Semantic Relatedness as an Inter-facet Metric for Facet Selection over Knowledge Graphs

Feddoul, Leila; Schindler, Sirko; Löffler, Frank

doi:10.1007/978-3-030-32327-1_10

Cited by 4 publications

(3 citation statements)

References 10 publications

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“…Scholarly Knowledge graphs are knowledge bases for representing scholarly knowledge [3]. Faceted search is a method that augments traditional search systems with a faceted exploration system, allowing users to narrow down search results by applying multiple filters based on the classification of the properties [1]. A faceted classification system lists each knowledge component along various dimensions, called facets, facilitating the classifications to be reached and managed in multiple forms.…”

Section: Introductionmentioning

confidence: 99%

Demonstration of Faceted Search on Scholarly Knowledge Graphs

Heidari

Ramadan

Stocker

et al. 2021

Companion Proceedings of the Web Conference 2021

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Scientists always look for the most accurate and relevant answer to their queries on the scholarly literature. Traditional scholarly search systems list documents instead of providing direct answers to the search queries. As data in knowledge graphs are not acquainted semantically, they are not machine-readable. Therefore, a search on scholarly knowledge graphs ends up in a full-text search, not a search in the content of scholarly literature. In this demo, we present a faceted search system that retrieves data from a scholarly knowledge graph, which can be compared and filtered to better satisfy user information needs. Our practice's novelty is that we use dynamic facets, which means facets are not fixed and will change according to the content of a comparison.

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Section: Introductionmentioning

confidence: 99%

Demonstration of Faceted Search on Scholarly Knowledge Graphs

Heidari

Ramadan

Stocker

et al. 2021

Companion Proceedings of the Web Conference 2021

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show abstract

“…Faceted search is a highefficiency search method with various applications. Faceted search is a method that augments traditional search systems with a faceted exploration system, allowing users to narrow down search results by applying multiple filters based on the classification of the properties [7]. A faceted classification system lists each knowledge component along various dimensions, called facets, facilitating the classifications to be reached and managed in multiple forms.…”

Section: Introductionmentioning

confidence: 99%

Leveraging a Federation of Knowledge Graphs to Improve Faceted Search in Digital Libraries

Heidari¹,

Ramadan²,

Stocker³

et al. 2021

Preprint

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Scientists always look for the most accurate and relevant answers to their queries in the literature. Traditional scholarly digital libraries list documents in search results, and therefore are unable to provide precise answers to search queries. In other words, search in digital libraries is metadata search and, if available, full-text search. We present a methodology for improving a faceted search system on structured content by leveraging a federation of scholarly knowledge graphs. We implemented the methodology on top of a scholarly knowledge graph. This search system can leverage content from third-party knowledge graphs to improve the exploration of scholarly content. A novelty of our approach is that we use dynamic facets on diverse data types, meaning that facets can change according to the user query. The user can also adjust the granularity of dynamic facets. An additional novelty is that we leverage third-party knowledge graphs to improve exploring scholarly knowledge.

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“…Based on our previously proposed workflow [1], we integrated all methods into an initial prototypical implementation [2]. While this leverages data from a specific KG, i.e., Wikidata [3], the methods we describe and use are generally applicable without or with only minimal changes to a wide range of KGs.…”

Section: Introductionmentioning

confidence: 99%

Automatic Facet Generation and Selection over Knowledge Graphs

Feddoul

Schindler

Löffler

2019

Lecture Notes in Computer Science

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With the continuous growth of the Linked Data Cloud, adequate methods to efficiently explore semantic data are increasingly required. Faceted browsing is an established technique for exploratory search. Users are given an overview of a collection's attributes that can be used to progressively refine their filter criteria and delve into the data. However, manual facet predefinition is often inappropriate for at least three reasons: Firstly, heterogeneous and large scale knowledge graphs offer a huge number of possible facets. Choosing among them may be virtually impossible without algorithmic support. Secondly, knowledge graphs are often constantly changing, hence, predefinitions need to be redone or adapted. Finally, facets are generally applied to only a subset of resources (e.g., search query results). Thus, they have to match this subset and not the knowledge graph as a whole. Precomputing facets for each possible subset is impractical except for very small graphs. We present our approach for automatic facet generation and selection over knowledge graphs. We propose methods for (1) candidate facet generation and (2) facet ranking, based on metrics that both judge a facet in isolation as well as in relation to others. We integrate those methods in an overall system workflow that also explores indirect facets, before we present the results of an initial evaluation.

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Semantic Relatedness as an Inter-facet Metric for Facet Selection over Knowledge Graphs

Cited by 4 publications

References 10 publications

Demonstration of Faceted Search on Scholarly Knowledge Graphs

Demonstration of Faceted Search on Scholarly Knowledge Graphs

Leveraging a Federation of Knowledge Graphs to Improve Faceted Search in Digital Libraries

Automatic Facet Generation and Selection over Knowledge Graphs

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