Knowledge Graphs Querying
Arijit Khan
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 (NLP), with different
focus and terminologies; and also in diverse topics ranging from graph
databases, query languages, join algorithms, graph patterns matching, to more
sophisticated KG embedding and natural language questions (NLQs). We aim at
uniting different interdisciplinary topics and concepts that have been
developed for KG querying. Second, many recent advances on KG and query
embedding, multimodal KG, and KG-QA come from deep learning, IR, NLP, and
computer vision domains. We identify important challenges of KG querying that
received less attention by graph databases, and by the DB community in general,
e.g., incomplete KG, semantic matching, multimodal data, and NLQs. We conclude
by discussing interesting opportunities for the data management community, for
instance, KG as a unified data model and vector-based query processing.