Compact, evolving community taxonomies using concept lattices Camille Roth Dept. Social and Cognitive Sciences, Univ. Modena and CREA — CNRS/Ecole Polytechnique, Paris

ICCS 14 — July 17-21, 2006; Aalborg, DK

Building taxonomies

Rationale Describe the taxonomy of a knowledge community, in particular scientific communities, that matches high-level descriptions. Epistemic communities Epistemic Community: group of agents sharing a common set of subjects, concepts, notions, issues; sharing a common goal of knowledge creation — Haas (1992) Definition here: “an epistemic community is the largest set of agents sharing a given set of notions” ∼ structural equivalence

Building taxonomies

Rationale Describe the taxonomy of a knowledge community, in particular scientific communities, that matches high-level descriptions. Epistemic communities Epistemic Community: group of agents sharing a common set of subjects, concepts, notions, issues; sharing a common goal of knowledge creation — Haas (1992) Definition here: “an epistemic community is the largest set of agents sharing a given set of notions” ∼ structural equivalence

Building taxonomies

Rationale Describe the taxonomy of a knowledge community, in particular scientific communities, that matches high-level descriptions. Epistemic communities Epistemic Community: group of agents sharing a common set of subjects, concepts, notions, issues; sharing a common goal of knowledge creation — Haas (1992) Definition here: “an epistemic community is the largest set of agents sharing a given set of notions” ∼ structural equivalence

Building taxonomies

Notions (N)

Formal framework Binary relation R between agents & notions

s1

Intent S ∧ of an agent set S: all notions used by every agent in S

s2

Prs

Lng

Extent N ? of a notion set N (S, N) is closed iff N = S ∧ and S = N ? Epistemic community: the extent of the intent of an agent set

NS

s3 s4

Agents (S)

Building taxonomies

Notions (N)

Formal framework Binary relation R between agents & notions

s1

Intent S ∧ of an agent set S: all notions used by every agent in S

s2

Prs

Lng

Extent N ? of a notion set N (S, N) is closed iff N = S ∧ and S = N ? Epistemic community: the extent of the intent of an agent set

NS

s3 s4

Agents (S)

Building taxonomies

Representing epistemic communities 1

structured into fields, with common concerns,

2

hierarchically: generalization / specialization,

3

overlapping.

From trees to lattices

Building taxonomies

Representing epistemic communities 1

structured into fields, with common concerns,

2

hierarchically: generalization / specialization,

3

overlapping.

From trees to lattices inspiration

application

ICCS

ICCS tree

inspiration

application

ICCS lattice

Building taxonomies Concept (Galois) lattices Consider the partially-ordered set of all epistemic communities {(S ∧? , S)|S ⊆ S} under (X , X ∧ ) < (X 0 , X 0∧ ) ⇔ X ⊂ X 0

( s 1 s 2 s 3s 4 ; ∅ ) ( s 1 s 2 s 3 ; Lng ) (s 1s 2 ; Lng Prs )

GL ( s 2 s 3 s 4 ; NS )

( s 2s 3 ; Lng NS )

( s 2 ; Lng Prs NS )

Managing taxonomies

Taxonomy selection & extraction Which ECs should we extract from the lattice? Given the assumptions, use agent set size — yet small isolated ECs could be interesting too. Create a partial taxonomy, with selection heuristics: partially-ordered set overlaying the lattice: “epistemic hypergraph”

( s 1 s 2 s 3s 4 ; ∅ ) ( s 1 s 2 s 3 ; Lng ) (s 1s 2 ; Lng Prs )

GL ( s 2 s 3 s 4 ; NS )

( s 2s 3 ; Lng NS )

( s 2 ; Lng Prs NS )

poset ( s 1 s 2 s 3s 4 ; ∅ ) ( s 1 s 2 s 3 ; Lng )

( s 2 s 3 s 4 ; NS )

Managing taxonomies

Taxonomy selection & extraction Which ECs should we extract from the lattice? Given the assumptions, use agent set size — yet small isolated ECs could be interesting too. Create a partial taxonomy, with selection heuristics: partially-ordered set overlaying the lattice: “epistemic hypergraph”

( s 1 s 2 s 3s 4 ; ∅ ) ( s 1 s 2 s 3 ; Lng ) (s 1s 2 ; Lng Prs )

GL ( s 2 s 3 s 4 ; NS )

( s 2s 3 ; Lng NS )

( s 2 ; Lng Prs NS )

poset ( s 1 s 2 s 3s 4 ; ∅ ) ( s 1 s 2 s 3 ; Lng )

( s 2 s 3 s 4 ; NS )

Managing taxonomies

Taxonomy selection & extraction Which ECs should we extract from the lattice? Given the assumptions, use agent set size — yet small isolated ECs could be interesting too. Create a partial taxonomy, with selection heuristics: partially-ordered set overlaying the lattice: “epistemic hypergraph”

( s 1 s 2 s 3s 4 ; ∅ ) ( s 1 s 2 s 3 ; Lng ) (s 1s 2 ; Lng Prs )

GL ( s 2 s 3 s 4 ; NS )

( s 2s 3 ; Lng NS )

( s 2 ; Lng Prs NS )

poset ( s 1 s 2 s 3s 4 ; ∅ ) ( s 1 s 2 s 3 ; Lng )

( s 2 s 3 s 4 ; NS )

Managing taxonomies Taxonomy evolution growth

1

Progress or decline of a field

(S1,N)

decrease

merging

2

Merging or scission of a field

(S2 ,N)

(S,N)

(S’,N’)

^

(S ∩ S’,(S ∩ S’) )

scission

(S2 ,N)

Empirical protocol

The zebrafish community MedLine abstracts including the term “zebrafish” over the period 1990-2003 each lemmatized term is a notion within an expert-based dictionary

Empirical results Hierarchical epistemic hypergraph 1990-1995 All (255)

Dev (168) Hom (67)

Mou (92)

Hum (34)

Brn (102)

Ver (75)

Pat (99) Spi (30)

Ven (50) Dor (49)

Gro (44) Sig (53)

Mou Dev (72) Hom Mou (40)

Dev Brn (81) Mou Hum (18)

Hom Hum (11)

Dev Pat (77) Ver Dev (68) Mou Ver (30)

Ven Dor (34) Brn Pat (62) Ver Pat (42)

Brn Ven (43) Brn Spi Crd (29)

Brn Dor (38)

Brn Ven Dor (30) Brn Spi Crd Ven (15)

Pwy (38)

Empirical results Hierarchical epistemic hypergraph 1998-2003 All (255)

Dev (150) Hom (57)

Mou (100)

Hum (100)

Brn (82) Ver (86)

Pat (90)

Dev Brn (62)

Mou Hum (58)

Hom Hum (38)

Gro (67)

Sig (133)

Mou Dev (71)

Hum Ver (44) Hom Mou (35)

Ven (40) Dor (40) Spi Crd (18)

Dev Pat (78) Ver Dev (70)

Sig Pwy (84) Gro Sig (51) Ven Dor (24)

Sig Rec (48) Gro Pwy (42)

Pat Brn (47)

Mou Ver (48)

Rec (67) Pwy (93)

Pwy Rec (34)

Ver Pat (58)

Gro Sig Pwy (39) Sig Pwy Rec (31)

Empirical results

Historical description 1

Research on brain and spinal cord depreciated,

2

The community started to enquire relationships between signal, pathway, and receptors,

3

Mouse-related research is stable, yet significant stress on human-related topics & new relationship to homologous genes and vertebrates: growing focus on differential studies.

Compared to expert-based descriptions

Empirical results

Historical description 1

Research on brain and spinal cord depreciated,

2

The community started to enquire relationships between signal, pathway, and receptors,

3

Mouse-related research is stable, yet significant stress on human-related topics & new relationship to homologous genes and vertebrates: growing focus on differential studies.

Compared to expert-based descriptions

Describing the structure of epistemic networks

Regarding SNA, applied epistemology, scientometrics... 1

hierarchically organized, overlapping communities

2

evolving partial taxonomies

3

binding automatically semantic content to agents,

4

underlining the importance of non-single-network properties