  Much research effort has been put to multilingual knowledge graph (KG)
embedding methods to address the entity alignment task, which seeks to match
entities in different languagespecific KGs that refer to the same real-world
object. Such methods are often hindered by the insufficiency of seed alignment
provided between KGs. Therefore, we propose an incidentally supervised model,
JEANS , which jointly represents multilingual KGs and text corpora in a shared
embedding scheme, and seeks to improve entity alignment with incidental
supervision signals from text. JEANS first deploys an entity grounding process
to combine each KG with the monolingual text corpus. Then, two learning
processes are conducted: (i) an embedding learning process to encode the KG and
text of each language in one embedding space, and (ii) a selflearning based
alignment learning process to iteratively induce the matching of entities and
that of lexemes between embeddings. Experiments on benchmark datasets show that
JEANS leads to promising improvement on entity alignment with incidental
supervision, and significantly outperforms state-of-the-art methods that solely
rely on internal information of KGs.
