DBpedia-Entity v2

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Recent years have witnessed the emergence of novel models for ad-hoc entity search in knowledge graphs of varying complexity. Since these models are based on direct term matching, their accuracy can suffer from a mismatch between vocabularies used in queries and entity descriptions. Although successful applications of word embeddings and knowledge graph entity embeddings to address the issues of vocabulary mismatch in ad-hoc document retrieval and knowledge graph noisiness and incompleteness, respectively, have been reported in recent literature, the utility of joint word and entity embeddings for entity search in knowledge graphs has been relatively unexplored. In this paper, we propose Knowledge graph Entity and Word Embedding for Retrieval (KEWER), a novel method to embed entities and words into the same low-dimensional vector space, which takes into account a knowledge graph's local structure and structural components, such as entities, attributes, and categories, and is designed specifically for entity search. KEWER is based on random walks over the knowledge graph and can be considered as a hybrid of word and network embedding methods. Similar to word embedding methods, KEWER utilizes contextual co-occurrences as training data, however, it treats words and entities as different objects. Similar to network embedding methods, KEWER takes into account knowledge graph's local structure, however, it also differentiates between structural components. Experiments on publicly available entity search benchmarks and state-ofthe-art word and joint word and entity embedding methods indicate that a combination of KEWER and BM25F results in a consistent improvement in retrieval accuracy over BM25F alone.

Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Datasetmentioning

confidence: 99%

Section: Datasetmentioning

confidence: 99%

Section: Mixture Modelmentioning

confidence: 99%

See 3 more Smart Citations

Joint Word and Entity Embeddings for Entity Retrieval from a Knowledge Graph

Nikolaev

Kotov

2020

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CACAO: Conditional Spread Activation for Keyword Factual Query Interpretation

Marx

Publio

Riechert

2019

Information retrieval is regarded as pivotal to empower lay users to access the Web of Data. Over the past years, it achieved momentum with a large number of approaches being developed for different scenarios such as entity retrieval, question answering, and entity linking. This work copes with the problem of entity retrieval over RDF knowledge graphs using keyword factual queries. It discloses an approach that incorporates keyword graph structure dependencies through a conditional spread activation. Experimental evaluation on standard benchmarks demonstrates that the proposed method can improve the performance of current state-of-the-art entity retrieval approaches reasonably.

Graph-Embedding Empowered Entity Retrieval

Gerritse

Hasibi

Vries

2020

Self Cite

In this research, we improve upon the current state of the art in entity retrieval by re-ranking the result list using graph embeddings. The paper shows that graph embeddings are useful for entity-oriented search tasks. We demonstrate empirically that encoding information from the knowledge graph into (graph) embeddings contributes to a higher increase in effectiveness of entity retrieval results than using plain word embeddings. We analyze the impact of the accuracy of the entity linker on the overall retrieval effectiveness. Our analysis further deploys the cluster hypothesis to explain the observed advantages of graph embeddings over the more widely used word embeddings, for user tasks involving ranking entities.