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A Hybrid Query Disambiguation Adaptive Approach for Web Information Retrieval
A Hybrid Query Disambiguation Adaptive Approach for Web Information Retrieval
KSII Transactions on Internet and Information Systems (TIIS). 2015. Jul, 9(7): 2468-2487
Copyright © 2015, Korean Society For Internet Information
  • Received : February 23, 2015
  • Accepted : June 23, 2015
  • Published : July 31, 2015
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About the Authors
Roliana Ibrahim
Faculty of Computing, Universiti Teknologi Malaysia (UTM), 81310 skudai, Johor Malaysia
Shahid Kamal
Faculty of Computing, Universiti Teknologi Malaysia (UTM), 81310 skudai, Johor Malaysia
Imran Ghani
Faculty of Computing, Universiti Teknologi Malaysia (UTM), 81310 skudai, Johor Malaysia
Seung Ryul Jeong
Graduate School of Business IT, Kookmin University, Korea

Abstract
In web searching, trustable and precise results are greatly affected by the inherent uncertainty in the input queries. Queries submitted to search engines are by nature ambiguous and constitute a significant proportion of the instances given to web search engines. Ambiguous queries pose real challenges for the web search engines due to versatility of information. Temporal based approaches whereas somehow reduce the uncertainty in queries but still lack to provide results according to users aspirations. Web search science has created an interest for the researchers to incorporate contextual information for resolving the uncertainty in search results. In this paper, we propose an Adaptive Disambiguation Approach (ADA) of hybrid nature that makes use of both the temporal and contextual information to improve user experience. The proposed hybrid approach presents the search results to the users based on their location and temporal information. A Java based prototype of the systems is developed and evaluated using standard dataset to determine its efficacy in terms of precision, accuracy, recall, and F1-measure. Supported by experimental results, ADA demonstrates better results along all the axes as compared to temporal based approaches.
Keywords
1. Introduction
W orld Wide Web (WWW) and search engines have get to be vital gears of our everyday life. In spite of huge enhancements being made to optimize the web search over the last decade, still much be done to cope with continually expanding size of the web and needs of the users. Today, web search optimization is an active research area and has gained remarkable attention of experts from both the industry and the academia [1] .
One of the significant difficulties in web search lies in inadmissible importance of results caused by ambiguity. Query terms are inherently ambiguous due to polysemy, and most of the queries are short, containing one to three terms only [2] . Consequently the ambiguous queries in terms of user intent and information needs, result into retrieval of many irrelevant pages. As the web size develops, ambiguity gets to be omnipresent and users are in more prominent need for viable method of disambiguation.
The ambiguity can be defined as “A lack of clear and exact use of words, so that more than meaning is possible1 . For instance in Wikipedia 2 when the phrase “World Cup” is searched, it returns 41 entries for different categories including FIFA World Cup, ICC Cricket World Cup, Rugby World Cup, Bandy World Cup, and so on.
In quest of web search optimization, the Temporal Information Retrieval (T-IR) has gained greater attention in recent years [3] . However, majority of these solutions either focus on development of suitable tools or perform behavioral analysis based on log data. Significant numbers of user search queries have strong temporal components or characteristics. These are the queries whose underlying intent may be to obtain newest information, past or anticipated events and largely depend on time. For instance referring to the “World Cup” example, the user might be interested in information about FIFA World Cup 2014 at Brazil. In this regard, if the user issues a query phrase “World Cup 2014”, it will make use of the temporal feature and will produce 12 ambiguous results i.e., ICC T20 World Cup at Bangladesh, FIFA World Cup at Brazil, Men’s Hockey World Cup at Netherlands, Alpine Skiing World Cup at Austria, FIBA 2014 at Spain and so on. A detailed overview of T-IR, its relevant challenges and opportunities can be found in [4] .
Context is an important source of information in computing environments. The term context is defined by the authors of [5] as “any information that can be used to characterize the situation of an entity”. An entity is a person, place, or object that is considered relevant to the interaction between a user and an application, including the user and applications themselves. According to the authors of [1] , the query disambiguation can be greatly improved by applying contextual information. For instance, referring to our example, if we add the contextual information (Brazil) and rephrase our search query as “World Cup 2014 Brazil”, this would produce more accurate results according to our intent. Hence, it is observable that context plays an important role in resolving the queries ambiguity.
Majority of the existing literature such as rule-based [6] , topological [7] , and ontological [6 , 8] approaches are based on temporal information retrieval. The T-IR based approaches whereas somehow refine the search results by exploiting various temporal features; however, due to lacking of contextual information result into large proportion of irrelevant information retrieval. In this paper, we propose an Adaptive Disambiguation Approach (ADA) that makes use of both the temporal and contextual information thereby retrieving the most accurate results in accordance with the search queries. The proposed approach is comprised of five stages namely: query input, query categorization, sub-query construction, results integration and improved clusters through feedback. Experimental based evaluation of ADA reveals improved performance in terms of accuracy, precision, recall, and F1-measure as compared to existing work.
The rest of the paper is organized as follows: We begin in Section 2 by unfolding the problem background information about the query ambiguity, temporal queries and contextual information used in a query. The related work is depicted in Section 3. Our proposed approach is characterized in Section 4. The implementation of the proposed approach is portrayed in Section 5. The outcomes and assessment is examined in point of interest in the Section 6 though the paper is closed in Section 7 alongside identification of future work.
http://www.macmillandictionary.com/dictionary/british/ambiguity
http://en.wikipedia.org/wiki/World_cup
2. Problem Background
Disambiguating the search intent and improving the accuracy of resulting information is a hot research area where numerous contributions have been made to address these issues. In order to develop a basic understanding of the problem background, we illustrate the compromised accuracy with the help of an example. For instance, Fig. 1 shows our search query “Cultural Show” given in well-known search engine Google 3 which produced 324,000,000 results in 0.39 seconds. The given term is ambiguous in terms of location and year that’s why resulting so many answers.
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Results after giving query “Cultural Show”
It is generally accepted that a few queries submitted to search engines are ambiguous by nature e.g. World Cup, Cultural Show etc. Different studies have investigated the inherent ambiguity issues of search queries in various ways. The work presented in [9 , 10] describes what are ambiguous queries. For instance referring to word “java” in search query, it is not clear whether the user intent is the application or the Indonesian city. The authors of [6 , 7] summarized the following categories of ambiguous queries:
  • ▪Category 1 (Ambiguous Query):a query having multiple meanings; e.g. “apple”which may refer to fruit or company.
  • ▪Category 2 (Broad Query):a query that covers a variety of subtopics and a user might look for one of the subtopics issuing another query; e.g. “World Cup 2014”which covers some subtopics such as FIFA, Hockey, ICC, and FIBA. A user usually issues such a query first and then narrows down to a subtopic.
  • ▪Category 3 (Clear Query):a query that covers a narrow topic and has specific meanings; e.g. “FIFA World Cup 2014”. A clear query usually means a successful search in which a user can find several results with a higher degree of quality in the first results page.
Similarly, a quantification of ambiguous queries is performed in [11] where in GISQC_DS dataset, 220 queries are identified as ambiguous. Likewise, the authors in [12] discuss how common the ambiguity in search queries is. Table 1 presents a summary of various contributions that have been made for query disambiguation.
Existing query disambiguating approaches
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Existing query disambiguating approaches
Different ranking algorithms are used by search engines (PageRank by Google, HITS by Ask.com) to assess the relevance of results. Similarly, Word-Sense Disambiguation (WSD) approaches deal with the process of identifying the sense of a word in a sentence, especially when the word has multiple meanings.
http://www.google.com
3. Related Work
In information retrieval, removal of ambiguity is of vital significance both from the user and system perspective [15] . In this section we briefly describe the various contributions that have been made for query disambiguation and search optimization.
Bunescu and Pasca were the first to use Wikipedia as a resource for disambiguation [17] . They expressed the disambiguation task to be a two-step process where a system must first identify the prominent terms in the text and secondly link them accurately. Although 84% of the accuracy has been claimed but it necessitates word connection with time highlights .Bunescu and Pasca’s work was initially limited to named entity disambiguation, and then enhanced by Mihalceain [18] thereby developing a more general system that linked all “interesting” terms and achieved 94.33% in terms of precision however keyword word extraction in view of time highlights can be used to enhance the execution.
Ricardo, at al. [16] highlighted the disambiguation of text queries with respect to temporal feature and attained precision, recall and f1-measure as 0.945, 0.92 and 0.943 separately. They proposed a two-stage process where relevant temporal expressions are extracted from results and then grouped into same clusters with respect to common year. Their approach was based on the idea of finding one non trivial term in text and focused on temporal clustering. Alonso et al. [19] first introduced the temporal clustering on the basis of topics and time. Their work solely relies on temporal features thereby compromising the accuracy of the results.
Link Text Topic Model (LTTM) based disambiguation approach has been proposed by Skaggas and Getoor in [20] , Although the 61.9% accuracy has been achieved but it resolves the link disambiguation problem only thereby lacking the capability to disambiguate the user queries. Hence it needed to utilize time highlights in order to enhance the performance.
Boston et al. [15] developed a system (called “Wikimantic”) for link disambiguation and query expansion in response to user queries for the retrieval of information graphics. In the developed system, they first disambiguate short text strings, followed by determination of the instant when the sequence of words should be disambiguated. The performance in terms of precision 0.87 has been attained but the main limitation of their system is that it only entertains short queries and the performance is greatly deteriorated when exposed to large queries. Furthermore, it attains low precision and recall as compared to other approaches.
Given a large text string, it’s always possible to find at least one trivial term to start the process. Ferragina and Scaiella [21] addressed this problem by employing a voting system that resolved all ambiguous terms simultaneously. The authors made claim of accomplishing 91.7, 89.9, and 90.8 as precision, recall and f1-measure independently. Their system makes use of various characteristics associated with different fragments of the input strings. Furthermore, due to unavailability of trivial terms in short text strings, its performance is greatly affected.
More recently, Anastasiu, D.C, et al [1] investigated the problem of query disambiguation by making use of keywords search and contextual information. First the articles were retrieved on the basis of both combined fragments of query as well as contextual terms. Next they retrieved the articles based on only query terms and finally similarities were computed. The authors accomplished the accuracy over yahoo as 33% and over google as 50% and henceforth can be enhanced by using the term publicizing concept for contextual as well as temporal features. Eventually, the commonly retrieved results were presented to users for their selection. A summary of the related work is presented in Table 2 .
Summary of query disambiguation approaches
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Summary of query disambiguation approaches
4. Adaptive Disambiguation Approach (ADA)
In this Section, we give detailed description of our adaptive approach of disambiguating text queries. Basically, we extend the work presented in [22] thereby incorporating the contextual information along with temporal features. Overall our adaptive approach is comprised of five-steps as depicted in Fig. 2 .
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Block diagram of Adaptive Disambiguation Approach
Furtherance in depiction about Fig. 2 , there are circles with distinctive hues i.e. blue and orange. The blue shaded circles are intended to demonstrate the procedures while the orange hued circles are speaking to the parallel procedure of same kind utilizing distinctive parameter depicted there as a part of. The five-steps of ADA are given as follows which are further described in the subsequent sub-sections:
  • • Query Input
  • • Query Categorization
  • • Sub-query Construction
  • • Results Integration
  • • Improved Feedback
- 4.1 Query Input
The input query can be explicit (combination of text and location) or implicit (just text). In this paper, we deal with the former one where the availability of contextual information is helpful in refining the search results. We denote the input query by q and consider a result set n of 10 entries against each query as shown in Fig. 3 . We use Google Web Services for information retrieval in accordance with the input queries.
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Search results against in response to query
- 4.2 Query Categorization
After taking the query from user, next we categorize it in accordance with its concept ambiguity. Similar approach has been adopted in [6 , 7 , 22] where three different types of concept queries are defined: ambiguous, broad and clear. In our approach, we further classify the ambiguous queries on the basis of both contextual and temporal information into contextual (location based), non-contextual (year based), and ambiguous (neither ensuring contextual nor temporal information) as shown in Table 3 .
Results recorded after execution
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Results recorded after execution
- 1.2.1 Contextual Queries
These are the queries which are delicate to location i.e. country or city name are recorded in the search results e.g. “poems” in the row 6 of Table 3 demonstrating the "1" as genuine esteem under the section "Country Information" while displaying the "0" as false under "year" and "city" sections independently.
- 1.2.2 Non-contextual Queries
The queries are said to be non-contextual if the corresponding results include some temporal features i.e. year e.g. Spartacus, eclipse, harry potter etc. demonstrating the value “1” under the column “year” in the rows 7,8,and 5 of Table 3 .
- 1.2.3 Ambiguous Queries
Ambiguous queries are those where no contextual information (country or city) and temporal features (year) are retuned in the search results after query execution e.g. art, books, amazon etc. as shown in rows 1, and 4 of Table 3 demonstrating “0” value under all columns.
As our approach is restricted to only two parameters namely, contextual and temporal information in the search results, hence these remaining ambiguous queries need to be refined in future by defining some other parameters like date of creation, author etc.
- 4.3 Results Filtering
In this phase, the obtained results are filtered based on the nature of the information found in query i.e. temporal as well as contextual. Based on temporal and contextual evidence, the search results are maintained in two separate databases “DbTemporal” and “DbContext”.
- 4.4 Results Integration and Selection
After organizing the results into corresponding databases, next similarities are computed in order to extract the most desired features from the records in each database. Next, the computed similar results from both the databases are integrated and presented to the users for selection according to their preferences.
- 4.5 Users’ Feedback
In last step of ADA, user will select the provided results according to their requirements. On the basis of user selection implicit feedback will be recorded. The implicit feedback mechanism will help in collecting user preferences to be used for search refinement, and providing appropriate query expansion suggestions in the future based on their track record.
5. Implementation of the Proposed Approach
Adaptive Disambiguation Approach (ADA) is implemented as middleware between users and Google search engine. For the implementation of ADA, we used the Eclipse IDE on top of a 64bit system with 8GB RAM and 2.5 GHz Corei5 processor. The Eclipse IDE offers the base workspace and an extensible plug-in system for customizing the environment. We implemented the underlined algorithm of ADA using JAVA programming language. The algorithm of ADA is given in Fig. 4 , which presents our hybrid approach.
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Disambiguation algorithm of ADA approach
The system, we developed to implement our hybrid ADA approach makes use of six different classes namely: Menu, Disambiguation, SearchonInternet, SearchEngine, Summarize and Feedback . Initially the Menu class executes a method getinput() to take a query for processing (in this case, we are using 220 queries identified as ambiguous in GISQC_DS data set [23] , (see appendix )). After taking the input, the Disambiguation initiates a constructor SearchonInternet () of the class SearchonInternet which then executes a method post() for class SearchEngine to be processed over the web. In response SearchEngine gives back the results to SearchonInternet which then suppress the results and send back to Disambiguation class. Next the Disambiguation class executes four methods namely; Locationcount() , CityCount() , CountryCount() and TemporalCount() in order to make a summary of the retrieved results.
After performing the summarization of results, the Disambiguation class executes a Boolean method to check the status, whether they are temporal or contextual. After collecting the results, Menu class sends them to the Feedback class as well as execute a method Display() to present them to user. Meanwhile, recordFeedback() methods is executed to record the results and then after selection made by user, store() method collects all the selections made by the user in order to refine the search results in future.
6. Results and Evaluation
In order to evaluate the capability of ADA in disambiguating the input queries, the performance evaluation was carried out in terms of precision, accuracy, recall, and F1-measure. For input queries, the standard GISQC_DS dataset [23] was considered where the performance of ADA was compared with GTE [24] .The GISQC_DS dataset consists of 450 queries manually extracted from Google Insights for Search. Using GTE, out of 450 input queries, 49% are concluded as ambiguous, 39% clear, and 12% broad. The Table 4 present a summary of the disambiguation by GTE approach. In this table, we have presented different query types that have been identified in the lietrature as ambiguous, clear and broad respectively. Total number of queries treated in this experiment are 450 given in first row of the Table 4 . Then there are three columns with headings query type, number of queries and query percentage respectively being identified. By observing the data given in the Table 4 , 220 queries are being identified as ambiguous presenting 49% of the whole query set. The clear queries are of number 176 out of 450 and hence presenting their share in 39% of the query set. Furthermore, 54 queries contributing in 12% of the query set as broad being identified.
Query disambiguation results using GTE
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Query disambiguation results using GTE
As our main concern is to address the ambiguity of input queries, therefore, we focused and processed only the 49% ambiguous queries of GISQC_DS dataset. Unlike GTE which considers the ambiguity solely from temporal perspective, our ADA takes into consideration both the temporal and contextual aspects of search results. Using ADA, we further categorized 49% of the ambiguous queries of GTE into three categories namely: contextual, non-contextual and ambiguous as shown in Table 5 . By making use of both the temporal and contextual aspects, ADA further enhances the disambiguation of the input queries thereby adding 62 contextual and 51 non-contextual giving 113 (51%) queries to the clear category. While 107 (49%) of the queries remained as ambiguos to be further investigated in future.
Query categorization statistics using ADA
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Query categorization statistics using ADA
As a result, ADA increases the number of clear queries from 176 to 289 and reduces the number of ambiguous queries from 220 to 107 as shown in Table 6 . While the number of broad queries remian same as 54 (12%) in Table 4 .
Query disambiguation results using ADA
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Query disambiguation results using ADA
The Fig. 5 show the relative performance of ADA against GTE in terms of query categorization. In the Figure, the lagend we have used consists of three structues namely; GTE to whom we have compared our approach in terms of ambiguous queries, our approach ADA and then third one is show the performance acheived by our approach in terms of the contributing percenatge of the ambigous queries. In Terms of ambiguous queries based on data from the Table 4 and Table 6 the difference between GTE and our approach is -25% i.e. in our approach the Number of ambigous quereis has been reduced from 49% to 24%. Similary in the case of clear quereis the number of quereis has been increased from 39% to 64% and hence caused an improvement of 25% in making the queries as clear to be processed further. Howevere, in case of third category i.e. broad queries the GTE and ADA produced the same results i.e. 12% and hence remain on same performance.
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Performance in terms of queries categorization
The primary goal of query disambiguation is to improve accuracy of the search results according to users aspirations. For the evaluation of an information retrieval mechanism, the most commonly used measures are precision, accuracy, recall, and F1-measure. In information retrieval context, precision (Eq. 1), is the percentage relevance of the retrieved documents with the user’s information needs. Accuracy (Eq. 2), is the degree of closeness of retreived documents to the actual intent of user. Similary, recall (Eq. 3), is the fraction of documents that are relevant to the query that are successfully retreived. Likewise, F1-measure (Eq. 4), is the weighted harmonic mean of precision and recall.
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Where True Positive (TP) is the number of locations /years are correctly identified as relevant, True Negative (TN) is the number of locations /years correctly identified as irrelevant or incorrect, False Positive (FP) is the number of locations /years wrongly identified as irrelevant and False Negative (FN) is the number of locations /years wrongly identified as relevant. In the wake of performing the expriment, 77, 79, 45 and 29 rundown search results are recognized as TP, TN, FP and FN independently if there should be an occurrence of GTE assessment while 117, 88, 9 and 6 results are being distinguished as TP, TN, FP and FN individually in assessment of our methodology ADA. Putting these got values in the above mathematical equations (1), (2), (3) and (4) we get the results as given below.
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The undergiven Table 7 Presents the got consequences of our methodology and the outcomes being accomplished while running the examination over GTE. It is being accepted that as for all these performance measures, the ADA beats the GTE approach.
Performance evaluation of ADA and GTE
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Performance evaluation of ADA and GTE
The relatively better performance of ADA based on the values being presented in Table 7 is demonstrated in Fig. 6 that are mainly attributed to the hybrid approach of making use of both the temporal and contextual features.
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Performance evaluation in terms of information retrieval measures
7. Conclusion
In this paper, we have proposed Adaptive Disambiguation Approach (ADA) using contextual information gathering of query results, whereas the results are concentrated by location (country/city) and temporal information. Unlike existing approaches, which rely on temporal features in query disambiguation, ADA is based on both temporal and contextual information. By making use of both the temporal features as well as keeping track of user’s current location, it addresses the inherent ambiguities in input queries and filters out irrelevant results in response to user’s search over the web. Using ADA, a large proportion of ambiguous queries (51% as shown in Table 5 ) are resolved and compared to existing temporal based approaches when tested on standard GISQC_DS dataset. Though 49% of the queries stay ambiguous because of absence of enough data being recovered after execution; thus the remaining queries are held for future work to be recognized in view of some different parameters. Similarly, ADA also outperforms the existing works in terms of the most common information retrieval measures (precision, accuracy, recall, and F1-measure). Part of the future work, we are keen to test ADA using multiple datasets to verify its robustness. In addition, we also plan to develop a small scale search engine which will enable us to carry out a full text analysis using the contextual information in search queries.
Acknowledgement
We might want to say thanks to Universiti Teknologi Malaysia and Ministry of Higher Education (MOHE) Malaysia (Vot No: 4F315) and Research University Grant Scheme (Vot No: Q.J130000.2528.05H84) for the offices and backing to direct this research. In addition we extend our gratitude to Higher Education Commission (HEC) of Pakistan and the Gomal University D.I.Khan.
BIO
Roliana Ibrahim is a senior lecturer at the Faculty of Computer Science and Information Systems, Universiti Teknologi Malaysia. She has been servicing UTM for more than 10 years after a few years’ experience working as a system developer in the industry. She received her BSc (Hons) Computer Studies from Liverpool John Moores University, MSc Computer Science from Universiti Teknologi Malaysia and PhD in the field of Systems Engineering from Loughborough University. Her current research project relates to the development of ontology based data warehousing and data mining model for oral cancer research data repository and improvement of data mining techniques for risk and survival analysis of cancer patients.
Shahid Kamal is a PhD student in the Software Engineering Research Group (SERG) at Faculty of Computing, Universiti Teknologi Malaysia. He received his BSc Computer Science from Gomal University, MSc Computer Science from ICIT, Gomal University D.I.Khan Pakistan. His research includes information systems, data mining, web search and its related issues and information integration. Besides, he is faculty member of the ICIT Gomal University D.I.Khan, Pakistan since 2007.
Imran Ghani is a Senior Lecturer at Faculty of Computing, Universiti Teknologi Malaysia (UTM), Johor Campus. He received his Master of Information Technology Degree from UAAR (Pakistan), M.Sc. Computer Science from UTM (Malaysia) and Ph.D. from Kookmin University (South Korea). He is the member of Software Engineering Research Group (SERG). He teaches Software Architecture and Design, Software Engineering, Secure Software Development and (Software) Application Development. Dr. Imran Ghani has 50+ publications in Journals, Proceedings, and Book Chapters.
Seung Ryul Jeong is a Professor in the Graduate School of Business IT at Kookmin University, Korea. He holds a B.A. in Economics from Sogang University, Korea, an M.S. in MIS from University of Wisconsin, and a Ph.D. in MIS from the University of South Carolina, U.S.A. Dr. Jeong has published extensively in the information systems field, with over 60 publications in refereed journals like Journal of MIS, Communications of the ACM, Information and Management, Journal of Systems and Software, among others. Dr. Jeong’s areas of interest are Process Management, Software Engineering, Systems Implementation, and Information Resource Management.
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