WORKSHOP REPORT

Usage Analysis and the Web of Data
Bettina Berendt
K.U. Leuven
Belgium
bettina.berendt@cs.kuleuven.be

Laura Hollink
TU Delft
Netherlands
l.hollink@tudelft.nl

Markus Luczak-Rösch
FU Berlin
Germany
markus.luczak-roesch@fu-berlin.de

Vera Hollink
CWI Amsterdam
Netherlands
v.hollink@cwi.nl

Knud Möller
NUI Galway
Irelandknud.moeller@deri.org

David Vallet
Universidad Autónoma de Madrid
Spaindavid.vallet@uam.es

Abstract
The workshop on Usage Analysis and the Web of Data (USEWOD2011) was the firstworkshop in the field to investigate combinations of usage data with semantics and the Web
of Data. Questions the workshop aims to address are for example: How can semantics help
in understanding usage data, how can semantic information be derived from usage data, andhow can we learn about usage of and on the emerging Web of Data, and what can we learn
from it? We report on the findings and results of this workshop, held on March 28, 2011 inconjunction with 20th International World Wide Web Conference, in Hyderabad, India.

1

Introduction

The purpose of the USEWOD2011 workshop1 was to investigate the synergy between semantics and semantic-web technology on the one hand and analysis and mining of usage data on
the other hand. The two fields are a promising combination. First, semantics can be used toenhance the analysis of usage data. Usage logs contain information that can help to better
understand users or to adapt a system to a user’s needs and preferences. However, usage
logs can be huge, especially on the Web, recording each single user click or query. In this1

As the main reference to refer to USEWOD2011 or the USEWOD dataset, please use [4]. The workshop
site is available at http://data.semanticweb.org/usewod/2011/, the proceedings have been published online at
http//arxiv.org.

workshop we will explore the question if semantic data can be employed to generalise overclicks and queries and allow analysis on a higher level of abstraction. Now that more andmore explicit knowledge is represented on the Web, in the form of ontologies, folksonomies,
or linked data, the question arises how these semantics can be used to aid large scale web
usage analysis and mining.
Second, usage data analysis can enhance semantic resources as well as Semantic Web
applications. Traces of users can be used to evaluate, adapt or personalise Semantic Web
applications. Since logs record real-life users, they provide an opportunity to create gold
standards for search or recommendation tools. In addition, logs can form valuable resourcesfrom which knowledge (e.g. in the form of ontologies or thesauri) can be extracted bottom-up.
Also, the emerging Web of Data demands a re-evaluation of existing usage mining techniques; new ways of accessing information enabled by the Web of Data imply the need todevelop or adapt algorithms, methods, and techniques to analyse and interpret the usage ofWeb data instead of Web pages. An important question at this time is how the Web of Data
is being used: how are datasets being accessed by human users and how by machines, whatkinds of queries are being performed, and what can we learn about the usage of semantic
applications? Ultimately only understanding of their usage (or non-usage!) can give bothsummative and formative evaluations of the adequacy of resources for their final destination:
their use in information processing, whether by people or by machines.
The primary goals of this workshop were to foment a new community of researchers fromvarious fields sharing an interest in usage mining and semantics and to create a roadmap for
future research in this direction. Several recent papers indicate an interest in bringing usagelog analysis and semantic technologies together [2, 3, 5, 8, 9, 12, 13, 15, 16], but the researchcommunities have so far remained isolated. USEWOD2011 forms an ideal opportunity forcross-fertilisation between the different communities and research fields. The workshop isof interest to researchers from the Semantic Web community who are working with usage
data. It is also relevant for people working on log analysis and data mining from the perspective of Information Science, Human Computer Interaction and User Modelling. Also,in the Information Retrieval community there is a growing interest in both usage data andlightweight semantics, such as Linked Data or folksonomies. Finally, we encouraged theparticipation of industry representatives, as they play the role of data providers and would
therefore ultimately benefit from the outcomes of the workshop.
The following set of topics was suggested for this workshop:• Analysis and mining of usage logs of semantic resources and applications.
• Inferring semantic information from usage logs.
• Methods and tools for semantic analysis of usage logs.
• Representing and enriching usage logs with semantic information.
• Usage-based evaluation methods and frameworks; gold standards for evaluation of semantic web applications.
• Specifics and semantics of logs for content-consumption and content-creation.
• Using semantics for recommendation, personalisation and adaptation.
• Usage-based recommendation, personalisation and adaptation of semantic web applications.
• Exploiting usage logs for semantic search.

• Data sharing, privacy, and privacy-protecting policies and techniques.
The workshop was opened by a keynote talk given by Dr. Markus Strohmaier on the topic
of Social Computation for the Web of Data: Motivation, Examples, and Outlook (Sect. 2).
This was followed by a series of talks presenting the various accepted submissions, including
two full paper and two short papers (Sect. 3), as well as two presentations which were related
to the USEWOD data challenge (Sect. 4). Rounding up the day was a plenary discussion on
obstacles holding back research in the area and future research directions (Sect. 5).

2

Keynote Talk

Dr. Strohmaier gave his well-received keynote on the topic of Social Computation for the
Web of Data: Motivation, Examples, and Outlook. Dr. Strohmaier is currently an assistantprofessor at the Knowledge Management Institute, Faculty of Computer Science at Graz
University of Technology in Austria. He was invited as a renowned expert on social streams
and social tagging systems, and has given many talks and published extensively in this area.
The following paragraph presents the abstract of his talk.
“Today, the early World Wide Web of documents is evolving into a web of data,
where data is stored in different kinds of databases, including unstructured, semistructured and structured repositories. This talk explores the role of social computation, i.e., the combination of social behavior and algorithmic computation, forlinked data. What distinguishes social-computational systems from other typesof systems is the unprecedented involvement of data about user behavior, goals
and motivations into the software systems structure. What can be observed insocial-computational systems is that the interaction between a user and the system is mediated by the aggregation of explicit or implicit data from other users.
This is the case with systems where, for example, user data is used to suggest
search terms (e.g., Google Autosuggest), to recommend products (e.g., Amazon
recommendations), to aid navigation (e.g., tag-based navigation) or to filter content (e.g., Digg.com). This makes social-computational systems a novel class ofsoftware systems and unique in a sense that potentially essential system properties
and functions are dynamically influenced by aggregate user behavior.”

3

Technical and Position Papers

Fortuna et al. [6] presented a full technical paper entitled User Modeling Combining AccessLogs, Page Content and Semantics, in which the authors investigate the combination ofsemantic and non-semantic access log sources to Web resources in order to model the user.
The authors’ proposed approach is evaluated on real-world data. The results of their study
indicate that their approach allows for building more accurate user models.
In User Modeling Combining Access Logs, Page Content and Semantics, Hollink and De
Vries [10] propose a query modification assistant based on usage log mining. Their approach
exploits semantic relations between instances appearing in usage logs in order to provide more
effective query suggestions to the user. The authors identify six possible situations in which
providing a query modification mechanism may improve the users’ search experience. Theirinitial experimental results indicate that semantic information can complement traditionalterm-based query modification approaches.
Arish Sureka [17] presented a position paper entitled Mining User Comment Activity for
Detecting Forum Spammers in YouTube, in which detection of spam comments in contentrelated sites is investigated. The author proposes an approach in which the logged activity ofa user is analysed in order to detect specific patterns that could indicate suspicious activity.
An empirical analysis is performed over YouTube, which demonstrates that the proposed
method can be effective for comment spammer detection.
In U-Sem: Semantic Enrichment, User Modeling and Mining Usage Data on the Social
Web Abel et al. [1] investigate the problem of obtaining and mining usage data for Social
Web applications. Their work presents a framework for the semantic enrichment and miningof user profiles from usage data obtained from such applications, by incorporating entity
extraction, entity identification and topic detection techniques.

4

Data challenge

USEWOD 2011 presented a data challenge to stimulate the exchange of ideas and methods
between the workshop participants and to relieve some of the difficulties of obtaining real-lifeusage data. Three months before the workshop we released two datasets2 of server log files ofLinked Open Data servers: Semantic Web Dog Food (SWDF: http://data.semanticweb.
org) and DBpedia (http://dbpedia.org/). The DBpedia logs consist of several monthsof log data from the linked data twin of Wikipedia, one of the focal points of the Web ofdata. SWDF is a constantly growing dataset of publications, people, and organisations in
the Web and Semantic Web area, covering several of the major conferences and workshops,
including WWW, ISWC and ESWC, and more recently also the Dublin Core conference.
These datasets represent a new category of web usage data, namely usage of the Web ofData. Participants were invited to come up with analyses, applications, alignments, etc. forthese datasets.
The DBpedia logs consist of all requests to the DBpedia server made at 23 days between
2009/07/01 and 2010/02/01. The Semantic Web Dog Food logs comprise all requests between
2008/11/01 and 2010/12/14 (some days missing due to server outage). Both data sets consistof logs of human users as well as machines. They contain requests for both single RDF orHTML documents and SPARQL queries. Some statistical details of the datasets can befound in Tab. 1; much more extensive details about a previous version of the dataset canalso be found in [14]. Logs are anonymised by replacing the IP address fields with ‘0.0.0.0’.
A hash of the original IP is appended to the log entry to enable participants to identify which
requests came from the same requestors. To allow basic location-based analyses, a field withthe country code of the original IP has been appended to the log entry. The mapping from
IPs to country codes was done using the GeoLite Country API3 .
At the time of writing, the dataset has been requested and downloaded by 19 different
research groups all over the world. Eight papers about the challenge data were submitted to
the workshop, two of which were accepted for presentation at USEWOD.
Markus Kirchberg from HP Singapore presented a paper titled From Linked Data to
Relevant Data — Time is the Essence [11]. In this paper the authors aim to predict the2
3

Details about the datasets at http://data.semanticweb.org/usewod/2011/challenge.html
http://www.maxmind.com/app/geolitecountry

SourceTime period# DaysSize# Requests

DBpedia

2009/07/01–2010/02/01

SWDF

2008/11/01–2010/12/14

23(per day)
720(per day)

7.476 GB
0.325 GB2.173 GB
0.003 GB

19,770,157
859,572
8,092,552
11,240# SPARQL
requests
5,268,125
229,049
2,287,973
3,178

Table 1: Statistics of the USEWOD 2011 data set

relevance of Linked Data entities at a given moment in time. They present a novel method
combining link analysis and usage analysis to produce time-windowed visualisations of theusage of an entity. They identify three important properties for predicting the relevance of anentity: the number of links between the entity and other entities, the depth of the traversals,
and the number of times a path was accessed within a time window.
The second accepted paper that used the challenge data was by Mario Arias Gallego,
Javier D. Fernández, Miguel A. Martı́nez-Prieto, and Pablo de la Fuente: An Empirical
Study of Real-World SPARQL Queries [7]. The paper describes an in-depth analysis of
the SPARQL queries in the USEWOD data set. From the analysis, the authors conclude
that most SPARQL queries are simple and include few triple patterns and joins. The mostcommon join types are Subject-Subject, Subject-Object and Object-Object. The graphpatterns are usually star-shaped. Chain-shaped patterns are rare and generally very short:
98% of the queries had a length of only one triple pattern. The authors show how thisknowledge about real-life SPARQL queries can be applied for optimising the performance of
query evaluation engines and RDF stores.
Research using the USEWOD data sets mainly focussed on the analysis of the log data.
The underlying linked data was explored in much less detail. The challenge participants aswell as the audience were convinced that taking these data into account will bring usage
analysis to the next level.

5

Plenary discussion

During the plenary discussion we discussed the main obstacles holding back research in the
USEWOD area. One problem appeared to be dominant in this field: the absence of a sizeable
amount of publicly available usage data. Usage data that is used in research projects usuallycannot be shared outside these projects, which prohibits comparing results.
To make more log data available to the community, we need to convince organisations
that publish data online to 1) record logs and 2) publish them. Efforts made within the
Linked Data community can serve as an example as this community has succeeded in persuading many organisations to publish their data in the Linked Data cloud. For making
usage data available, the most promising organisations are non-profit organisations publishing information on non-privacy sensitive topics, for instance cultural heritage institutions.
The main incentive for these organisations is the perspective that research on usage data of asystem can yield insights that can help to improve the system. Once some organisations will
have been persuaded to published their logs, there may be enough momentum to convinceother organisations as well, as happened with Linked Open Data.
We also discussed issues related to the storage of usage data. RDF was suggested as

a data format. For anonymisation a standard tool, such as an Apache plug-in, should be
developed that allows organisations to anonymise the logs in-house in a standardised way. For
sharing log files a central logfile repository is needed. The USEWOD organisation committed
themselves to setting up such a repository.
The combination of usage data and the web of data provides a wide variety of unexploredresearch directions. For instance, participants mentioned that analysis of usage data can beenhanced by using location information. Usage data can also be used to detect errors in
the web of data or to identify concepts and entities that are no longer useful. Moreover, as
the web of data is growing ever larger, ranking entities by relevance becomes increasingly
important. Usage data can be a key feature in such relevance ranking.

6

Conclusions

The research presented in the USEWOD workshop as well as the lively discussion afterwards,
shows the importance and timeliness of research on the combination of usage data and theweb of data. Therefore, the organisers are planning a follow-up workshop next year. A newand extended dataset should be provided for a second data challenge, and as a resource forthe community. In the meantime, the current USEWOD data set will remain available. As
the absence of logs is viewed as the main problem for research in this area, the USEWOD
data set can make an important contribution to the field.

Acknowledgements We were fortunate to have a highly qualified program committee;
we thank them for their contribution to making the workshop a success. We would also liketo explicitly thank our keynote speaker Markus Strohmaier, as well as our two sponsors: theLATC project4 , which sponsored the best paper award, and Gridline5 , which sponsored the
USEWOD2011 data challenge.

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