paper-with-me

Papers

Which Knowledge Graph Is Best for Me?

2018-09-28 · Michael Färber, Achim Rettinger

In recent years, DBpedia, Freebase, OpenCyc, Wikidata, and YAGO have been published as noteworthy large, cross-domain, and freely available knowledge graphs. Although extensively in use, these knowledge graphs are hard to compare against each other in a given setting. Thus, it is a challenge for researchers and developers to pick the best knowledge graph for their individual needs. In our recent survey, we devised and applied data quality criteria to the above-mentioned knowledge graphs. Furthermore, we proposed a framework for finding the most suitable knowledge graph for a given setting. With this paper we intend to ease the access to our in-depth survey by presenting simplified rules that map individual data quality requirements to specific knowledge graphs. However, this paper does not intend to replace our previously introduced decision-support framework. For an informed decision on which KG is best for you we still refer to our in-depth survey.

📄 PDF Abstract BibTeX arXiv:1809.11099

Code (0)

등록된 구현이 없습니다.

Tasks

Knowledge GraphsSurvey

Similar Papers 제목 키워드 기반

Who killed Lilly Kane? A case study in applying knowledge graphs to crime fiction

2020-11-24 · Mariam Alaverdian, William Gilroy, Veronica Kirgios, Xia Li 외

We present a preliminary study of a knowledge graph created from season one of the television show Veronica Mars, which follows the eponymous young private investigator as she attempts to solve the murder of her best fri…

Knowledge Graphs

Neural Regression with Embeddings for Numerical Attribute Prediction in Knowledge Graphs

2026-08-27 · Rupesh Sapkota, Louis Mozart Kamdem Teyou, Moshood Yekini, Caglar Demir 외 arxiv

In recent years, transductive knowledge graph embedding models have been applied to tasks such as link prediction and query answering. Although knowledge graphs often contain rich numerical attributes, most embedding mod…

Knowledge Graph EmbeddingKnowledge GraphsLink Prediction

Benchmark and Best Practices for Biomedical Knowledge Graph Embeddings

2020-06-24 · WS 2020 7 · David Chang, Ivana Balazevic, Carl Allen, Daniel Chawla 외

Much of biomedical and healthcare data is encoded in discrete, symbolic form such as text and medical codes. There is a wealth of expert-curated biomedical domain knowledge stored in knowledge bases and ontologies, but t…

Graph EmbeddingKnowledge Graph EmbeddingKnowledge Graph EmbeddingsKnowledge Graphs+1

A Method of Query Graph Reranking for Knowledge Base Question Answering

2022-04-27 · Yonghui Jia, Wenliang Chen

This paper presents a novel reranking method to better choose the optimal query graph, a sub-graph of knowledge graph, to retrieve the answer for an input question in Knowledge Base Question Answering (KBQA). Existing me…

Graph RankingKnowledge Base Question AnsweringQuestion AnsweringReranking

BiQUE: Biquaternionic Embeddings of Knowledge Graphs

2021-09-29 · EMNLP 2021 11 · Jia Guo, Stanley Kok

Knowledge graph embeddings (KGEs) compactly encode multi-relational knowledge graphs (KGs). Existing KGE models rely on geometric operations to model relational patterns. Euclidean (circular) rotation is useful for model…

Knowledge Graph EmbeddingsKnowledge GraphsTranslation