Methods inspired by nature and Semantic Web

1. Methods inspired by nature and Semantic Web
Rata Gheorghita Mugurel MOC2, Ivanciu Adriana MLC2
gheorghita.rata@infoiasi.ro, adriana.ivanciu@infoiasi.ro
Semantic web is an extension of the current web,
intended to provide an improved cooperation
between humans and machines1.
Genetic algorithms
Genetic algorithms and search engines
In the book Enhancing the power of the Internet, in the chapter
Intelligent Information Search, the authors2 say that there were many
approaches that were studied regarding the way of how this domain
can be improved. There are two major problems, according to the
authors: classical information models and information retrieval
model itself. The most techniques were focused to the first problem.
For the second one, probabilistic methods were the most popular in
the past. Even if artificial intelligence and fuzzy theory had a great
contribution, the evolving of genetic algorithms and neural networks
gathered the attention. Although manual knowledge acquisition
1 Berners-Lee, T. Hendler, J. Lassila, O. The semantic web. Scientific American, 28-37 (2001).
2
Enhancing the power of the Internet
By Masoud Nikravesh, Ben Azvine, Ronald Yager, Lotfi A. Zadeh

2. 2 Rata Gheorghita Mugurel MOC2, Ivanciu Adriana MLC2
process was the base for the search systems, data mining was an
important technique for obtain knowledge in an automatic process.
The power of genetic algorithm was proved when were used in
the process of extracting keywords and establish its weights. The
same authors say that genetic algorithms and genetic fuzzy system
have great results regarding Search engines. In the same domain
(Search engines), neural network-based methods are lesser extent.
According to Hsinchun (1998), which is quoted in this paper,
genetic algorithms are used to search in a dynamic manner on a
keyword dictionary and return a list of related Web pages. The
search process is described as following:
 The population is formed from chromosomes that have a
fixed length
 Chromosomes represents user preferences
 A fitness value is associated with each chromosomes
 Genes contain the user keyword and a number that
represents the frequency of the keyword occurrence in a
web document (witch is a candidate for the solution)
 After the user evaluates the documents returned, the fitness
value is adjusted, considering the score computed by the
system.
Going further, metagenetic algorithms are used to optimize the start
population. One of these combines two genetic algorithms. The first
is used to generate the start population with values from keywords
index and the second creates a population with logic operators
corresponding to each member from the first algorithm. The first
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3. Methods inspired by nature and Semantic Web
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algorithm can be easily replaced with a random selection for a faster
search.
SWARMS
SWARMS3 (semantic web added rich mining systems) is a
platform for knowledge management. It store the information in
ontologies, can extract the network structure from the ontology and
search (mining) the semantic data. This system is applied in many
domains mainly in online news industry and social networking. To
simple queries the SPARQL works great. But the more the queries
became big and complicated, SPARQL will not satisfy the
requirements anymore. In this case the developers appeal to methods
inspired by nature. Another reason is that the metadata in Semantic
Web is not always well structure, and a classic algorithm is hard to be
adapted.
The search in Semantic Web context is based on semantic similarity
and it measure the similarity between objects from ontology. The
semantic similarity is computed from hierarchy similarities, property
similarities, label similarities and access similarities (Zongmin Ma,
Huaiqing Wang, 2009). These can be computed with some probabilistic
algorithms. The same authors propose a Semantic similarity based on
cached models. The search algorithm should respect two rules: return
an approximate optimal solution and the time spent on its searching
3
The Semantic Web for Knowledge and Data Management: Technologies and Practices
By Zongmin Ma, Huaiqing Wang
3

4. 4 Rata Gheorghita Mugurel MOC2, Ivanciu Adriana MLC2
must be finite. The best algorithms class that fit these specifications is
the one inspired from nature and genetics.
The authors used a genetic algorithm for training the model and
create the initial cache. The base elements of the genetic algorithm are:
 the population have 50 chromosomes;
 the mutation probability is 0.2;
 the algorithm will stop when the fitness is 0.9 or the
generations number reach 100.
Below is a chart that represents the two search ways and its time
performance per number of requests:
Performance of Ontology Cache Cache
disabled
Cache
enabled
2500
T
i
m 2000
e
1500
C
o
n 1000
s
u
m 500
i
n
g 0
0 1000 2000 3000 4000
Request Count
Performance of Time Consuming
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5. Methods inspired by nature and Semantic Web
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Details can be found in the document from the point 3 of the
Bibliography.
Human Similarity theories for the semantic web
In the paper Human Similarity theories for the semantic web, the
author4 shares his opinion about how human mind representation can
be useful for making the web documents more ‘friendly’ for the
computers. He thinks that the way of how human mind represents the
data, in order to be easy to find similarities can be manipulated, studied
and used for ontology building and other web semantic activities,
generally speaking. Giving the fact that the users of the computers are
human after all, he thinks that semantic web has a lot in common with
humans and both humans and computers have to deal with a big
quantity of information. One of the domains that can help Semantic
Web is Psychology, in his opinion. In order to solve problems, humans
are using inductive and deductive reasoning, they have to follow causal
chains, to solve problems and to make decisions. In RDF, the data
structure language for Semantic Web, the concepts witch are
considered fundamental are resources, properties and statements. The
first category is represented by objects. The objects can be anything
like humans, books or activities. This resources have properties like
names, chapters and physical locations. The statement is the link
between the property and the resource. The author thinks that
4
Jose Quesada, Max Planck Institute, Human development
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6. 6 Rata Gheorghita Mugurel MOC2, Ivanciu Adriana MLC2
psychologists and Semantic Web have the same interest in a certain
way, represented by the fact that both tries to model the world using the
formalism. Although there are big differences between the two
domains, the author believes that there is a level of convergence
between them.
Conclusion
In nature we can find an impressive number of algorithms that can be
used to solve different problems from different domains including
Semantic Web. Nature will always surprise and will offer patterns,
algorithms, processes that will inspire solving technologies problems
with a good result.
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7. Methods inspired by nature and Semantic Web
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Bibliography
1. Semantic web service composition based on ant colony optimization
method
Ghafarian, T.; Kahani, M.
Networked Digital Technologies, 2009. NDT apos;09. First
International Conference on
Volume , Issue , 28-31 July 2009
2. Enhancing the Power of the Internet Series: Studies in Fuzziness and
Soft Computing , Vol. 139 Nikravesh, M.; Azvine, B.; Yager, R.;
Zadeh, L.A. (Eds.) 2004
3. The Semantic Web for Knowledge and Data Management:
Technologies and Practices By Zongmin Ma, Huaiqing Wang, IGI
Global, 2009
4. Human Similarity theories for the semantic web, Jose Quesada, Max
Planck Institute, Human development presented in Nature inspired
for the Semantic Web (NatuReS) October 27, 2008
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