Difference between revisions of "FACES"
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FACES stands for '''FAC'''eted '''E'''ntity '''S'''ummarization. It is an entity summarization system that combines three dimensions : ''diversity'', ''popularity'', and ''uniqueness''. | FACES stands for '''FAC'''eted '''E'''ntity '''S'''ummarization. It is an entity summarization system that combines three dimensions : ''diversity'', ''popularity'', and ''uniqueness''. | ||
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| + | == 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. | ||
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| + | Definitions 1-4 defines basic notions related to entity summaries. They are as stated in [1]. | ||
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| + | '''Definition 1''' : A data graph is a digraph G = V, A, Lbl<sub>V</sub> , Lbl<sub>A</sub> , 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<sub>V</sub> : V → E ∪ L and (iv) Lbl<sub>A</sub> : A → P are labeling functions that map nodes to entities or literals and edges to properties. | ||
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| + | '''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<sub>V</sub> , Lbl<sub>A</sub> if there exists a ∈ A such that Lbl<sub>A</sub> (a) = Prop(f ), Lbl<sub>V</sub> (Src(a)) = e and Lbl<sub>V</sub> (Tgt(a)) = Val(f ). | ||
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| + | '''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. | ||
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| + | '''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. | ||
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| + | = 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. <br> | ||
| + | [2] Fisher, D.H.: Knowledge acquisition via incremental conceptual clustering. Machine learning 2(2), 139–172 (1987) <br> | ||
| + | [3] Gennari, J.H., Langley, P., Fisher, D.: Models of incremental concept formation. Artificial intelligence 40(1), 11–61 (1989) <br> | ||
Revision as of 16:23, 24 November 2014
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, LblV , LblA , 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) LlbV : V → E ∪ L and (iv) LblA : 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, LblV , LblA if there exists a ∈ A such that LblA (a) = Prop(f ), LblV (Src(a)) = e and LblV (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)
