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Afzal et al. Journal of Biomedical Semantics 2011, 2(Suppl 1):S4
http://www.jbiomedsem.com/content/2/S1/S4

RESEARCH

JOURNAL OF
BIOMEDICAL SEMANTICS

Open Access

Mining semantic networks of bioinformatics
e-resources from the literature
Hammad Afzal2,3, James Eales1, Robert Stevens1, Goran Nenadic1*
From Semantic Web Applications and Tools for Life Sciences (SWAT4LS), 2009
Amsterdam, The Netherlands. 20 November 2009
* Correspondence: G.
Nenadic@manchester.ac.uk
1
School of Computer Science,
University of Manchester, Oxford
Road, Manchester, M13 9PL, UK
Full list of author information is
available at the end of the article

Abstract
Background: There have been a number of recent efforts (e.g. BioCatalogue,
BioMoby) to systematically catalogue bioinformatics tools, services and datasets.
These efforts rely on manual curation, making it difficult to cope with the huge influx
of various electronic resources that have been provided by the bioinformatics
community. We present a text mining approach that utilises the literature to
automatically extract descriptions and semantically profile bioinformatics resources to
make them available for resource discovery and exploration through semantic
networks that contain related resources.
Results: The method identifies the mentions of resources in the literature and
assigns a set of co-occurring terminological entities (descriptors) to represent them.
We have processed 2,691 full-text bioinformatics articles and extracted profiles of
12,452 resources containing associated descriptors with binary and tf*idf weights.
Since such representations are typically sparse (on average 13.77 features per
resource), we used lexical kernel metrics to identify semantically related resources via
descriptor smoothing. Resources are then clustered or linked into semantic networks,
providing the users (bioinformaticians, curators and service/tool crawlers) with a
possibility to explore algorithms, tools, services and datasets based on their
relatedness. Manual exploration of links between a set of 18 well-known
bioinformatics resources suggests that the method was able to identify and group
semantically related entities.
Conclusions: The results have shown that the method can reconstruct interesting
functional links between resources (e.g. linking data types and algorithms), in
particular when tf*idf-like weights are used for profiling. This demonstrates the
potential of combining literature mining and simple lexical kernel methods to model
relatedness between resource descriptors in particular when there are few features,
thus potentially improving the resource description, discovery and exploration
process. The resource profiles are available at http://gnode1.mib.man.ac.uk/bioinf/
semnets.html.

© 2011 Afzal et al; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons
Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in
any medium, provided the original work is properly cited.

Afzal et al. Journal of Biomedical Semantics 2011, 2(Suppl 1):S4
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Background
The rapid increase in the amount of bioinformatics data produced in recent years has
resulted in the huge influx of bioinformatics electronic resources (e-resources), such as
online-databases [1], data-analysis tools [2], Web services [3] etc. Still, many users rely
on a limited set of tools that have been used and developed locally in their groups or
by their collaborators, since discovering and using new resources became one of the
major issues and bottlenecks in bioinformatics. Therefore, a number of communitywide efforts such as BioCatalogue [4] and BioMoby [5] have been initiated to systematically catalogue the “bioinformatics resourceome”. By collecting and annotating
resources using keywords and ontological concepts, such catalogues facilitate access to
both bioinformaticians and Semantic Web crawlers and agents that can orchestrate
their use. However, the annotation process depends on a typically slow manual curation process that hinders the growth of such curated resources to keep pace with the
very field they attempt to catalogue. For instance, the number of registered services in
BioCatalogue (there were more than 1,600 of them as of March 2010) is lagging behind
the total number of Web services available online: it is estimated that there are ~3500
life science Web services in Taverna alone [6]. This fact calls for the development of
semi-automatic methods for resource annotation and their cataloguing in order to
maximise the utility of e-resources by making them widely available to the community.
One of the key aims of providing bioinformatics resources with semantic descriptions
is to improve resource discovery. Semantically-described resources can not only be
searched, browsed and discovered by using keyword-based queries (for instance, via
their names or task descriptions), but also on the basis of the semantic relatedness of
their functionalities or their input/output parameters. For example, a user can search
for a Web service that corresponds to a particular input, output or operation performed. If, however, the retrieved services do not fulfill the exact requirement or are
not available, the user may explore similar services (for example, with more generic/
specific input/output, but still with a related functionality). This process has been
facilitated by concept-based annotations using domain ontologies that have been used
to annotate the resources (as in myGrid [7] and BioCatalogue). Descriptions of services
may also have “pre-computed” similar services (see Figure 1) so that the users can
identify them without additional searches.
When manually assigned annotation tags and/or related resources are not available,
we hypothesise that automated approaches could be used to improve the discovery
process by generating semantic networks and clusters of similar bioinformatics
resources. In this paper we propose a methodology to automatically build such networks from the literature. In our previous work, we have shown that the vast amounts
of scientific literature related to bioinformatics resources can be tapped in order to
automatically extract their key semantic functional features [8]. Here we do not aim to
fully characterise resources (e.g. as presented in BioCatalogue), but rather to extract
their descriptors that can be used to semantically link related instances. Traditionally,
similar or related instances have been identified by using lexical comparisons of their
names and names of their parameters (input/output) and operations. Such approaches
rely on authors using similar vocabularies to name operations, parameters and messages. In order to measure semantic relatedness, in this paper we present a kernelbased similarity approach that uses lexical and semantic properties of resource

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Figure 1 A snapshot of a Web service description taken from BioCatalogue.

mentions as extracted from the literature. Finally, we do not aim to characterise the
quality or provenance of resources: the aim is to provide an exploration space for the
users to discover related resources.

Methods
The methodology is based on three concepts: mentions of bioinformatics resources,
semantic resource descriptors, and similarity functions. Bioinformatics resources represent e-resources that are used by bioinformaticians while performing in-silico experiments [9]. Mentions of bioinformatics resources are identified in the literature using
term identification [8]; their semantic profiles comprising semantic descriptors are also
generated from the literature [8,10]; finally, the resources are inter-connected with
each other on the basis of similarity between their semantic profiles that is measured
using various similarity metrics.
Identification of bioinformatics resources in text

We have focused on the four major classes of resources: Algorithms, Applications,
Data and Data Resources. These have been engineered from the myGrid ontology [11].
Table 1 shows example resource instances belonging to these classes.
In our previous work, we have described a set of text mining tools that can be used
to identify, classify and extract mentions of e-resources in the literature [8]. The
method is based on identification of key terminological heads assigned to each of the
semantic classes (e.g. alignment and method are “linked” to Algorithms, while sequence
and record point to a Data entity) and specific lexico-syntactic patterns (enumerations,
coordination, etc.) in which such instances occur.

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Table 1 Examples of semantic classes and their instances
Semantic
class

Example instances

Algorithm

SigCalc algorithm, CHAOS local alignment, SNP analysis, KEGG Genome-based approach,
GeneMark method, K-fold cross validation procedure

Application

PreBIND Searcher program, Apollo2Go Web Service, FLIP application, Apollo Genome Annotation
curation tool, GenePix software, Pegasys system

Data
Data
resource

GeneBank record, Genome Microbial CoDing sequences, Drug Data report
PIR Protein Information Resource, BIND database, TIGR dataset, BioMOBY Public Code repository

Harvesting semantic descriptors

Semantic resource descriptors are the key terminological phrases used in the existing
textual descriptions of bioinformatics resources to refer to concepts and specific roles
(e.g. input/output parameters, etc). These have been used in the existing resource
descriptions (BioCatalogue, BioMoby, EBI web services [12], etc.) to denote functionalities, dependencies, input/output constraints, etc. For example, frequent descriptors are
gene expression, phylogenetic tree, microarray experiment, hierarchical clustering, amino
acid sequence, motif, etc. We use such descriptors to profile a given resource (see
below). Two sources were combined to build a dictionary of bioinformatics resource
descriptors. The first source is the list of terms collected from the bioinformatics
ontology used in the myGrid project. This list contains 443 terms describing concepts
in informatics (the key concepts of data, data structures, databases and metadata);
bioinformatics (domain-specific data sources e.g. model organism sequencing databases,
and domain-specific algorithms for searching and analysing data e.g. a sequence alignment algorithm); molecular biology (higher level concepts used to describe bioinformatics data types, used as inputs and outputs in services e.g. protein sequence, nucleic
acid sequence); and tasks (generic tasks a service operation can perform e.g. retrieving,
displaying, aligning). The second source includes automatically extracted terms (recognised by the TerMine service [13]) and frequent noun phrases obtained from existing
descriptions of bioinformatics Web resources available from BioCatalogue.
Semantic profiling of resources

For each bioinformatics resource that is identified in the literature, we build its semantic
profile by harvesting all descriptors that co-occur with the resource in the same sentence
in a given corpus (see Figure 2 for an example). These profiles are used to establish
semantic similarities between resources by comparing the descriptors (used as features)
that have been assigned to them. We note that descriptors do not represent a comprehensive description of a resource, but rather an approximation extracted from the literature.
Some of these descriptors may be generic (e.g. gene) and some very specific (e.g. DDBJ).
Linking semantically related resources

We explored three methods to link semantically related resources. The first approach
is based on lexical similarity between resource names (Method 1). The second
approach takes into account the number of shared descriptors between resources
(Method 2). However, resource representations using descriptors can be sparse
(an average number of descriptors per resource is 13.77, see Table 2 in Results),
in particular given a high number of potential descriptors (see Results). This suggests

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Kyoto Encyclopaedia of Genes and Genomes (KEGG)

Semantic Descriptors
data | database | DDBJ | EBI | enzyme | GenBank |
Gene Ontology | gene | genome | Kyoto Encyclopedia | microarray data |
pathway | protein | transcription factor | protein-protein interaction |
UniProt

Figure 2 Sample of semantic resource descriptors for the Kyoto Encyclopaedia of Genes and Genomes
(KEGG).

that it is not likely that many resources will share exactly the same descriptors. We
therefore use a third approach that introduces lexical smoothing of descriptors
(Method 3).
Method 1: lexical comparison of resource names

This method relies on lexical word-based similarity between resource names. We use
the concept of lexical profiles to estimate similarity. The lexical profile of a term comprises all possible linear combinations of word-level substrings present in that term
[14]. For example, the lexical profile of term ‘protein sequence alignment’ comprises
the following terms protein, sequence, alignment, protein sequence,sequence alignment,
protein sequence alignment. The similarity between two resources is then calculated as
a similarity between lexical profiles of their names. Formally, let LP(s1) and LP(s2) be
lexical profiles (represented as vectors) of names of resources s1 and s2. Then the similarity function is defined as a cosine [15,16] between vectors LP(s1) and LP(s2):
Sim1(s1 , s2 ) =

< LP(s1), LP( s2 ) >
LP(s1) ⋅ LP( s2 )

(1)

where < x, y> is the inner product between vectors x and y , and |x| is the norm of x.
We note that resources that share longer substrings will have a higher similarity value.

Table 2 The statistics of bioinformatics e-resources found in the BMC Bioinformatics
corpus
Semantic Class

Total number of instances

Average number of descriptors

Algorithm

5,722

11.47

Application

2,076

10.38

Data

2,662

18.77

Data Resource

1,992

12.94

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Method 2: shared descriptors

Here we use the standard bag-of-descriptors approach, where each resource is represented as a bag of its descriptors and the similarity is based on exact matches between
them. This method compares the resources using the inner product that measures the
degree of descriptor sharing [14,15]:
Sim2(s1 , s2 ) =

< s1 , s2 >
| s1 | ⋅ |s2 |

(2)

where s1 and s2 are binary profile vectors that represent the semantic descriptors
assigned to the resources being compared. Instead of binary weights (descriptors is/is
not present in a resource’s profile), we can use a variant of term frequency – inverse
document frequency (tf-*idf) weights. tf*idf is a statistical measure that is used to measure the importance of a term (or word) in a document as compared to whole collection of documents [15,16]. Here we use it to estimate how important and
discriminative a given descriptor is for a given resource. We combine the relative frequency of co-occurrence of the descriptor and resource, and the inverse frequency of
the descriptor with regard to all resources:
tf*idf(d , s) =

freq(d , s)

∑ freq(d , s)
i

⋅ log

total _ number _ of _ resources
number _ of _ resources _ that _ have _ d

(3)

where d is a descriptor and s is a resource. Although the frequency of a common
descriptor may be high, its tf*idf would be counter-balanced if it appears with a number of different resources. Each resource vector in this case comprises the tf*idf
weights for all the descriptors that appear with the resource.
Method 2 relies on resources sharing exactly the same descriptors. However, in many
cases descriptors may not be exactly the same, but may be related and this should be
reflected in the similarity of the associated resources. This is particularly important as
the number of descriptors assigned to some resources is low, reducing the probability
that other resources will have those descriptors. Therefore, we introduce an approach
that takes into account the similarity between descriptors.
Method 3: lexical similarity of shared descriptors

In this method, we have used kernel functions to enhance the comparison process
between bioinformatics resources retrieved from the literature by incorporating lexical
profiles of their features. This approach is inherent to our method of employing the
descriptors, as descriptors (used as features to describe resources) have been retrieved
from the contextual sentences that are related to resources. Various similarity kernels
can be used for comparisons (e.g. bag-of-words kernels [17,18], string kernels [19],
etc.). Here we introduce a kernel function that uses lexical relatedness between
descriptors to measure the similarity between the resources. The main motivation
behind this approach is that resources can share related but not exactly the same
descriptors. We therefore use a kernel that takes into account descriptor smoothing by
incorporating a similarity measure between descriptors themselves in the function that
calculates similarity between resources. Formally, let S = {s 1 , …, s k } be the set of
e-resources whose descriptions have been collected from the literature. Let D = {d1, …,
dm} be the set of all descriptors, where m is the total number of descriptors. In order

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to measure similarity between resources, we first build a descriptor similarity matrix
A(m x m), where each element aij corresponds to the similarity between descriptors di
and dj. This similarity is calculated as the cosine between the lexical profiles of the
descriptors. More precisely,
a ij = sim(di , d j ) =

< LP(di ), LP(d j ) >
LP(di ) ⋅ LP(d j )

(4)

Then, the similarity between two resources s1 and s2 is calculated as:
Sim3( s1 , s2 ) = s1 ⋅ A ⋅ s2

(5)

where
⎡ sim(d1 , d1) sim(d1 , d2 )
⎢ sim(d , d ) sim(d , d )
2 1
2 2
A = A(di , d j ) = ⎢
⎢
...
...
⎢
⎣ sim(dm , d1) sim(dm , d2 )

sim(d1 , dm ) ⎤
sim(d2 , dm ) ⎥
⎥
⎥
...
⎥
... sim(dm , dm )⎦

...
...
...

(6)

Note that vectors s1 and s2 are normalised and can contain either binary or tf*idf feature weights.
Figure 3 shows an example of two resources and their similarities calculated using
the three methods. It demonstrates the utility of using semantic descriptors of
resources, and employing the kernel-based similarity functions to measure the semantic relatedness between bioinformatics resources.

Results
Here we demonstrate the development of networks of related resources by using each
of the three methods introduced above. The networks are visualised as weighted,
undirected graphs where nodes are resources and edges represent relatedness between
them. This relatedness is estimated using the three similarity functions (as explained in
Methods), where the weight of an edge represents the strength of the relationship
between the two connected nodes. We also investigate different methods of exploring
and visualising our similarity matrices. Specifically, we use hierarchical clustering dendrograms, heatmap visualisations and semantic networks.
Data

Table 2 gives the number of bioinformatics resources that were identified in a corpus
of 2,691 full-text articles published by the journal BMC Bioinformatics. The details of
the extraction process are given in [8].
We extracted a total of 12,452 e-resources and 1,518 descriptors. Table 3 presents
the most frequent single word, two- and three-word descriptors. Each of the
e-resources has been assigned a set of associated descriptors (13.77 descriptors on
average; see Table 2 for details for the specific classes).
Exploration of Semantic Networks of Bioinformatics Resources

Here we assess the utility of resource descriptors for semantic profiling and linking of
bioinformatics resources. We do this by exploring our hypothesis that bioinformatics
resources can be semantically linked via resource descriptions. We do not aim to

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Figure 3 Example of measuring similarities between two bioinformatics resources (Kalign Algorithm and
ClustalW program) using the three methods of similarities: lexical similarity between the resource names, shared
descriptors between the resources, and shared descriptors between resources after their lexical smoothing.

evaluate individual semantic profiles and the quality of extracted descriptors, but rather
the usefulness of links based on them. For this we have manually identified an evaluation sample of 18 resources that are commonly used in bioinformatics (see Table 4)
and that we have extensive experience with. The sample contains resources from all
four resource classes, and each of these has occurred in more than 120 sentences in
Table 3 The most frequent single-word, two-word and three-word descriptors, along
with their total frequency in the corpus
Single word descriptors

Two-word descriptors

Three-word descriptors

gene: 13,585

gene expression: 1,147

protein-protein interaction: 308

method: 8,203

secondary structure: 887

multiple sequence alignment: 295

protein: 6,417

protein sequence: 780

gene expression data: 262

sequence: 5,991

protein structure: 574

amino acid sequence: 257

analysis: 4,287

microarray experiment: 488

Smith-Waterman algorithm: 48

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Table 4 A sample of resources used for exploration
Resource name

Resource
class

Number of
Sentences

Number of
descriptors

Gene ontology (GO)

Data resource

6757

289

Support vector machine (SVM)
Protein data bank (PDB)

Algorithm
Data resource

2456
904

134
102

Hidden Markov model (HMM)

Algorithm

602

94

Principal components analysis (PCA)

Algorithm

599

18

Position-specific scoring matrix (PSSM)

Algorithm

457

24

Self organising map (SOM)

Algorithm

305

137

Medical subject headings (MeSH)

Data resource

261

138

Neural network

Algorithm

256

158

Markov chain Monte Carlo (MCMC)
Expression profile

Algorithm
Data

252
252

132
136

Basic local alignment search tool (BLAST)

Application

238

160

Phylogenetic tree

Data

233

175

Structural classification of proteins (SCOP)

Data resource

216

114

Kyoto encyclopaedia of genes and genomes
(KEGG)

Data resource

187

143

Clusters of orthologous groups (COG)

Data resource

163

94

ChIp-chip data

Data

126

66

Pairwise alignment

Data

123

80

our corpus. The aim of the evaluation was to establish whether the literature-extracted
links correspond to semantic relatedness between the resources, i.e. if groupings of the
18 resources reflect their roles and functions. The limited size of the evaluation set
allowed us to comprehensively analyse and examine all links and learn lessons on a set
of familiar resources through thorough link and cluster analyses that were performed
by a domain expert (JE).
The data has been generated using the three methods for deriving semantic relatedness between resources as described above. However, the results and similarities presented here are restricted to the selected 18 resources.
Method 1: lexical comparison of resource names. As expected, this method did
not yield useful results as very little similarity was found between resource names, in
particular in smaller sets of resources. Non-zero similarity was only obtained between
Protein data bank and ChIp-chip data (similarity of 0.28) and Basic local alignment
search tool and Pairwise alignment (0.18).
Method 2: shared descriptors. We derived mutual similarity scores for the 18
resources based on shared semantic descriptors. Two experiments were performed:
one with binary-valued features and one with tf*idf weights. In both cases, this method
identified significant relatedness between many resources (see Figure 4 for heat-maps).
Clearly, the addition of descriptors improved our ability to derive a measure of semantic similarity between related resources whose names are lexically disparate. However,
while the binary-weighted scores brought many similarities between a number of
resources (making it difficult to define any clear semantic relationships from these
data), the tf*idf scores were significantly more discriminative (see Figure 4B), clearly
highlighting related resources.
To further highlight the subtle differences and similarities between the resources in
the sample, we applied a hierarchical clustering algorithm [21] to the two matrices of

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Figure 4 Heatmap representations of the matrix of shared descriptor similarity scores between resources
(method 2). (A) The scores based on binary weights. (B) The scores based on tf*idf. Heatmaps generated
by R function ‘heatmap’ [20]. Note that the heatmap diagonals (self-similarity) are intentionally left white to
make them easier to interpret, and that the heatmaps are different scales.

scores. The tree in Figure 5A highlights some interesting clusters of the examined
resources when only binary indication of descriptors’ presence was used. Binary
weights provided a spread of similarity scores, which better suited hierarchical clustering. In the resulting dendrogram, many resources have been grouped together based
on their class (e.g. PCA and MCMC are algorithms; COG and PDB are data resources
as well as KEGG and MeSH). However, the cluster of pairwise alignment and HMM
highlights the semantic theme of sequence analysis. It is interesting that BLAST was
not linked to these, while it makes a protein-related cluster with COG, SCOP and

Figure 5 Hierarchical clustering of e-resources using the shared descriptors similarity matrix (method 2).
(A) The scores based on binary weights. (B) The scores based on tf*idf. Distances were calculated as (1 –
Sim2). Ward’s minimum variance clustering method [21] was used to cluster the data. The tree was
generated using R function ‘hclust’ [20].

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PDB. Furthermore, KEGG, BLAST, COG, SCOP, PDB and MeSH form their own
group, which does not highlight any obvious semantic relationships; a likely reason is
that these resources represent very common and fundamental resources in bioinformatics, so share quite a large group of generic descriptors.
While providing a flatter structure, Figure 5B highlights more reliable associations,
typically between data resources and algorithms. For example, BLAST and PDB are
closely related, as are pairwise alignment and PSSM, highlighting the semantic theme
of sequence analysis. Together, pairwisealignment, PSSM, BLAST and PDB make a
group that share a theme of being related to sequence analysis. Phylogenetic tree and
COG form their cluster (COG is an attempt to phylogenetically group proteins [22]). It
is also interesting that HMM, SVM and neural networks are all grouped together,
representing a machine learning theme (classifiers), while GO and MeSH make their
own cluster as the only semantic hierarchical resources in the set, which are typically
used for annotations.
Even though similarity data alone can identify important semantic links, we further
explored the importance of the number and strength of links between resources. In
Figure 6 we present the similarity data as edges in a network connecting each node
(representing individual resources) with those that have some similarity to it. Each
edge is weighted by the similarity between the resources it connects, so that edges that
appear thick represent strong relationships and weak relationships are represented by

Figure 6 Semantic network of bioinformatics resources (using method 2 and values shown in Figure 4).
Node size represents frequency in the corpus; edge thickness represents how similar the two connected
nodes are. Node colour is determined by the semantic class of the node: red for Data, green for Data
resource, blue for Algorithm and yellow for Application. (A) The scores based on binary weights. (B) The scores
based on tf*idf. The image was generated using Cytoscape [23], the network was laid out using the
Cytoscape layout algorithm ‘Edge-Weighted Spring Embedded’, using the edge weight data in the network.

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thin edges. We have removed all edges that have a weight below the median edge
weight for the network, or below the median weight for a given node. Nodes that are
left with no edges are not presented in the resulting networks. Our intention with this
was to remove edges that exist due to chance alone and to better highlight the strongest relationships in the network.
The strongest links in Figure 6A are between HMM, SVM and neural network, identifying the machine learning theme. There is also a strong link between Gene Ontology
and PDB, reflecting the fact that PDB identifiers are mapped to the GO terms. Gene
Ontology and SVM are also strongly linked, most probably because SVM methods have
been widely used for protein annotations using GO (see, for example, [24]). Figure 6B
(based on tf*idf) brings all algorithm instances into a sub-network. There is also a subnetwork related to sequence analysis.
Method 3: lexical similarity of shared descriptors. The results of calculations for
linking the resources considering the lexical similarities between their descriptors are
summarised in figures 7, 8 and 9.
Figure 7 has many similarities with Figure 4. As before, the tf*idf scores were more
selective in linking resources than binary features. However, as expected, descriptor
smoothing has introduced more similarities than Method 2 in tf*idf-based similarities
(compare figures 4B and 7B).
Figure 8A shows an informative cluster made of PCA, Expression profile and ChIpchip (PCA is a commonly used method to analyse both protein expression data
(expression profiles) and ChIp-chip data). Data resources phylogenetic tree and pairwise
alignment have been clustered together, both of which are common data forms in
sequence analysis. GO-PDB and SVM-neural network also made their own groupings.
Links in Figure 8B bring together another cluster related to sequence analysis group
(BLAST, pairwise alignment and PSSM). There is again a clear sub-tree with machine
learning classifiers.
The networks given in Figure 9 present the strongest grouping of resources based on
their class. Data nodes (represented in red) and Algorithm nodes (blue) are strongly
linked to each other. The strongest edge weights again occur between resources that

Figure 7 Heatmap representation of the matrix of lexically smoothed descriptor similarity scores between
resources (method 3). (A) The scores based on binary weights. (B) The scores based on tf*idf. Heatmaps
generated by R function ‘heatmap’ [20]. Note that the heatmaps are represented using different scales.

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Figure 8 Hierarchical clustering of e-resources using the lexically smoothed descriptor similarity matrix
(method 3). (A) The scores based on binary weights. (B) The scores based on tf*idf. Distances were
calculated as (1 – Sim3). Ward’s minimum variance clustering method [21] was used to cluster the data.
The tree generated using R function ‘hclust’ [20].

Figure 9 Semantic network of bioinformatics resources (using method 3 and values shown in Figure 7).
Node size represents frequency in the corpus; edge thickness represents how similar the two connected
nodes are. Node colour is determined by the semantic class of the node: red for Data, green for Data
resource, blue for Algorithm and yellow for Application. (A) The scores based on binary weights. (B) The
scores based on tf*idf. The image was generated using Cytoscape (see Figure 6 for further details).

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appear most frequently in the corpus, suggesting that frequency normalisation may be
needed to reduce this impact. Gene Ontology, in particular, is linked to all other
resources, and that is primarily a product of its ubiquity in the literature and therefore
the tendency for many descriptors and resources to be linked to it. A very strong link
between pairwise alignment and BLAST was only highlighted using the tf*idf weights
(Figure 9B).

Discussion
In order to establish similarity between resources, their literature-based profiles are
compared using three levels of representations: the lexical similarity between resource
names (method 1); the similarity calculated on the basis of shared semantic descriptors
(method 2), and the same similarity smoothed by considering lexically similar descriptors (method 3). As expected, the first method failed to capture any implicit links
between resources as it relied solely on the surface level clues originating from the
names of resources. Of course, in a larger set it is likely that some resources will be
lexically linked, but many non-lexical links would be missed. The second approach
performed better in that sense, and was able to identify interesting clustering patterns
between the resources that did not have any lexical resemblance. At the third level, in
contrast to considering the exact match between resource descriptors, we devised a
descriptor-based kernel matrix that incorporated the approximate lexical similarities
between the descriptors (using their lexical profiles). The approximate similarities
helped in linking the resources that shared the descriptors that were not exactly the
same, but were related (see Figure 3). By further analysing the associated semantic profiles, we can see that significant relatedness between resources typically originates from
sharing a number of generic descriptors, in particular single-word ones (see Table 3).
Many of these have a generic nature (e.g. method, analysis, gene, etc.) and are not discriminatory enough for establishing semantic relatedness at non-generic levels. This
problem was addressed through using tf*idf-based scoring weights assigned to descriptors (considering the frequency of descriptors appearing in profiles of different
resources), which resulted in more informative and semantically-relevant groupings of
resources. Previous results in annotation of texts (and entities modelled by text features) with various categories (including ontological structures) have shown that tf*idf
was typically a measure of choice and outperformed other measures by discriminating
features that have been over-represented [15,16]; for example, it was widely used to
annotate protein function with concepts from the Gene Ontology [25-28].
Semantic networks generated from the literature can be useful for both bioinformaticians that are exploring resources corresponding to their needs, and resource curators,
who could accelerate their work by discovering and annotating sets of related
resources. For example, an interesting pattern emerged whilst experimenting with
Method 3, whereby resources would cluster together based on their class (i.e. the
resources that belonged to the same class (such as Algorithm, Data Resource, etc.)
tended to appear closer in the network). On the other hand, Method 2 revealed some
interesting functional links (linking data types and algorithms). It remains an open
question as to which of these clustering patterns is most useful for semantic resource
discovery and/ or curation. Selecting and varying the threshold for the edge weight in
our network representations can also discard weaker links – we have experimented

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with discarding all edges that are below the global or local median value. We plan to
further explore improving the querying of resource profiles by organising them in an
RDF-store that would facilitate retrieval of related resources, and then to test these in
curation and service discovery tasks. Of course, this would include developing resource
identification, normalisation and disambiguation techniques, as some resources may
appear under different names/acronyms. We also note that better coverage would be
possible if anaphoric expressions in sentences are resolved.
Related work

The domain of life sciences has witnessed many efforts in the direction of utilizing
Semantic Web technologies, where particular focus has been on data annotation (e.g. a
number of protein function databases), using both manual and automated approaches.
These efforts have recently been extended to semantic description of resources (e.g.
services and tools) that are used to analyse, explore and visualise such data. These
approaches include assigning meta-data about functionalities, inputs and outputs. The
majority of automatic approaches to service annotation rely on the data available in
Web Service Description Language (WSDL) files associated with Web services. Such
files describe programmatic interfaces to services, including data types, input and output message formats and the operations provided. For example, Lerman and colleagues
[29] presented work on automatic labelling of input and output of Web services using
meta-data based classification relying on terms extracted from the associated WSDL
files. The underlying heuristic behind the meta-data based classification is that similar
data types tend to be named by similar names and/or belong to operations or messages
that are similarly named. Similarly, Hess and Kushmerick [30] used machine learning
to classify Web services using information given in WSDL files of the services that
include port types, operations and parameters along with any documentation available
about the Web service. Information in a WSDL file is treated as normal text, and the
problem of Web service and its metadata classification is addressed as a text classification problem. Liu and Wong [31] used clustering to identify homogenous service
“communities”, where features were also extracted from associated WSDL files. They
also use simple text processing and statistics to identify content-describing terms, and
argue that clustering services in functional groups can facilitate more effective service
discovery.
Carman and Knoblock, on the other hand, reported on invoking new/unknown services and comparing the data they produce with that of known services, and then use
the meta-data associated with the known services to add annotations to the unknown
resources [32]. Belhajjame and colleagues [33] used known annotations of parameters
belonging to components in a workflow to infer the unknown annotations of other
parameters (in other components). Here, semantic information of operation parameters
is inferred based on their connections to other (annotated) components within existing
tried-and-tested workflows. Apart from deriving new annotations, this method can
inspect the parameter compatibility in workflows and can also highlight conflicting
parameter annotations. Similarly, Dong et al. [34] used clustering-based approach in
which parameters of service operations were used to find similar services.
To the best of our knowledge, the work reported here is the first attempt to extract
semantic networks of bioinformatics resources from the literature. Since the number of

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features in our approach for many instances was small, we have used kernels to
“expand” the feature space by taking into account feature similarities. This is similar to
latent semantic kernels (LSK, [35]) and semantic smoothing [36]. These methods also
try to bypass the problem of exact matching by deriving “conceptual” indices using
either statistical analysis of word co-occurrence across documents (LSK) or information from a static, external semantic network (semantic smoothing). Our method, on
the other hand, relies on dynamic lexical similarities between features. We note that
matrix A in formula (5) can be generated in different ways (as long as it is positive
semi-definite i.e. represents a kernel [35]). Future work, therefore, will explore combining various kernels to build this similarity matrix, including combination of LSK and
similarities based on the myGrid ontology.

Conclusions
In this paper we proposed and explored a literature-based methodology for building clusters and semantic networks of functionally related e-resources in bioinformatics. The
main motivation is to facilitate the resource discovery approaches that would improve the
availability and utility of these resources to the community. The methodology revolves
around semantic descriptors that are frequently used by bioinformatics resource providers
to semantically describe the resources. The semantic descriptors have been automatically
compiled and each e-resource has been assigned a set of descriptors co-occurring with the
given e-resource in a full-text article corpus. As a proof-of-concept, the approach was
evaluated on a subset of manually selected resources that the authors were familiar with.
The results suggest that the method was able to group and link semantically related
entities. Semantic networks that were based on tf*idf-weights in particular were more
informative in “recovering” semantic relatedness between the resources. We envisage that
such semantic networks will be useful to both bioinformaticians who are exploring and
discovering new resources, and resource curators (e.g. in the BioCatalogue project).
Furthermore, by providing an RDF-store of extracted profiles, tools and services can be
integrated and queried together with the rest of the “bioinformatics resourceome”, in
particular as part of service/data search engines and crawlers.
One of the major issues with the literature-based approach to resource profiling is
that many resources do not appear frequently and are represented by small descriptor
sets. Therefore, we explored expanding the feature space by using the “kernel trick”
[35], where (lexical) similarity between features is taken into account when calculating
similarity between instances. The results demonstrate the potential of even simple
kernel methods (using lexical profiles) to model relatedness between resource descriptors. We anticipate that further work will be required to explore the most relevant
weights for semantic descriptors to counter-balance the impact of frequent (and less
informative) features. Other kernels (such as contextual and distributional similarities,
ontology-based similarities, string kernels etc.) need to be explored and could provide
better resolution of the complex interrelationships between features (descriptors) and
consequently between bioinformatics resources. Finally, further studies would be
needed to establish which methods would be most suited in supporting resource curation and discovery tasks.

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Acknowledgements
This work was partially supported by the UK Biotechnology and Biological Science Research Council (BBSRC) via the
BioCatalogue project and by the Science Foundation Ireland under Grant No. SFI/08/CE/I1380 (Lion-2) awarded to HA.
JE is funded by the e-LICO project (EU Grant agreement number 231519). GN was partially supported by grant
144013 (Representations of Logical Structures and their Application in Computer Science, Ministry of Science, Serbia).
This article has been published as part of Journal of Biomedical Semantics Volume 2 Supplement 1, 2011: Semantic
Web Applications and Tools for Life Sciences (SWAT4LS), 2009. The full contents of the supplement are available
online at http://www.jbiomedsem.com/supplements/2/S1.
Author details
School of Computer Science, University of Manchester, Oxford Road, Manchester, M13 9PL, UK. 2College of
Telecommunication Engineering, National University of Sciences and Technology, Islamabad, Pakistan. 3Digital
Enterprise Research Institute, National University of Ireland, Galway, Ireland.
1

Authors’ contributions
HA implemented the literature mining modules; HA and JE developed and applied kernel metrics; JE performed data
analysis and evaluation; RDS and GN conceived and supervised the study. The manuscript was initially drafted by HA
and JE. All authors read and approved the manuscript.
Competing interests
The authors declare no competing interests.
Published: 7 March 2011
References
1. Nucleic Acids Research. 2009, 37(DB), (January).
2. Toolbox at the EBI. [http://www.ebi.ac.uk/Tools//].
3. Nucleic Acids Research. 2009, 37(Web Server), (July).
4. Goble CA, Belhajjame K, Tanoh F, Bhagat J, Wolstencroft K, Stevens R, Nzuobontane E, McWilliam H, Laurent T, Lopez R:
BioCatalogue: A Curated Web Service Registry for The Life Science Community. In 3rd International Biocuration
Conference 2009, Berlin Germany.
5. Wilkinson MD, Links M: BioMOBY: an open source biological web services proposal. Briefings in Bioinformatics 2002,
3:331-41.
6. Oinn T, Greenwood M, Addis M, Alpdemir N, Wroe C, et al: Taverna: lessons in creating a workflow environment for
the life sciences. Research Articles. Concurr. Comput. : Pract. Exper 2006, 18(10):1067-1100.
7. Stevens R, Robinson A, Goble C: myGrid: personalised bioinformatics on the information grid. In Bioinformatics (ISMB
Supplement) 2003, 302-304.
8. Afzal H, Stevens R, Nenadic G: Mining Semantic Descriptions of Bioinformatics Web Resources from the Literature.
Proceedings of the 6th European Semantic Web Conference on The Semantic Web: Research and Applications Heraklion,
Crete, Greece, Springer-Verlag; 2009, 535-549.
9. Eales JM, Pinney JW, Stevens RD, Robertson DL: Methodology capture: discriminating between the “best” and the
rest of community practice. BMC Bioinformatics 2008, 9:359.
10. Afzal H, Stevens R, Nenadic G: Towards Semantic Annotation of Bioinformatics Services: Building a Controlled
Vocabulary. In Proc. of the Third International Symposium on Semantic Mining in Biomedicine 2008, 5-12, Turku, Finland.
11. Wolstencroft K, Alper P, Hull D, Wroe C, Lord PW, Stevens RD, Goble CA: The myGrid Ontology: Bioinformatics Service
Discovery. International Journal of Bioinformatics Research and Applications 2007, 3:326-340.
12. Web Services at the EBI. [http://www.ebi.ac.uk/Tools/webservices/].
13. Termine Web Service. [http://www.nactem.ac.uk/software/termine/].
14. Nenadic G, Ananiadou S: Mining Semantically Related Terms from Biomedical Literature. ACM Transactions on Asian
Language Information Processing 2006, 22-43.
15. Salton G, Buckley C: Term-weighting approaches in automatic text retrieval. Information Processing and Management
1998, 513-523.
16. Shatkay H: Hairpins in bookstacks: information retrieval from biomedical text. Brief Bioinform 6:222-238.
17. Teytaud O, Jalam R: Kernel-based text categorization. In International Joint Conference on Neural Networks ,
(IJCNN’2001), Washington DC (2001).
18. Pahikkala T, Pyysalo S, Ginter F, Boberg J, Jarvinen J, et al: Kernels incorporating word positional information in
natural language disambiguation tasks. Proc. of the Eighteenth International Florida Artificial Intelligence Research Society
Conference AAAI Press; 2005.
19. Lodhi H, Saunders C, Shawe-Taylor J, Cristianini N, Watkins C: Text classification using string kernels. J. Mach. Learn
2002, 2:419-444.
20. R Development Core Team: R, a language and environment for statistical computing. R Foundation for Statistical
Computing 2009, Vienna, Austria.
21. Romesburg HC: Cluster analysis for researchers. Lulu Press, North Carolina 2004.
22. Koonin E: The Clusters of Orthologous Groups (COGs) Database: Phylogenetic Classification of Proteins from
Complete Genomes., The NCBI Handbook, Chapter 22 (NLM).
23. Cytoscape. [http://www.cytoscape.org/].
24. Vinayagam A, König R, Moormann J, Schubert F, Eils R, Glatting KH, Suhai S: Applying Support Vector Machines for
Gene ontology based gene function prediction. BMC Bioinformatics 2004, 5:116.
25. Rice S, Nenadic G, Stapley B: Mining Protein Function from Text Using Term-based Support Vector Machines. BMC
Bioinformatics 2005, 6(Suppl 1):S22.

Page 17 of 18

Afzal et al. Journal of Biomedical Semantics 2011, 2(Suppl 1):S4
http://www.jbiomedsem.com/content/2/S1/S4

26. Verspoor K, Cohn J, Joslyn C, Mniszewski S, Rechtsteiner A, Rocha LM, Simas T: Protein annotation as term
categorization in the gene ontology using word proximity networks. BMC Bioinformatics 2005, 6(Suppl 1):S20.
27. Lin KHY, Hou WJ, Chen HH: Gene Ontology Annotation Using Word Proximity Relationship. In Proc. Second Int. Symp.
Semantic Mining 2006.
28. Malone BM, Perkins AD, Bridges SM: Integrating phenotype and gene expression data for predicting gene function.
BMC Bioinformatics 2009, 10(Suppl 11):S20, doi:10.1186/1471-2105-10-S11-S20.
29. Lerman K, Plangrasopchok A, Knoblock C: Automatically Labelling the Inputs and Outputs of Web Services. In
Proceedings of AAAI-2006 2006, 149-181, Boston, MA, USA.
30. Hess A, Kushmerick N: Learning to Attach Semantic Metadata to Web Services. In Proc. 2nd International Semantic
Web Conference (ISWC2003). Volume 2870/2003. Sanibel Island, Florida, USA, Springer Berlin / Heidelberg; 2003:258-273.
31. Liu W, Wong W: Discovering homogenous service communities through web service clustering. Proceedings of the
2008 AAMAS international conference on Service-oriented computing: agents, semantics, and engineering LNCS 5006,
Springer-Verlag;69-82.
32. Carman MJ, Knoblock CA: Learning Semantic Descriptions of Web Information Sources. Twentieth International Joint
conference on Artificial Intelligence 2007, 1474-1480, Hyderabad India.
33. Belhajjame K, Embury SM, Paton NW, Stevens R: Automatic annotation of Web services based on workflow
definitions. ACM Trans 2007, 2(2):1-34, Web.
34. Dong X, Halevy A, Madhavan J, Nemes E, Zhang J: Similarity search for Web services. In Proceedings of the Thirtieth
international conference on Very large data bases 2004, 30:372-383, Toronto, Canada, VLDB Endowment.
35. Shawe-Taylor J, Cristianini N: Kernel Methods for Pattern Analysis. Cambridge University Press; 2004.
36. Siolas G, d’Alche Buc F: Support Vector Machines based on a Semantic Kernel for Text Categorization. In Proceedings
of the International Joint Conference on Neural Networks. Volume 5. IEEE Press; 2000:205-209.
doi:10.1186/2041-1480-2-S1-S4
Cite this article as: Afzal et al.: Mining semantic networks of bioinformatics e-resources from the literature.
Journal of Biomedical Semantics 2011 2(Suppl 1):S4.

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