Inproceedings3195: Unterschied zwischen den Versionen
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Larry Gonzalez (Diskussion | Beiträge) Keine Bearbeitungszusammenfassung |
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|Abstract=In this paper, we propose a novel data-driven schema for large-scale | |Abstract=In this paper, we propose a novel data-driven schema for large-scale heterogeneous knowledge graphs inspired by Formal Concept Analysis (FCA). We first extract the sets of properties associated with individual entities; these property sets (aka. characteristic sets) are annotated with cardinalities and used to induce a lattice based on set-containment relations, forming a natural hierarchical structure describing the knowledge graph. We then propose an algebra over such schema lattices, which allows to compute diffs between lattices (for example, to summarise the changes from one version of a knowledge graph to another), to add diffs to lattices (for example, to project future changes), and so forth. While we argue that this lattice structure (and associated algebra) may have various applications, we currently focus on the use-case of modelling and predicting the dynamic behaviour of knowledge graphs. Along those lines, we instantiate and evaluate our methods for analysing how versions of the Wikidata knowledge graph have changed over a period of 11 weeks. We propose algorithms for constructing the lattice-based schema from Wikidata, and evaluate their efficiency and scalability. We then evaluate use of the resulting schema(ta) for predicting how the knowledge graph will evolve in future versions. | ||
heterogeneous knowledge graphs inspired by Formal Concept | |||
Analysis (FCA). We first extract the sets of properties associated | |||
with individual entities; these property sets (aka. characteristic sets) | |||
are annotated with cardinalities and used to induce a lattice based on | |||
set-containment relations, forming a natural hierarchical structure | |||
describing the knowledge graph. We then propose an algebra over | |||
such schema lattices, which allows to compute diffs between lattices | |||
(for example, to summarise the changes from one version of a | |||
knowledge graph to another), to add diffs to lattices (for example, to | |||
project future changes), and so forth. While we argue that this lattice | |||
structure (and associated algebra) may have various applications, | |||
we currently focus on the use-case of modelling and predicting the | |||
dynamic behaviour of knowledge graphs. Along those lines, we | |||
instantiate and evaluate our methods for analysing how versions | |||
of the Wikidata knowledge graph have changed over a period of 11 | |||
weeks. We propose algorithms for constructing the lattice-based | |||
schema from Wikidata, and evaluate their efficiency and scalability. | |||
We then evaluate use of the resulting schema(ta) for predicting how | |||
the knowledge graph will evolve in future versions. | |||
|Download=20180221-www-dynamics-wikidata.pdf | |Download=20180221-www-dynamics-wikidata.pdf | ||
|Projekt=Cfaed, DIAMOND, HAEC B08 | |Projekt=Cfaed, DIAMOND, HAEC B08 | ||
Version vom 9. März 2018, 11:21 Uhr
Modelling Dynamics in Semantic Web Knowledge Graphs with Formal Concept Analysis
Larry GonzálezLarry González, Aidan HoganAidan Hogan
Larry González, Aidan Hogan
Modelling Dynamics in Semantic Web Knowledge Graphs with Formal Concept Analysis
Proceedings of the 27th Web Conference (WWW), Lyon, France, April 23–27, 2018, to appear
Modelling Dynamics in Semantic Web Knowledge Graphs with Formal Concept Analysis
Proceedings of the 27th Web Conference (WWW), Lyon, France, April 23–27, 2018, to appear
- KurzfassungAbstract
In this paper, we propose a novel data-driven schema for large-scale heterogeneous knowledge graphs inspired by Formal Concept Analysis (FCA). We first extract the sets of properties associated with individual entities; these property sets (aka. characteristic sets) are annotated with cardinalities and used to induce a lattice based on set-containment relations, forming a natural hierarchical structure describing the knowledge graph. We then propose an algebra over such schema lattices, which allows to compute diffs between lattices (for example, to summarise the changes from one version of a knowledge graph to another), to add diffs to lattices (for example, to project future changes), and so forth. While we argue that this lattice structure (and associated algebra) may have various applications, we currently focus on the use-case of modelling and predicting the dynamic behaviour of knowledge graphs. Along those lines, we instantiate and evaluate our methods for analysing how versions of the Wikidata knowledge graph have changed over a period of 11 weeks. We propose algorithms for constructing the lattice-based schema from Wikidata, and evaluate their efficiency and scalability. We then evaluate use of the resulting schema(ta) for predicting how the knowledge graph will evolve in future versions. - Projekt:Project: Cfaed, DIAMOND, HAEC B08
- Forschungsgruppe:Research Group: Wissensbasierte SystemeKnowledge-Based Systems
@inproceedings{GH2018,
author = {Larry Gonz{\'{a}}lez and Aidan Hogan},
title = {Modelling Dynamics in Semantic Web Knowledge Graphs with Formal
Concept Analysis},
booktitle = {Proceedings of the 27th Web Conference (WWW), Lyon, France, April
23{\textendash}27, 2018},
year = {2018},
month = {April}
}
