Knowledge Compilation forCore Competence Extraction in Organizations

Simona Colucci1, Eufemia Tinelli2, Silvia Giannini2, Eugenio Di Sciascio2,Francesco M. Donini1

1Dipartimento di Scienze Umanistiche, della Comunicazione e del Turismo(DISUCOM),

Università della Tuscia, Viterbo, Italy

2Dipartimento di Ingegneria Elettrica e dell'Informazione (DEI),Politecnico di Bari, Bari, Italy

s.giannini@deemail.poliba.it

16th International Conference on Business Information Systems (BIS 2013)Pozna«, Poland | 19 - 20 June, 2013

Introduction Problem Formulation Algorithm Description Performance Evaluation Conclusions

Outline

1 Introduction

2 Problem Formulation

3 Algorithm Description

4 Performance Evaluation

5 Conclusions

Silvia Giannini Knowledge Compilation for Core Competence Extraction

Introduction Problem Formulation Algorithm Description Performance Evaluation Conclusions

The concept of Core Competence

A Knowledge Management (KM) process

"Core competencies are a company collective knowledge abouthow to coordinate diverse production skills and integrate multiple

streams of technologies. Identifying core comptencies helps in supportcompetitive advantage, articulate a strategic intent, and allocateresources to build cross-unit technological and production links."

(G. Hamel, and C.K.A. Prahalad, The core competence of the corporation. Harvard Business, in HarvardBusiness Review May-June (1990) 79�90)

The force of core competencies (also denoted by Core Competence) is feltas decisively in services as in manufacturing.

Core Competence is a corporate, not unit, resource.

Core Competence does not diminish with use.

Building Core Competence is usually seen as an ambitious task.

Silvia Giannini Knowledge Compilation for Core Competence Extraction

Introduction Problem Formulation Algorithm Description Performance Evaluation Conclusions

Business Information Management

Database Technologies

Integrated enterprise suites(e.g., http://www.monster.com,http://www.careerbuilder.com)

Semantic Technologies

Machine-understandable representationand processing of the informativecontent

Knowledge Compilation:

addresses the computational di�culties of deduction in knowledge basesexpressed through a logical formalism;

combines the representation power of a logical language, with thescalability and e�ciency of information processing in a DBMS.

Silvia Giannini Knowledge Compilation for Core Competence Extraction

Introduction Problem Formulation Algorithm Description Performance Evaluation Conclusions

Contributions

A Knowledge Compilation approach to Core Competence evaluation.

1 OFF-LINE REASONINGpre-processing of a company intellectual capital, described in a DescriptionLogics (DLs) Knowledge Base (KB), in an appropriate relational databaseschema.

2 ON-LINE REASONINGquerying of the data structure coming out from the �rst phase throughstandard SQL-queries for e�cient Core Competence Extraction.

The service has been implemented in the I.M.P.A.K.T. (InformationManagement and Processing with the Aid of Knowledge-based Technologies)framework.

Silvia Giannini Knowledge Compilation for Core Competence Extraction

Introduction Problem Formulation Algorithm Description Performance Evaluation Conclusions

Outline

1 Introduction

2 Problem FormulationDescription Logics notesFrom KB to R-DBNon-standard inference services for HR Management

3 Algorithm Description

4 Performance Evaluation

5 Conclusions

Silvia Giannini Knowledge Compilation for Core Competence Extraction

Introduction Problem Formulation Algorithm Description Performance Evaluation Conclusions

Description Logics notes

Knowledge-based Competence Modeling

We need a formal language in order to provide semantic-based and automatedCore Competence evaluation services

The Core Competence extraction approach grounds on aDLs formalization of the knowledge domain

A family of logic-based knowledge representation languages

They provide useful inference services

Silvia Giannini Knowledge Compilation for Core Competence Extraction

Introduction Problem Formulation Algorithm Description Performance Evaluation Conclusions

Description Logics notes

DL formalism

DL alphabet DL possible building elements

Concept names: set of objectsEmployee, ObjectOrientedProgramming

Role names: relations betweenconceptshasDegree, knowsLanguage

+

ConjunctionJava u Cplusplus

Universal Role Quanti�cation∀ knowsLanguage.English

Full Existential Quanti�cation∃ hasDegree.Law

Concrete features: predicateswith concrete domainsyears, lastDate

Concept Descriptions: complex expressions combining concept and rolenames through constructors

Trade-o� between expressiveness and computational complexity of a DL LSilvia Giannini Knowledge Compilation for Core Competence Extraction

Introduction Problem Formulation Algorithm Description Performance Evaluation Conclusions

Description Logics notes

T -Box and A-Box

T -Box: formalizes the intensional knowledge of a domain of interest

Concept De�nitions (AssetManager ≡ Manager u∀hasKnowledge.AssetAllocation)Concept Inclusions (Java v ObjectOrientedProgramming)

A-Box: formalizes the extensional knowledge of a domain of interest

Concept Assertions (AssetManager(jack))

Role Assertions (knows(jack,john))

Silvia Giannini Knowledge Compilation for Core Competence Extraction

Introduction Problem Formulation Algorithm Description Performance Evaluation Conclusions

Description Logics notes

DL Reasoning Services

Subsumption w.r.t. a T -Box

Given a T -Box T, and two concepts C1 and C2, is C1 more speci�c than C2(C1 v C2) according to the axioms in T?

e.g. Competence = ∃ basicKnowledge.ProgrammingPro�le = ∃ basicKnowledge.Java u ∃ hasMasterDegree.ComputerScience

Competence subsumes Pro�le, formally Pro�le v Competence

Silvia Giannini Knowledge Compilation for Core Competence Extraction

Introduction Problem Formulation Algorithm Description Performance Evaluation Conclusions

From KB to R-DB

Human Resources (HR) Management

Real-life examples suggest using at least the following DL building elements:

conjunction;

universal quanti�cation;

existential quanti�cation;

concrete features.

ALE(D)

Silvia Giannini Knowledge Compilation for Core Competence Extraction

Introduction Problem Formulation Algorithm Description Performance Evaluation Conclusions

From KB to R-DB

Human Resources (HR) Management

Reasoning complexity leads to the adoption of the following constructors:

conjunction;

universal quanti�cation;

existential quanti�cation;

concrete features.

FL0(D)

Silvia Giannini Knowledge Compilation for Core Competence Extraction

Introduction Problem Formulation Algorithm Description Performance Evaluation Conclusions

From KB to R-DB

Curriculum Vitae (CV) representation

The T -Box

Employee Profile(M

0)

Industry

(M1)

ComplementarySkill(M

2)

Level

(M3)

Language

(M5)

JobTitle(M

6)

Knowledge

(M4)

Submodules Mi, i ≥ 1, describe Curriculum Vitae (CV) sections and aremodeled according to FL0(D) ;+ it allows for extending the T -Box whenever a new category of work-related

features is identi�ed.

Main module M0: it models the properties (entry points) needed toimports all the previous sections describing an employee CV.

The T -Box represents an ontology for the HR domain and currentlyincludes nearly 5000 concepts.

Silvia Giannini Knowledge Compilation for Core Competence Extraction

Introduction Problem Formulation Algorithm Description Performance Evaluation Conclusions

From KB to R-DB

Curriculum Vitae (CV) representation

The T -Box

Employee Profile(M

0)

Industry

(M1)

ComplementarySkill(M

2)

Level

(M3)

Language

(M5)

JobTitle(M

6)

Knowledge

(M4)

We consider only the Knowledge submodule in Core Competence Extraction:

it models the hierarchy of possible employee skills and technical toolsusage ability.

it may be speci�ed through:type - experience role (e.g., developer, administrator)year - experience levellastdate - last temporal update of work experience

Silvia Giannini Knowledge Compilation for Core Competence Extraction

Introduction Problem Formulation Algorithm Description Performance Evaluation Conclusions

From KB to R-DB

Curriculum Vitae (CV) representation

The A-Box

Role free - includes only concept assertions P (a), stating that theemployee a is described by features P .

Pro�le

Given the ontology T , a pro�le P = u(∃R0j .C) is a concept in ALE(D),

where R0j , 1 ≤ j ≤ 6, is an entry point, and C is a concept in FL0(D)

modeled in Mj .

Silvia Giannini Knowledge Compilation for Core Competence Extraction

Introduction Problem Formulation Algorithm Description Performance Evaluation Conclusions

From KB to R-DB

CV translation

We need to manage the atomic information:

FL0(D) concepts are normalized according to the Concept-CenteredNormal Form (CCNF).

Silvia Giannini Knowledge Compilation for Core Competence Extraction

Introduction Problem Formulation Algorithm Description Performance Evaluation Conclusions

From KB to R-DB

CV translation

Silvia Giannini Knowledge Compilation for Core Competence Extraction

Introduction Problem Formulation Algorithm Description Performance Evaluation Conclusions

From KB to R-DB

CV translation

T -Box informative content (A, B, C concepts)

concepts de�nitions (A ≡ B)

subsumption relations via concept inclusions (A v C)

pro�le descriptions (P = A)

A-Box informative content

pro�le descriptions istances P (a)

Extra-ontological personal informations

Silvia Giannini Knowledge Compilation for Core Competence Extraction

Introduction Problem Formulation Algorithm Description Performance Evaluation Conclusions

From KB to R-DB

Relational schema design rules

T -Box informative content

Table CONCEPT(conceptID, name, level) stores the CCNF of all theFL0(D) concepts (part (a))

Silvia Giannini Knowledge Compilation for Core Competence Extraction

Introduction Problem Formulation Algorithm Description Performance Evaluation Conclusions

From KB to R-DB

Relational schema design rules

T -Box informative content

Tables PARENT, ANCESTOR, CHILD, DESCONCEPT map the recursiverelationships over table CONCEPT (part (a))

Silvia Giannini Knowledge Compilation for Core Competence Extraction

Introduction Problem Formulation Algorithm Description Performance Evaluation Conclusions

From KB to R-DB

Relational schema design rules

T -Box informative content

A Table Rj(pro�leID, groupID, conceptID, value, lastdate) is created foreach entry point R0

j , j > 0 (part (b))

Silvia Giannini Knowledge Compilation for Core Competence Extraction

Introduction Problem Formulation Algorithm Description Performance Evaluation Conclusions

From KB to R-DB

Relational schema design rules

T -Box informative content

Each atom of CCNF(C) of a conjunct ∃R0j .C is stored in a di�erent tuple

of table Rj with the same groupID (part (b))

Silvia Giannini Knowledge Compilation for Core Competence Extraction

Introduction Problem Formulation Algorithm Description Performance Evaluation Conclusions

From KB to R-DB

Relational schema design rules

A-Box informative content

Table PROFILE includes pro�leID and extra-ontological structuredinformation (e.g., personal data, work-related information) (part (b))

Silvia Giannini Knowledge Compilation for Core Competence Extraction

Introduction Problem Formulation Algorithm Description Performance Evaluation Conclusions

Non-standard inference services for HR Management

The reasoning service

Objective: Automatically extract Core Competence, by identifying a commonknow-how in a signi�cant portion of personnel (k employees, with k set as athreshold value by the people in charge for the strategic analysis).

The Knowledge Compilation approach solves subsumption in FL0(D) only viaSQL queries to the presented R-DB schema, without reference to anyexponential-time inference engine.

Silvia Giannini Knowledge Compilation for Core Competence Extraction

Introduction Problem Formulation Algorithm Description Performance Evaluation Conclusions

Non-standard inference services for HR Management

A logic-based approach

Least Common Subsumer (LCS)

Let C1, . . . , Cn be a collection of nconcepts in a DL L. The LeastCommon Subsumer (LCS) ofC1, . . . , Cn is a concept D in L suchthat D is the most speci�c conceptsubsuming all the elements of thecollection.

k-Common Subsumer (k-CS)

Let C1, . . . , Cn be a collection of nconcepts in a DL L and let k < n. Ak-Common Subsumer (k-CS) ofC1, . . . , Cn is a concept D in L suchthat D is an LCS of k concepts amongC1, . . . , Cn.

Informative k-Common Subsumer(IkCS)

Given k < n, an Informativek-Common Subsumer (IkCS) of theconcepts C1, . . . , Cn in a DL L is aconcept D such that D is a k-CSstricltly subsumed by theLCS(C1, . . . , Cn) and addinginformative content to it.

Best Informative Common Subsumer(BICS)

Given k < n, a Best InformativeCommon Subsumer (BICS) of theconcepts C1, . . . , Cn in a DL L is aconcept B such that B is an IkCS forC1, . . . , Cn, and for every k < j ≤ nevery j-CS is not informative.

Silvia Giannini Knowledge Compilation for Core Competence Extraction

Introduction Problem Formulation Algorithm Description Performance Evaluation Conclusions

Outline

1 Introduction

2 Problem Formulation

3 Algorithm Description

4 Performance Evaluation

5 Conclusions

Silvia Giannini Knowledge Compilation for Core Competence Extraction

Introduction Problem Formulation Algorithm Description Performance Evaluation Conclusions

The Core Competence Extraction Algorithm

On-line Reasoning Steps for Knowledge Compilation

1 Pro�les Subsumers Matrix (PSM) computation

Pro�le Concept Components listingStrict Match execution

2 Common Subsumers Enumeration (CSE)

Pro�les Subsumers Matrix evaluationLCS, BICS and k-CS sets extraction

Silvia Giannini Knowledge Compilation for Core Competence Extraction

Introduction Problem Formulation Algorithm Description Performance Evaluation Conclusions

The Core Competence Extraction Algorithm

1 Pro�les Subsumers Matrix computation

Pro�les Subsumers Matrix

Let P = {P (a1), . . . , P (an)} be a set of n employee pro�les. Letj ∈ {1, . . . , 6} be an entry point, and Dk ∈ {D1, . . . , Dm} be the Pro�leConcept Components (PCC) w.r.t j deriving from the collection P.The Pro�les Subsumers Matrix (PSM) S = [sik], with 1 ≤ i ≤ n and1 ≤ k ≤ m, is de�ned as:

sik =

{1 if P (ai) strictly matches the component Dk

0 otherwise

Silvia Giannini Knowledge Compilation for Core Competence Extraction

Introduction Problem Formulation Algorithm Description Performance Evaluation Conclusions

The Core Competence Extraction Algorithm

1 Pro�les Subsumers Matrix computation

Idea: Extract the common know-how, expressed in form of atomicinformation, shared by the same group of employees, with cardinalitygreater or equal to k.

Example

Mario Rossi: Cplusplus (5 years), Java (5 years), Visual Basic (5 years)

Daniela Bianchi: Cplusplus (2 years), Java (6 years), Visual Basic (1 years)

Elena Pomarico: CplusPlus, Java, Visual Basic

Carmelo Piccolo: VBScript, Process Performance Monitoring

Lucio Battista: DBMS (2 years)

Mariangela Porro: DBMS (2 years), Internet Technologies (2 years)

Nicola Marco: DBMS (5 years), Internet Technologies (5 years)

Domenico De Palo: OOprogramming (6 years), Arti�cial intelligence (4 years), Internet technologies (4years)

Silvia Giannini Knowledge Compilation for Core Competence Extraction

Introduction Problem Formulation Algorithm Description Performance Evaluation Conclusions

The Core Competence Extraction Algorithm

1 Pro�les Subsumers Matrix computation

Idea: Extract the common know-how, expressed in form of atomicinformation, shared by the same group of employees, with cardinalitygreater or equal to k.

D1 D2 D3 D4 D5 D6 D7 D8 D9 D10 D11 ...

1 1 1 1 1 1 0 1 0 1 1 1 ...

2 1 1 1 1 1 0 1 0 1 1 1 ...

3 1 1 0 0 0 1 0 0 0 0 0 ...

4 1 1 0 0 0 1 0 1 0 0 0 ...

5 1 1 0 0 1 1 0 1 0 0 0 ...

6 1 0 1 0 0 0 0 0 0 0 0 ...

7 1 0 1 1 0 0 0 0 1 1 1 ...

8 1 1 1 1 1 0 1 1 0 0 0 ...

Table: Portion of the previous Example Pro�le Subsumers Matrix

Silvia Giannini Knowledge Compilation for Core Competence Extraction

Introduction Problem Formulation Algorithm Description Performance Evaluation Conclusions

The Core Competence Extraction Algorithm

1 Pro�les Subsumers Matrix computation

Idea: Extract the common know-how, expressed in form of atomicinformation, shared by the same group of employees, with cardinalitygreater or equal to k.

D1 ∃hasKnowledge.ComputerScienceSkillD2 ∃hasKnowledge.(ComputerScienceSkillu =2 years)D3 ∃hasKnowledge.ProgrammingLanguageD4 ∃hasKnowledge.OOPD5 ∃hasKnowledge.(ComputerScienceSkillu =5 years)D6 ∃hasKnowledge.(DBMSu =2 years)D7 ∃hasKnowledge.(OOPu =5 years)D8 ∃hasKnowledge.(InternetTechnologiesu =2 years)D9 ∃hasKnowledge.C++D10 ∃hasKnowledge.VisualBasicD11 ∃hasKnowledge.Java...

Table: Description of D1, . . . , D11 reported in the previous Table

Silvia Giannini Knowledge Compilation for Core Competence Extraction

Introduction Problem Formulation Algorithm Description Performance Evaluation Conclusions

The Core Competence Extraction Algorithm

1 Pro�les Subsumers Matrix computation

Pro�le Concept Components

Let P = u(∃R0j .C) be a pro�le, with concept C in FL0(D) written in CCNF

C1 u . . . u Cm. Let j ∈ {1, ...6} be an entry point.

The Pro�le Concept Components (PCC) of P w.r.t. j are de�ned as follows:

if Ck, with 1 ≤ k ≤ m, is a concept name, then ∃R0j .C

k is a PCC of P ;

if Ck, with 1 ≤ k ≤ m, is a concrete feature, then the concept ∃R0j .(C

k u Cf ) is

a PCC of P , for each f ∈ {1, . . .m} such that f 6= k and Cf is a concept name;

for each Ck, if Ck = ∀R.E, with 1 ≤ k ≤ m, then, for each Eh PCC of E, thederiving PCC of P are: i) ∃R0

j .∀R.Eh; ii) ∃R0j .(∀R.Eh u Cf ), for each

f ∈ {1, . . .m} such that f 6= k and Cf is a concept name.

The process of PCC listing exploits principally tables CONCEPT andANCESTORS.

Silvia Giannini Knowledge Compilation for Core Competence Extraction

Introduction Problem Formulation Algorithm Description Performance Evaluation Conclusions

The Core Competence Extraction Algorithm

1 Pro�les Subsumers Matrix computation

Strict Match

Given a set P = {P (a1), . . . , P (an)} of employee pro�les and a setFS = {fs1, . . . , fss} of required features fsi = ∃R0

j .Ci, i = {1, . . . , s},representing a set of PCC of the collection P, the Strict Match process returnsall pro�les in P providing all features fsi in FS.

Thanks to CCNF, Strict Match can retrieve employee pro�les more speci�cthan FS, i.e., linked by a subsumption relation to the set of PCC in FS.Strict Match is performed only through standard SQL queries Qs(fsi)built on-the-�y on the basis of the translation of fsi in a set of syntacticelements to search for in the proper Rj table.

The �nal result of Strict Match is the intersection of pro�les returned byall performed Qs(fsi) queries.

Silvia Giannini Knowledge Compilation for Core Competence Extraction

Introduction Problem Formulation Algorithm Description Performance Evaluation Conclusions

The Core Competence Extraction Algorithm

1 Pro�les Subsumers Matrix computation

Silvia Giannini Knowledge Compilation for Core Competence Extraction

Introduction Problem Formulation Algorithm Description Performance Evaluation Conclusions

The Core Competence Extraction Algorithm

1 Pro�les Subsumers Matrix computation

Example Query

Pro�le Concept Component:fs = ∃hasknowledge.(Javau ≥6 years)

Query:SELECT profileIDFROM hasKnowledge as RWHERE conceptID = (SELECT conceptID

FROM conceptWHERE name='Java')

AND EXISTS (SELECT *FROM hasKnowledgeWHERE conceptid=(SELECT conceptID

FROM conceptWHERE name='years')

AND value >= 6 AND profileid=R.profileid AND groupid=R.groupid)

Silvia Giannini Knowledge Compilation for Core Competence Extraction

Introduction Problem Formulation Algorithm Description Performance Evaluation Conclusions

The Core Competence Extraction Algorithm

2 Common Subsumers enumeration

Referring to the PSM of the set P = {P (a1), . . . , P (an)}, and to a conceptcomponent Dk ∈ {D1, . . . , Dm} deriving from P, a Core Competence is theunion of the most speci�c features (i.e., pro�le concept components Dj) sharedby the same group of k employees, where k is a prede�ned threshold.

D1 D2 D3 D4 D5 D6 D7 D8 D9 D10 D11 ...

1 1 1 1 1 1 0 1 0 1 1 1 ...

2 1 1 1 1 1 0 1 0 1 1 1 ...

3 1 1 0 0 0 1 0 0 0 0 0 ...

4 1 1 0 0 0 1 0 1 0 0 0 ...

5 1 1 0 0 1 1 0 1 0 0 0 ...

6 1 0 1 0 0 0 0 0 0 0 0 ...

7 1 0 1 1 0 0 0 0 1 1 1 ...

8 1 1 1 1 1 0 1 1 0 0 0 ...

Table: Portion of the previous Example Pro�le Subsumers Matrix

Silvia Giannini Knowledge Compilation for Core Competence Extraction

Introduction Problem Formulation Algorithm Description Performance Evaluation Conclusions

The Core Competence Extraction Algorithm

2 Common Subsumers enumeration

Referring to the PSM of the set P = {P (a1), . . . , P (an)}, and to a conceptcomponent Dk ∈ {D1, . . . , Dm} deriving from P, a Core Competence is theunion of the most speci�c features (i.e., pro�le concept components Dj) sharedby the same group of k employees, where k is a prede�ned threshold.

D1 D2 D3 D4 D5 D6 D7 D8 D9 D10 D11 ...

1 1 1 1 1 1 0 1 0 1 1 1 ...

2 1 1 1 1 1 0 1 0 1 1 1 ...

3 1 1 0 0 0 1 0 0 0 0 0 ...

4 1 1 0 0 0 1 0 1 0 0 0 ...

5 1 1 0 0 1 1 0 1 0 0 0 ...

6 1 0 1 0 0 0 0 0 0 0 0 ...

7 1 0 1 1 0 0 0 0 1 1 1 ...

8 1 1 1 1 1 0 1 1 0 0 0 ...

Table: Portion of the previous Example Pro�le Subsumers Matrix

LCS = ∃hasKnowledge.ComputerScienceSkill

Silvia Giannini Knowledge Compilation for Core Competence Extraction

Introduction Problem Formulation Algorithm Description Performance Evaluation Conclusions

The Core Competence Extraction Algorithm

2 Common Subsumers enumeration

Referring to the PSM of the set P = {P (a1), . . . , P (an)}, and to a conceptcomponent Dk ∈ {D1, . . . , Dm} deriving from P, a Core Competence is theunion of the most speci�c features (i.e., pro�le concept components Dj) sharedby the same group of k employees, where k is a prede�ned threshold.

D1 D2 D3 D4 D5 D6 D7 D8 D9 D10 D11 ...

1 1 1 1 1 1 0 1 0 1 1 1 ...

2 1 1 1 1 1 0 1 0 1 1 1 ...

3 1 1 0 0 0 1 0 0 0 0 0 ...

4 1 1 0 0 0 1 0 1 0 0 0 ...

5 1 1 0 0 1 1 0 1 0 0 0 ...

6 1 0 1 0 0 0 0 0 0 0 0 ...

7 1 0 1 1 0 0 0 0 1 1 1 ...

8 1 1 1 1 1 0 1 1 0 0 0 ...

Table: Portion of the previous Example Pro�le Subsumers Matrix

BICS = ∃hasKnowledge.ComputerScienceSkillu =5 years

Silvia Giannini Knowledge Compilation for Core Competence Extraction

Introduction Problem Formulation Algorithm Description Performance Evaluation Conclusions

The Core Competence Extraction Algorithm

2 Common Subsumers enumeration

Referring to the PSM of the set P = {P (a1), . . . , P (an)}, and to a conceptcomponent Dk ∈ {D1, . . . , Dm} deriving from P, a Core Competence is theunion of the most speci�c features (i.e., pro�le concept components Dj) sharedby the same group of k employees, where k is a prede�ned threshold.

D1 D2 D3 D4 D5 D6 D7 D8 D9 D10 D11 ...

1 1 1 1 1 1 0 1 0 1 1 1 ...

2 1 1 1 1 1 0 1 0 1 1 1 ...

3 1 1 0 0 0 1 0 0 0 0 0 ...

4 1 1 0 0 0 1 0 1 0 0 0 ...

5 1 1 0 0 1 1 0 1 0 0 0 ...

6 1 0 1 0 0 0 0 0 0 0 0 ...

7 1 0 1 1 0 0 0 0 1 1 1 ...

8 1 1 1 1 1 0 1 1 0 0 0 ...

Table: Portion of the previous Example Pro�le Subsumers Matrix

ICS3 = ∃hasKnowledge.(DBMSu =2 years)

Silvia Giannini Knowledge Compilation for Core Competence Extraction

Introduction Problem Formulation Algorithm Description Performance Evaluation Conclusions

The Core Competence Extraction Algorithm

2 Common Subsumers enumeration

Referring to the PSM of the set P = {P (a1), . . . , P (an)}, and to a conceptcomponent Dk ∈ {D1, . . . , Dm} deriving from P, a Core Competence is theunion of the most speci�c features (i.e., pro�le concept components Dj) sharedby the same group of k employees, where k is a prede�ned threshold.

D1 D2 D3 D4 D5 D6 D7 D8 D9 D10 D11 ...

1 1 1 1 1 1 0 1 0 1 1 1 ...

2 1 1 1 1 1 0 1 0 1 1 1 ...

3 1 1 0 0 0 1 0 0 0 0 0 ...

4 1 1 0 0 0 1 0 1 0 0 0 ...

5 1 1 0 0 1 1 0 1 0 0 0 ...

6 1 0 1 0 0 0 0 0 0 0 0 ...

7 1 0 1 1 0 0 0 0 1 1 1 ...

8 1 1 1 1 1 0 1 1 0 0 0 ...

Table: Portion of the previous Example Pro�le Subsumers Matrix

ICS3 = ∃hasKnowledge.(OOPu =5 years)

Silvia Giannini Knowledge Compilation for Core Competence Extraction

Introduction Problem Formulation Algorithm Description Performance Evaluation Conclusions

The Core Competence Extraction Algorithm

2 Common Subsumers enumeration

Referring to the PSM of the set P = {P (a1), . . . , P (an)}, and to a conceptcomponent Dk ∈ {D1, . . . , Dm} deriving from P, a Core Competence is theunion of the most speci�c features (i.e., pro�le concept components Dj) sharedby the same group of k employees, where k is a prede�ned threshold.

D1 D2 D3 D4 D5 D6 D7 D8 D9 D10 D11 ...

1 1 1 1 1 1 0 1 0 1 1 1 ...

2 1 1 1 1 1 0 1 0 1 1 1 ...

3 1 1 0 0 0 1 0 0 0 0 0 ...

4 1 1 0 0 0 1 0 1 0 0 0 ...

5 1 1 0 0 1 1 0 1 0 0 0 ...

6 1 0 1 0 0 0 0 0 0 0 0 ...

7 1 0 1 1 0 0 0 0 1 1 1 ...

8 1 1 1 1 1 0 1 1 0 0 0 ...

Table: Portion of the previous Example Pro�le Subsumers Matrix

ICS3 = ∃hasKnowledge.(InternetTechnologiesu =2 years)

Silvia Giannini Knowledge Compilation for Core Competence Extraction

Introduction Problem Formulation Algorithm Description Performance Evaluation Conclusions

The Core Competence Extraction Algorithm

2 Common Subsumers enumeration

Referring to the PSM of the set P = {P (a1), . . . , P (an)}, and to a conceptcomponent Dk ∈ {D1, . . . , Dm} deriving from P, a Core Competence is theunion of the most speci�c features (i.e., pro�le concept components Dj) sharedby the same group of k employees, where k is a prede�ned threshold.

D1 D2 D3 D4 D5 D6 D7 D8 D9 D10 D11 ...

1 1 1 1 1 1 0 1 0 1 1 1 ...

2 1 1 1 1 1 0 1 0 1 1 1 ...

3 1 1 0 0 0 1 0 0 0 0 0 ...

4 1 1 0 0 0 1 0 1 0 0 0 ...

5 1 1 0 0 1 1 0 1 0 0 0 ...

6 1 0 1 0 0 0 0 0 0 0 0 ...

7 1 0 1 1 0 0 0 0 1 1 1 ...

8 1 1 1 1 1 0 1 1 0 0 0 ...

Table: Portion of the previous Example Pro�le Subsumers Matrix

ICS3 = ∃hasKnowledge.(C++ u VisualBasic u Java)

Silvia Giannini Knowledge Compilation for Core Competence Extraction

Introduction Problem Formulation Algorithm Description Performance Evaluation Conclusions

Outline

1 Introduction

2 Problem Formulation

3 Algorithm Description

4 Performance Evaluation

5 Conclusions

Silvia Giannini Knowledge Compilation for Core Competence Extraction

Introduction Problem Formulation Algorithm Description Performance Evaluation Conclusions

What is I.M.P.A.K.T.

Information Management and Processing with the Aid ofKnowledge-based Technologies

An integrated system managing three enterprise business services based onknowledge management:

1 Skill Matching1

2 Team Composition2

3 Core Competence Extraction

Technologies:Java client-server applicationJena API � to access the ontology modelPellet reasoner � ontology o�-line pre-processing phasePostgreSQL 9.1 DBMS - on-line querying phase

1E. Tinelli, S. Colucci, S. Giannini, E. Di Sciascio, and F.M. Donini, Large scale skillmatching through knowledge compilation In: Proc. of ISMIS 2012, Springer-Verlag (2012)192�201.

2E. Tinelli, S. Colucci, E. Di Sciascio, and F.M. Donini, Knowledge compilation forautomated team composition exploiting standard SQL In: Proc. of SAC 2012, ACM (2012)1680�1685.

Silvia Giannini Knowledge Compilation for Core Competence Extraction

Introduction Problem Formulation Algorithm Description Performance Evaluation Conclusions

I.M.P.A.K.T. GUI

Silvia Giannini Knowledge Compilation for Core Competence Extraction

Introduction Problem Formulation Algorithm Description Performance Evaluation Conclusions

I.M.P.A.K.T. GUI

Silvia Giannini Knowledge Compilation for Core Competence Extraction

Introduction Problem Formulation Algorithm Description Performance Evaluation Conclusions

I.M.P.A.K.T. GUI

Silvia Giannini Knowledge Compilation for Core Competence Extraction

Introduction Problem Formulation Algorithm Description Performance Evaluation Conclusions

I.M.P.A.K.T. GUI

Silvia Giannini Knowledge Compilation for Core Competence Extraction

Introduction Problem Formulation Algorithm Description Performance Evaluation Conclusions

Datasets

Real Dataset

180 CV from 3 di�erent employment agencies

Pro�les refer to individual working in ICT domain to simulate ICT industry

Adopted also for an ontology re�nement phase

Synthetic Dataset

Obtained by a KB instances generator:

Automatically creates satis�able pro�les

Allows for setting the average number of technical skills and the number offeatures for each entry point

Silvia Giannini Knowledge Compilation for Core Competence Extraction

Introduction Problem Formulation Algorithm Description Performance Evaluation Conclusions

Evaluation parameters

Evaluation about:

data complexity (number of pro�les)

expressiveness complexity (number of features3)

Two di�erent test campaigns:

1 Comparison with reference to a fully logic-based implementation.

2 Scalability capabilities.

Generic Speci�c

1st test campaign DS1 DS2 (subsets of real dataset)

2nd test campaign DS3 DS4 (subsets of synthetic dataset)

For the same number of pro�les, in DS1 (resp. DS3) there is a smaller numberof resulting Pro�le Concept Components than in DS2 (DS4).

3We considered only Technical Knowledge (i.e., concepts of the form ∃hasKnowledge.C)

Silvia Giannini Knowledge Compilation for Core Competence Extraction

Introduction Problem Formulation Algorithm Description Performance Evaluation Conclusions

First test campaign4: Comparison

PSM average computation time [ms] CSE average computation time [ms]

n = {5, 10, 15, 20} is the number of pro�le.

k is set to 3. Each value is obtained by averaging 10 runs of the stepsinvolved.

Here, the matrix creation is the most computationally expensive process(tpsm is bigger than tcse).

4Tests executed on Intel Dual Core server machine, (2.26GHz processor, 4GB RAM).

Silvia Giannini Knowledge Compilation for Core Competence Extraction

Introduction Problem Formulation Algorithm Description Performance Evaluation Conclusions

First test campaign: Comparison

PSM average computation time [ms] CSE average computation time [ms]

Data complexity : execution time increases (especially tcse) with thenumber n of analyzed pro�le.

Expressiveness complexity : a signi�cant di�erence between tcse in DS1

and DS2.

Silvia Giannini Knowledge Compilation for Core Competence Extraction

Introduction Problem Formulation Algorithm Description Performance Evaluation Conclusions

First test campaign: Comparison

PSM average computation time [s]5

PSM average computation times with reference to the number of pro�leconcept components, deriving from a fully logic-based approach.

The results are in the order of seconds [s], compared to the resultsobtained with the knowledge compilation approach ([ms]).

5S. Colucci, E. Tinelli, E. Di Sciascio, F.M. Donini: Automating competence managementthrough non-standard reasoning. Engineering Applications of Arti�cial Intelligence 24(8) (2011)1368 � 1384

Silvia Giannini Knowledge Compilation for Core Competence Extraction

Introduction Problem Formulation Algorithm Description Performance Evaluation Conclusions

Second test campaign: Scalability

PSM and CSE average computation times [s]

Datasets Cardinality (n)500 1000 2000

tpsmDS3 0.87 1.1 1.74DS4 3.91 9.24 27.29

tcseDS3 0.21 0.46 1.4DS4 66.06 235.81 912.57

Table: Core Competence extraction times (in seconds)

500 randomly created pro�es, then extended to 1000 and 2000.

k is adaptively set to 0.3 · nEach value is obtained by averaging 10 runs of the steps involved.

Silvia Giannini Knowledge Compilation for Core Competence Extraction

Introduction Problem Formulation Algorithm Description Performance Evaluation Conclusions

Second test campaign: Scalability

PSM and CSE average computation times [s]

Datasets Cardinality (n)500 1000 2000

tpsmDS3 0.87 1.1 1.74DS4 3.91 9.24 27.29

tcseDS3 0.21 0.46 1.4DS4 66.06 235.81 912.57

Table: Core Competence extraction times (in seconds)

The PSM creation is still the most computationally expensive process forDS3 (tpsm is bigger than tcse).

Silvia Giannini Knowledge Compilation for Core Competence Extraction

Introduction Problem Formulation Algorithm Description Performance Evaluation Conclusions

Second test campaign: Scalability

PSM and CSE average computation times [s]

Datasets Cardinality (n)500 1000 2000

tpsmDS3 0.87 1.1 1.74DS4 3.91 9.24 27.29

tcseDS3 0.21 0.46 1.4DS4 66.06 235.81 912.57

Table: Core Competence extraction times (in seconds)

In presence of complex pro�les (DS4), i.e. a huge number of conceptcomponents, tcse dramatically raises.

We hypothesize there could be a critical value of concept componentsafter which the most time-consuming phase switches from the PSMComputation to the Common Subsumers Enumeration.

Silvia Giannini Knowledge Compilation for Core Competence Extraction

Introduction Problem Formulation Algorithm Description Performance Evaluation Conclusions

Outline

1 Introduction

2 Problem Formulation

3 Algorithm Description

4 Performance Evaluation

5 Conclusions

Silvia Giannini Knowledge Compilation for Core Competence Extraction

Introduction Problem Formulation Algorithm Description Performance Evaluation Conclusions

Conclusions and Future Works

Proposal: Knowledge Compilation approach for Core Competence Extraction.

+ It improves performances in terms of execution times, w.r.t. classicallogic-based approach.

+ It adopts standard SQL-queries to compute the same informative contentas advanced inference services.

+ It makes the computational costs of the process a�ordable also for largeorganizations, while retaining the full expressiveness of the logic-basedapproaches.

Notes on Performance:

The number of pro�les is highly relevant in the common subsumersenumeration process.

The most computationally expensive process is the pro�le subsumersmatrix creation, under a threshold of pro�les concept components.

Current work:

A most comprehensive test campaign

Database optimization: denormalized modeling and table partitioning

Silvia Giannini Knowledge Compilation for Core Competence Extraction