=Paper=
{{Paper
|id=Vol-2180/paper-44
|storemode=property
|title=GraFa: Faceted Search & Browsing for the Wikidata Knowledge Graph
|pdfUrl=https://ceur-ws.org/Vol-2180/paper-44.pdf
|volume=Vol-2180
|authors=José Moreno-Vega,Aidan Hogan
|dblpUrl=https://dblp.org/rec/conf/semweb/Moreno-VegaH18a
}}
==GraFa: Faceted Search & Browsing for the Wikidata Knowledge Graph==
https://ceur-ws.org/Vol-2180/paper-44.pdf
GraFa: Faceted Search & Browsing for the
Wikidata Knowledge Graph
José Moreno-Vega and Aidan Hogan
IMFD Chile & Department of Computer Science, University of Chile
Abstract. We present a demo of the GraFa faceted search and brows-
ing interface over the Wikidata knowledge graph. We describe the key
aspects of the interface, including the types of interactions that the sys-
tem allows, the ranking schemes employed, and other features to aid
usability. We also discuss future plans for improving the system.
Online Demo: http://grafa.dcc.uchile.cl/
1 Introduction
Faceted browsing [7] has become a popular paradigm for interacting with data on
the Web, where a number of authors have proposed systems for faceted browsing
interfaces over Semantic Web knowledge-bases (see [3,5] for surveys). However,
many such systems are demonstrated for small, uniform datasets, where we could
not find an available system that would work for a dataset as large (many triples)
and diverse (many properties and classes) as Wikidata [6]. These large, diverse
datasets are precisely those most in need of intuitive user interfaces.
To bridge this gap, we propose the Graph Facets (GraFa) system designed
to offer faceted search and browsing over large, diverse RDF graphs. An impor-
tant feature of the GraFa system is that it provides exact faceted views, meaning
that the facets offered to restrict the current results set offer exact counts and are
exactly those that will lead to non-empty results upon selection. However, result
sets for very common types of (intermediate) queries – such as human or human
AND gender male, etc. – can reach into the millions of entities. Computing the
exact facets for such large results while maintaining interactive response times
is technically challenging and does not appear to be well-supported by avail-
able faceted search tools. To improve scalability while maintaining efficiency,
the GraFa system thus incorporates novel indexing schemes that, in an offline
phase, pre-compute and store exact facets for large results sets.
In our paper accepted in the research track [4], we describe the GraFa
system in detail, including the faceted browsing interactions it permits, the in-
dexing scheme used to improve query performance, the implementation based on
Lucene, performance experiments over Wikidata, as well as an initial user eval-
uation of the system. These results show that by pre-indexing the exact facets
for 141 queries identified as generating more than 50,000 results, the worst-case
response times for the system are under 3 seconds. In addition, initial user eval-
uation results provide feedback for further directions in which the system can
be improved. We refer the reader to [4] for detailed results.
In the demo track, we propose to offer attendees of ISWC a live demo of
the GraFa system for performing faceted search and browsing over Wikidata.
We also wish to discuss possible features that could be added to the system in
future, other datasets or use-cases to which Grafa could be applied, as well
as to gain feedback and identify potential topics for collaboration. The demo
we plan to provide is publicly available here: http://grafa.dcc.uchile.cl/. In
this paper, we provide details on user interactions, details on the prototype, and
current limitations; for more information on performance, back-end, indexing,
usability, etc., we refer to our paper in the research track [4].
2 User Interactions
Figure 1 provides an overview of the user in-
teractions that the GraFa system currently
supports. The user is first presented with the
option of searching by keyword (e.g., "nick
drake") or by selecting a type IRI (e.g., wd:Q5
(human)). Each result set is associated with a
list of facets from which the user may itera-
tively select further restrictions of the current
results. We will now discuss each of these in-
teractions in further detail.
For the initial keyword search, a ranked
list of entities are returned that have match-
ing keywords in their labels, aliases or de- Fig. 1: Overview of user interac-
scriptions; the properties corresponding to tions supported by GraFa
such values must be configured in the sys-
tem (in the case of Wikidata, we use rdfs:label, skos:altLabel and
schema:description). Furthermore, a set of supported languages must be con-
figured, where the labels, aliases and descriptions of these languages will be
indexed, as available; the demo is configured for both Spanish and English.
Rather than search by keyword, the user can instead opt to perform a type
search; here, types are defined as values for a configured list of type properties (in
the case of Wikidata, we use wdt:P31 (instance of)). Given that users will often
not know the required IRI of a type, the GraFa interface offers auto-completion
on the labels and aliases of types in the graph, where suggested types are ranked
according to a PageRank score and are annotated with the number of results
with that type. For example, if a user types in the partial label hum, GraFa
will suggest human (3595226 results) as the first result; if selected, GraFa will
search for entities of type wd:Q5; on the other hand, if the user types in per, the
first suggestion will be person (3595226 results), which if selected will also
trigger a query for entities of type wd:Q5 (for which person is an alias).
Whether the user begins with a keyword or type search, GraFa will gener-
ate a list of resulting entities. For each entity in the results, its label, aliases,
description and an image are displayed; the label is presented as a hyperlink
that will dereference the entity IRI. In the case of keyword search, results are
ranked by a combined query-relevance and PageRank score. In the case of type
search, results are ranked purely by PageRank score. In either case, a list of
facets are computed for the current results set. Each facet is a property and a
list of values that at least one entity in the current result set is associated with.
The facet view displays all possible properties, with a count of the number of
entities with some value for that property. Upon selecting a property, the user
can perform an auto-complete search on the label of a particular value, or can
browse possible values in a drop-down list. Once a value is selected, a new result
set is generated for the active conjunction of restrictions, where these results are
ordered by PageRank; a new facet view is also generated. The user may continue
iteratively adding facets until they are satisfied with the results, or until they
reach a single result (facets with zero results are never offered).
3 Demo
We have implemented a prototype of the GraFa system for experimental pur-
poses and to gather initial feedback and expressions of interest. The system
uses Apache Lucene as a back-end store, managing (1) full-text search index-
ing for keyword and auto-complete prefix searches, (2) structured indexes for
type and facet selection, (3) indexes for cached queries generating more than
50,000 results. The front-end is implemented as a Java servlet, with interactive
autocomplete features being based on Javascript libraries. The source code is
available from the following repository: https://github.com/joseignm/GraFa/.
We will demonstrate an instance of the GraFa prototype indexing a dump
of Wikidata. More specifically, the demo indexes the “truthy” dump of Wikidata
from 2017/09/13, containing 1.77 billion triples and 74.1 million entities. On a
machine with an Intel Xeon E5-2609 v3 CPU, 32 GB of RAM, and 2 × 2TB
Seagate 7200 RPM 32MB Cache SATA, indexing the data takes approximately
5 days, with the majority of time (4.5 days) taken to compute and index all
queries with more than 50,000 results [4]. In performance experiments on type
and facet selections, the worst-case response times are around 3 seconds [4].
In Figure 2, we provide an example screenshot of the GraFa demo where
the user has searched for lighthouses on the Adriatic Sea, and is now considering
further filtering the 14 available results by country.1
4 Limitations and Future Work
We see the current GraFa system as offering a baseline system for future devel-
opment, where indeed the current version has a number of limitations that we
have yet to address, including (1) support for datatypes and range queries, (2)
support for existential value queries, (3) support for class/property hierarchies
and potentially other forms of inference, (4) incremental updates. We note that
1
See http://grafa.dcc.uchile.cl/search?instance=Q39715&properties=P206%23%23Q13924
Fig. 2: Results for lighthouses located next to the Adriatic Sea, further showing
possible values for countries by which the current results can be restricted
existing faceted browsing systems support some of these features, where, for ex-
ample, Broccoli [2] offers range queries, while SemFacet [1] offers reasoning
capabilities. It would thus be interesting to see if similar techniques could be
combined into GraFa in the future, what performance cost such new features
would imply for faceted browsing over a dataset such as Wikidata, and indeed,
what sorts of benefits they could bring for users of GraFa.
Acknowledgements This work was supported by the Millennium Institute for Foun-
dational Research on Data (IMFD) and by Fondecyt Grant No. 1181896.
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