How and Why is An Answer (Still) Correct? Maintaining Provenance in Dynamic Knowledge Graphs

07/29/2020
by   Garima Gaur, et al.
0

Knowledge graphs (KGs) have increasingly become the backbone of many critical knowledge-centric applications. Most large-scale KGs used in practice are automatically constructed based on an ensemble of extraction techniques applied over diverse data sources. Therefore, it is important to establish the provenance of results for a query to determine how these were computed. Provenance is shown to be useful for assigning confidence scores to the results, for debugging the KG generation itself, and for providing answer explanations. In many such applications, certain queries are registered as standing queries since their answers are needed often. However, KGs keep continuously changing due to reasons such as changes in the source data, improvements to the extraction techniques, refinement/enrichment of information, and so on. This brings us to the issue of efficiently maintaining the provenance polynomials of complex graph pattern queries for dynamic and large KGs instead of having to recompute them from scratch each time the KG is updated. Addressing these issues, we present HUKA which uses provenance polynomials for tracking the derivation of query results over knowledge graphs by encoding the edges involved in generating the answer. More importantly, HUKA also maintains these provenance polynomials in the face of updates—insertions as well as deletions of facts—to the underlying KG. Experimental results over large real-world KGs such as YAGO and DBpedia with various benchmark SPARQL query workloads reveals that HUKA can be almost 50 times faster than existing systems for provenance computation on dynamic KGs.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
08/17/2021

Computing and Maintaining Provenance of Query Result Probabilities in Uncertain Knowledge Graphs

Knowledge graphs (KG) that model the relationships between entities as l...
research
04/28/2023

LitCQD: Multi-Hop Reasoning in Incomplete Knowledge Graphs with Numeric Literals

Most real-world knowledge graphs, including Wikidata, DBpedia, and Yago ...
research
09/21/2022

Evaluating Continuous Basic Graph Patterns over Dynamic Link Data Graphs

In this paper, we investigate the problem of evaluating Basic Graph Patt...
research
11/06/2020

Complex Query Answering with Neural Link Predictors

Neural link predictors are immensely useful for identifying missing edge...
research
11/20/2017

Spec-QP: Speculative Query Planning for Joins over Knowledge Graphs

Organisations store huge amounts of data from multiple heterogeneous sou...
research
03/12/2023

ALIST: Associative Logic for Inference, Storage and Transfer. A Lingua Franca for Inference on the Web

Recent developments in support for constructing knowledge graphs have le...
research
01/30/2013

Dynamic Jointrees

It is well known that one can ignore parts of a belief network when comp...

Please sign up or login with your details

Forgot password? Click here to reset