually changing original index


Human Evaluation vs. Automatic Evaluation

Taking into account• Indexing variability

• Automatic evaluation compares with a specific indexing choice

• Especially important if thesaurus doesn’t match book subject

• Evaluation variability• Only one expert judgment is considered per book

• Evaluation set bias• Dually-indexed books (may) present specific

characteristics


New Developments, Outside of Paper!• Reviews: you should add actual results of good

general alignment systems as compared to your scenario

• Ontology Alignment Evaluation Initiative• http://oaei.ontologymatching.org/2007• State-of-the-art aligners applied to specific cases

• This paper: grounding for an OAEI Library track• KB vocabularies• Evaluation in re-indexing scenario


Automatic Evaluation

• There is a gold standard for re-indexing scenario

• General method: for dually indexed books, compare existing Brinkman annotations and new ones


Depot

1.4Mbooks

1Mbooks

GTT Brinkman

250Kbooks


Automatic Evaluation

• Book level: Precision & Recall for matched books • Books for which there is one good annotation• Minimal hint about users’ (dis)satisfaction

• Annotation level: P & R for candidate annotations• Note: counting over annotations and books, not rules

and concepts• Rules & concepts used more often are more important


Automatic Evaluation Results

Notice: for exactMatch only


Manual Evaluation Method

Variant 1, for supervised setting

• Selection of 100 books• 4 KB evaluators• Paper forms + copy of books

OAEI 2007: Results from the Library TrackPaper Forms


Annotation Transl.: Manual Evaluation Results

Research question: quality of candidate annotations

• Measures used: cf. automatic evaluation

• Performances are consistently higher

[Left: manual evaluation, Right: automatic evaluation]



Research question: evaluation variability

• Krippendorff’s agreement coefficient (alpha)

• High variability: overall alpha=0.62• <0.67, classic threshold for Computational

Linguistics tasks• But indexing seems to be more variable than usual

CL tasks



Research question: indexing variability• Measuring acceptability of original book

indices

• Kripendorff’s agreement for indices chosen by evaluators• 0.59 overall alpha confirms high variability


Conclusions

• Better characterization of alignment scenarios is needed

• For a single case there are many scenarios and variants

• Requires to elicit requirements• And evaluation criteria


Discussion: Differences between scenarios?

• Concept-based search• Re-indexing• Integration of one thesaurus into the other• Thesaurus merging• Free-text search aided by thesauri• Navigation



Semantics of alignment

• A core of primitives that are be useful• broader/narrower, related

• Some constructs are more specific• “AND” combination for re-indexing

• Interpretation of equivalence?• Thesaurus merging: “excavation” = “excavation”• Query reformulation: “excavation” = “archeology;

Netherlands”



Multi-concept alignment

• Useful for re-indexing or concept-based search• Less for thesaurus re-engineering scenarios

• Combinations are not fully dealt with by thesaurus formats

• But simple links involving a same concept can be useful• C1 BT C2• C1 BT C3



Precision and recall

• Browsing -> emphasis on recall• For other scenarios, it depends on the setting

Supervised vs. unsupervised


Thanks!

the value of usage scenarios for thesaurus alignment in cultural heritage context

Documents

reindexing of gtt

gtt book

gtt concepts

thesaurus alignment

reindexing scenarioscenario

reindexing scenarioif

brinkman conceptstitle

brinkman conceptsintegration