Fuzzy

A Hybrid Method for User Query Reformation and Classification

Said M. Fathalla

Department of Mathematics & Computer Science Faculty of Science, Alexandria University

Alexandria, Egypt [email protected]

Yasser F. Hassan Department of Mathematics & Computer Science

Faculty of Science, Alexandria University Alexandria, Egypt

[email protected]

Maged El-Sayed Department of Information Systems and Computers

Faculty of Commerce, Alexandria University Alexandria, Egypt [email protected]

Abstract. This paper presents a hybrid method for user query reformation and classification depending on fuzzy semantic-based approach and KNN classifier. The overall processes of the system are: query preprocessing, fuzzy membership calculation and query classification and reformation. Classification is performed using KNN classifier not just by keyword-based semantic but using a sentence-level semantics. A semantic model - EnrichSearch - has been developed for applying this approach. After classification, user’s query is reformulated to be submitted to a search engine which gives better results than submitting the original query to the search engine. Experiments show significant enhancement in search results over traditional keyword-based search engines’ results.

Keywords—semantic web; fuzzy logic; ontology; KNN

I. INTRODUCTION The important goal of the semantic web is to make the

meaning of information explicit so that it will be machine-accessible information which enabling more effective access to knowledge contained in heterogeneous information environments, such as the web [1]. Current search engines are not very effective because they are based on simple query-document matches without considering the semantics of the keywords [2] and also the intended semantic of a keyword is difficult to know if the keyword has more than one semantic according to the context.

Fuzzy sets are the type of sets in set theory that are imprecise and no boundaries. Accordingly, they only relay on the degree of their memberships. As the extension of web grows, the thorny problem of data heterogeneity is also increasing. However, the review/comparability of the fuzzy sets, crisp sets and semantic web technologies were given based on their extensions [3].

Synonyms are different words with similar meanings. Words that are synonyms are said to be synonymous, so synonymous are have the same membership values to a category. For example the keywords “movies” and “films” are synonymous. Also Short words such as “msg” and “err” are considered to be synonymous for the original words “message” and “error” respectively. Due to Polysemy, a homonym is one of a group of words that share the same spelling and the same

pronunciation but have different meanings. It is important to identify the intended meaning of each keyword, this done by using the classification of the text.

The main contribution of this work is the disambiguation of the meaning of keywords through its context (getting the intended meaning of keywords) because working with keywords individually means lose the context which is the only thing that can help to deal with ambiguity. The intended meaning of keywords is used in query reformation to get better results rather than submitting the original query to a search engine and also to avoid too many irrelevant results from retrieval which improve the retrieval effectiveness. The task of automatically classifying a natural language text into a predefined set of semantic categories based on their content is called text categorization [4]. In this work, text categorization is done not just for simple word or phrase-based but also semantics-based classification. For example, consider the keyword "switch", if a user working in hardware maintenance formulates the problem statement ”turn the power switch on”, if a user working in network formulates the query ” power on network switch”.

Many researches in query enrichment uses ontologies to extract keywords meanings to enrich the query but in our research we does not use only ontology but we use our classification algorithm to extract the intended meaning (which is denoted by InSem(wi)) according to the meaning of the overall query.

This paper is organized as follows. In section II, a related work is discussed. In section III, an overall architecture of the proposed methodology is discussed including the training stage and classification and reformation stage. Section IV discusses the ontology development process. Section V shows our experimental results. Finally in section VI, we conclude and describe our vision for the future work.

II. RELATED WORK The ARCH system [5] is a personalized IR system that

enhance the user query using both of user profile which contains long term user interests which limit the scope of the system to what’s in user’s profile which is not necessary related to what the user is currently searching for. Researches

ICCTA 2012, 13-15 October 2012, Alexandria, Egypt

132978-1-4673-2824-1/12/$31.00 ©2012 IEEE

such as [6] rely on question classification and exact matching between questions’ keywords and answers without using semantics. The publicly available Microsearch system [7] which enriches search results presentation with metadata extracted from results and report on some of the early feedback have been received. Such approach faces many problems such as losing metadata, most users ignore feedback and performance problems due to extracting and summarizing embedded metadata.

III. OVERALL ARCHITECTURE OF THE PROPOSED METHODOLOGY

The work is divided into two main stages. The first is the training stage in which the system has been trained to adjust the weights of the keywords that falls into more than one category. The second is the working stage in which user can submit the query to the system for classification and reformation. Fig. 1 shows a layered approach for the proposed model in which layers show the generic architecture of the model as follows:

User Interface Layer: involves the users who submit the query to the system in form of keywords.

Text Tokenizer Layer: splits the query statement provided from the above layer to a set of keywords and provides it to the underlying Semantic query layer.

Semantic Query Layer: takes the main keywords from the above layer and formulate a semantic query using the underlying Formal Query Layer.

Formal Query Layer: provides a formal query language –which is SPARQL-that can be used to retrieve semantic data from the underlying Semantic data layer.

Semantic Data Layer: contains the semantic data represented in RDF repository.

Figure 1. Layered approach for the proposed methodology

A. The Training Stage The training is done by adjusting the membership value of

a keyword to a certain category. This stage entails the computation of fuzzy degree of similarity that ranges between two edges “0” and “1”. Consider a set of recognized keywords K = {k1, k2, …,kn}, set of relevant

categories C = {c1, c2, …, cm}.The relevance of keywords K to categories C is therefore expressed by a fuzzy relation

μ:K C [0, 1] (1) where the membership value of μ (ki, cj) specifies the

degree of relevance of keyword ki to category cj. The membership values of this relation are determined by using a set of training examples, where each training example contains a text query and its category. Equation (2) shows the possible values of membership. The larger the value; the better the match of keyword to category. In particular, the membership of “1” means a perfect assign of this word to that category – i.e. this word does not belong to other category in the ontology- , a membership of “0” means this word does not belong to this category at all and the keywords that have different meaning (homonyms) belongs to different categories has a value between 0 and 1.

(2) Let TS be a set of training examples consisting of n queries and their classifications.

TS = {⟨q1, c(q1)⟩,⟨q2, c(q2)⟩ , …,⟨qn, c(qn)⟩ } Each query in the training set is represented by a set of term-frequency pairs as follows.

q = {⟨k1, w1⟩, ⟨ k2,w2⟩, …,⟨kn, wn⟩ } where wj is the occurrence frequency of keyword kj in the

query. Given a set of training queries TS, the membership value μ (ki, cj) is calculated from the total number of occurrences of keyword ki in category cj divided by the total number of keyword frequency ki in all categories as follows.

μ , =∑wi ,{wi∈qk qk∈TS C(qk )=cj }∑wi ,{wi∈qk qk∈TS }

(3) Table I, II and III show an illustration example for

membership value calculations. Suppose query q1 and q2 are belong to category C1 and q3 belongs to C2 and there are only three keywords {k1, k2, k3} appears in TS. A training set structure is shown in table I, table II shows the term-frequency to each category and table III shows the calculated value for each keyword to each category.

TABLE I. TRAINING SET STRUCTURE

Query Keywords

Category K1 K2 K3 q1 q2 q3

1 2 0

2 1 1

0 0 3

C1 C1 C2

TABLE II. THE TERM-FREQUENCY TO EACH CATEGORY

Keyword Category

C1 C2

K1 K2 K3

3 3 0

0 1 3


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TABLE III. CALCULATED VALUE FOR EACH KEYWORD TO EACH CATEGORY

The training algorithm works in three main steps:

I. Input: the Training set (TS). II. Calculate μ (ki, cj) according to (3).

III. Update ontology with new weights

B. The Classification and Reformation Stage Fig. 2 shows the overall architecture of the proposed

model in classification and reformation stage which contains the main components of the model and shows the overall process starting from accepting user’s query as input to produce the final classification of the query and the enriched query. The overall process of EnrichSearch compromise three major steps:

Step1 (Preprocessing): the interface model takes a query from the user as input and performs query tokenization.

Step2 (Semantic Annotation): an ontology-based annotation is used in extractor model to relate the

keyword appearing in the query to an ontology concept that defines it using SPARQL language [8] and Jena [9]. This step removes any ambiguity that may occur for homonyms.

Step3 (Query Classification): query classification is performed using the classification algorithm in Fig.4.

1) Query-category fuzzy similarity measure Let a test query q = {⟨t1, ( 1)⟩, ⟨t2, ( 2)⟩,… , ⟨tm , ( )⟩}, where ( ) represents the

membership degree that term i belongs to q. The fuzzy similarity between q and a category ci is calculated using (4).

( , ) =∑ ( , )⊗ ( )∑ ( , )⊕ ( )

(4)

Where ⊗ ⊕ are fuzzy conjunction (t-norm) and disjunction (t-conorm) and ( ) is the membership value of a keyword to a query. The conjunction operator could be generalized for any triangular norm, also called t norm and denoted by t(x, y), which is a mapping [0, 1] [0, 1] into [0, 1]. In the same way, it is possible to define fuzzy disjunction operators as a triangular conorms, also called t conorm and denoted by s(x, y), as a mapping [0, 1] [0, 1] into [0, 1] [10]. The most commonly used conjunction and disjunction operators are Einstein Product and sum which used to calculated t-norm and t-conorm as shown in (5) and (6).

Figure 2. Architecture of the proposed model (classification and reformation stage)

keyword Category

C1 C2

K1 K2 K3

1 0.75

0

0 0.25

1


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t(x, y) = .

2−( + − . ) (5)

s(x, y) = . 1+ .

(6) The test query to be classified is represented as a Boolean

features vector that assigns ( ) to one for every keyword k that occurs in q at least once and zero if the keyword k is not found in q as shown in (7).

(7)

2) Query Classification The query classification is performed using the k-nearest-

neighbor (KNN) classifier according to the query-category similarity. The KNN is selected as a baseline classifier as it is easy to implement and often results in very good classification performances in many practical applications [11]. Fig. 3 illustrates how classification is performed. Given the similarity measure of the given query to each category sim(q,Ci),the final classification is C according to (8) such that [12] C C1, C2,…, CM .

d(q, C ) = min d(q, sim(q,Ci)) i=1,2,…,M (8)

Figure 3. Query classification using KNN

Figure 4. Classification algorithm

3) Query reformation

The intended semantic for each keyword ki is extracted from the ontology according to the final classification of the query to formulate the enriched query according to (9). For example consider the keyword “switch” in the two queries statements “how to switch the HDD Rack on” and “I have a

problem in the network switch connected to my computer”. This keyword has two meanings (the switch device that switch the power on or off and the switch device which connect computers in a network) so the problem is which meaning should the machine decide to take in the enriched query? The answer is that the machine decide to take the first meaning (which is the intended meaning according to query classification) when the user submit the first query and it will take the second meaning when the user submit the second query according to the classification of the query statement. Finally, the enriched query is submitted to keyword-based search engines which give better results than submitting the original query.

ki, EnrichedQuery= “classification output“+ki“OR” InSem(ki) (9)

IV. ONTOLOGY DEVELOPMENT The semantic web uses the resource description format data

model (RDF) to describe data models, including concepts and relationships between them. The main purpose of developing ontology is to share common understanding of the structure of information among people or software agents so that it becomes computer readable and understandable. We have developed a well-defined ontology as a way to specify keyword semantics and the relationship between these keywords. In the construction process of the ontology, we follow the steps in the ontology-building process suggested in [1]. For the implementation and modelling of the domain ontology we chose Protégé [13] as a development tool. Fig. 5 shows a mapping of some RDF concepts to ontology. Some of properties types as discussed in the next sub sections.

Figure 5. Mapping RDF concepts to ontology

A. Inverse Property

The property hasCategory has an inverse property which is categoryOf that is if switch hasCategory network, then because of inverse property, it could be inferred that Network is category of switch.


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B. Transitive Properties Fig. 6 shows an example of the transitive property

hasSynonym. If the individual Network has a synonym that is Net, and Net has a synonym that is Mesh, then we can infer that Network has a synonym that is Mesh. Inverse property is indicated by the dashed line in Fig. 6.

Figure 6. An example of a transitive property hasSynonym

C. Symmetric Property

Fig. 7 shows an example of a symmetric property. If the individual Fuzzy is related to the individual Uncertain via the hasSynonym property, then, it could be inferred that Uncertain must also be related to Fuzzy via the hasSynonym property. In other words, if Fuzzy is the synonym of Uncertain, then Uncertain must be the synonym of Fuzzy.

Figure 7. An example of a symmetric property

D. Reasoning

One of the main services offered by a reasoner is to test whether or not one class is a subclass of another class. By performing such tests on the classes in an ontology it is possible for a reasoner to compute the inferred ontology class hierarchy. Another standard service that is offered by reasoners is consistency checking. Based on the description (conditions) of a class the reasoner can check whether or not it is possible for the class to have any instances. Protégé 4 allows different OWL reasoners to be plugged in; the reasoner shipped with Protégé is called FaCT++ [14]. The ontology can be ‘sent to the reasoner’ to automatically compute the classification hierarchy, and also to check the logical consistency of the ontology.

The W3C has proposed the Resource Description Framework (RDF) [15] for exposing the meaning of a document to the web community of people, machines, and intelligent agents. Rules provide the natural and wide-accepted mechanism to perform automated reasoning, with mature and available theory and technology. This has been identified as a design issue for the semantic web, as clearly stated by Tim Berners-Lee et al [16]. The mapping rules that we consider are following.

hasSynonym(x,y) hasSynonym(y,z) hasSynonym(x,z) hasSynonym(x,y) hasSynonym (y,x)

E. Online Validation Tool

There are many online validation tools that check and visualize RDF documents. The most powerful RDF online validation is that provided by W3C [17]. Fig. 8 shows a graph representation of a resource by W3C online validation service.

V. EXPERIMENTAL RESULTS The solution was built using Jena, a java framework for

building semantic web applications. Briefly, Jena provides an API programmatic environment for RDF and its derivatives. It can query RDF based data models, either in memory or persisted in a data store, using the SPARQL RDF query language [18]. Since all semantic data that we created are described in RDF format, they can be queried with the SPARQL query language. Currently, we integrate a snapshot with a textbox for users inputting their queries [19].

Rather than using the semantics of individual keywords in the query statement which breaks the relationship between keywords, the proposed method utilized the relationship of a keyword with the keyword that appears next (if any) by searching the ontology using the two words as one concept, if found then use the semantic of them as one concept rather than using the semantic of each one individually. In this section we will describe the data sets that we used for evaluation, experiments procedures and results. Queries in the study were designed to test various search features including single word search and phrase search. We used different collection of query sets to evaluate the effectiveness of fuzzy similarity approach in query classification process. The first collection is the AOL query set and the second collection is the DMO query set. These data sets represent a different number of categories. We divided the dataset into training set and testing set. Two third of queries had been marked as the training set and the remaining one third for the testing set. For evaluation, we use only the top 3 categories, which consist of about 84% of the queries. Samples of data set are shown in Table IV.

TABLE IV. SAMPLE FROM DATA SET

Search query Classification Dell network switch NETWORK Dell database server DATABASE network router NETWORK DHCP NETWORK IP NETWORK switch table NETWORK query table DATABASE The performance presented in this section is measured

from the accuracy to predict the class of test queries. The classification accuracy is defined as the percentage of test


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queries that are correctly classified. For a query in the test set that was assigned into several classes, the classification is considered correct if it is same as the given classification. The classification accuracy in our model is 89.2%.

We compared the results of a given query to a keyword-based search engine (we chose Google) with the results of the enriched query produced by our solution. The enriched query excludes many irrelevant results as shown in Table V. The comparisons of the original query and the enriched query in this regard are shown in Fig. 9.

The experimental results show that our solution enhances the results retrieved by the keyword-based search engines rather than using the original query.

TABLE V. A COMPARISON BETWEEN THE RESULTS OF GOOGLE FOR A USER QUERY AND THE CORRESPONDING ENRICHED

QUERY (JUNE, 20 2012)

No. Original Query Enriched Query

Google Results

for Original Query

Google Results

for Enriched

Query

Difference in Results

(Pages)

1 What is the network switch?

Network: what is the network group of computers connected together switch a device that connect computers in a network

About 291,000,00

0 results

About 2,310,000

results

288,690,000

2 DHCP

Network :DHCP Dynamic Host Configuration Protocol

42,100,000 results

2,600,000 results 39,500,000

3 truncate table

Database: truncate SQL command table storage unit

1,160,000 results

616,000 results 544,000

Figure 8. Graph representation of a resource produced by W3C online validation service.

Figure 9. Comparison between precision of results using original query and enriched query.


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VI. CONCLUSION EnrichSearch was designed as a plug-in for any search

engine. Thus, implementation of the EnrichSearch focused on the modification of the query string itself, instead of modifying the target search engine directly which is easier. The proposed solution provides a new method for query statement classification by using the semantics of the query keywords. Classification is done not just by keyword-based semantic but using a sentence-level semantics which means that the system can utilize the relationship between keywords (if any) to get the intended semantic of a keyword. Another benefit of this approach is that the algorithm is continuously improving itself by adjusting the weight of keywords which increases the classification accuracy by time. Ambiguity comes from homonyms is reduces by getting the intended meaning of keywords.

Our proposed solution facilitates online searching for user query by using the already-implemented keyword-based search engines (such as YAHOO! and Google) which produced by many companies and research labs with the benefit of semantic web and fuzzy logic to get better results than submitting the original query. This avoid many irrelevant results to the intended meaning of the user and save time to search for the needed information in the huge result set returned by the keyword-based search engines for the original query. Our future work is to apply ontology learning to enrich the ontology and use multiple ontologies in different domains not just only one domain.

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