Entity Summary
Check back on Friday May 16th 2014 for more details...
Creating Faceted (divesified) Entity Summaries
Creating entity summaries has been of contemporary interest in the Semantic Web community in the recet past. In our approach called FACES: FACed Entity Summaries, we are interested in generating diversified and user friendly summaries.
Problem
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.
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) LlbTemplate:Sub : V → E ∪ L and (iv) LblA : A → P are labeling functions that map nodes to entities or literals and edges to properties.
Evaluation
System | k = 5 | FACES % ↑ | k = 10 | FACES % ↑ | time/entity in seconds |
---|---|---|---|---|---|
FACES | 1.4314 | NA | 4.3350 | NA | 0.76 sec. |
RELIN | 0.4981 | 187 % | 2.5188 | 72 % | 10.96 sec. |
RELINM | 0.6008 | 138 % | 3.0906 | 40 % | 11.08 sec. |
SUMMARUM | 1.2249 | 17 % | 3.4207 | 27 % | NA |
Ideal summ agreement | 1.9168 | 4.6415 |
System | k = 5 | FACES %↑ | k = 10 | FACES %↑ |
---|---|---|---|---|
FACES | 1.8649 | NA | 5.6931 | NA |
RELIN | 0.7339 | 154 % | 3.3993 | 69 % |
RELINM | 0.8695 | 114 % | 4.1551 | 37 % |
SUMMARUM | 1.6484 | 13 % | 4.4919 | 27 % |
Ideal summ agreement | 2.3194 | 5.6228 |
Experiment | FACES % | RELINM % | SUMMARUM % |
---|---|---|---|
Experiment 1 | 84 % | 16 % | NA |
Experiment 2 | 54 % | 16 % | 30 % |
k = 5 | k = 10 | ||
---|---|---|---|
Google search API | Sindice seach API | Google search API | Sindice search API |
3.5 | 3.4 | 0.5333 | 0.5428 |
Dataset
Evauation data is available for download download