IDEL: In-Database Entity Linking with Neural Embeddings
Table 1. The WebNLG data set provides a manually labeled ground truth for 10 different entity types, such as products, locations, persons and brands. It features several thousands of manually annotated sentences. Moreover, its structured representation describes entities with at least 20 up to 52 attributes. However, most attribute values are populated only sparsely: in this table, “Avg. tuple density” denotes the portion of non-null attribute values for all entities of a particular type. Hence, most structured entity tuples have only a few attribute values different from NULL.
['Entity Family', 'Entity type', 'Instances', 'Tuples', 'Sentences', 'Sentences/Instance', 'Columns', 'Avg. tuple density']
Instances denotes the number of distinct entities for each type in the relational data. Tuples denotes the number of tuples in the relational data for each distinct instance. Sentences denotes the number of sentences that contain at least one of these instances in text data. Sentences/Instance counts the average ratio of how often an instance is represented in a sentence. In particular, for types City and ComicsCharacter we observe relatively very few sentences representing each entity. As a result, the system might learn less variances during training and sampling for these data types. Columns denotes the number of distinct attributes in the relational schema for this entity type. Avg. tuple density denotes the proportion of an attribute with non-NULL values averaged over all tuples for this type. We observe that all entity types feature sparsely populated attribute values only. Some entity types are described with a rather large number of attribute values (up to 52), while most entity types contain 20∼30 attributes. For example, the entity types WrittenWork and Airport are described with roughly 50 sparsely populated attribute values.