Klint: Assisting Integration of Heterogeneous Knowledge
Authors: Jacobo Rouces, Gerard de Melo, Katja Hose
IJCAI 2016 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | using integration algorithms based on linguistic annotations in Frame Base [Rouces et al., 2016], extended with support vector machine learning from a labeled training set. and Figure 1: Example of Klint s interface: integrating elements from DBpedia Klint used the contextual and lexical information from the source elements to suggest two candidate values for the integrated type (selected node, conflict ), for which the correct assigned value, Hostile encounter-conflict.n was the first suggestion. |
| Researcher Affiliation | Academia | Jacobo Rouces Aalborg University, Denmark EMAIL Gerard de Melo Tsinghua University, China EMAIL Katja Hose Aalborg University, Denmark EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper refers to external websites (http://www.framebase.org/ and http://www.w3.org/wiki/Converter To Rdf), but there is no explicit statement or link indicating that the source code for Klint itself is publicly available. |
| Open Datasets | Yes | extended with support vector machine learning from a labeled training set. and Figure 1: Example of Klint s interface: integrating elements from DBpedia |
| Dataset Splits | No | The paper does not provide specific details on validation dataset splits, percentages, or methodology. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU or CPU models used for running experiments. |
| Software Dependencies | No | The paper mentions formats like RDF and SPARQL endpoints, but it does not specify any particular software names with version numbers or library dependencies. |
| Experiment Setup | No | The paper does not provide specific details about the experimental setup, such as hyperparameter values, training configurations, or system-level settings. |