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Linked Data and PageRank-based classification


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In this article, we would like to present new approach to classification with Linked Data and PageRank. Our research is focused on classification methods that are enhanced by semantic information. The semantic information can be obtained from ontology or from Linked Data. DBpedia was used as source of Linked Data in our case. Feature selection method is semantically based so features can be recognized by nonprofessional users because they are in a human readable and understandable form. PageRank is used during feature selection and generation phase for expansion of basic features into more general representatives. It means that feature selection and processing is based on a network relations obtained from Linked Data. The features can be used by standard classification algorithms. We will present the promising preliminary results that show the easy applicability of this approach to different datasets.
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Michal Nykl, Karel Ježek
Department of Computer Science and Engineering, Faculty of Applied Sciences, University of West Bohemia, Pilsen
Univerzitni 22, 306 14 Pilsen, Czech Republic
Martin Dostal, Dalibor Fiala
NTIS, Faculty of Applied Sciences, University of West Bohemia, Pilsen
Univerzitni 8, 306 14 Pilsen, Czech Republic
This is preprint version, original version is:
NYKL, Michal, JEŽEK, Karel, DOSTAL, Martin and FIALA, Dalibor. Linked Data and PageRank based classification.
In: IADIS International Conference Theory and Practice in Modern Computing 2013 (part of MCCSIS 2013).
Praha: IADIS Press, 2013, pp. 61-64. ISBN: 978-972-8939-94-6.
In this article, we would like to present new approach to classification with Linked Data and PageRank. Our research
is focused on classification methods that are enhanced by semantic information. The semantic information can be
obtained from ontology or from Linked Data. DBpedia was used as source of Linked Data in our case. Feature selection
method is semantically based so features can be recognized by nonprofessional users because they are in a human
readable and understandable form. PageRank is used during feature selection and generation phase for expansion of basic
features into more general representatives. It means that feature selection and processing is based on a network relations
obtained from Linked Data. The features can be used by standard classification algorithms. We will present the promising
preliminary results that show the easy applicability of this approach to different datasets.
Linked Data, PageRank, classification, feature selection
Document classification is an important part of document management systems and other text processing
services. Today`s methods are usually statistically oriented that means a big amount of data is required for
training phase of these classification algorithms. The preparation of sufficient classification training sets and
proper feature selection methods is challenging task even for domain specialists. So the common solution of
this problem is based on relatively comprehensive corpuses that contain a lot of documents divided into
different classification classes. Statistical methods are trying to discover relations between terms
and classification classes during training phase.
Our approach recognizes interesting keywords and expands them using semantic information obtained
from Linked Data. For example based on a feature MySql we can do the feature expansion into databases
without explicit occurrence of this word in document content. The classification phase can process these
parent concepts and use them for correct pairing of documents and classification classes.
Next, we will explain basic principles of Linked Data and introduce the primary variant of PageRank that
will be used in next sections. Related work is discussed in Section 2 and our approach to feature selection is
described in Section 3. Preliminary evaluation of our method was performed with 20 News Groups dataset.
The results are presented in Section 4.
1.1 Linked Data and PageRank
The concept of Linked Data was first introduced by Tim Berners-Lee (Berners-Lee, 2006). He formulated
four rules for machine readable content on the Web:
Use URIs as names for things.
Use HTTP URIs so that people can look up those names.
When someone looks up a URI, provide useful information using the standards
Include links to other URIs so that they can discover more things.
More specific is the idea of Linked Open Data, which is based on the presumption of freely published
data without restrictions in usage or additional fees.
The PageRank algorithm was developed in 1998 by Page and Brine (Brine, 1998) as approach to web
pages ranking through exploring hyperlinks structure on the Internet. The importance of each web page
depends on the number and PageRank value of all web pages that link to it. This approach also known
as “Random surfer walking has been studied and improved for citation analysis (Ma, 2008). Our modified
version of PageRank (1) corresponds to its matrix definition (Langville, 2006) where Px(a) is a value of node
a in iteration x, d is a damping factor usually set to 0.85, V is a set of all nodes in the graph, U is a set of
nodes with link to node a, D is a set of all dangling nodes and wij is weight of link from node i to node j.
   
 
 (1)
Document classification can be defined as content-based assignment of one or more predefined categories
(classification classes) to documents. We can distinguish two phases in document classification processing,
the learning phase and the classification phase. In the learning phase user define categories by giving training
documents for each of these categories. Quality improves with increasing number of training documents.
This is a weak point of document classification because the solid training collection is required.
There have been many supervised learning techniques for document classification. Some of these
techniques include Naive Bayes, k-nearest neighbor, vector approaches e.g. Rochio, support vector machines,
boosting (Schapire, 1999), rule learning algorithms (Cohen, 1996), Maximum Entropy and Latent semantic
DBPedia was used as a source of Linked Data presented in this article. We use a local copy of Linked
Data stored in our relation database for performance purposes, but SPARQL endpoint could also be used.
DBPedia is semantically enriched Wikipedia that was successfully employed previously for computing
semantic relatedness of documents. WikiRelate! (Strube, 2006) combines path based measures, information
content based measures, and text overlap based measures. Explicit Semantic Analysis (Gabrilovich, 2007)
uses machine learning techniques to explicitly represent the meaning of a text as a weighted vector
of Wikipedia-based concepts.
Another approach to document classification (Wang, 2005) proposed term graph model as improved
version of the vector space model. The aim of this model is to represent the content of a document with
relationship between keywords. This model enables to define similarity functions and PageRank-style
algorithm. The vectors of PageRank score values were created for each document. The rank correlation and
the term distance were used as a similarity measures to assign document to classification class. An alternative
approach to document classification uses hypernyms and other directly related concepts (Bloehdorn, 2004;
Ramakrishnanan, 2003). Next step in document classification can be marked as feature expansion with
additional semantic information from ontology (De Melo, 2007). This approach (De Melo, 2007)
is exploiting the external knowledge for mapping terms to regions of concepts. For exploration of related
concepts, the traversal graph algorithm is used.
Feature selection is the most important part of our approach. This method and its connection with Linked
Data and PageRank consist of following steps:
1. Basic features are selected from documents on the base of the TFIDF (other methods, e.g.
2, can be
used too).
2. The features of each document are mapped to Linked Data nodes (identified with URI).
The mapping is based on the full or partial compliance between feature (term) and name of the node
in a corresponding language. This enables to form the first version of the graph.
3. One step expansion of the graph by Linked Data is executed. The Graph expansion illustrates
Fig. 1, where the original (basic) nodes are marked as I and the expanded nodes are marked as II.
The weights of all links are assigned. Variants of weighting see below (Fig. 2).
4. PageRank algorithm is applied to this graph.
5. If at least one of the last added nodes have a higher PageRank score, than any of the older nodes we
continue with step 3, otherwise the nodes with the highest scores represent the selected features.
Figure 1. Graph expansion with PageRank
We have investigated three options for initialization of the PageRank algorithm (see Fig. 2), where the
steps of node expansion in a graph are marked with I, II and III:
a) All edges are assigned the same weight equal to 1.
b) The basic nodes are advantaged with self-citation edge with increased weight.
c) The basic nodes are advantaged with self-citation edge. The edges to the new nodes are
penalized base on the quadratic distance of path from the basic node.
Our evaluation of this three possibilities shows, that the variant c) achieves the best results due to
effective limitation in expansion of the basic nodes. The other versions requires explicit limiting criterion for
graph expansion. In the next section, this variant was used for evaluation of our feature selection approach.
Figure 2. Initialization of the PageRank
For evaluation purposes the subset of 20 News groups collection was used. The reason was that our source of
Linked Data was not sufficient to distinguish between similar categories in 20 News groups collection like and Another problem was overtraining that occurs with
approximately 100 training documents for 1 category.
Our approach will be completely based on the path length in a graph of nodes from Linked Data. But for
preliminary results and evaluation purposes, we decided to compare our feature selection method and
standard statistical approach with the same vector space classification algorithm (Rocchio). The comparison
(see Fig. 3) is done with macro-averaging F1(β=1) measure. The number of testing documents is determined
as 20% of training documents.
Figure 3. Graph F1 measure for Rocchio classification algorithm
Our method for document classification with Linked Data is promising especially in tasks with inadequate
training sets or for quick filtering of existing documents. In those cases the training phase could be very
expensive and inappropriate waste of time for user. Our method allows the definition of assigning categories
using only a single node from Linked Data with automatic expansion on both sites category definition and
feature selection. In the future, we would like to eliminate the overtraining problem and we would like to
create a solid method for document classification directly based on the graph analysis.
This work was supported by the grants GAČR P103/11/1489 and NTIS CZ.1.05/1.1.00/02.0090.
Berners-Lee, T., 2006. URL:, date: 2006-07-27, cited: 2013/01/12.
Bloedhorn, S. and Hotho, A., 2004. Boosting for Text Classification with Semantic Features. WebKDD’04. Seattle, USA.
Brine, S. and Page, L., 1998. The anatomy of a large-scale hypertextual Web search engine. Computer Networks and
ISDN Systems, Vol. 30, Issues 1-7, pp 107-117.
Cohen, W. and Singer Y., 1996. Context-sensitive learning methods for text categorization. ACM SIGIR ’96.
De Melo, G. and Siersdorfer, S., 2007. Multilingual text classification using ontologies. 29th EC on IR, Rome, Italy.
Gabrilovich, E. et al, 2007. Computing semantic relatedness using Wikipedia-based explicit semantic analysis.
Langville, A.N. et al., 2006. Google’s PageRank and Beyond: The Science of Search Engine Ranking. USA.
Ma, N. et al, 2008. Bringing PageRank to the citation analysis. Information Processing & Management, 44, 800810.
Schapire R. and Singer Y., 1999. BoosTexter: A boosting-based system for text categorization. Machine Learning.
Strube, M. and Ponzetto, S.P., 2006. WikiRelate! Computing semantic relatedness using Wikipedia. AAAI’06, USA.
Wang, W. et al, 2005. Term Graph Model for Text Classification. ADMA’05. Wuhan, China, pp. 19-30.
... In study [8], the PageRank algorithm is modified to correspond to the matrix of the graph where Px(a) is a value of node a in iteration x, d is a damping factor (for web graph orginally set to about 0.85), V is the vertex set whose nodes in the graph, U is a set of nodes with link to node a, D symbolize the set of all dangling nodes and w ij is link weight for each lin k ...
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URL:, date
  • T Berners-Lee
Berners-Lee, T., 2006. URL:, date: 2006-07-27, cited: 2013/01/12.