Trust Evaluation Using an Improved Context Similarity Measurement

International Journal of Business Information Systems Strategies (IJBISS) Volume 3, Number 1, February 2014
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Trust Evaluation Using an Improved Context
Similarity Measurement
Mohsen Raeesi1
, Mohammad Amin Morid 2
, Mehdi Shajari3
Department of Computer Engineering and Information Technology,
Amirkabir University of Technology, Iran
Abstract
In context-aware trust evaluation, using ontology tree is a popular approach to represent the relation
between contexts. Usually, similarity between two contexts is computed using these trees. Therefore, the
performance of trust evaluation highly depends on the quality of ontology trees. Fairness or granularity
consistency is one of the major limitations affecting the quality of ontology tree. This limitation refers to
inequality of semantic similarity in the most ontology trees. In other words, semantic similarity of every two
adjacent nodes is unequal in these trees. It deteriorates the performance of contexts similarity computation.
We overcome this limitation by weighting tree edges based on their semantic similarity. Weight of each
edge is computed using Normalized Similarity Score (NSS) method. This method is based on frequencies of
concepts (words) co-occurrences in the pages indexed by search engines. Our experiments represent the
better performance of the proposed approach in comparison with established trust evaluation approaches.
The suggested approach can enhance efficiency of any solution which models semantic relations by
ontology tree.
Keywords
Trust and Reputation, Context similarity, Ontology tree, Weighted ontology tree, Normalized
Similarity Score
1. Introduction
Trust is a critical concept in mutual collaboration in dynamic e-commerce systems. It is defined
as a particular level of subjective probability using which, an agent assesses it and another agent
will perform a particular action before it can monitor such action [1]. In the context of e-
commerce systems, the actions are the e-commerce transactions. The trusting agent is called the
trust or entity, and the trusted agent is called the trustee entity.
To evaluate the trustee’s trustworthiness for a certain trust scope, context attributes is one of the
two kinds of input analyzed by trust or [2]. Context attributes represent contextual information
that the trust or requires in order to complete the evaluation of the trustee’s trustworthiness. As a
formal definition, context is any information that can be used to characterize the situation of an
entity [3]. Context value for all the contexts may not be available. So, it is essential to have a
mechanism for evaluating the unavailable trust value of certain context, using the available trust
value of another context. It can be done in many different ways such as multiplying the trust
value of the trustee in the available context into the similarity between available and unavailable

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contexts. As a result, computing the similarity between two contexts is crucial for trust evaluation
in e-commerce systems.
There are many researches which attempted to compute unknown trust value of certain context,
using the known trust value of another context. A significant portion of the researches utilize
ontology trees for context modeling such as [4], [5]. These researches often exploit node distance
to compute similarity. There is an underlying assumption in this exploitation: each two adjacent
nodes have equal semantic distance or granularity of nodes in each level is identical. This
underlying assumption is not true in most of the trees and it deteriorates the performance of trust
evaluation. This research attempts to transcend this limitation by offering a novel weighted
ontology tree, which is independent of the tree’s structure. Our experiments on real data extracted
context from Epinions.com shows that weighting trees improves the performance of trust
evaluation.
The remainder of this paper is organized as follows. Section 2 surveys related works in context
modeling and computing similarity between the contexts. In Section 3, essential materials for the
proposed method are discussed in two subsection, similarity computation and ontology tree
construction. Our suggested model is described in Section 4. Sections 5 and 6 are related to
experimental setup and results, followed by a conclusion in Section 7.
2. Related Work
There are several previous works which aim to compute the mentioned similarity between to
context in trust evaluation. To do so, in all of the researches first they used a model for context
representation and then they introduced a method for computing similarity between the contexts.
Therefore, we split this section according to these two steps.
2.1 Context Modeling
In order to compute the similarity between two contexts, the first step is to model the context
which is known as context representation or context modeling. Any approach is used for the
context modeling results different types of the similarity computation. Three popular types of
these approaches are: ontology tree, key word based modeling and task based modeling [6].Of
course, there are several other approaches which can be used in context modeling but they are not
as popular as the above approaches. Strang et al. have a survey on these approaches [7].
2.1.1 Ontology tree
Ontology tree is referred to the approach which the contexts are represented in a context ontology
tree hierarchical structure. Each node in this tree represents a context and is split into two lower
level contexts and the low level contexts are sub-context of the node. For example, Figure 1
shows ontology tree for network context and its sub-contexts [2].

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Figure 1.Example of an ontology tree
In [4] they make use of an ontology tree of services using DAML-S6, where each node in the tree
representing a type of service. Using ontology tree for representing game application running on
a gaming device is another work which is done by [2].Here, a game application is composed by a
game manager component (GM) and by one game scenario component (GS). In [8] they
introduced a belief-theoretic reputation estimation model for multi-context communities. They
employed an ontology tree to show consumer experience reports and beliefs about various
products of a website (i.e. Epinion.com).
One of the limitations of these ontology tree approaches is that the tree may be constructed
unfairly or granularity inconsistent. In particular, on branch of a node may be split generally
while the other branch is split in more details which will be discussed in more details later. In this
paper we mainly focus on this approach and introduce a method to overcome its limitations.
2.1.2 Keyword Based Modeling
Second common approach for context representation is using a combination of keywords to show
a context. Each keyword is referred to a different context and by ensemble the keywords the
result collection is a context. For example in all the papers there is a keyword section which
introduces the main concepts which the paper has been written around it. Our paper keywords
are: Trust, Context, Weighted Similarity, and Ontology. In [9] they used this approach for context
representation. They considered a file-server application having three types of services (i.e.,
contexts): upload PDF File with keywords {write, pdf, file}, upload DOC File with keywords
{write, doc, file}, login with keywords {LoginInfo, userName, passWD}.
The main advantage of this approach is its simplicity. Contrary to the previous approach, there is
no need to perform any preprocessing to construct a tree and it can be applicable in any context.
But their disadvantage is their limitation in extension. There are some situations where it is not
possible to specify the context by using some simple labels.

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2.1.3 Task Based Modeling
The third approach is more applied method and is built on tasks. Suppose that we are working on
a certain environment with certain jobs. In such a situation, the collection of tasks which can be
done is limited and will not be exceeded from a certain threshold. Therefore, in such cases each
task can be considered as a context. Here, each task is composed of several sub-tasks which are
knowntask’s aspect or task’s attribute. An aspect is the smallest element of a task which describes
a special attribute of it. In [6] they worked on several tasks such as: “Tom is wondering about
trusting Bob to guide him in London when it is stormy”. Here, the task is model as: Location:
London, Weather: stormy, Subject: guide. As it may be guessed the task’s aspects are: Location,
Weather and Subject. This approach is also employed in other researches such as [4, 9, 10].
This kind of context modeling cannot be used in general and is limited to specific cases. In
particular, when we are facing with a situation where the collection of possible tasks is limited,
the tasked based modeling can be an appropriate solution.
There are several other approaches which can be used in context modeling but they are not as
popular as the above approaches. For more study the different approaches can be found in [9].
2.2 Computing Similarity between the Contexts
After identification of a model to represent a context, the next step is to specify a method to
compute similarity between the contexts. In this section the goal is to introduce these methods
which have been used in previous researches.
In [4] similarity between two contexts is computed by the distance between to node in the
context’s ontology tree:
( 1, 2) =
( , )
(1)
Here, the distance of two nodes is defined as the least number of intermediate nodes for one node
to traverse to another node. For example, in Figure 2 which shows services ontology tree, service
s1 and s2 has a distance of 3.
Figure 2.Services in a context ontology tree [4]
[2] introduced another similarity computation method for contexts which are represented in an
ontology tree. Here, the similarity between two nodes is calculated as the ratio between the

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number of shared nodes from the source node and the sink node to the root node, and the total
number of nodes from the source and the sink to the root node. For example in Figure 2 s1 and s2
has a distance of 3/5.
[9] considered any context as a set of keywords and they computed the similarity between two
contexts by using the set theory. Here, the similarity between two contexts, Si and Sj, with their
individual keywords sets, K(Si) and K(Sj), is defined as the ratio between the set’s intersect and
the set’s union:
, =
( ) ∩ ( )
( ) ∪ ( )
(2)
As it was elaborated, one approach for context representation is considering a context as task. In
[11] the similarity D(S1,S2) between two tasks s1 and s2 is obtained from the comparison of the
task attributes.
, = 1 − ∑ , − , (3)
where n is the number of task attributes, Si,l is the l-th attribute of task Si, and Sj,l is the l-th
attribute of task Sj.
In [6] in order to measure similarity among contexts, they used the idea of the bipartite SimRank
which is an extension of the basic SimRank algorithm [12] to bipartite domains consisting of two
types of objects. Such domains are naturally modeled as graphs, with nodes representing objects
and edges representing relationships. Here, they formed a graph with contexts and aspects as
nodes. In this graph each context points to their aspects (Figure 3). The recursive intuition behind
this algorithm is that in many domains, similar objects are related to similar objects. More
precisely, contexts A and B are similar if they are related to aspects b and c, respectively, and b
and c are themselves similar. The base case is that aspects are similar to themselves.
Figure 3.Graph model of context in [6]
3. Methods and Materials
The proposed solution utilizes concept similarity computation and ontology tree as two base
materials. In each of these areas, there is rich literature representing the importance of the
research topic. We choose the proper method based on our requirements and the experiment

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result comparison of the methods. In this section we describe used methods and relating subjects.
Next two subsections introduce our method on concept similarity computation and semantic
hierarchical structure respectively.
3.1 Normalized Similarity Score
To develop the ability of text understanding for computers, two major approaches are adopted so
far: using expert-created semantic structure and automatically extracting semantic relation from
human-written text. Based on the first approach, several large and long-term projects are
established such as Cyc [13] and WordNet [14]. These projects try to establish semantic web of
vast variety of concepts, which comes at enormous effort and cost. Despite of these efforts by
knowledgeable human experts, this approach has a significant limitation: In comparison with
available information on the web, the total entered information is limited [15]. Covering this
limitation, the second approach is developed in the recent years. The new approach utilizes the
large public available user-generated data on the web to achieve semantic relations which is
accessible on public available search engines. Most of the methods based on the second approach
employ aggregate page-count estimates of search-queries to extract semantic relations. In this
research, we use the second approach for concept similarity computation. Poor quality of the first
approach in our evaluations directs us to the second approach.
Concept similarity can be determined out of co-occurred words’ frequency in articles
automatically. Normalized Similarity Score (NSS) uses these frequencies to measure semantic
relatedness between words [16]. This score is derived from Normal Google Distance (NGD) [15].
In order to utilize NGD as a relatedness measure -rather than a distance measure-Lindsey
converts NGD scores into similarity scores by subtracting NGD from the its maximum score.
Therefore NSS computes the relatedness between two terms a and b as follows:
( , ) = 1 − ( , ) (4)
NGD measures the distance between two terms by the symmetric conditional probability of their
co-occurrences [17]. It means that NGD assumes that the probability of word x co-occurring
along with word y is high when the similarity between their concepts is “near” to each other and
vice versa. NGD is formulated as following equation:
( , ) =
max(log ( ) , log ( )) − ( , )
− min(log ( ) , log ( ))
(5)
where f(x) is the number of times a search engine hits for the search term x; f(x, y)is the number
of times this search engine hits both of x and y simultaneously; and M is the total number of
pages that can potentially be retrieved in search engine (e.g., Google can potentially retrieve
around 10 billion pages) [18]. Originally, NGD was developed for using by Google search
engine; nevertheless it is applicable in other search engines as well. In the present research Bing
is selected as a search engine due to its better performance.
3.2 Ontology tree construction
There are two practical approaches for constructing ontology trees: utilizing Word Net
hierarchical semantic structure and extracting ontology tree from e-commerce website categories.

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Each of these approaches suffers from several problems. To alleviate the problems, combining
these two approaches is one of possible solutions. In the current research, this solution is used.
The rest of this subsection introduces these approaches and details the strength and weakness of
them.
3.2.1 Using the Word Net Ontology tree
Word Net [7, 8] is a hierarchically organized lexical system motivated by theory of
psycholinguistics that was developed at Princeton University in the 1990s. As a conventional
online dictionary, Word Net lists alphabetically concepts important to a particular subject along
with explanation. The major advantage of Word Net is linking the words based on semantic
relations between their meanings [21]. The most frequently encoded semantic relation among
synsets is the super-subordinate relation i.e. hypernym-hyponym. This relation links more
general synset to the specific ones. Hypernym represents is-a relationship among the words.
Contrarily, hyponym is inverse-is-a relationship. As an example, {digitalcamera#1} is a
hyponym for {camera#1} and a hyponym for {webcam#1}. Figure 4 depicts the hypernyms tree
for webcam. Hypernym-hyponym relation can be utilized to extract semantic hierarchy structure
(or ontology tree). But, another problem exists yet. It is possible that a word have multiple parents
in at the same level of hierarchy. To face this issue, we select one of the more significant parents
based on the meaning of them. For example, however {camera} has two hypernym:
{photographic equipment} and {television equipment, video equipment}, we use {photographic
equipment} for ontology tree extraction. Because our mean by the word camera is a device for
take photograph.
Figure 4. Hypernyms tree of “webcam”
Using WordNet hierarchical semantic structure is widespread in research projects; however, this
structure is not applicable in real applications for a few reasons. First, concepts are categorized by
their semantics rather than their applications. It makes two close concepts to become far from
each other in real world context. For example, while in real stores both monitor and monitor
cleaner are in the same category, in a semantic tree they are not. Second, WordNet is unfair. It
means that the abstraction ratio in the tree levels is not equal for all concepts. This problem makes
similar words to be at different depths in the ontology tree. More clarification regarding to the
mentioned problems is shown in Figure 5. This figure displays the positions of three similar
0:webcam
1:digital camera
2:camera
3:photographic equipment
4:equipment
5:instrumentality, instrumentation
6:artifact
7:physical object, object
8:entity
3:television equipment, video equipment
4:electronic equipment
5:equipment
6:instrumentality, instrumentation
7:artifact
8:physical object, object
9:entity

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words in WordNet tree: Mouse, Keyboard and Laptop. As seen, while all electronic stores
categorize “mouse” and “keyboard” in the same level,WordNet does not. In addition, exhibited
distance and depth difference between “keyboard” and “Laptop” does not seem to be true.
Figure 5. Semantic granularity is not equal all over the WordNet. Depth of Mouse, Keyboard and Laptop in
WordNet hierarchical semantic structure does not make sense.
3.2.2 Using the ontology tree extracted from website categories
Extracting ontology tree from product categories of e-commerce websites is another approach to
overcome the limitation of the Word Net tree. However, there is not any publicly available
dataset based on this approach. Several website such as Netflix have flat categories, while
hierarchical structure is necessary for our purpose. Another essential requirement of ontology tree
is granularity consistency i. e. each hierarchy level of tree should be almost in same semantic
detail level. Among directory and ecommerce websites (such as Yahoo Dir., and Amazon) eBay
comparatively satisfy this requirement more preferable. Moreovere Bay has another benefit: its
ontology tree includes comprehensive range of shopping concepts, since it sells various kinds of
goods. Figure 5 depicts the full ontology tree extracted from eBay.
electronic device
mouse
keyboard
8
device
keyboard machine
computers
digital computer
personal computer
portable computer
laptop computer
8
personal computer
portable computer
laptop computer

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Figure 6.Ontology tree which is extracted from eBay categories
Despite the mentioned advantage of eBay ontology tree, it is far from a mature ontology tree yet.
This tree covers a few contexts in comparison with WordNet. In addition, the contexts are
categorized by their applications rather than their semantics, contrast to WordNet. It makes two
distant concepts to become adjacent in the ontology tree. For example contrary to common sense,
in Figure 6 “Home” is the parent (more general concept) of “Baby”.
As aforementioned, ontology tree of WordNet and eBay is on the two end of a semantic-applied
spectrum. WordNet is completely semantic, while eBay is applied. Each of them causes a specific
difficulty. A reasonable approach to reduce difficulty is combining two previous approaches.
Hence we prefer combination approach
6.
4. Proposed Approach
In this paper, we attempted to show an advanced ontology tree for context representation
overcoming the limitation of the previous trees. Afterward, we detail an enhanced method for
computing the similarity between two contexts based on the proposed tree. To do so, in the
section first, we reveal the limitation of the previous methods and then the proposed enhanced
solution will be shown.
4.1 Limitation of ontology context modeling
In section 2, we elaborated three approaches for context modeling and pointed out their
limitations. Among these approaches the most popular one is the context modeling using
ontology tree. As discussed before, the most important limitation of this approach is that the tree
may be constructed unfairly. In particular, one branch of a node may be split abstractly while the
Motors Electronics
Cameras
Digital
Camera
Camrecorde
rs
Camera
Accessories
Lens &
filters
Telescope
Cell Phones Computers
Accessories
Tablets
Networking
Laptops
Printer
Collectibles &
Art
9
6.
Products
Computers
Accessories
Tablets
Networking
Laptops
Printer
TV
Collectibles &
Art
Home
Baby
Crafts
Home &
Garden
Pet Supplies
Toys
Entertainments Books Fashion
Clothing
Accessories
9
6.
Fashion
Clothing
Shoes
Accessories

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other branch is split in more details. In other words, this tree is granularity inconsistent. The
limitation is illustrated in the following (Figure 7):
Figure 7.An example of unfair constructed ontology tree for the computer science concepts
As shown, computer science is split to software and hardware. Afterward, the hardware node is
split to VHDL programming language while the software is split to programming language;
afterward object oriented language and finally the java programming language. As seen, in the
above tree VHDL and java are both a programming language in hardware and software context
but their distribution is not equitable. In particular, the distance between hardware and VHDL is
an edge while the distance between software and java is three edges and so it is not an equitable
distribution. Therefore, the VHDL node should be split into more nodes in order to have a fairly
constructed ontology tree. As it is clear, this unfair construction of the ontology tree causes
several problem in the context’s similarity computation methods which are based on these
ontology trees.
4.2 Context modeling based on weighted ontology tree
In favor of overcome to the described limitation, we suggest to use a weighted ontology tree
instead of the traditional trees. Edges weights in this tree represent the similarity between their
corresponding nodes. To clarify the issue, it is illustrated by the Figure 8. By specifying the
similarity between the nodes of an edge, the distance between any two arbitrary nodes can be
specified more equitable. Therefore, the total distance between hardware and VHDL is equal to
the total distance between software and Java (i.e. 14). The reason is that, despite of splitting the
software node in more details the distance between the split branches is not much and so both
total distances become equitable.

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Figure 8.Fairly constructed ontology tree for the computer science concepts
In order to implement the above solution it is needed to construct a weighted ontology tree. To
do so, first we need to have a method for computing the similarity between the nodes of an edge
as their weighted distance. To achieve this, we use the Normalized Similarity Score (NSS)
method defined in subsection 3.1. This method is based on frequencies of concepts (words) co-
occurrences in the pages indexed by search engines. Here, each context is a concept, which has
its own meaning in the dictionaries. The ontology tree’s edge will be labeled by the similarity
between its two ends nodes. Afterward, the similarity between any two arbitrary contexts can be
computed by multiplying the edges weight on the path between them in their ontology tree. For
instance in Figure 9, multiplying w1, w2, w3 and w4 results the similarity between S1 and S2.
Thus, we can formulate the similarity between any two arbitrary contexts Ci and Cj as:
, =
1
∏ ∈ ,
(6)
where the Si and Sj are the related node of and in the ontology tree. In addition, denotes
the weight of edges in the unique path between Si and Sj. Using the above method, distance
between two nodes and the edges’ weights have impact on similarity simultaneously.

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Figure 9.Services in a context ontology tree
5. Experimental Setup
Over the last decade, publications on computational trust model have significantly increased.
However, these researches seldom have evaluation on real data. Among the research that
evaluated their model, most of them have used simulation techniques using stochastic generated
data. Therefore, evaluation of trust models with real data is still required to investigate their
practical consequences. In the present research, we aim to evaluate our proposed method on a real
data set. To do so, two notable issues should be carefully considered:
1. There is no public dataset available on trust area including context of each transaction (based
on our literature review). Regarding available datasets such as Epinions, transactions are not
linked with their related real record to find their context; therefore, data should be collected
from scratch.
2. There is not any standard process to evaluate the results in context-aware trust modeling,
thus a process for evaluation of the proposed method should be suggested. The process
should depict the difference between the accuracy of the trust modeling in the simple and
weighted ontology.
To cover the above concerns, we considered several solutions, which are studied in the following
subsections.
5.1. Data collection
We extract our data set from Epinions.com. Epinions is a review website where ordinary users
can assign rating and write reviews about product and seller. Also they can assign a trust rating
representing helpfulness, to reviewers. Users can access to recommendations, criticisms, and
reviews for products; however, only registered users are permitted to participate in rating a
product or writing reviews at Epinions [22].
To collect data, various popular e-commerce sites such as eBay, Amazon, and Epinions were
investigated. Each of these websites has its own limitations to be used in our evaluation. For
instance, eBay offers the average rating of all customers (reputation) on each seller, whereas each
transaction rating is needed for our study, because we should determine the context of each

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transaction as well as its corresponding rating. Regarding Amazon, since buyers only rate
products (not sellers), computing trust of a seller in other contexts is impossible. Contrary to eBay
and Amazon, Epinions.com can be a suitable choice for our purpose, despite of its shortcomings.
In Epinions users can rate seller as well as products. Moreover it offers the ratings on seller
separately. As a result, Epinions does not have the aforementioned limitations of eBay and
Amazon.
Data collection on Epinions encounters with a two major challenges. First, what kind of reviews
should be collected? And second, how can we collect these kinds of reviews? This subsection
elaborates on these challenges, and how we finally collected an appropriate dataset for our
experiments.
First, only trustee who has multiple contexts is appropriate for our experiments. Therefore,
products’ ratings (most of the Epinions reviews) are inappropriate for our requirement, since a
product is not definable as a trustee and does not have multiple contexts. Regardless of the ratings
on products, one category of Epinions.com remains including multi-context data: "Online Store
and Services". In this category, users can rate e-stores (not product) and write reviews about
them. As these stores sell various kinds of products, the corresponding user reviews could be in
different contexts. Thus, we collect the required data from reviews of "Online Store and Services"
category. Also, among these reviews, only those regarding a unique context are usable. To be
exact, overall reviews without focusing on any context or reviews deal with multiple contexts
(concerning purchasing numerous products) are not suitable for our purpose.
Second, context of each rating is not clearly available in Epinions, and so, automatic gathering of
context data by conventional web scrapper is impossible. Therefore, in order to identify the
context of a review, its text should be studied by human. For instance, if in a review, a user has
commented on the quality of a Lego, bought for her child, the toy context should be assigned to
this review. Moreover, in addition to difficulty of context identification, appropriate sample size
is another limitation of data collection. For each seller, we require at least two contexts including
approximately 30 ratings on each context, while most of the sellers do not have as many ratings.
Figure 10. Overview of fields that collected
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Amazon.
experiments.
13
Amazon.
experiments.

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The process of data collection on Epinions regarding the above challenges is as follows. We skim
thousands of reviews and ignore reviews that are unrelated, without any specific context or with
multiple contexts. The ratings’ data of the remained reviews is collected according to their seller-
context classification separately. Finally, sellers which do not have at least two contexts including
approximately 30 ratings are removed from data. Despite of the mentioned challenges, we
gathered ratings data off our sellers in different contexts supporting our experiments. These
sellers are eBay, Overstock, Beach Camera and Amazon. Figure 10 depicts some part of the
collected data in Laptop context. This data consist of five fields: context, rate, rating date,
description and URL (i.e., link to the source of the review).
5.2. Evaluation Criteria
As aforementioned, final goal of this paper is to predict the trust value of a certain user in an
unknown context, based on their trust value in a known context. To evaluate the accuracy of our
prediction, an evaluation measure introduced by Liu et al. [4] is utilized. This measure calculates
outcome error from formula (7). This formula is a kind of “Prediction Error” type. This type of
error calculation is one of the most widespread perform anceevaluation criteria exploited in
several other papers on trust models [24, 25, 26] .According to these papers, prediction error of a
trust evaluation model can be computed as follow:
=
−
5
× 100 (7)
Where Predicted_Rate is the predicted trust value of the trust evaluation model, and Real_Rate is
the actual trust value.
6. Experimental Results
To evaluate our proposed method, at first, it is applied on several subtrees extracted from the base
ontologytree (see Figure 6), elaborated in the previous section. Second, all the tree edges are
weighted using Normalized Similarity Score detailed in subsection 3.1. The resultedweighted
subtrees are shown in Figure 11.

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A) Subtree between Cell-phone and Laptop for eBay data B) Subtree between Digital-Cam and TV for Beach Camera data
D) Subtree between Digital-Cam and Book for Amazon data E) Subtree between Clothing and Book for Overstock data
Figure 11.Subtrees constructed to evaluate proposed method
In the third step of experiment process, the trust evaluation criteria, described in subsection 5.2, is
applied to both our proposed method and the Liu et al. similarity computation method [4]
formulated in equation (1), in order to compare weighted and un weighted similarity computation
methods respectively. These methods try to predict trust in an unknown context using a known
context. As Figure 10 compares the error of these predictions, our proposed method outperforms
the prediction results. The reason for deficiency of the un weighted method is its static approach
on similarity computation. As mentioned in subsection 4.1, this method considers only the path

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length between two concepts in the ontology tree. As a result, when the tree is unfair, the path
length between two concepts is not remarkable which represents the similarity lower than real
value. For example, in left bottom subtree of Figure 5, adding "work" and "publication" nodes
between "Books" and "Products" increases granularity of this branch. Accordingly, the subtree
become sun fair and the similarity will be decreased to 0.2 and the trust value on the target
context is predicted with less accuracy. On the contrary, our proposed method decreases this
drawback using semantic similarity of each parent and child nodes.
Figure 12.Comparison between prediction error rate of the proposed method and un weighted
method [4] on real data
The most performance improvement of Figure 12 is occurred on eBay and Amazon cases.
Related subtrees of these cases (see Figure 11) explain the reason. These subtrees are fairer
compared to others. In eBay case adding "phone" causes semantic fair of the two branches, while
adding "Cell phone" increases granularity of the left branch. Also, in Amazon tree "Book" and
"Digital Camera" are in the same level of tree according to our expectation, while in other trees
leaves have dissimilar levels.
Until know, error of proposed method was compared to unweighted method error proportionally,
whereas absolute error pattern of our method is another substantial issue, shown on Figure 12
results. This figure exhibits that the least prediction error achieved by Amazon. The reason of this
achievement can be due to Amazon’s expertise in book context, which has gained popularity for
the electronic market. Accordingly, its trust value on book is higher than other contexts.
Therefore, our method can predict Amazon trust on "Digital Camera" context accurately. In
figure 14 the relative expertise between two contexts is defined as "rate difference". It signifies
the absolute difference between real trusts rates on two contexts. Figure 13 represents the relation
between the rate difference and the proposed method error. The more prediction error increases,
the less rate difference decreases. It indicates that expertise has an important influence on

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prediction performance, and the high Pearson correlation coefficient between these variables
(about -0.95) confirms this claim.
Figure 13. Relation between real trust rate difference and proposed method error
7. Conclusion
This research transcended a limitation of previous ontology tree context modeling to improve
context similarity measurement. An important limitation of context modeling using ontology tree
is that the tree may be constructed unfairly or granularity inconsistent. In other words, the
semantic similarity of each two adjacent nodes is unequal in the ontology tree. The proposed
approach overcomes this limitation by weighting edges based on their semantic similarity.
Weight of each edge is computed based on Normalized Similarity Score (NSS) method. This
method is based on frequencies of concepts (words) co-occurrences in the pages indexed by
search engines. Using the proposed approach, trust value prediction of a certain user in an
unknown context, based on their trust value in a known context becomes more accurate. Thus,
this approach can be implemented in a wide range of web applications from a small business
environment to a large market-place such as electronic shopping systems.
To test the success of the proposed approach, we collect customer reviews about four e-
commerce sellers in Epinions.com. For each seller reviews of at least two contexts were
collected. It is assumed that trust value in a context is known and the other is unknown. We
compute trust value in the unknown context from the known context. We perform this
computation twice, once with weighted ontology tree and once with unweighted. The difference
between these two results show the performance of the proposed approach compared with
previous approach.
Our experimental results showed the performance of the proposed approach over unweighted
ontology tree. The prediction error of trust evaluation with weighted ontology tree is 8 to 21
percent lower than unweighted one under different scenarios. As tree become fairer after
weighting, the performance improvement becomes more obvious. In addition to relative error
0.00
0.20
0.40
0.60
0.80
1.00
1.20
1.40
1.60
Book>>DigiCam Book>>Clothing Cellphone>>Laptop DigiCam>>TV
Rate Difference Proposed method Error
Amazon Overstock eBay Beach Camera

18
(performance compared with previous approach), absolute value of error also follows a certain
pattern. The absolute error of the suggested approach was less when we utilize trust value of the
context which trustee is expert on that context. If we define expertness as difference between real
ratings of known and unknown context, expertness has high negative correlation with absolute
error. Amazon is an example of this fact. Amazon is an expert website in the context of book.
Accordingly, predicting the trust values of Amazon in other contexts based on book context is
more accurate. It is worth noting that this feature is often useful. Most of the times we know the
trust value in popular context of a seller and we require predicting trust values of other contexts.
The novelty of the current research relies on two major facts. First, the proposed approach
improved the performance of trust evaluation in unknown contexts. Second, we collect a real trust
data set including context information with considerable effort. This is done while previous
researches on context trust evaluation either do not asses their models or use simulation for test.
Obviously, the result on real data is more creditable than simulation. In addition to the mentioned
contributions, this study has other contributions such as: the procedure of evaluating the
proposed approach, the method of ontology tree construction, and using automatically extracting
semantic relation from human-written text for weighting ontology tree.
As a future work, the proposed approach should be evaluated on larger data set and other
application (instead e-commerce). Another option for continuing this research is comparing the
performance of weighted and un weighted ontology tree outside the area of trust and reputation.
Furthermore, suggesting a method for expertness measurement enables us to estimate the
performance of trust evaluation. Another avenue of exploration is to extend suggested similarity
computation method to normalize the edges’ weight in each problem. It can be embedded to our
model with configurable parameters.
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