STL: A similarity measure based on semantic and linguistic information

STL : A Similarity Measure Based on Semantic, Terminological and Linguistic Information
Nitish Aggarwal
joint work with Tobias Wunner, MihaelArcan
DERI, NUI Galway
firstname.lastname@deri.org
Friday,19th Aug, 2011
DERI, Friday Meeting

Overview
Motivation & Applications
Why STL?
Semantic
Terminology
Linguistic
Evaluation
Conclusion and future work
2

SemanticAnnotation
Similarity between corpus data and ontology concepts
SAP AG held €1615 million in short-term liquid assets (2009)
“dbpedia:SAP_AG” “xEBR:LiquidAssets” at “dbpedia:year:2009”
3

SemanticSearch
Similarity between Query and index object
SAP liquid asset in 2010
Current asset of SAP last year
“dbpedia:SAP_AG” “xEBR:liquid asset” at “dbpedia:year:2010”
Net cash of SAP in 2010
SAP total amount received in 2010
4

OntologyMatching & Alignment
Similarity between ontology concepts
ifrs:StatementOfFinancialPosition
xebr:KeyBalanceSheet
Assets
Ifrs:Assets
ifrs:BiologicalAssets
xebr:SubscribedCapitalUnpaid
Ifrs:CurrentAssets
Ifrs:NonCurrentAssets
xebr:FixedAssets
xebr:CurrentAssets
ifrs:PropertyPlantAndEquipment
xebr:TangibleFixedAssets
xebr:IntangibleFixedAssets
xebr:Amount Receivable
xebr:Liquid
Assets
Similarity = ?
Similarity = ?
ifrs:CashAndCashEquivalents
Ifrs:TradeAndOtherCurrentReceivables
Ifrs:Inventories
5

Classical Approaches
String Similarity
Levenshteindistance, Dice Coefficient
Corpus-based
LSA, ESA, Google distance,Vector-Space Model
Ontology-based
Path distance, Information content
Syntax Similarity
Word-order, Part of Speech
6

Why STL?
Semantic
Semanticstructure and relations
Terminology
complex terms expressing the same concept
Linguistic
Phrase and dependency structure
7

STL
Definition
Linear combination of semantic, terminological and linguistic
obtained by using a linear regression
Formula used
STL = w1*S + w2*T + w3*L + Constant
w1, w2, w3 represent the contribution of each
8

Semantic
WuPalmer
2*depth(MSCA) / depth(c1) + depth(c2)
Resnik’s Information Content
IC(c) = -log p(c)
Intrinsic Information Content (Pirro09)
Overcome the analysis of large corpora
9

Cont.
Intrinsic information content(iIC)
.
where sub(c) is number of sub-concept of given concept c.
Pirro_Similarity
10

Cont.
MSCA
subconcepts = 48
IC (TFA) = 0.32
Assets
Subscribed Capital Unpaid
Fixed Assets
Current Assets
Pirro_Sim = 0.33
Pirro_Sim =?
Stocks
Tangible Fixed Assets
Amount Receivable
subconcepts = 6
IC (AR) = 0.69
subconcepts = 9
IC (TFA) = 0.60
Amount Receivable [total]
Amount Receivable with in one year
Amount Receivable after more than one year
Other Tangible Fixed Assets
Property, Plant
and Equipment
Payments on account and asset in construction
Furniture Fixture and Equipment
Trade Debtors
Other Fixture
Land and Building
Other Debtors
Plant and Machinery
Other Property, Plant
and Equipment
Property, Plant
and Equipment [Total]
11

Limitation
Does semantic structure reflect a good similarity?
not necessarily
e.g. In xEBR, parent-child relation for describing the layout of concepts
“Work in progress” is not a type of asset, although both are linked via the parent-child relationship
12

Terminology
Definition
Common naming convention
Ngram Vs subterms
In financial domain, bigram ”Intangible Fixed” is a subtring of ”Other Intangible Fixed Assets” but not a subterm.
Terminological similarity
maximal subterm overlap
13

Cont.
Trade Debts Payable After More Than One Year
[[Trade][Debts]][Payable][After More Than One Year]
[SAP:Payable]
[Ifrs:After More Than One Year]
[Investoword:Debt]
[FinanceDict:Trade Debts]
[Investopedia:Trade]
Financial[Debts][Payable][After More Than One Year]
Financial Debts Payable After More Than One Year
14

Multilingual Subterms
Translatedsubterms
Available in otherlanguages
Advantage
Reflect terminological similarities that may be available in one language but not in others.
”Property Plant and Equipment”@en
”Sachanlagen”@de
”Tangible Fixed Asset” @en
15

Linguistic
Syntactic Information
Beyond simple word order
phrase structure
Dependency structure
Phrase structure
Intangible fixed : adj adj > ??
Intangible fixed assets : adj adj n > NP
Dependency structure
Amounts receivable : N Adv : receive:mod, amounts:head
Received amounts : V N : receive:mod, amounts:head
16

Evaluation
Data Set
xEBR finance vocabulary
269 terms (concept labels)
72,361(269*269) termpairs
Benchmarks
SimSem59: sample of 59 term pairs
SimSem200 : sample of 200 term pairs (under construction)
17

Experiment
An overview of similarity measures
18

Experiment Results (Simsem59)
STL formula used
STL = 0.1531 * S + 0.5218 * T + 0.1041 * L + 0.1791
Correlation between similarity scores & simsem59
Semantic
Contribution
Terminology
Contribution
Linguistic
Contribution
19

Conclusion
STL outperforms more traditional similarity measures
Largest contribution by T (Terminological Analysis)
Multilingual subterms performs better than monolingual
20

Future work
Evaluation on larger data set and vocabularies (IFRS)
3000+ terms
9M term pairs
richer set of linguistic operations
“recognise” => “recognition”
by derivation rule verb_lemma+"ion”
Similarity between subterms
“Staff Costs” and "Wages And Salaries"
21

STL: A similarity measure based on semantic and linguistic information

by Nitish Aggarwal

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