Morph-KGC is an engine that constructs RDF knowledge graphs from heterogeneous data sources with the R2RML and RML mapping languages. Morph-KGC is built on top of pandas and it leverages mapping partitions to significantly reduce execution times and memory consumption for large data sources.

Morph-KGC is an engine that constructs RDF knowledge graphs from heterogeneous data sources with the R2RML and RML mapping languages. Morph-KGC is feature-rich with support for YARRRML, RML-FNML, RML-star, RML views, Python user-defined functions and more!

Morph-KGC is an engine that constructs RDF knowledge graphs from heterogeneous data sources with the R2RML and RML mapping languages. Morph-KGC is built on top of pandas and it leverages mapping partitions to significantly reduce execution times and memory consumption for large data sources. User-friendly mappings with YARRRML.

Morph-KGC can be used as a library, providing different methods to materialize the RDF or RDF-star knowledge graph. It integrates with RDFLib, Oxigraph and Kafka to easily create and work with knowledge graphs in Python. The methods in the API accept the config as a string or as the path to an INI file. Materialize the knowledge graph to RDFLib.

2025年1月3日 · Morph-KGC is an engine that constructs RDF knowledge graphs from heterogeneous data sources with the R2RML and RML mapping languages. Morph-KGC is built on top of pandas and it leverages mapping partitions to significantly reduce execution times and memory consumption for large data sources.

Our experimental results show that, compared with state-of-the-art techniques, the use of mapping partitions in Morph-KGC presents the following advantages: (i) it decreases significantly the time required for materialization, (ii) it reduces the maximum peak of memory used, and (iii) it scales to data sizes that other engines are not capable of...

Powerful RDF Knowledge Graph Generation with RML Mappings - Releases · morph-kgc/morph-kgc

2022年3月23日 · morph-kgc is an engine that constructs RDF knowledge graphs from heterogeneous data sources with R2RML and RML mapping languages. Morph-KGC is built on top of pandas and it leverages mapping partitions to significantly reduce execution times and memory consumption for large data sources.

2020年8月9日 · 动态知识图谱补全（Dynamic KGC），它涉及不止知识图谱G中的实体或关系，即一些没有出现的词，或者后期想对知识图谱进行补全的场景下。 其实如果能够构造动态的知识图谱，具有更好的现实意义，不过自然难度也会加大. TransE，即使其满足 h + r ≈ t，尾实体是头实体通过关系平移 (翻译)得到的，但它不适合多对一和多对多，所以导致TransE在复杂关系上的表现差。 公式如下. TransH模型，即将实体投影到由关系构成的超平面上。 值得注意的是它是非对称映 …

现有的知识图谱补全（KGC）方法可以分为两类：基于嵌入的方法和基于文本的方法。 基于嵌入的方法将每个实体和关系映射到低维向量，而不使用任何辅助信息。