Automated Identification of Web Queries using Search Type Patterns
Alaa Mohasseb1, Maged El-Sayed2 and Khaled Mahar1 1College of Computing & Information Technology, Arab Academy for Science, Technology & Maritime Transport, Alexandria, Egypt 2Department of Information Systems & Computers, Alexandria University, Alexandria, Egypt
Keywords: Information Retrieval, User Intent, Web Queries, Web Searching, Search Engines, Query Classification.
Abstract: The process of searching and obtaining information relevant to the information needed have become increasingly challenging. A broad range of web queries classification techniques have been proposed to help in understanding the actual intent behind a web search. In this research, we are introducing a new solution to automatically identify and classify the user's queries intent by using Search Type Patterns. Our solution takes into consideration query structure along with query terms. Experiments show that our approach has a high level of accuracy in identifying different search types.
1 INTRODUCTION ambiguous and most of the queries might have more than one meaning, therefore using only the terms to The main goal of any information retrieval system is identify search intents is not enough, two queries to obtain information relevant to information needs. might have exactly the same set of terms but may Search engines can better help the user to find reflect two totally different intents, therefore his/her needs if they can understand the intent of the classifying web queries using the structure of the user. Identifying such intent remains very difficult; query in addition to terms and characteristics may one major task in identifying the intent of the search help in making the classification of queries more engine users is the classification of the query type. accurate. There are many different proposed classifications In our research, we propose a solution that of web queries (Morrison, et al., 2001, Broder, 2002, automatically identifies and classifies user's queries Kellar, et al., 2006, Baeza-yates, et al., 2006, using Search Type Patterns. Such Search Type Ashkan, et al., 2009, Lewandowski, et al., 2012, Patterns are created from studying different web Bhatia, et al., 2012). Broder’s classification of web queries classification proposals and from the queries (Broder, 2002) is one of the most commonly examination of various web logs. A Web Search used classifications. It classifies web queries to three Pattern is constructed from one or more terms, such main types: Informational queries, Navigational terms are categorised and introduced in the form of queries and Transactional queries. taxonomy of search query terms. Some researches (Choo, et al. 2000, Morrison, et We have developed a prototype to test the al., 2001, Broder, 2002, Rose, et al., 2004, Kellar, et accuracy of our solution. Experimental results show al., 2006) used different manual methods to classify that our solution accurately identified different users’ queries like surveys and field studies. Other search types. researches used automated classification techniques The rest of the paper is organized as follows: like supervised learning, SVM…etc. (Lee, et al., Section 2 highlights the different proposed 2005, Beitzel, et al., 2005, Baeza-yates, et al., 2006, classification techniques used in web query Liu, et al., 2006, Ashkan, et al., 2009, Mendoza, et identification. Section 3 provides detailed al., 2009, Jansen, et al., 2010, Kathuria, et al., 2010). explanation of the extended classification of web One drawback of the solutions that were search queries and the different type of each introduced so far is that they do not take into category. Section 4 provides a detailed description consideration the structure of the queries. Queries of the proposed solution. Section 5 covers submitted to search engines are usually short and experiments and results and finally Section 6 gives
Mohasseb A., El-Sayed M. and Mahar K.. 295 Automated Identification of Web Queries using Search Type Patterns. DOI: 10.5220/0004849402950304 In Proceedings of the 10th International Conference on Web Information Systems and Technologies (WEBIST-2014), pages 295-304 ISBN: 978-989-758-024-6 Copyright c 2014 SCITEPRESS (Science and Technology Publications, Lda.) WEBIST2014-InternationalConferenceonWebInformationSystemsandTechnologies
conclusion and future work. "download"...etc. Ambiguous queries include queries that can’t be identified directly because the user interest is not clear. 2 PREVIOUS WORK Morrison, et al., (2001) classified search goals into Find, Explore, Monitoring and Collect, this classification focus on three variables: the purpose 2.1 Search Types of the search, the method used to find information and the contents of searched information. According to (Broder, 2002) web searches could be classified according to user's intent into three categories: Navigational, Informational and 2.2 Classification Methods Transactional. Many researches (Liu, et al., 2006, and Techniques Jansen, et al., 2008, 2010, Mendoza, et al., 2009, Kathuria, et al., 2010, Hernandez, et al., 2012) have Researchers have used different manual and based their work on Broder’s classification of user automated classification methods and techniques to query intent. Others like (Lee, et al., 2005) used identify users query intent. navigational and informational queries only due to Broder, (2002) classified user’s query manually lack of consensus on transactional query and to by using a survey of AltaVista users as one of the make classification task more manageable. methods to determine the type of queries, the survey Rose, et al., (2004) and (Jansen, et al., 2008) was done online and users were selected randomly. extended the classification of Informational, Users were asked to describe the purpose of their Navigational and Transactional queries by adding search, queries that were neither Transactional nor level two and level three sub-categories. Navigational were assumed to be Informational, the Lewandowski, et al., (2012) proposed two new final results of the survey showed that 24.5% of the query intents, Commercial and Local. According to queries were Navigational, Informational queries their work, the query might have a Commercial accounted for 39% of the queries and transactional potential like the query: "commercial offering" or accounted for 36% of the queries. In addition Broder the user might search for information near his has analysed a random set of 1000 queries from the current location. daily AltaVista log, queries that were neither Bhatia, et al., (2012) classified queries to four Transactional nor Navigational were assumed to be classes: Ambiguous, Unambiguous but Informational, results showed that 20% of queries Underspecified, Information Browsing and were Navigational, 48% were Informational and Miscellaneous. 30% were Transactional. Calderon-Benavides, et al., (2010) and Ashkan, Choo, et al., (2000) and Kellar, et al., (2006) et al., (2009) proposed other classification of queries used questionnaire survey for manual classification that classified user intent into dimensions and facets. of queries and since participants in this kind of These dimensions and facets are extracted from classification were low in number, the results can’t user’s queries to help the identification of user intent be considered reliable. when searching for information on the web like In addition to the questionnaire survey (Kellar, et Genre, objective, specificity, scope, topic, task, al., 2006) conducted one-week field study to classify authority sensitivity, spatial sensitivity and time data using a custom web browsing and analysed the sensitivity (Calderon-Benavides, et al., 2010). data for only 21 participants. Ashkan, et al., (2009) classified query intent into Rose, et al., (2004) argued that user goals can be two dimensions, Commercial and Non-commercial deduced from looking at user behaviour available to and Navigational and Informational. the search engine like the query itself, result Kellar, et al., (2006) classified web informational clicked…etc. This approach has limitation that the task based on three main informational goals, goal-inferred from the query may not be the user Information Seeking, Information Exchange and actual goal. Information Maintenance. Lewandowski, et al., (2012) analysed click- Baeza-yates, et al., (2006) established three through data to determine Commercial and categories for user search goal, Informational, Not Navigational queries and used crowdsourcing Informational and Ambiguous. Informational query approach to classify a large number of search when the user’s interest is to obtain information queries. available on the web. Not Informational include Liu, et al., (2006) also used click-through data specific transactions or resources like "buy", for query type identification. Queries were randomly
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selected and manually classified by three assessors 3 BACKGROUND using voting to decide queries category. This work relied on decision tree algorithm and used precision 3.1 Web Search Queries Classification and recall to test effectiveness of the query type identification. The following sections describe in details each of Lee, et al., (2005) proposed two types of the categories we considered in our work. These features, past user click behaviour and Anchor-link categories are based on work done by (Broder, 2002, distribution. Results showed that the combination of Rose, et al., 2004 and Jansen, et al., 2008). these two techniques could correctly identify the goals for 90% of the queries. 3.1.1 Informational Searching Hernandez, et al., (2012) introduced a solution that automatically classifies queries using only the Informational Searching has five sub-categories: text included in the query, based on the feature and a) Informational - Directed (I, D): the goal of characteristics described by (Broder 2002, Jansen, et this category is to learn something in particular al., 2008, Dayong, et al., 2010). More than 1692 about a certain topic, or to answer a specific queries were manually classified then two machine- question, both open and closed ended. This category learning algorithms, naïve Bayes and Support Vector has level two sub-categories: Machine (SVM), were used. Results showed that the a.1) Informational - Directed - Open (I, D, O): two machine-learning algorithms suited more this category may take many forms either a question Informational and Transactional queries; results of to get an answer for an open-ended question or one Navigational queries were very low with naïve with unconstrained depth or to find information Bayes and null with SVM. These Results indicate about two or more topics. Examples: "why are that using only the content of words in the queries is metals shiny?" and "honeybee communication". not sufficient to find all user intents. a.2) Informational - Directed - Closed (I, D, Ashkan, et al., (2009) classified 1700 queries and C): queries in this category can be a question to find manually labelled the selected queries then used ads one specific or unambiguous answer or to find click-through and query features to determine the information about one specific topic. Examples: query intent. "capital of Brazil" and "what is a prime number?" Beitzel, et al., (2005) and Baeza-Yates, et al., b) Informational - Undirected (I, U): the (2006) used supervised learning to determine query purpose of this category is to know anything and intents. In addition to supervised learning (Baeza- everything about a topic, most queries in this type Yates, et al., 2006) applied unsupervised learning are related to science, medicine, history and news then combined both techniques to identify user and celebrities (Rose, et al., 2004). Examples: search goal. "Shawn Johnson", "Vietnam war” and Jansen, et al., (2008) developed a software “hypertension". application that automatically classified queries c) Informational - List (I, L): plural query using web search engine log of over a million and a terms are a highly reliable indicator of this category half queries. Results showed that more than 80% of (Rose, et al., 2004), the goal of this type of queries is web queries were Informational, Navigational and to find a list of suggested websites or candidates or Transactional queries each represent about 10% of list of suggestions for further research. Examples: web queries. To validate their approach 400 queries "list of Disney movies", "London universities", and from Dogpile transaction log were randomly "things to do in Atlanta". selected and manually coded, 74% of the queries d) Informational - Find (I, F): the goal of this were successfully classified and the remaining 25% category is to find or locate something in the real were vague or multi-faceted queries. world like a product or service. Most product or Kathuria, et al., (2010) automatically classified shopping queries have the locate goal (Rose, et al., queries using k-means clustering, results for this 2004), for example: "apple store location in New technique showed that more than 75% of web Jersey" and "cheap apple MacBook pro". queries are Informational in nature and 12% each for e) Informational - Advice (I, A): the goal of navigational and transactional queries. this category is to get ideas, suggestions, advice or instructions about something and may take many forms like a question. Examples: "How to download iTunes" and "writing a book".
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3.1.2 Navigational Searching results page. This category has level two sub- categories: Navigational Searching has two sub-categories: d.1) Transactional - Results Page - Links (T, a) Navigational to Transactional (N, T): the R, L): the resources of this kind of queries appear in URL or website user is searching for is a the title, summary, or URL of the search engine transactional site. Examples: “amazon.com” and results page. Example: “searching for title of a “ebay.com”. conference paper to locate the page numbers”. b) Navigational to Informational (N, I): the d.2) Transactional - Results Page - Other (T, URL or website user is searching for is an R, O): the resources of this kind of queries does not informational site. Examples: “google.com” appear on the search engine results page but and“yahoo.com”. somewhere else on the search engine results page. Example: “spelling check of a certain term”. 3.1.3 Transactional Searching 3.2 Characteristics of Web Search Transactional Searching has the following sub- categories: Queries a) Transactional - Obtain (T, O): the goal of this type of queries is to obtain specific resource or 3.2.1 Informational Search Characteristics object, not to learn some information but just to use One of the major characteristics of Informational the resource itself. This category has the following Searching is the use of natural language phrases level two sub-categories: (Jansen, et al., 2008). Queries for such search may a.1) Transactional - Obtain - Online (T, O, O): consist of informational terms like "list" and the resources of this type of queries will be obtained "playlist"…etc., question words like "who", "what", online, meaning that the user might search for "when"…etc. Searches related to Advice, help and something to just look at it on the screen. Examples: guidelines like "FAQs" or "how to"…etc., ideas and "meatloaf recipes" and "Adele Songs lyrics". suggestions terms, recent information and news like a.2) Transactional - Obtain - Offline (T, O, F): "weather". the resources of this type of queries will be obtained Some queries consisting of multimedia like offline and may require additional actions by the videos are considered informational like "how-to- user, meaning that the user might search for do" videos. Topics related to science, medicine, something to print or save to use it later offline. history, news and celebrities are also considered Examples: "Bon Jovi wallpapers" and "windows 7 informational, (Rose, et al., 2004). screensavers". b) Transactional - Download (T, D): the 3.2.2 Navigational Search Characteristics resource of this type of query is something that needs to be installed on a computer or other Navigational Searching queries contain organization, electronic device to be useful like finding a file to business, company and universities name, domain download. This category has level two sub- suffixes like ".com",".org"…etc. also prefixes such categories: as "www" or "http" and “web” as the source. Some b.1) Transactional - Download - Free (T, D, Navigational queries contain URLs or parts of URLs F): the downloadable file is free. Examples: "free (Jansen, et al., 2008). online games" and "free mp3 downloads". Most queries consisting of people names, b.2) Transactional - Download - Not Free (T, including celebrities, are not considered D, N): the downloadable file is not necessarily free. navigational. According to (Rose, et al., 2004) a Examples: "safe haven book download" and "Kelly search for a celebrity such as “Justin Timberlake” Clarkson songs download". will result in a fan or media sites, and usually the c) Transactional - Interact (T, I): this type of goal or objective of searching for a celebrity is not queries occurs when the intended result of the search just visiting a specific site. is a dynamic web service, and requires further interaction with a program or a resource. Examples: 3.2.3 Transactional Search Characteristics "currency converter", "stock quote”, “buy cell phones", and "weather". According to (Jansen, et al., 2008) queries in d) Transactional - Results Page (T, R): the Transactional Searching is related to obtaining terms goal of this category is to obtain a resource that can like "lyrics", "recipes", "patterns"…etc., download be printed, saved, or read from the search engine terms like, "software"…etc. Also queries containing
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"audio", "video" and "images” are considered to be how term categorization and Search Type Patterns transactional. were constructed. Queries related to entertainment terms like Table 1 shows detail of all term categories in our "pictures", "games"…etc., and e-commerce. Interact solution and Figure 1 shows the taxonomy terms such as "buy", "chat", "book", "order”…etc., organization of these categories. and file extensions like “jpeg”, “zip…etc., (Jansen, et al., 2008). Table 1: List of Term Categories. Category Name Abbreviation Terms Action Verb-Interact Buy, Reserve, AV_I 4 PROPOSED SOLUTION terms Order…etc. Action Verb-Locate AV_L Locate, Find. Action Verb-Locate All Locate & Our solution mainly relies on Search Type Patterns AV_IL (STPs). These patterns generalize web search & Interact terms Interact terms. Action Verb- queries of different types and could be used in AV_D Download Download identifying the query class and hence the user's Write, create, Action Verbs AV intent. We have constructed 1182 different Search drive…etc. Type Patterns. Examples of these patterns are given Can, may, Auxiliary Verb AuxV in sections 4.1 and 4.2. Due to space limitation we will…etc. couldn’t give a comprehensive listing of these Linking Verbs LV Is, are, was…etc. patterns. Verbs V All Verbs Our proposed Search Type Patterns cover all Adjective Free Adj_F Free categories discussed in section 3.1 above except Adjective Online Adj_O Online Adjective Free & Navigational search sub-categories and the Adj_OF Free & Online Transactional-Results page category. The reason of Online excluding these categories is because it is not Adjective Adj All Adjectives Almost, barely, possible to determine the intent of the query without Adverb Adv performing the search and monitoring the user’s highly…etc. Determiners D A, An, The…etc. interaction with the result, which falls outside the Conjunction Conj And, as, but…etc. scope of our work since our solution is not based on 1st, second, Ordinal Numbers NN_O processing the search results. For example, if a user 70th…etc. searches for: “UCLA University”, he might be Cardinal Numbers NN_C 1, 50, ten...etc. interested in browsing the site to know more Numeral Numbers NN All numbers information (Navigational-to-Informational) or to Phil Collins, Clint register a course (Navigational-to-Transactional). Celebrities Name PN_C Eastwood, The Each Search Type Pattern (STP) is composed of Beatles…etc. a sequence of term categories (tc). STP =
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Table 1: List of Term Categories. (Cont.) Table 1: List of Term Categories. (Cont.) Category Name Abbreviation Terms Institutions, Category Name Abbreviation Terms Yale university, Associations, Clubs, All CN_Ent, PN_IOG Warren middle Info. Terms, File & Parties, Foundations CN_EFI CN_File & school…etc. Entertainment and Organizations CN_IFT Companies, Wallpapers, All PN_CO, Obtain Offline CN_OF Geographical Areas, documents…etc. PN_CGIP PN_G, PN_PB & Institutions, Lyrics, PN_IOG Obtain Online CN_OO Places…etc. Recipes…etc. Celebrities, All PN_BCEE & Obtain Online & Entertainment, PN_BCC Obtain CN_OB PN_CGIP Offline Companies…etc. File, Entertainment, All CN_EFI & Coach, Pepsi, Informational & CN_OBEF Brand Names PN_BN CN_OB Gucci…etc. Obtain Terms uTorrent, Software & History, News, PN_SA Photoshop, History & News CN_HN Applications War, Rumour. Skype…etc. Translation, iPad, Oreo Interact terms CN_I Products PN_P reservation…etc. cookie…etc. Locate CN_L Location Brand, Products, All PN_BN, Site, Website, PN_BSP Site, Website, URL CN_SWU Software…etc. PN_P and PN_SA URL, Webpage. Brand, Products, All PN_BCC & Common Noun – All singular Entertainment, PN_BBC CN_OS PN_BSP Other- Singular common nouns Companies…etc. Common Noun- All plural CN_OP Revolutionary Other- Plural common nouns History and News PN_HN war, American Common Noun- Other Common CN_O Civil war…etc. Other Nouns Christian, All Common Common Noun CN Religious Terms PN_R Muslim, God, Nouns Allah…etc. Pronoun Pron. I, Me, You…etc. Christmas, Holidays, Days, Noun N All Nouns PN_HMD Saturday, .com, .org, Months Domain Suffix DS November…etc. .us…etc. Religious Terms, All PN_R & Prefixes DP http, www. Holidays, Days, PN_HR For, of, PN_HMD Preposition PP Months about…etc. Specific Terms How, How far, Health Terms PN_HLT related to health How QW_How How many, How & medicine. much, How often Specific Terms Science Terms PN_S What QW_What What related to Science. When QW_When When Health & Science All PN_S & PN_HS Where QW_Where Where Terms PN_HLT Who QW_Who Who Proper Noun PN All Proper Nouns Which QW_Which Which Weather, All question Database and Servers CN_DBS Question Words QW Dictionary…etc. words Advice, ideas, Advice CN_A instruction, suggestion, tips. 4.1 Constructing Search Type Patterns Download, and Term Category Taxonomy Download CN_D Software Music, Movie, In order to construct Search Type Patterns and term Entertainment CN_Ent Sport, Picture, categories we have used 80,000 randomly selected Game…etc. queries from AOL 2006 datasets. We have taken the File Type CN_File MP3, PDF…etc. following steps: List, Step 1- parsing the 80,000 queries and Informational Terms CN_IFT Playlist…etc. automatically extracting terms in the queries.
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Step 2- manually performing initial tcsup for both tcxi and tcyi. Such super-category might categorization for the terms. already exist or it might be created, in this case we Step 3- processing the queries and converting merge the two Query Patterns QPx and QPy in one each query to a Query Pattern. A Query Pattern (QP) new pattern that contains tcsup instead of tcxi and tcyi. is a representation of the original query where each For example, the four Query Patterns:
DS DP
N
D QW PP PN Adv Conj QW V Adj What QW NN Which Pron
PN PN BBC PN PN CN HR HN HS Adj QW QW Where PN OF When NN BCC QW NN LV C PN How O BSP
PN AV QW BCEE Aux Adj Who PN PN V F S CGIP Adj CN PN
O OB PN P EF HMD AV CN PN PN PN IL AV O HLT BN E D CN CN D PN DBS PN SA AV CN R AV PN L OB CN I CN Ent CN EFI CN L PN OS OP BDN PN PN
CN SWU CN G CO OF CN PN CN IFT PN C CN CN CN OO CN CN IOG PN HN A I Ent File PB Figure 1: Terms Categorization.
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we are going to illustrate in the next Section. The Phase 1- query parsing: this step is mainly resulting database contains 10,440 terms classified responsible for extracting user’s query terms. Unlike into the classes shown in Table 1. most other ir solutions, our solution does not destroy In addition to the term categories, we were able the query structure by removing stop-words and wh- to identify 1182 Search Type Patterns. Table 1 and question words. Such important query components Table 2 show the distribution of these patterns by are exploited in determining the query type. The search type. system simply takes the user’s query and parses it to We validated our Search Type Patterns using a facilitate the mapping of each word to the right dataset containing 1953 queries from AOL that were category. For example given the two queries: query manually classified and used in (Mendoza, et al., 1: “what is the capital of romania?” And query 2: 2009). “list of movies by steven spielberg” as inputs, the system extracts the following terms from query 1: Table 2: level 1 Search Type Patterns Distribution. “what”, “is”, “the”, ”capital”, "of", “romania”, and extracts the following terms from query 2: Type of search Total Informational 838 “list”, “of”, “movies”, ”by”, “steven spielberg”. Transactional 336 Phase 2- Query Pattern Formulation: the Navigational 8 system converts the query to a Query Pattern by mapping terms in the query to corresponding term Table 3: Level 2 and Level 3 Search Type Patterns categories. First the system checks for compound Distribution. terms (phrases) and then it processes single terms. The system maps each term to the most specific sub- Type of search Total category. If a term is not found in the terms Informational -List 155 database, the system assumes that the term is a Informational -Find 164 Proper Noun, since Proper Nouns are infinite and we Informational -Advice 121 do not maintain an exhaustive list of them. After Informational -Undirected 51 Informational -Directed -Open 113 determining term category for all terms in the user Informational -Directed -Closed 234 query we then process consecutive terms that were Transactional -Obtain - Online 59 identified as Proper Nouns. We convert such Transactional -Obtain -Offline 76 sequence of Proper Nouns to a single Proper Noun Transactional -Interact 28 since no Search Type Pattern contains consecutive Transactional -Download –Free 104 independent Proper Nouns. Transactional -Download -not Free 69 The result of applying step 2 to query 1 is: "What"QW_What, "is"LV, "the"D, 4.2 Classifying Search Engine Queries "capital"CN_OS, "of"PP, "Romania"PN_G. As a result, the Query Pattern for query 1 is: Our solution automatically identifies and classifies PN PN PN> then it is modified to PN BCEE PN Sear ch CGIP !"# CN_Ent + PP + PN> by merging the two !"# !# $. " # Term !"# !" )*# +# Type !"# $" # !"# %# Categor y !" !"# consecutive Proper Nouns into a single Proper !"# !"# '# !"# !$# %& # +, ‐# Patterns (&' # Term Taxonomy Noun. Phase 3- Query Type Classification: In this Figure 2: Proposed System Framework. step the system attempts to match the Query Pattern generated in step 2 with the most appropriate Search 302 AutomatedIdentificationofWebQueriesusingSearchTypePatterns Type Patterns to determine the Query type. For some Table 4, shows classification detail by search Query Patterns, like the Query Pattern of query 1, type. Informational queries have the highest this will be straightforward. This Query Pattern frequency with 4245 queries then transactional matches a Search Type Pattern in the Search Type queries with 2783 queries. Navigational queries have Informational-Directed-Closed. the lowest frequency with only 726 queries. Table 5 For other queries, like query 2, the Query Type shows the breakdown of the result to sub-categories. does not fully match any Search Type Pattern. In Our experiments show that 944 out of the 1182 this case we retrieve all Search Type Patterns that different Search Type Patterns were used in partially match the Query Pattern and we use the classifying the 10,000 queries that were used in our term categories taxonomy to determine which experiment. Search Type Patterns better match the Query Pattern. For example the Query Pattern 5 EXPERIMENTS In this research, we have introduced a framework to automatically identify and classify search engine We developed a prototype in Java to test our user queries. Unlike other solutions, our solution proposed solution. Our prototype utilizes the 1182 relies on both query terms and query structure in different Search Type Patterns that we have order to determine the user intent. We have constructed and also use the taxonomy of term categorized search queries through introducing categories shown in Figure 1 and Table 1. This Search Type Patterns. Our framework consists of taxonomy of term categories contains 10,440 three main steps: (1) parsing user’s query, (2) different terms and types. formulating Query Patterns, and (3) Classifying To test the accuracy of our solution, 10,000 query type. queries were randomly selected from AOL 2006 Experiments show that our solution has achieved dataset and tested using the system. The selected high accuracy in classifying queries. As a future queries are different from those used in constructing work we will examine and analyze more queries the Search Query Patterns. Results of the experiment from different search engine datasets in order to show that our solution had identified and classified extend the ability of our system to identify more 7754 of the queries. After examining the remaining queries. We also plan to conduct more experiments unclassified 2246 queries, we found that 927 of them on larger datasets and compare our results to results were not identified due to vagueness or mistakes. obtained from other approaches. This make the accuracy of the classification 85.5% of the queries without mistakes. 303 WEBIST2014-InternationalConferenceonWebInformationSystemsandTechnologies REFERENCES Services and Use, 26(4), 261-269. Lewandowski, D., Drechsler, J., & Mach, S., 2012. Deriving query intents from web search engine Ashkan, A., Clarke, C. L.,Agichtein, E., &Guo, Q., 2009. queries. 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