Demonstration of Faceted Search on Scholarly Knowledge Graphs

Heidari, Golsa; Ramadan, Ahmad; Stocker, Markus; Auer, Sören

doi:10.1145/3442442.3458605

Cited by 7 publications

(2 citation statements)

References 3 publications

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“…The faceted search is an intuitive method of IR, where users can explore and refine search results by applying filters, or facets, along various dimensions [16], [17]. In our application, we distinguish between two facet types based on their functionality in querying and classifying data: (i) semantic type facets and (ii) property facets.…”

Section: Semantic Searchmentioning

confidence: 99%

Semantifying the PlanQK Platform and Ecosystem for Quantum Applications

Martyniuk,

Karam,

Falkenthal

et al. 2023

Knowledge Graphs: Semantics, Machine Learning, and Languages

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Quantum computing is currently experiencing rapid progress. Due to the complexity and continuous growth of knowledge in this field, it is essential to store information in a way that allows an easy access, analysis and navigation over reliable resources. Knowledge graphs (KGs) with machine-readable semantics offer a structural information representation and can enhance the capabilities of knowledge processing and information retrieval. In this paper, we extend the platform and ecosystem for quantum applications (PlanQK) for a KG. Specifically, we describe how the quantum computing knowledge, which is submitted on the platform by researchers and industry actors, is incorporated into the graph. Moreover, we outline the semantic search over the PlanQK KG.

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Section: Semantic Searchmentioning

confidence: 99%

Semantifying the PlanQK Platform and Ecosystem for Quantum Applications

Martyniuk,

Karam,

Falkenthal

et al. 2023

Knowledge Graphs: Semantics, Machine Learning, and Languages

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“…More specifically, Scholarly KGs such as ORKG [16], MAG [39], OpenAIRE [25] are becoming popular way to represent research data. Such KGs are used for search [4,14], question answering [17], Fig. 7.…”

Section: Related Workmentioning

confidence: 99%

Knowledge Graph Induction enabling Recommending and Trend Analysis: A Corporate Research Community Use Case

Mihindukulasooriya¹,

Mike²,

Rossiello³

et al. 2022

Preprint

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A research division plays an important role of driving innovation in an organization. Drawing insights, following trends, keeping abreast of new research, and formulating strategies are increasingly becoming more challenging for both researchers and executives as the amount of information grows in both velocity and volume. In this paper we present a use case of how a corporate research community, IBM Research, utilizes Semantic Web technologies to induce a unified Knowledge Graph from both structured and textual data obtained by integrating various applications used by the community related to research projects, academic papers, datasets, achievements and recognition. In order to make the Knowledge Graph more accessible to application developers, we identified a set of common patterns for exploiting the induced knowledge and exposed them as APIs. Those patterns were born out of user research which identified the most valuable use cases or user pain points to be alleviated. We outline two distinct scenarios: recommendation and analytics for business use. We will discuss these scenarios in detail and provide an empirical evaluation on entity recommendation specifically. The methodology used and the lessons learned from this work can be applied to other organizations facing similar challenges.

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