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Knowledge Graphs Querying

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.

Diversity, Equity and Inclusion Activities in Database Conferences: A 2022 Report

The Diversity, Equity and Inclusion (DEI) initiative started as the Diversity/Inclusion initiative in 2020 [4]. The current report summarizes our activities in 2022. Our responsibility as a community is to ensure that attendees of DB conferences feel included, irrespective of their scientific perspective and personal background. One of the first steps was to establish the role of the DEI chairs at DB Conferences, with the DEI team dedicated to providing leadership to help our community achieve this goal. In this leadership role, the DEI team is advising DEI chairs at DB conferences, serving as a memory of DEI events at conferences, building an agreed-upon vision, and committing to working together to devise a set of measures for achieving DEI. That is pursued via actions led by our core members (Figure 1) and liaisons of individual executive bodies (Figure 2): REACH OUT collects data and experiences from our community. INCLUDE monitors and recommends inclusion efforts. ORGANIZE focuses on in-conference organization efforts, such as adopting a code of conduct. INFORM communicates through various channels. SUPPORT coordinates DEI support from executive bodies and sponsors. SCOUT collates DEI efforts from other communities. COORDINATE manages all actions. Two new actions: MEDIA preserves and disseminates the digital media produced by DEI@DB events. ETHICS establishes and promotes ethics guidelines for publications in our community.