=Paper=
{{Paper
|id=Vol-2456/paper53
|storemode=property
|title=LimesWebUI -- Link Discovery Made Simple
|pdfUrl=https://ceur-ws.org/Vol-2456/paper53.pdf
|volume=Vol-2456
|authors=Mohamed Ahmed Sherif,Pestryakova Svetlana,Kevin Dreßler,Axel-Cyrille Ngonga Ngomo
|dblpUrl=https://dblp.org/rec/conf/semweb/SherifSDN19
}}
==LimesWebUI -- Link Discovery Made Simple==
https://ceur-ws.org/Vol-2456/paper53.pdf
LimesWebUI– Link Discovery Made Simple?
Mohamed Ahmed Sherif, Pestryakova Svetlana, Kevin Dreßler, and Axel-Cyrille
Ngonga Ngomo
Paderborn University, Data Science Group, Pohlweg 51, D-33098 Paderborn, Germany
E-mail: {fistName.lastName}@upb.de
Abstract. In this paper we present LimesWebUI, our web interface of Limes.
Limes, the Link Discovery Framework for Metric Spaces, is a framework for dis-
covering links between entities contained in Linked Data sources. LimesWebUI
assists the end user during the link discovery process. By representing the link
specifications (LS) as interlocking blocks, our interface eases the manual creation
of links for users who already know which LS they would like to execute. How-
ever, most users do not know which LS suits their linking task best and therefore
need help throughout this process. Hence, our interface provides wizards which
allow the easy configuration of many link discovery machine learning algorithms,
that does not require the user to enter a manual LS. We evaluate the usability of
the interface by using the standard system usability scale questionnaire. Our over-
all usability score of 76.5 suggests that the online interface is consistent, easy to
use, and the various functions of the system are well integrated.
1 Introduction
Establishing links between knowledge bases is one of the key steps of the Linked Data
publication process.1 A plethora of approaches has thus been devised to support this
process [2]. Limes2 was designed as a declarative framework to address two main chal-
lenges of time-efficiency and accuracy.
The formal specification of Link Discovery (LD) adopted herein is akin to that
proposed in [3]. Given two (not necessarily distinct) sets S resp. T of source resp.
target resources as well as a relation R, the goal of LD is to find the set M = {(s, t) ∈
S × T : R(s, t)} of pairs (s, t) ∈ S × T such that R(s, t). In most cases, computing M
is a non-trivial task. Hence, in Limes [3], we aim to approximate M by computing the
mapping M 0 = {(s, t) ∈ S × T : σ(s, t) ≥ θ}, where σ is a similarity function and θ
is a similarity threshold. For example, one can configure Limes to deduplicate census
records by comparing the dates of birth, family names and given names of persons.
We call the equation which specifies M 0 a link specification (short LS). The gram-
mar we use for representing LSs in Limes is based on set semantics. This grammar
assumes that LSs consist of two types of atomic components: (i) similarity measures m,
?
Copyright c 2019 for this paper by its authors. Use permitted under Creative Commons
License Attribution 4.0 International (CC BY 4.0).
1
http://www.w3.org/DesignIssues/LinkedData.html
2
Source code as well as manual are available at https://github.com/dice-group/LIMES
2 Sherif et al.
which allow the comparison of property values or portions of the concise bound descrip-
tion of 2 resources and (ii) operators ω, which can be used to combine these similarities
into more complex specifications. Without loss of generality, we define an atomic sim-
ilarity measure a as a function a : S × T → [0, 1]. An example of an atomic similarity
measure is the edit similarity dubbed edit3 . Every atomic measure is a measure. We
define a filter as a function f (m, θ). We call a LS atomic when it consists of exactly
one filtering function. A complex LS can be obtained by combining two specifications
through an operator such as AND, OR and MINUS.
2 Limes Web User Interface
LIMES Web user interface (LimesWebUI4 ) is our novel tool to ease the usage of Limes.
The aim of LimesWebUI is to aid our users throughout the configuration as well as the
execution process of Limes. LimesWebUI consists of the following main components:
1. The prefixes component consists of the set of name spaces to be used through the
rest of the Limes configuration process. In most cases, our interface is able to auto-
matically find the common prefixes5 . In case the user wants to add a custom prefix,
(s)he still can type the prefix manually.
2. In the data source and target components, the user can define the source and target
sets of resources to be linked. In particular, the Endpoint field provide a list of
common endpoints, where the user can select the one that provide the datasets
(s)he interested on. Still, the user can manually input other endpoints if not in the
provided list. Then, in the Restriction field the user can select the class within
the dataset to retrieve its instances for linking. Note that, our web UI is able to
retrieve all the classes automatically for the user via a SPARQL query.
3. The manual metric component. Once the user chooses the source/target datasets’
endpoints and classes, LimesWebUI will automatically load the respective proper-
ties within the source/target instances. The user can either use our interface either
to build a manual LS or to configure one of our machine learning algorithms to
learn it. For building the manual metric, our interface provides a workspace that
uses a custom version of the Blockly API 6 , where the user can simply drag and
drop the LS elements from the toolbox. Using our work-space, the user can de-
fine complex LS which consists of multiple measures, operators and preprocessing
functions. Figure 1 shows an example of a complex LS in our work-space. Note
that, our interface is able to save/load the work-space for later use.
4. The machine learning (ML) component consists of: (1) The ML algorithm name to
be used. e.g., Wombat [5] and Eagle [4]. (2) The type of the ML algorithm. i.e., su-
pervised batch, supervised active or unsupervised. (3) A list of parameters for fine
tuning the currently selected ML algorithm. Note that, the list of the default param-
eters of the currently selected ML algorithm will be loaded initially by our web UI.
3
We define the edit similarity of two strings s and t as (1 + Levenshtein(s, t))−1 .
4
Publicly accessible at http://limes.aksw.org
5
From https://prefix.cc/context
6
https://developers.google.com/blockly
LimesWebUI– Link Discovery Made Simple?? 3
Fig. 1. Manual metric example.
Figure 2 shows an example of using LimesWebUI for configuring the unsupervised
version of the Wombat-simple ML algorithm.
5. In the acceptance and review components, the user can define the acceptance and
review thresholds. i.e., the similarity threshold by which a link should be considered
by Limes as accepted or to-be-reviewed link. Hence, Limes save such a link into
either the accepted/review file.
6. Using the output component, the user can choose an output serialization such as
turtle, n-triples, tab separated values and comma separated values.
Finally, the user is able to display the generated XML configuration file, save or
even run it. LimesWebUI assigns a unique execution ID (EID) for each linking task.
In case an execution takes time, the user can simply close his browser and check for
the status of his task later using his/her EID. Once an execution is done, the resulted
accepted and to-be-reviewed links are stored in our server, where the user can retrieve
them at any time using the respective EID.
Fig. 2. Using LimesWebUI for configuring the machine learning algorithm Wombat.
4 Sherif et al.
I needed to learn a lot of things before I could get going with this system (10)
I felt very confident using the system (9)
I found the system very cumbersome to use (8)
Avg.
I would imagine that most people would learn to use this system very quickly (7)
STD.
I thought there was too much inconsistency in this system (6)
I found the various functions in this system were well integrated (5)
I think that I would need the support of a technical person to be able to use this system (4)
I thought the system was easy to use (3)
I found the system unnecessarily complex (2)
I think that I would like to use this system frequently (1)
0 1 2 3 4 5 6
Fig. 3. Result of usability evaluation using SUS questionnaire.
Evaluation. To assess the usability of our system, we used the standard System Usabil-
ity Scale (SUS) [1] questionnaire7 . The survey was posted through the mailing lists of
the DICE research group (https://dice-research.org) and was filled by 24 users.
The results of our SUS shown in Figure 3. We achieved a mean usability score of 76.5
indicating a high level of usability according to the SUS score. The responses to ques-
tion 1 suggests that our system is adequate for frequent use (average score to question
1 = 3.4±1.2) by users of all types. The responses to question 3 (average score 3.7±1.2)
suggests that the interface is easy to use and the responses to question 5 indicates that
the various functions are well integrated (average score 4.1 ± 1.1). However, the re-
sponse to question 10 (average score 2.9 ± 1.3) indicates that users need to learn some
basic concepts before they can use the system effectively.
Acknowledgment. This work has been supported by the BMVI projects LIMBO (GA no.
19F2029C) and OPAL (GA no. 19F2028A), Eurostars Project SAGE (GA no. E!10882)
as well as the H2020 project SLIPO (GA no.731581).
References
1. James R Lewis and Jeff Sauro. The factor structure of the system usability scale. In Interna-
tional conference on human centered design, pages 94–103. Springer, 2009.
2. Markus Nentwig, Michael Hartung, Axel-Cyrille Ngonga Ngomo, and Erhard Rahm. A sur-
vey of current link discovery frameworks. Semantic Web, 8(3):419–436, 2017.
3. Axel-Cyrille Ngonga Ngomo. On link discovery using a hybrid approach. J. Data Semantics,
1(4):203–217, 2012.
4. Axel-Cyrille Ngonga Ngomo and Klaus Lyko. EAGLE: efficient active learning of link spec-
ifications using genetic programming. In Extended Semantic Web Conference 2012.
5. Mohamed Ahmed Sherif, Axel-Cyrille Ngonga Ngomo, and Jens Lehmann. WOMBAT - A
Generalization Approach for Automatic Link Discovery. In 14th ESWC, 2017.
7
Our survey is available at: https://forms.gle/ayZE7KVwH4VhTULk6