Exploring knowledge graphs for exploratory search

Sarrafzadeh, Bahareh; Vechtomova, Olga; Jokic, Vlado

doi:10.1145/2637002.2637019

Cited by 23 publications

(12 citation statements)

References 15 publications

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“…Exploratory search in knowledge graphs. Exploratory methods overcome the rigidity of declarative languages in graphs, such as SPARQL [35]. Entity search allows for automatic completion of a set of seed entities (persons, organizations, places).…”

Section: Related Workmentioning

confidence: 99%

Graph-Query Suggestions for Knowledge Graph Exploration

Lissandrini

Mottin

Palpanas

et al. 2020

Proceedings of the Web Conference 2020

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We consider the task of exploratory search through graph queries on knowledge graphs. We propose to assist the user by expanding the query with intuitive suggestions to provide a more informative (full) query that can retrieve more detailed and relevant answers. To achieve this result, we propose a model that can bridge graph search paradigms with well-established techniques for informationretrieval. Our approach does not require any additional knowledge from the user and builds on principled language modelling approaches. We empirically show the effectiveness and efficiency of our approach on a large knowledge graph and how our suggestions are able to help build more complete and informative queries.

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Section: Related Workmentioning

confidence: 99%

Graph-Query Suggestions for Knowledge Graph Exploration

Lissandrini

Mottin

Palpanas

et al. 2020

Proceedings of the Web Conference 2020

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“…As the largest single knowledge source Wikipedia is used in many query recommendation methods, such as the search direction discovery method proposed in the research by Yuvarani et al [ 23 ]. The Linked Data project connects many formal knowledge sources, including Wikipedia, and thus has also been used for query recommendations [ 24 ]. A key problem in using formal knowledge sources for query recommendation is decisions regarding ranking of the different recommendations.…”

Section: Related Workmentioning

confidence: 99%

Recommending Queries by Extracting Thematic Experiences from Complex Search Tasks

Zhao

Zhang

et al. 2018

Entropy

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Since complex search tasks are usually divided into subtasks, providing subtask-oriented query recommendations is an effective way to support complex search tasks. Currently, most subtask-oriented query recommendation methods extract subtasks from plain form search logs consisting of only queries and clicks, providing limited clues to identify subtasks. Meanwhile, for several decades, the Computer Human Interface (CHI)/Human Computer Interaction (HCI) communities have been working on new complex search tools for the purpose of supporting rich user interactions beyond just queries and clicks, and thus providing rich form search logs with more clues for subtask identification. In this paper, we researched the provision of subtask-oriented query recommendations by extracting thematic experiences from the rich form search logs of complex search tasks logged in a proposed visual data structure. We introduce the tree structure of the visual data structure and propose a visual-based subtask identification method based on the visual data structure. We then introduce a personalized PageRank-based method to recommend queries by ranking nodes on the network from the identified subtasks. We evaluated the proposed methods in experiments consisting of informative and tentative search tasks.

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“…By representing specific domain knowledge in a unified graphical form, the domain knowledge from experts can also be fused, improving the quality of the knowledge graph. Second, a demand driven knowledge service is transformed to path navigation in the knowledge graph [33]. The meta knowledge and its relationships in the graph form a solution to the specific question.…”

Section: Non-linear Fusion On Fragmented Knowledgementioning

confidence: 99%

Knowledge Engineering with Big Data

Chen

et al. 2015

IEEE Intell. Syst.

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In the era of Big Data, knowledge engineering has to face fundamental challenges by fragmented knowledge from heterogeneous, autonomous sources with complex and evolving relationships. The knowledge representation, knowledge acquisition, and knowledge inference techniques developed in the 1970s and 1980s, driven by research and development of expert systems, need to be updated to cope with both fragmented knowledge from multiple sources in the Big Data revolution, and in-depth expertise from domain experts. This article presents BigKE, a 3phase "online learning -knowledge fusion -knowledge service" knowledge engineering framework with Big Data, with three fundamental research problems: (1) fragmented knowledge modeling and online learning from multiple information sources, (2) non-linear fusion on fragmented knowledge, and (3) automated demand-driven knowledge navigation. The knowledge graph representation is advocated in BigKE. We also compare BigKE with existing models for Big Data, such as the 4P medical model, the IBM 4V model, 5 R's, and the HACE theorem.

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Exploring knowledge graphs for exploratory search

Cited by 23 publications

References 15 publications

Graph-Query Suggestions for Knowledge Graph Exploration

Graph-Query Suggestions for Knowledge Graph Exploration

Recommending Queries by Extracting Thematic Experiences from Complex Search Tasks

Knowledge Engineering with Big Data

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