
TL;DR
This survey reviews recent advances in knowledge graph querying, covering interdisciplinary methods, deep learning techniques, and challenges like incomplete data and natural language questions, highlighting future opportunities.
Contribution
It unifies diverse research topics on KG querying from multiple communities and discusses emerging challenges and future directions for data management.
Findings
Deep learning enhances KG embedding and question answering.
Multimodal KGs present new querying challenges.
Incomplete KGs affect query accuracy.
Abstract
Knowledge graphs (KGs) such as DBpedia, Freebase, YAGO, Wikidata, and NELL were constructed to store large-scale, real-world facts as (subject, predicate, object) triples -- that can also be modeled as a graph, where a node (a subject or an object) represents an entity with attributes, and a directed edge (a predicate) is a relationship between two entities. Querying KGs is critical in web search, question answering (QA), semantic search, personal assistants, fact checking, and recommendation. While significant progress has been made on KG construction and curation, thanks to deep learning recently we have seen a surge of research on KG querying and QA. The objectives of our survey are two-fold. First, research on KG querying has been conducted by several communities, such as databases, data mining, semantic web, machine learning, information retrieval, and natural language processing…
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Taxonomy
TopicsSemantic Web and Ontologies · Advanced Graph Neural Networks · Graph Theory and Algorithms
