Generating LOD from Web: A Case Study on
Building Integrated Museum Collection Data
Fuyuko Matsumura1 , Fumihiro Kato2 , Tetsuro Kamura3,4 ,Ikki Ohmukai1,3 , and Hideaki Takeda1,3
12National Institute of Informatics, {fuyuko, i2k, takeda}@nii.ac.jp
Research Organization of Information and Systems, fumi@nii.ac.jp
3
The Graduate University for Advanced Studies
4
Tokyo University of the Arts, kamura.tetsuro@noc.geidai.ac.jp

Abstract. In this paper, a workflow was developed to enable eﬃcient
data extraction from web and integration them with the cooperation ofweb developers and data professionals who specialized in a certain field.
This paper introduces how we applied the workflow to build Linked Data
for “LODAC Museum”, a dataset on museum collection data in Japan.

1Introduction

The Linked Open Data for ACademia (LODAC)1 project has started to integrate
and publish academic information of Japan as Linked Open Data (LOD) datasets
to enhance interdisciplinary sharing and reuse of various datasets. Linked Data
Integration Framework (LDIF)[2] aims to automatically integrate and map datato common vocabularies using RDF data included in websites; however, most ofwebsites do not have RDF data and how to generate LOD from websites written
in HTML is important to increase useful LOD. Therefore, a workflow to generate
LOD from web is developed in this paper. It is characterized by separating of
web programming and metadata mapping. This paper introduces the workflow
to generate LOD consisting of key-value pairs taking “LODAC Museum”[1], adataset on museum collection data in Japan as an example.

2The Workflow for Generation of Linked Data onMuseum Collection

Museum collection data are transformed into LODAC Museum by the followingsteps. The original collection data are obtained by web scraping for museumsites and translated into Resource Description Framework (RDF).
1. Extracting data from web pages: Collect key-value pairs data from web pagesof artworks in multiple sources using Apache Nutch and Solr.
2. Mapping vocabularies: Map keys in extracted data to a common schema bymuseum professionals.
3. Integrating unique items: Identify the same items (artwork, creator, museum)across museum collections and associate them to single identifiers.
1

http://lod.ac

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234. Publishing: Publish data as Linked Data with permalinks that work as identifiers for items, accessible through the SPARQL endpoint.
5. Versioning: Store snapshots of crawled original HTML files, extracted keyvalue pairs, and transformed RDF files into the git repository to enable totrace the process of data transformation and retry data generation fromoriginal files.
The main feature of our system is that the key-value pairs extraction frommuseum websites and metadata mapping are completely divided and can be
independently processed, because it is assumed that two diﬀerent types of professionals are needed. Prior to the data extraction from HTML, it is needed tofind parts which include key-value pairs from HTML and write XPath for theparts into the setting files of Nutch by web developers. On the other hand, when
extracted key-value pairs are mapped to RDF, it is desired that the professionals
in the target domain handle metadata mapping because vocabularies and sentences of websites are possibly quite hard to understand for people who are notmuseum professionals.
When the construction of LODAC Museum was started, this workflow had
not implemented yet and scraped data were manually transformed into RDF inprograms. After the workflow was implemented, it additionally extracted 578,500
key-value pairs from websites of 38 institutions. Since some of them were mapped
to multiple properties, those data were converted into 890,588 RDF triples according to the mapping rule, and appended to the RDF store.

3Discussion

Some methods have been proposed to generate RDF from websites and some ofthem are trying to automatically extract desired information from websites usingmachine learning techniques. However, expressions of collection data are diﬀerent
between each website of museums in Japan. For instance, when collection data
is expressed as a table, some sites display the creator’s name with the title of awork in the same cell while others display the birth year with the title. Moreover,each museum uses diﬀerent terms to describe each attribute. Therefore, it seems
suitable that museum professionals manually implement mapping rules and thosegenerated rules can also be used as training data for automatic extraction of
museum collection data from websites in the future.
Furthermore, we plan to develop a user interface for metadata mapping andsemi-automatic data integration based on text matching; moreover, further verification of our data conversion system are needed using other datasets.

References
1. Kamura, T., Takeda, H., Ohmukai, I., Kato, F., Takahashi, T., and Ueda, H.: Study Support andIntegration of Cultural Information Resources with Linked Data, Proc. of the 2nd International
Conference on Culture and Computing, pp.177–178 (2011)
2. Schultz, A., Matteini, A., Isele, R., Bizer, C. and Becker, C.: LDIF - Linked Data Integration
Framework, Proc. of the 2nd International Workshop on Consuming Linked Data (COLD2011)(2011)

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