=Paper=
{{Paper
|id=None
|storemode=property
|title=R3 - A Related Resource Recommender
|pdfUrl=https://ceur-ws.org/Vol-585/paper4.pdf
|volume=Vol-585
}}
==R3 - A Related Resource Recommender==
https://ceur-ws.org/Vol-585/paper4.pdf
1st International Workshop on Adaptation, Personalization and REcommendation in the Social-semantic Web (APRESW 2010)
R3 - A Related Resource Recommender
Thomas Kurz, Tobias Bürger and Rolf Sint
Salzburg Research Forschungsgesellschaft
Jakob Haringer Str. 5/3, 5020 Salzburg, Austria
firstname.lastname@salzburgresearch.at
Abstract. Due to the ever growing amount of content in the Web of
Data, the retrieval of relevant information is challenging. Currently, effi-
cient resource recommendation methods are lacking, that could ease the
exploration of data in the Web of Data. To alleviate this situation, this
paper proposes the R3 resource recommendation framework for retrieval
of data in the Linked Open Data (LOD) cloud. It analyses relevant search
engines and interlinking frameworks and, based on that, proposes the R3
framework which is illustrated both in theoretical and practical details.
The framework enables the recommendation of (RDF) resources from
the LOD cloud based on textual, structural, or semantic similarity.
1 Introduction
The goal of Linking Open Data (LOD) community is to bootstrap the Seman-
tic Web (the “Web of Data”) by publishing and interconnecting datasets using
RDF[1]. The outcome of this movement is the so called LOD cloud which grew
to 13.1 billion triples and 142 million RDF links in the last two years and it is
still growing [2].
As within the traditional, document-centric Web, search and retrieval of infor-
mation is of utmost importance. Similarly, a big challenge for a specific end user
or application, operating on the Web of Data, is to find relevant data that serves
their specific needs. Despite the fact, that Linked Data browsers and search en-
gines are available to explore content in the LOD cloud, means to issue complex
queries by ordinary users or to recommend content in the cloud based on par-
ticular interests, are currently lacking. In case a user is searching for the city
of Berlin using a LOD search engine, he is able to retrieve resources with many
properties such as their names, descriptions, latitude, longitude, or density of
population. If she now would like to retrieve related resources such as a ranked
list of cities ordered by geographical distance and/or density of population or
resources with similar structure (like countries or provinces) ranked on the se-
mantic similarity of their textual description, she will fail with current search
engines. Similarly the recommendation of related resources could allow the user
to issue a “Query by Example” by defining some kind of a fake-resource and use
it as query base, which would be a novel form for searching the Web of Data.
In order to alleviate this situation, this paper investigates the state of the art
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in LOD search engines and interlinking frameworks (Section 2) and, based on
that, proposes the R3 resource recommendation framework that is capable of
recommending data from the LOD cloud based on the semantic, structural, or
textual similarity of given resources. The framework allows to query for related
things in the LOD cloud based on a given resource and is illustrated including its
requirements, conceptual architecture, and implementation aspects (Section 3).
Finally, details are given on how to further advance and implement the frame-
work (Section 4).
2 Resource Discovery and Interlinking in the LOD Cloud
There are some applications on the web, which allow the user to search or browse
the web of data. Supplementary to that there are so called Interlinking Frame-
works that can be used to check the resources of two or more different datasets
pairwise for similarity. Because of the analogies to our approach these frame-
works should also be considered in the following discussion.
2.1 Browsers and Search Engines
Sindice1 , as described in [3], is a scalable index of the Semantic Web. It crawls
the Web for RDF Documents and Microformats and indexes resulting resource
URIs, Inverse Functional Properties (IFPs) and keywords. A human user can
access these documents through a simple user interface, based on indexes men-
tioned above.
Sigma2 is rather a semantic information mashup enabled by Sindice than a
self-contained semantic search service. Nevertheless it enriches a lot of its func-
tionalities with some nice additional features. It works as Web of Data browser
where the user can start from any entity (found by a fulltext search) and then
browse to the resulting page. The resources index is build out of from sites which
use RDF, RDFa or Microformats.
The Open Link Search3 will list entities with a user-defined text pattern occur-
ring in any literal property value or label. It also supports Entity URI lookup.
The Search can be redefined by filtering type, property value, etc.
It is also possible to execute SPARQL queries by using the SPARQL endpoint.
Some demo queries are predefined and can easily be altered via text input fields.
Falcons4 is described in [5] as a service for searching and browsing entities on the
Semantic Web. It is a keyword-based search engine for the Semantic Web URIs
and provides different query types for object, concept and document search.
Falcons also gives the facility of facetting over types by dynamically recommend-
ing ontologies. The recommendation is based on a combination of the TF-IDF
technique and the popularity of ontologies.
1
http://sindice.com/
2
http://sig.ma/
3
http://lod.openlinksw.com/
4
http://iws.seu.edu.cn/services/falcons/objectsearch/index.jsp
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Watson5 offers keyword based querying to obtain a URI-list of semantic doc-
uments in which the keywords appear as identifiers or in literals of classes, prop-
erties, and individual. Search options make it possible to restrict the search space
to particular types of entities (classes, properties or individuals) and to partic-
ular elements within the entities (e.g. local name, label, comment).
SWSE6 is a search engine for the RDF Web. Similar search engines currently
provided for the HTML Web it looks like a ordinary fulltext search. But the
information retrieval capabilities of SWSE are much more powerful because of
the inherent semantics of RDF and other Semantic Web languages.
Swoogle7 allows a user to search through ontologies, instance data, and terms
of the Semantic Web. Furthermore it supports browsing the Web of Data. This
search engine also uses an archive functionality to identify and provide different
versions of Semantic Web documents.
Like described above, each considered semantic search service provides a cer-
tain amount of functionalities. Some of them are part of two or more services,
others are exclusive to one certain engine. Though it is possible to search for
appearance of a given resource in some of them, neither it is possible to find re-
lated resources for a resource and its RDF triples nor to define on which triples
the relationship should be calculated on. Also the search engines do not consider
a semantic similarity of queries and content, which definitely could increase the
quality of result. But there are applications in the area of Semantic Web which
match some of these requirements in certain ways - the interlinking frameworks.
2.2 Interlinking frameworks
Interlinking frameworks for semantic web data try to detect related and link
resources in different datasets. In [8] several frameworks are compared to each
other concerning their functionalities, which brings us to the decision that the
Silk8 approach is rather related to our goals.
Silk[7] is a framework for detecting explicit RDF links between data items within
different data sources. Using the declarative Silk - Link Specification Language
(Silk-LSL), developers can specify which types of RDF links should be discovered
between data sources and, based on arbitrary metrics and aggregation functions,
which resources should be declared as related. Silk accesses the interlinking can-
didates via the SPARQL protocol.
The usage of different metrics and aggregation functions for different types of
properties can be adopted to our resource recommender. In addition we can
remodel Silk-LSL in some ways (e.g. alternative metrics) and use it as query
syntax. This language makes it also possible to define the appropriated data-
sources by query.
5
http://kmi-web05.open.ac.uk/WatsonWUI/
6
http://swse.deri.org/
7
http://swoogle.umbc.edu/
8
http://www4.wiwiss.fu-berlin.de/bizer/silk/spec/
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3 R3 - A Conceptual Overview
Our intent is to build a recommender service, which allows to query for related
resources from various (predefined) datasources based on a given resource. But
what is relatedness, what factors have an impact on it and how can we implement
such a recommender service? This is discussed in the following sections.
3.1 Requirements
In case of RDF resources there are various factors which define relatedness. On
the one hand the RDF structure itself (predicates and non-literal objects) reveals
something about how similar two resources are. On the other hand the literal
properties can be compared according to their types towards different metrics.
That can be simple ones like euclidean metric for numbers, or more complex like
semantic similarity of texts. A user should be able to specify the factors that
are used to find relevant related resources, and also its impact on the result. In
addition to that the whole recommendation process should be calculated in an
adequate time. So we can specify requirements below:
1. Recommend related resources from the LOD cloud based on a given RDF
resource.
2. Consider semantic similarity of texts and structural similarity of resources.
3. Offer a comparison mechanism for literals with adjustable metrics.
4. Allow user defined feature boost; that means a certain feature (e.g. property
x or structure) has a higher relevance on relatedness than others.
5. Return related resources ordered by relevance.
3.2 Conceptual Architecture
The concept to fulfill these requirements is illustrated in Figure 1. The data must
be fetched from the LOD cloud, combined and indexed; it should be queryable
via a specific search syntax. This process is described more precisely in this sec-
tion.
Data Consolidation
The service gets recommendable resources out of the Linked Data Cloud. Since
it should possible, to build a multi-source index, there must be a kind of ontology
alignment. Thus preprocessed data is stored directly into the index. The single
datasources must be reindexed in given time intervals.
Resource Recommender Index
A core index can provide lot of metrics like euclidean distance, date similarity,
string equality, etc. Semantic similarity which can be used to evaluate the se-
mantic distance of texts and RDF structures is more complex, therefore we need
a supplemental semantic index. Semantic textual indices (one for each defined
property) as well as the semantic structure index (one for the whole dataset) are
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Fig. 1: Design and workflow of R3
build out of the core index.
Resource Query
To get recommended resources based on a given one, the recommender provides
a query language, whereby the user can specify, which features should be in-
cluded in the calculation according to which metric. Furthermore the factor how
intensive a specific feature impacts the result and how the diverse values are
combined is configurable by query. To restrict the set of base resources the user
can define the included datasets. The searchresult is list of resources ranked by
relevance.
3.3 Implementation
Datasets, which build our resources base is taken out from the LOD cloud via
SPARQL. To map different resources from sources we use a simple mapping ta-
ble. Complex ontology matching strategies like in [9] are also possible.
Because of its high scalability, its fast query processing and the possibility to
use integrated functions and numerical as well as token-based comparison, we
decided to use SOLR9 as our index base. A lot of metrics like euclidean distance,
date similarity, string equality, etc. are provided by or can be directly integrated
into SOLR index. As described, for more complex metrics we need supplemental
semantic indices build out of the SOLR index.
Text-based Semantic Index
A potential semantic index can be a Semantic Vector Index. This approach bases
upon the Vector Space Model wherein every document is represented as a vec-
tor in an n-dimensional term space according to appearing terms. The Semantic
9
http://lucene.apache.org/solr/
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Vector Package10 is able to build such an Index (which can be queried for se-
mantic related documents) out of the basic Lucene Index.
Structure-based Semantic Index
The semantic vector index can also be used to index the semantic similarity of
RDF structures. Therefore not every word or text module is integrated in the
term model but the URI, RDF predicates and non-literal objects of a resource.
Figure 2 shows the semantic similarity of a subset of dbpedia resources. To illus-
trate this semantic space we build a structure distance matrix of this resources
and scaled it to two dimensions using classical multidimensional scaling (MDS)
offered by the R statistics software11 . We highlighted resources of different types
which shows that related resources have a similar RDF structure.
Fig. 2: Evaluation of Structure Index
Query Language
As mentioned, the SILK Link Specification Language12 can be used as inspira-
tion for a query format that fulfills our query requirements and allows to specify
the basic resource (set of RDF triples or URI), the considered datasets (SPARQL
endpoints used from data consolidator), relevant features and its impact and the
applied metrics (taken from a fix set). Figure 3 shows an simple query example.
4 Further Work
In this paper we described the conceptual architecture of a resource recommen-
dation framework for the Semantic Web. Our future work includes the implemen-
tation of this concept and a practical evaluation with real datasets. In a further
step we plan to optimize the Semantic Vector package, which is used in one core
10
http://code.google.com/p/semanticvectors/
11
http://www.r-project.org/
12
http://www4.wiwiss.fu-berlin.de/bizer/silk/spec/#specification
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Fig. 3: Sample for a Recommander Query
component of the framework, to enhance its scalability and performance. The
resulting recommender will be integrated into the KiWi13 system.
References
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13
http://kiwi-project.eu/
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