# Automatic Semantic Modeling for Structural Data Source with the Prior Knowledge from Knowledge Base

arXiv: https://arxiv.org/abs/2212.10915 · HTML: https://mlsanity.com/paper/2212.10915/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 36 references that are in the map.
- First posted on arXiv 21 Dec 2022.

## Builds on (most cited first, 3 of 3)

- [A Survey on Knowledge Graphs: Representation, Acquisition and Applications](https://mlsanity.com/paper/2002.00388/) (2020)
- [Learning the Semantics of Structured Data Sources](https://mlsanity.com/paper/1601.04105/) (2016)
- [Evaluating approaches for supervised semantic labeling](https://mlsanity.com/paper/1801.09788/) (2018)

## Abstract (arXiv)

A critical step in sharing semantic content online is to map the structural data source to a public domain ontology. This problem is denoted as the Relational-To-Ontology Mapping Problem (Rel2Onto). A huge effort and expertise are required for manually modeling the semantics of data. Therefore, an automatic approach for learning the semantics of a data source is desirable. Most of the existing work studies the semantic annotation of source attributes. However, although critical, the research for automatically inferring the relationships between attributes is very limited. In this paper, we propose a novel method for semantically annotating structured data sources using machine learning, graph matching and modified frequent subgraph mining to amend the candidate model. In our work, Knowledge graph is used as prior knowledge. Our evaluation shows that our approach outperforms two state-of-the-art solutions in tricky cases where only a few semantic models are known.