FACES

FACES stands for FACeted Entity Summarization. It is an entity summarization system that combines three dimensions : diversity, popularity, and uniqueness.

Preliminaries

Problem Statement - An entity is usually described using a conceptually different set of facts to improve coverage. We want to select a ‘representative’ subset of this set in a good summary to uniquely identify the entity.

Definitions 1-4 defines basic notions related to entity summaries. They are as stated in [1].

Definition 1 : A data graph is a digraph G = V, A, Lbl_V , Lbl_A , where (i) V is a finite set of nodes, (ii) A is a finite set of directed edges where each a ∈ A has a source node Src(a) ∈ V, a target node Tgt(a) ∈ V, (iii) Llb_V : V → E ∪ L and (iv) Lbl_A : A → P are labeling functions that map nodes to entities or literals and edges to properties.

Defintion 2 : A feature f is a property-value pair where Prop(f ) ∈ P and Val(f ) ∈ E ∪ L denote the property and the value, respectively. An entity e has a feature f in a data graph G = V, A, Lbl_V , Lbl_A if there exists a ∈ A such that Lbl_A (a) = Prop(f ), Lbl_V (Src(a)) = e and Lbl_V (Tgt(a)) = Val(f ).

Definition 3 : Given a data graph G, the feature set of an entity e, denoted by FS(e), is the set of all features of e that can be found in G.

Definition 4 : Given FS(e) and a positive integer k < |FS(e)|, summary of entity e is Summ(e) ⊂ FS(e) such that |Summ(e)| = k.

References

[1] Cheng, Gong, Thanh Tran, and Yuzhong Qu. "RELIN: relatedness and informativeness-based centrality for entity summarization." In The Semantic Web–ISWC 2011, pp. 114-129. Springer Berlin Heidelberg, 2011.
[2] Fisher, D.H.: Knowledge acquisition via incremental conceptual clustering. Machine learning 2(2), 139–172 (1987)
[3] Gennari, J.H., Langley, P., Fisher, D.: Models of incremental concept formation. Artificial intelligence 40(1), 11–61 (1989)