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Automatic Facet Generation and Selection over Knowledge Graphs
Title: | Automatic Facet Generation and Selection over Knowledge Graphs |
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Authors: | Leila Feddoul, Sirko Schindler, and Frank Löffler |
Source: | SEMANTiCS 2019 |
Place: | Karlsruhe, Germany |
Date: | 2019-09-09 |
Type: | Conference Paper |
Abstract: |
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. |
File: | Feddoul2019_Chapter_AutomaticFacetGenerationAndSel |
URL: | https://doi.org/10.1007/978-3-030-33220-4_23 |