Semantic Enrichment of Mappings

Semantic Enrichment ofMappings

Patrick Arnold

WDI-Lab, Abteilung für Datenbanken, Universität Leipzig

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Outline

04/19/2023Abteilung für Datenbanken, Inst. für Informatik, Universität Leipzig

1. Motivation2. Goals3. Related Work4. Determining the Relation Type5. Implementation6. First Results7. Conclusions

19.04.2023WDI-Lab, Abteilung für Datenbanken, Universität Leipzig3

1. Motivation

Classic approaches in schema/ontology matching provide only little information about the correspondences Source node Target node Confidence

Further details are commonly omitted What kind of relation?

equal, is-a, part-of, overlap Simple correspondence vs. complex correspondence?

(first name, last name) ↔ name Transformation functions?

gross price = net price * (1 + sales taxes) name = first name + “ “ + last name


1. Motivation

Our intentions: Mapping enrichment Enhance a mapping by adding further or more-specific

information to its correspondences Useful for merging and transforming schemas/ontologies

Workflow: Input: A mapping Mapping enrichment carried out in an independent system

(blackbox) Output is an enriched mapping

Implies a new, more-specific format


1. Motivation

Typical relation types Equal Is-a Part-of Overlap

Inverse types: Equal Inverse is-a Has-a Overlap


2. Goals

First Focus: Detecting the relation type of a correspondence Investigate linguistic methods on element level Extension by existing strategies possible equal, is-a, inverse is-a

Later… Relation type detection on instance level Exploiting background knowledge Correspondence type, transformation rules, …


3. Related Work

Several projects dealing with this problem Mainly based on the following methods:

Using dictionaries, thesauri, corpora WordNet, GermaNet Includes tokenization, normalization of strings etc.

Using background knowledge The Open University: Using Swoogle to retrieve multiple

ontologies referring to a concept Exploiting the structure between ontologies Exploiting Reasoning, Bayes Nets, Feature Vectors etc. Search Engines (Google)


3. Related Work

SMatch Complex strategy using WordNet to determine the following

relations: Equal, more-general, less-general, overlap, mismatch “Overlap” offers few interesting information (concepts are

somehow related…) Approach: To each word in a label, annotate all meanings of

this word found in WordNet Compare/match the meanings of the words Exploit the relations offered by WordNet


3. Related Work

TaxoMap Focus on geographic ontologies Detect relations equal, is-a, inv is-a and is-close

Focus rather on the correspondence itself, not on the type Is-a relation if a label in node S appears in node T and is a full

word Use WordNet as additional source

Working on manually pre-defined branches of WordNet instead of the entire thesaurus

Useful for domain-specific ontologies Recall: 23 %, Precision: 83 %


3. Related Work

LogMap Uses reasoning algorithms to repair/discover mappings

Based on Horn logics and Dowling-Gallier-Algorithm Use background knowledge (thesauri)

Detects full correspondences and weak correspondences No specific relation detection per se


4. Relation Type Determination4.1 Introduction

Typically, there is no link between the syntax and semantics of words stool, chair, seat… refer to the same object stool, school, tool, pool, wool… have nothing in common!

Things change when it comes to compounds… blackbird is a bird high school is a school



Compound: Two words A, B of a language form a new word AB apple + tree → apple tree sun + glasses → sunglasses forth + with → forthwith

A, B can be noun, verb, adjective/adverb, preposition We are normally interested in nouns

19.04.202313


WDI-Lab, Abteilung für Datenbanken, Universität Leipzig

No compounds are... Compositions AB where A (or B) is not an official word

broom, nausea Derivations

discard, unload, increase, compound Compositions AB where A and B are not semantically

related door (do + or), wither (wit + her)



Unlike non-compounds, semantics can be generally derived from the compound’s syntax Especially in nouns

blackboard is a board handbag is a bag

Germanic languages are left-branching Germanic: school bus, central intelligence agency Romanic: rio de las palmas (= palm river)

In English, no changes are applied to the words: German: Ort + Eingang → Ortseingang, Stadt + Bau → Städtebau English: city + limit → city limit, city + planning → city planning


4. Relation Type Determination4.2 Classification

From an Linguistic point of views…

* C A, C B, AB ~⊈ ⊈ R B

Description Example Relation (AB : B)

Endocentric A+B denote something more specific than B

lecture hallblackboard

AB B⊂

Exocentric A+B denote something more specific of an unexpressed term C

doughnutbuttercup

AB C *⊂

Copulative / appositional

A+B denote something that is the sum of what A and B denote

bittersweetBosnia-Herzeg.actor-director

AB ⊂(A B)∪


4. Relation Type Determination4.2 Classification

From the English point of view… Closed form

database, playground, blackbird Hyphened form

bus-driver, single-minded, small-appliance industry Open form

web space, container ship, computer scientist

From a POS point of view… noun-noun, adjective-noun, verb-verb, …


4. Relation Type Determination4.3 First Conclusions

From the knowledge now gained, we can enrich correspondences in schemas in two ways: Set the relation type to is-a instead of equal (1) Remove or at least doubt an existing correspondence (2)

For (1) we assume that AB B⊂ (cookbook, book, 0.8, equal) → (cookbook, book, 0.8, is-a)

For (2) we assume that If A is not a word in AB, the correspondence is likely to be false: (stool, tool, 0.9, equal) → false? (refund, fund, 0.7, equal) → false? (discharge, charge, 0.7, equal) → false?


4. Relation Type Determination4.4 Mismatches

A word changed its spelling over the centuries: butterfly (“flutter-by”, “beat fly”, …) Weiße Elster (from Czech: alstra = water)

A compound is of literal meaning (metaphor): Completely different meaning

computer mouse, gravy train, buttercup Obvious origin (in a broad sense being related):

airport, birdhouse, downtown, snowman



Inaccuracies in (vernacular) language e.g., in biology: strawberry, blackberry, raspberry etc.

Neither is a berry in the biological sense (yet tomato, banana, grape, pumpkin, melon etc. are)



For detecting the relation type, the mismatch problem has no negative effect on the mapping The correspondence is wrong after all

(buttercup, cup, equal) is as wrong as (buttercup, cup, is-a) Enrichment has no negative effect on the mapping per se

Still, enhanced methods can be used to reduce the mismatches


5. Implementation5.1 Goals

Specify the following relation types on linguistic methods: equal (default), is-a, inverse is-a Missing: part-of and overlap English and German language

Main focus on English language

Possibly apply mapping repair Remove correspondences that seem clearly wrong

Test & Evaluation


5. Implementation5.1 Goals

First concentrate on the element level Use linguistic knowledge as presented before Different cases to be distinguished

Single items vs. itemizations


5. Implementation5.2 Cases

Simple Case (1:1) Source and target node consist of one item

blackboard ↔ board high school ↔ school international database conference ↔ conference


5. Implementation5.2 Cases

Complex Cases (1:n, n:1, n:m) Source/target node consist of several item

blackboard, whiteboard ↔ board wine ↔ white wine, red wine beer, wine ↔ wine computers, laptops ↔ computers


5. Implementation5.3 Node Level vs. Path level

Relation type depends on the perspective… Node level vs. Path level Relation is often…

is-a on node level equal on path level


5. Implementation5.3 Node Level vs. Path level

Source Target

+ Apparel + Children - Shoes - Caps - …

+ Apparel + Children Shoes + Caps + …

Source Target

+ Kids + Apparel + Shoes + Caps + …

+ Clothing + Children Shoes + Caps + …


5. Implementation5.4 Requirements

Benchmarks / Gold Standards (English language) Manually defined

Dictionary / Thesauri

More-specific data structure Correspondence: source node, target node, confidence, type Node: A list of items Item: A list of word Word: single word vs. compound


5. Implementation5.5 Generating Benchmarks

Benchmarks More difficult than in standard mappings In some cases even for humans difficult to decide

Birdhouse is a house? Airport is a port?

How to judge correspondences in an evaluation? car = bike → FALSE car = auto → TRUE motorbike bike → ?⊂


5. Implementation5.6 Challenges

Exocentric compounds Airport, buttercup, saw tooth, …

Compounds in itemizations (French wine, German wine — French wine) inverse is-a (French wine, German wine — European wine) is-a (French wine, German wine — Mosel wine) overlap (French wine, German wine — Italian wine) mismatch



Plurals (Christian churches — church) (red wine, white wine — wines)

Short forms Infant colic — colic (equal instead of is-a)

Node Level vs. Path Level Compound extending/skipping levels in the schema



Limited recall Strong dependency to input (mapping) Some is-a relations cannot be detected with simple

linguistic methods (car, vehicle) (wine, beverage) (cell phones, communication devices)


6. First Results

Web ↔ Yahoo 421 Correspondences 68 subset-correspondences

Found 50 subset-relations, with 34 being correct Recall: 50.0 % Precision: 68.0 % f-Measure: 59.0 %


6. First Results

Google Health ↔ Yahoo Health (excerpt) 396 Correspondences 31 subset-correspondences

Found 20 subset-relations, with 15 being correct Recall: 48.3 % Precision: 75.0 % f-Measure: 61.6 %


6. First Results

Main issues observed… Imprecise labels

infant colic — colic (equal) Uterine-Fibroids — Uterus.Fibroids (equal) picture frames — frames (equal in field “arts”)

Node-Path-Discrepancies “No-Compound”-Subsets

vehicle — car (isa)


7. Conclusions

Mapping Enrichment Relation type Simple vs. complex correspondences

Transformation rules

Relation Type Determination Linguistic approach on element level

Compounds, itemizations Advanced methods

Instance level, background knowledge etc. Increase recall, keep up precision


Discussion

Thank You!

Semantic Enrichment of Mappings

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