Knowledge Base Embeddings: Semantics and Theoretical Properties
Research on knowledge graph embeddings has recently evolved into knowledge base embeddings, where the goal is not only to map facts into vector spaces but also constrain the models so that they take into account the relevant conceptual knowledge available. This paper examines recent methods that have been proposed to embed knowledge bases in description logic into vector spaces through the lens of their geometric-based semantics. We identify several relevant theoretical properties, which we draw from the literature and sometimes generalize or unify. We then investigate how concrete embedding methods fit in this theoretical framework.
Code (0)
등록된 구현이 없습니다.
Tasks
Knowledge Graph EmbeddingsMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Exploring Sentence Vectors Through Automatic Summarization
Vector semantics, especially sentence vectors, have recently been used successfully in many areas of natural language processing. However, relatively little work has explored the internal structure and properties of spac…
SentenceSentence EmbeddingsExploring the Combination of Contextual Word Embeddings and Knowledge Graph Embeddings
``Classical'' word embeddings, such as Word2Vec, have been shown to capture the semantics of words based on their distributional properties. However, their ability to represent the different meanings that a word may have…
Entity TypingKnowledge Graph EmbeddingsRepresentation LearningWord EmbeddingsThe Interplay of Semantics and Morphology in Word Embeddings
We explore the ability of word embeddings to capture both semantic and morphological similarity, as affected by the different types of linguistic properties (surface form, lemma, morphological tag) used to compose the re…
LEMMATAGWord EmbeddingsExploring Sentence Vector Spaces through Automatic Summarization
Given vector representations for individual words, it is necessary to compute vector representations of sentences for many applications in a compositional manner, often using artificial neural networks. Relatively litt…
SentenceSentence EmbeddingsExploring Semantic Properties of Sentence Embeddings
Neural vector representations are ubiquitous throughout all subfields of NLP. While word vectors have been studied in much detail, thus far only little light has been shed on the properties of sentence embeddings. In thi…
Machine TranslationReading ComprehensionSemantic Textual SimilaritySentence+3