Table understanding approaches for extracting knowledge from heterogeneous tables
Table understanding methods extract, transform, and interpret the information contained in
tabular data embedded in documents/files of different formats. Such automatic …
tabular data embedded in documents/files of different formats. Such automatic …
Seeping semantics: Linking datasets using word embeddings for data discovery
Employees that spend more time finding relevant data than analyzing it suffer from a data
discovery problem. The large volume of data in enterprises, and sometimes the lack of …
discovery problem. The large volume of data in enterprises, and sometimes the lack of …
Semantic labeling: a domain-independent approach
Semantic labeling is the process of mapping attributes in data sources to classes in an
ontology and is a necessary step in heterogeneous data integration. Variations in data …
ontology and is a necessary step in heterogeneous data integration. Variations in data …
[HTML][HTML] Learning-based network path planning for traffic engineering
Recent advances in traffic engineering offer a series of techniques to address the network
problems due to the explosive growth of Internet traffic. In traffic engineering, dynamic path …
problems due to the explosive growth of Internet traffic. In traffic engineering, dynamic path …
Recent advances and future challenges of semantic modeling
In recent years, the efforts of both governmental and commercial institutions to exchange
and publish data have significantly increased. Data published by these institutions is usually …
and publish data have significantly increased. Data published by these institutions is usually …
A fully automated approach to a complete semantic table interpretation
In recent years, there has been an increasing interest in extracting and annotating tables on
the Web. This activity allows the transformation of text data into machine-readable formats to …
the Web. This activity allows the transformation of text data into machine-readable formats to …
Survey of tools for linked data consumption
There is a large number of datasets published as Linked (Open) Data (LOD/LD). At the same
time, there is also a multitude of tools for publication of LD. However, potential LD …
time, there is also a multitude of tools for publication of LD. However, potential LD …
Learning semantic models of data sources using probabilistic graphical models
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 …
contained in the data. Building semantic models is a prerequisite to automatically publishing …
RODI: Benchmarking relational-to-ontology mapping generation quality
Accessing and utilizing enterprise or Web data that is scattered across multiple data sources
is an important task for both applications and users. Ontology-based data integration, where …
is an important task for both applications and users. Ontology-based data integration, where …
[HTML][HTML] SeMi: A SEmantic Modeling machIne to build Knowledge Graphs with graph neural networks
G Futia, A Vetrò, JC De Martin - SoftwareX, 2020 - Elsevier
Abstract SeMi (SEmantic Modeling machIne) is a tool to semi-automatically build large-scale
Knowledge Graphs from structured sources such as CSV, JSON, and XML files. To achieve …
Knowledge Graphs from structured sources such as CSV, JSON, and XML files. To achieve …