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CoaKG: A Contextualized Knowledge Graph Approach for Exploratory Search and Decision Making

Authors Veronica dos Santos , Daniel Schwabe , Altigran Soares da Silva , Sérgio Lifschitz

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ACM Subject Classification
  • Information systems → Decision support systems
  • Information systems → Information integration
  • Information systems → Graph-based database models
Keywords
  • Knowledge Graphs
  • Context Search
  • Decision Support

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Abstract

In decision-making scenarios, an information need arises due to a knowledge gap when a decision-maker needs more knowledge to make a decision. Users may take the initiative to acquire knowledge to fill this gap through exploratory search approaches using Knowledge Graphs (KGs) as information sources, but their queries can be incomplete, inaccurate, and ambiguous. Although KGs have great potential for exploratory search, they are incomplete by nature. Besides, for both Crowd-sourced KGs and KGs constructed by integrating several different information sources of varying quality to be effectively consumed, there is a need for a Trust Layer. Our research aims to enrich and allow querying KGs to support context-aware exploration in decision-making scenarios. We propose a layered architecture for Context Augmented Knowledge Graphs-based Decision Support Systems with a Knowledge Layer that operates under a Dual Open World Assumption (DOWA). Under DOWA, the evaluation of the truthfulness of the information obtained from KGs depends on the context of its claims and the tasks carried out or intended (purpose). The Knowledge Layer comprises a Context Augmented KG (CoaKG) and a CoaKG Query Engine. The CoaKG contains contextual mappings to identify explicit context and rules to infer implicit context. The CoaKG Query Engine is designed as a query-answering approach that retrieves all contextualized answers from the CoaKG. A Proof of Concept (PoC) based on Wikidata was developed to evaluate the effectiveness of the Knowledge Layer.

Cite As Get BibTex

Veronica dos Santos, Daniel Schwabe, Altigran Soares da Silva, and Sérgio Lifschitz. CoaKG: A Contextualized Knowledge Graph Approach for Exploratory Search and Decision Making. In Transactions on Graph Data and Knowledge (TGDK), Volume 3, Issue 1, pp. 4:1-4:27, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025) https://doi.org/10.4230/TGDK.3.1.4

Author Details

Veronica dos Santos
  • Informatics Department, Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Brazil
Daniel Schwabe
  • Informatics Department Associate Reseacher, Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Brazil
Altigran Soares da Silva
  • Institute of Computing (IComp), Federal University of Amazonas (UFAM), Manaus, Brazil
Sérgio Lifschitz
  • Informatics Department, Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Brazil

Funding

  • da Silva, Altigran Soares: This research is partially supported by FAPEAM under the NeuralBond Project (UNIVERSAL 2023 Proc. 01.02.016301.04300/2023-04); and by CNPq under Projects IAIA (406417/2022-9) and TILD-IAR (408490/2024-1); and an individual grant from CNPq to Altigran da Silva (307248/2019-4).
  • Lifschitz, Sérgio: This research is partially supported by CAPES (PUC-Rio's institutional funding).

Supplementary Materials

References

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