Name
Abstract | ||
|---|---|---|
A semantic model of a data source is a representation of the concepts and relationships contained in the data. Building semantic models is a prerequisite to automatically publishing data to a knowledge graph. However, creating these semantic models is a complex process requiring considerable manual effort and can be error-prone. In this paper, we present a novel approach that efficiently searches over the combinatorial space of possible semantic models, and applies a probabilistic graphical model to identify the most probable semantic model for a data source. Probabilistic graphical models offer many advantages over existing methods: they are robust to noisy inputs and provide a straightforward approach for exploiting relationships within the data. Our solution uses a conditional random field (CRF) to encode structural patterns and enforce conceptual consistency within the semantic model. In an empirical evaluation, our approach outperforms state of the art systems by an average 8.4% of F1 score, even with noisy input data.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3308558.3313711 | WWW '19: The Web Conference on The World Wide Web Conference WWW 2019 |
Keywords | Field | DocType |
Semantic models, knowledge graph, linked data, ontology, probabilistic graphical models, semantic web | Conditional random field,F1 score,Ontology,Data mining,Computer science,Semantic Web,Linked data,Artificial intelligence,Graphical model,Probabilistic logic,Machine learning,Semantic data model | Conference |
ISBN | Citations | PageRank |
978-1-4503-6674-8 | 1 | 0.36 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Binh Vu | 1 | 4 | 4.50 |
| Craig A. Knoblock | 2 | 5229 | 680.57 |
| Jay Pujara | 3 | 86 | 14.81 |