Knowledge Base Completion

This paper addresses the problem of corpus-level entity typing, i.e., inferring from a large corpus that an entity is a member of a class such as “food” or “artist”. The …...

We explore some of the practicalities of using random walk inference meth- ods, such as the Path Ranking Algorithm (PRA), for the task of knowledge base completion. We show that …...

Most of previous work in knowledge base (KB) completion has focused on the problem of relation extraction. In this work, we focus on the task of inferring missing entity type …...

Representation learning of knowledge bases aims to embed both entities and relations into a low-dimensional space. Most existing methods only consider direct relations in representation learning. We argue that multiple-step …...

Models that learn to represent textual and knowledge base relations in the same con- tinuous latent space are able to perform joint inferences among the two kinds of re- lations …...

Path queries on a knowledge graph can be used to answer compositional ques- tions such as “What languages are spoken by people living in Lisbon?”. However, knowledge graphs often have …...

We enrich a curated resource of common- sense knowledge by formulating the prob- lem as one of knowledge base comple- tion (KBC). Most work in KBC focuses on knowledge bases …...

Modeling relation paths has offered sig- nificant gains in embedding models for knowledge base (KB) completion. How- ever, enumerating paths between two en- tities is very expensive, and existing ap- …...

AbstractConsiderable effort has been devoted to building commonsense knowledge bases. However, they are not available in many languages because the construction of KBs is expensive. To bridge the gap between …...

Knowledge Base Completion infers missing facts from existing ones in knowledge bases. As recent Open Information Extraction systems allow us to extract ever larger (yet incomplete) open-domain Knowledge Bases from …...

AbstractRepresenting the semantic relations that exist between two given words (or entities) is an important first step in a wide-range of NLP applications such as analogical reasoning, knowledge base completion …...

AbstractDuring the past few decades, knowledge bases (KBs) have experienced rapid growth. Nevertheless, most KBs still suffer from serious incompletion. Researchers proposed many tasks such as knowledge base completion and …...

AbstractCollaboratively constructed knowledge bases play an important role in information systems, but are essentially always incomplete. Thus, a large number of models has been developed for Knowledge Base Completion, the …...

AbstractIn this work, we aim at equipping pre-trained language models with structured knowledge. We present two self-supervised tasks learning over raw text with the guidance from knowledge graphs. Building upon …...

AbstractThis paper explores learning rich self-supervised entity representations from large amounts of associated text. Once pre-trained, these models become applicable to multiple entity-centric tasks such as ranked retrieval, knowledge base …...

AbstractThe aim of knowledge base completion is to predict unseen facts from existing facts in knowledge bases. In this work, we introduce the first approach for transfer of knowledge from …...

AbstractKnowledge base completion (KBC) has benefitted greatly by learning explainable rules in an human-interpretable dialect such as first-order logic. Rule-based KBC has so far, mainly focussed on learning one of …...