Entity Recognition (ER) is a key component of relation extraction systems and many other
natural-language processing applications. Unfortunately, most ER systems are restricted to …

Many large-scale knowledge bases simultaneously represent two views of knowledge
graphs (KGs): an ontology view for abstract and commonsense concepts, and an instance …

Fine-grained entity ty** (FGET) is an important natural language processing (NLP) task. It
is to assign fine-grained semantic types of a type taxonomy (eg, Person/artist/actor) to entity …

Vocabularies are increasingly being developed on platforms for hosting version-controlled
repositories, such as GitHub. However, these platforms lack important features that have …

Populating ontology graphs represents a long-standing problem for the Semantic Web
community. Recent advances in translation-based graph embedding methods for populating …

Knowledge graph (KG) schema, which prescribes a high-level structure and semantics of a
KG, is significantly helpful for KG completion and reasoning problems. Despite its …

Categorizing entities by their types is useful in many applications, including knowledge base
construction, relation extraction and query intent prediction. Fine-grained entity type …

Today, many software architecture design methods consider the use of architectural patterns
as a fundamental design concept. When making an effective pattern selection, software …

The Semantic Web aims to extend the current Web standards and technologies so that the
semantics of Web contents is machine processable. For the Semantic Web vision to become …

Understanding customers demands and needs is one of the keys to success for large
enterprises. Customers come to a large enterprise with a set of requirements and finding a …