Knowledge Base Population
32 papers with code • 1 benchmarks • 3 datasets
Knowledge base population is the task of filling the incomplete elements of a given knowledge base by automatically processing a large corpus of text.
Latest papers with no code
STable: Table Generation Framework for Encoder-Decoder Models
The output structure of database-like tables, consisting of values structured in horizontal rows and vertical columns identifiable by name, can cover a wide range of NLP tasks.
A Generative Model for Relation Extraction and Classification
Relation extraction (RE) is an important information extraction task which provides essential information to many NLP applications such as knowledge base population and question answering.
GenRE: A Generative Model for Relation Extraction
Relation extraction (RE) is an important information extraction task which provides essential information to many NLP applications such as knowledge base population and question answering.
Entity Linking Meets Deep Learning: Techniques and Solutions
Entity linking (EL) is the process of linking entity mentions appearing in web text with their corresponding entities in a knowledge base.
PERLEX: A Bilingual Persian-English Gold Dataset for Relation Extraction
The main motivations of this research stem from a lack of a dataset for relation extraction in the Persian language as well as the necessity of extracting knowledge from the growing big-data in the Persian language for different applications.
FarsBase-KBP: A Knowledge Base Population System for the Persian Knowledge Graph
While most of the knowledge bases already support the English language, there is only one knowledge base for the Persian language, known as FarsBase, which is automatically created via semi-structured web information.
Data Augmentation for Personal Knowledge Base Population
Cold start knowledge base population (KBP) is the problem of populating a knowledge base from unstructured documents.
Linking Graph Entities with Multiplicity and Provenance
The graph model is versatile, thus, it is capable of handling multiple values for an attribute or a relationship, as well as the provenance descriptions of the values.
Uncovering Probabilistic Implications in Typological Knowledge Bases
The study of linguistic typology is rooted in the implications we find between linguistic features, such as the fact that languages with object-verb word ordering tend to have post-positions.
Learning Relational Representations by Analogy using Hierarchical Siamese Networks
We address relation extraction as an analogy problem by proposing a novel approach to learn representations of relations expressed by their textual mentions.