In this article, we describe a system that reads news articles in four different languages and
detects what happened, who is involved, where and when. This event-centric information is …

The enormous quantity of digital data necessitates automation, which among other things
can help link unstructured to structured data. Such a task requires a systematic approach of …

The purpose of knowledge graph entity disambiguation is to match the ambiguous entities to
the corresponding entities in the knowledge graph. Current entity ambiguity elimination …

Abstract Industry 4.0 standards, such as AutomationML, are used to specify properties of
mechatronic elements in terms of views, such as electrical and mechanical views of a motor …

Abstract Named Entity Disambiguation (NED) is the task of disambiguating named entities in
a natural language text by linking them to their corresponding entities in a knowledge base …

Abstract Named Entity Disambiguation (NED) algorithms disambiguate mentions of named
entities with respect to a knowledge-base, but sometimes the context might be poor or …

The vast amount of web data enables us to build knowledge bases with unprecedented
quality and coverage. Named Entity Disambiguation (NED) is an important task that …

Abstract “跨语言实体消歧是在源语言句子中找到目标语言相对应的实体,
对跨语言自然语言处理任务有重要支撑. 现有跨语言实体消歧方法在资源丰富的语言上能得到较 …

Embedding models turn words/documents into real-number vectors via co-occurrence data
from unrelated texts. Crafting domain-specific embeddings from general corpora with limited …

Abstract Named Entity Recognition (NER) is a challenging learning task of identifying and
classifying entity mentions in texts into predefined categories. In recent years, deep learning …