Volume 3, Issue 5, May 2014
Page 420
Abstract
Search engine is pedestal machine on two-dimensional logic and possibility hypothesis which are short of concept based i.e. fact based existent human understanding and advanced way of thinking and precision to formulate answers.QA (Question answering) system is hybrid machine with analysis ability to fetch data from diverse knowledge source assorted format and formulate a answer to user search query. Research domain in advanced Information retrieval is machine to prospect a information seeking engine (search engine advancement).formulate precise answer to user search question is aspire of QA system .current search engines are keyword base with sort of semantic incorporation (semantic search in Google) Enhancement in search engines is three tier, user question understanding (user model, context, intent), text/data-mining analytics with machine learning (Support vector machine(SVM),Artificial neural networks(ANN)),and adha Rank (bings algorithm) to formulate trainable machine to reduce search space and time.QA system is a semantic concept based framework which identifies the nucleus keywords and phrases incorporating meaning i.e. sense of word to phrase ,sense of phrase to sentence ,to corpus which abolish the disambiguation of NL(natural language) in search engines. A prototype design of Answer engine is proposed that performs concept map of user question .the semantic concept framework enhances the ability of reckoning in Answering system. GUI interface performs mapping of user question in concept group (ontology class i.e. Spatial ontology, domain ontology, task ontology, universal ontology).text analytics formulates extraction of Answer term, phrases from corpus or knowledge dataset (unstructured & structured) using n-gram. Machine learning algorithms facilitate intelligence in search corpus. Ranking algorithm presents information on relevance precision mapped to user search intent. AdaRank repetitively build ‘frail rankers’ on the foundation of re-weighted training data and lastly linearly join the feeble rankers for making ranking forecast. The training process of AdaRank is exactly enhancing the performance measure used. AdaRank significantly optimizes the base technologies BM25, Ranking SVM, and Rank Boost.QA system are future of search engine, expert systems, and rule based systems. Current paradigm information processing machines to intelligent information machine which perform time variant search on large dataset with higher precision to question. This paper a search analysis has been carried out on 20 research article on Advance IR technologies.
The search analysis present in detailed study on three methodologies (question understanding, text analytics, machine learning and ranking algorithm) when optimized invent enhanced search machine, QA system. Search paper concludes that intelligence in information processing has evolved search engines, rule based machines, expert systems to hybrid reasoning machines-QA Systems.
Keywords: search engine, semantic, concept, QA, Machine learning,
1.
I
NTRODUCTIONSearch engines have evolved into reasonably advanced Machines in the precedent era. Algorithms developed have become optimized to formulate semantics, user intention, and broader collective prototype on the mesh. Optimistic outcome have been presented by search engines and they dole out our every requirements. But search engines like Google, Yahoo, AOL, and others donor provide precise answers to user questions. Search engine answer simple questions “who is president of India “how is stock market. But complex questions “why president manmohan visited Iran and not Iran? Require advance reasoning. Search engine are information retrieval (IR) system that extract information and present sort links to information on user search questions with advent of Big data analytics and enormous vertical web to retrieve relevant information from vast information in time constrain is a immense test and research area. Search engines extract relevant (recall based) information from huge set of knowledge source. Search engines (text based) are IR system that discover complete archive and text in diverse configure. In the present day user’s needs are precise answers to questions relatively than a time overwhelming search throughout the sort of links to pages and complete extracted document. Advancement in search engines is syntactic to concept based semantic search. Words or terms are nucleus entities in syntactic engine with high recall and low precision (short of polysemy, synonymy identification)[14]. Semantic embark on sense of core word or phrase in question, with higher precision answer. Concept pedestal search is deployed in Automatic Answering to search question. Current search engines are dataset management system which confine, accumulate, run, recover, scrutinize & exhibit information according to the particular user seek obligation. An interface to intellectual human process to multimedia data through graphical interface to information database. Search engine classify passage with trait mining from corpus .A corpus is symbolized with a linear vector of features, with words, phrases union as merged principles values. Conventional classification on keywords is instant overwhelming and asking price bearing. Machine learning algorithm trim down the search space and time amid concept search (intellect to machine). Concept mining frame map context of user search in question module and first-rate categorized cluster for information search. Sense mapping process is hub task of concept mining. Words are futile unless perspective is taken in
Search Engines to QAS: Explorative Analysis
Mtech.Student A.d.Kadam, Prof.S.P.Medhane and Prof.S.D.Joshi, Mtech student S.V.Shinde
Volume 3, Issue 5, May 2014
Page 421
deliberation; words from diverse meaning construal when placed in group or phrases. Terms structure diverse meaning among identical words positioned at unlike point in verdict to create new thought. Concluding concepts are formation based and ontology plots’ the formation to deduce the sense. Phrases with same keywords placed in diverse arrangement imply idea alteration .Concept extraction and question structuring are correlated progression under pinged in concept graph. Feature is cluster of words that construct sense to sentence, paragraph, document ultimately. Features are charasterics of data which classify information processing and searching faster. In order the extract concept context identification needs to be done. In past and current state large number of text data mining methods are formulated to perform diverse knowledge work tasks. Methods incorporated are associative canon mining, intermittent item set mining, sequential prototype mining, maximum pattern Extraction, and closed pattern Extraction, the majority of methodologies are time framework and definite quarter to find exact pattern. A unlock investigate question is how to employ fat pattern engender from database for information discovery. Optimized pattern algorithm in text analytics is step towards Search engine optimization. Text analytics is interesting feature invention from text statistics. Explorative challenge in text analytics to formulate precise answer using pattern mining algorithm. Research area in search engine is to find best text analytics extraction method. In IR systems Golden stand models are rocchio model, probabilistic model [5] [6] SVM (support Vector machines,) and BM25 formulate extraction of relevant information filtering. Reckoning calculated by term based method or keyword based methods in search engines have higher relevance and have advanced to phrase weighting hypothesis in machine intelligence and IR. Research vision demonstrate phrase based search method are short of polysemy and synonyms .phrase or keyword multiple meaning in miscellaneous context-polysemy, phrase have multiple sense, synomus keyword with multiple meaning. sense extraction of many phrase is hesitant to answer client desire .research investigation suggest that term research better optimization to GUI are better keyword point as expression clutch “sense” have not found per formative in IR record[7][8] .phrases resolve the ambiguity of NLP interpreted by client but answer lack in phrase search is 1) status of phrase is related to term 2) phrase count is low 3) irrelevant phrase are high .chronological prototype in text mining are outperform disadvantages of phrase technique due to good arithmetical possessions like keywords .bunged concept based chronological patterns mining taxonomy models (PTM) are propound [8].gradual rise in performance is achieved in text analytics using closed pattern in word and phrase based extraction .research scholar articles picture that concept based approach is better in measures of performance of “confidence” &”support” to user. Machine learning algorithms reduce the search space and time with intelligence in selecting categorized corpus for search and extraction of data.SVM is found to be better classifier on large data set .vertical ranking algorithms present in information in ranked format. What? Search engine technology cannot perform is synthesis of information from various knowledge source to present a solitary answer to user search question. Expert system rule based machine are domain specific system that answer user search question .search engines need to be upgraded to hybrid system which understand (interpret user question) optimized search (text analytics) with intelligence (machine learning system) with decision support for optimized performance.Even though search engines pit fall in fact (real world) knowledge, precision map to relevance, Deduction and precisiation [14].quantum IQ in search technology is facilitated with QA systems. Question Answering is a field of Information Retrieval, Information Extraction and Natural Language Processing which is concerned with providing precise answers automatically, to questions posed by humans in a natural language. Which have deductive reasoning ability and synthesis capability these machine are future to search technology which interact with user to facilitate precise answers to questions. Discovery track (DT) in search engine to QAS map, DT= (DT1, DT2, DT3) D4. DT1(Concept Extraction) DT2 (text Analytics and Machine Learning) DT3(learning Ranking algorithm)and DT4 (decision support system).
2.
APPRAISAL SEARCH 2.1concept extractionVolume 3, Issue 5, May 2014
Page 422
For example: search question1 and question2 are complex questioner containing same words and phrases even in structure and need beyond semantic (concept map) to understand the Question posted to search engine. Here who is writer who wrote book (abstract) named (name entity recognition) “first Indian prime-minister” biography in 1980- book was in past written by author (he can be the president himself also) .the concept maps from actor to action to event{author who wrote “first Indian president” in 1980) .second scenario actor-first priminister action-who wrote hisbiography-autobiography event.
A. For example: Complex Questions
1. “who wrote the “the first Indian prime-minister “a biography in 1980. Contains the same keywords but has a completely different meaning than: 2. “Who was first Indian priminister to have written his biography”. Concept of Question1
Actor: -Indian prime-minister Action: - wrote biography Event- 1980 How: (not Expressed). Concept of Question 2
Actor: - Indian prime-minister Action:-will be travelling Event- unknown. How: abstract (himself-as his biography) Search engines when incorporate concept can answer such question
B .For example: complex Answer Extraction
1. The President of the America spoke today about the global economy”
The data corpus contains same corpus with same keywords but have diverse concept 2. “The President of India will be travelling this week to the United States in economy class. Concept of Answer1:-
Actor: -The President of the United States Action: - spoke (voice- is a mean of reaching; other can be through signals, army, ref, etc) When/Where (event location):- today Target: -about the global economy How: -by Voice (implicit he reaches the audience by voice) Tense: - Past {spoke}
Concept of answer2:-
Actor: - The President of India Action:-will be travelling When/Where (location):- this week Target: - to the United States How: - In economy class”. Tense: - Future {will be}
These are some of the co-ordinates of phrase/words .machine understands from concepts interested questions of user. This is futuristic scope of GUI development for search engine which sense questions and answers from dataset corpus
Ontology
Framework on concept and relations that describe entity or objects and relation in them, vocabulary to categorize terms and relationship and constraints in them [2]. Ontology help to formulate search question of user and class of ontology’s (spatial ontology, domain ontology, universal ontology interpret the question sense.
2.2 Context identification.
Context is characteristic of situation of interaction or phrase that defines the functioning environment. Context in precise is situation expression current context is defined on parameters as sight, thinker point, state of affairs constraint context sensitive information is urge of what exactly user is search spatial Question interface help context sentive information retrieval in search engines[7] location based services Android Assistant incorporate Context of information retrieval. A co relation exists between environment of question asking of user to information fetched .Actor in context identification are user or machine. A user context has direct influence on outcome of system, a choice of diverse information might exist open to dissimilar user.schilit et al (1994) describe context “where you are (place) ,who you are in place ,and what assets are in close proximity .which propose context is more alert on entity environment as disparate to her internal state.morse et al(2000) define context as “unspoken situational information “ more precisely by dey[1] et al “ any information to characterize the state of user. Context is HCI and need for context aware information. Whose context is question of context-aware IR
1. User context 2. Program context 3. Paper context 4. System context.
Volume 3, Issue 5, May 2014
Page 423
Figure 1: Context model2.3 Text analytics
Text analytics is core area of research in information retrieval system. Text mining is research are that influences Information retrival,Natural language processing(NLP),information extraction, clustering, classification, concept extraction, web mining with machine learning and artificial intelligence influencing domain. Text analytics is the automatic and semi-automatic mining of unspoken, formerly unidentified, and latent useful information and prototype, from a huge volume of amorphous textual data, in natural language text. Text analytics, apiece corpus or text file is symbolized by a array of vector, with co-ordinates are diverse keywords in corpus. Research area in text analytics is to categorize textual information with such elevated co-ordinates. With elevated co-ordinates, text-analytics algorithms also deal with word ambiguities of pronouns, synonyms, and irrelevant data, user spell mistakes, acronym, acronyms and unstructured data format. Innovative approaches in text mining facilitate advancement in search engines. Efficient pattern discovery in text Analytics is recent advent in Natural language processing. Updatable machine with new discovered patterns deployed and evolved, to build effective search machines for finding relevant patterns [8].scholar method [8] would build effective pattern extraction machine.
Figure 2: Text analytics
2.4 machine learning algorithm
Machine learning would enhance search engine reducing search space and time by categorizing information in relevant classes using learnable classifiers. Machine learning is learning process incorporated in machines where machines inculcate human knowledge. machine learning has golden standard categorization approach 1)semi supervised 2) supervised 3)unsupervised .semi-supervised in human learning process which discover new learnable approach in machine and best approach in machine learning.H.A Simon , R.S Michaliski ,vapnik et[9][10] al have proposed machine learning approaches. Concluding tag are that dynamic mixed prototype machines are state of learning machines integrating new facts and human knowledge .machine learning reduce the search space and time for information search. Machine learning approaches have build rote learning machines, inductive learning ensemble learning machine, inductive learning support vector machine ,artificial neural network (ANN) .In current state Active learning is upgradable classifier which under ping unstamped facts set for training ,owing to specialist and general capability of classifier machine learning wrap up stream-based active learning, pool-based active learning and for mutable
Methodologies.
Every classifier methods have some plus points and drawback point .coordinate disaster problem, PU problem excess -fitting ,simplification are associated question in machine learning hence a DSS system method would assist to pick finest context based method for existing Question solving i.e Pragmatic methodology with a mixed model is chosen in hybrid design of search engine(QA system).
2.5 Ranking algorithm.
Volume 3, Issue 5, May 2014
Page 424
implement Naïve Bayes, k-Means, EM, KNN, Apriori etc. selecting of proper algorithm depends on classification and dataset under search. Ranking is presentation of information fetched by text retrieval engine to user based on efficiency parameters and performance measure i.e. recalls precision, F- measure, ultimately information gain.. addressable research area is learning to rank for search engine. In research a sculpt is automatically fashioned with some training dataset and then is exploited for ranking of answers extracted. The integrity of a mold is generally evaluated with performance trials MAP (Mean Average Precision) and NDCG (Normalized Discounted Cumulative Gain). superlatively a learning algorithm tutor a ranking system that tin straight optimized performance trial with respect to the training data. current methods, conversely, are only capable to train ranking system by diminish loss functions slackly linked to the recital measures. For example, Rank Boost and Ranking SVM train ranking system by curtailing categorization mistake on example pair. To treaty with the difficulty, we intend a story erudition algorithm inside the frame of boosting, which can diminish a loss task unswervingly clear on the recital measures. Our algorithm, referred to as AdaRank, repeatedly assemble ‘frail rankers’ on the origin of re-weighted training facts and lastly linearly merge the frail rankers for building ranking prophecy. The training procedure of AdaRank is precisely to of enhancing the recital measure worn. Trial results on four yardstick datasets exhibit that AdaRank drastically exceed the plinth stripe technique of Ranking SVM, BM25 and RankBoost. Adha rank is powerful algorithm.2.6 decision support system (Web graphs and CART).
decision support system would select best pattern mining method with concept mapping ontology incorporation(spatial ontology GUI for GIS question answer) with active machine learning algorithm and ranking Algorithm(adha rank) Web graphs are used in Web mining where each Web Page is termed as node and Links are termed as edges based on the incoming links and outgoing links these graphs are plotted, node may contain statistical information or document feature basis on this the document can be rank and relevance can be calculated.
CART: Classification and regression tree. It is a recursive growing process:
Given the data represented at a node, either declares that node to be a leaf (and state what category to assign it to) or find another property to use to spilt the data into subsets. Thus it’s a generic tree-growing methodology known as CART[16]. CART approach six general questions as noted below:
i. How many decision outcomes or splits will be there at a node?
ii. Should properties ne restricted to binary-values or multi valued, which property should be tested at a node. iii. When to declare node to be as leaf.
Iv.How to optimize a large tree.
v. How to detecting impure node and assign it to new category. VI.How missing data is handled.
The derivation tree e.g. of CART based on grammar: - The Geography Book sold over 1000 copies
Figure 3: Web Cart
3. RESEARCH SCRUTINY QUESTIONS.
We tackle two inspection questions in research map article. The major discovery (DAQ1) is motivated by the desire to see how search engines can be upgraded to hybrid system QA system. The subsequent question (DAQ2) is navigated alone principal optimization method (SEO) in search technology. Distinctively, few research articles incite the requirement for advance answer engines search technology is taken in advancement with compiled research articles in advancement in IR technology. The research question answer are been soughed in research space (RS) comprising text analytics (Research space1-RS1) concept extraction/context identification (RS2) , machine learning (RS3) with Ranking algorithm, Decision support system /Algorithm(RS4). The two breakdown questions lecture to in review article are: DAQ1: Why need of purgation of search engines to answering machines?
DAQ2: how to build upgradable search engine machine with selective optimized method?
Volume 3, Issue 5, May 2014
Page 425
the practice tools used enhance search engines the need of answer engine and tools and implementation in current state of research(s) utilized as subject(s).For diverse reading we story the feature of the future slant development . For case, we story if a tactic can achieve high search answering machine with existing frame model of search engines. How diverse frame can be conceptually connected to formulate high end answering system from search engines.4.
LITERATURE
SEARCH
We formulated research space with area in advancement in IR system via gaze to knowledge sources that observe in upkeep with research articles and invited research papers. Particularly, we opted sources that (1) grasp link tartan on chronicle piece of writing, gathering course, and library division; (2) wrap in material as scanty as might be predictable beneath the situation; and (3) demonstrate up in additional exact assessment on software edifice point. The preferred knowledge set are: Google Scholar, IEEE Explore Digital Library, ACM Digital Library, and Wikipedia and open source articles on mesh.. From our research area we formulated a questions string incorporating associated technologies and research domains and area: S mine fact terms commencing the inspection questions S engender a dilapidated of correspondent keywords and phrases and barter spell check for the noteworthy phrases S provide search Question pro vector array Question string. subsequent is the Question cord which is exploited for writing research review: ((search engine’ OR ’intelligent Information system AND ’text mining’ AND concept extraction AND machine learning ,AND Ranking algorithm AND Decision algorithms OR system AND expert system AND answering machine And QA System OR QAS OR ’Questioner technology ’ OR ’hybrid information machines)) for every of the selected knowledge source we enclose information yield from question string reliant in the lead the facts formation desirable in the record line. Based on knowledge search figure 1 pie chart forwards search area covered .
5. EXISTING SEARCH SYSTEM
Present search engines are customary data management search system which extract complete document or part of relevant Para from structured or unstructured corpus or knowledge resource or present in link sort to knowledge repositories on web, that many contain answers to user questions. search engine perform filtering of information with text analytics and clusters positive relevant information for user search question from large dataset [21].search engines are prominently classed in categories 1) Text based engine (document search engines) 2) web based search engines –perform search on web pages3) personalized search systems (incorporated click stream, browser log-user intent modeled systems) 4) closed domain search system-corpus is collection of company, institute dataset. Search engines incorporate question from user which search string of keywords .this question identifies several data source on match to keyword and concept with degree of relevance user question are matched with dataset with objects as text file, image, and audio i.e. multimedia data. Search engine donot store corpus or knowledge dataset but uphold the text article surrogates or metadata .search working consists of four teir Process methodology 1) crawling 2) indexing 3) ranking 4) relevance feedback
1.Crawling- Automated Indexers, Web Robots, Web Spider or a Bot that create search engine index navigates web and read web snippets and other information.web crawler are been developed with dedicated crawling mechanism
2.Indexing: pointer formation to set of documents for ease of search and reduce computing power to retrieve faster results search engine perform indexing regular expression parser facilitate it.
3.Ranking: user search question, the index is asked to recover the text mainly relevant question. Ranking is done on relevant document on map of degree of relevance ,importance and precision
4.Relevance feedback -purify ranking, early set of ranking answer. Client that select only revenant set of information from rank sort.. Based on the information in these documents a more appropriate ranking is presented (for example, the query may be expanded using the terms contained in the first set of relevant documents
Figure 4: Existing Search System.
Volume 3, Issue 5, May 2014
Page 426
engines .QA system formulate deduction and reasoning in answer extracted. Interactive QA system interacts with user (POST) for question reformulation which facilitates precise answer modeling. Track Map of QA system are future path to advancement of search engines.6. QA SYSTEM
QA system is a meadow of Information retrival (IR), Information Extraction (IE) and Natural Language Processing (NLP) search area in automated answering search questions inquired by user in a natural language [4]. QA systems mine answers from structured, unstructured collection of corpus question taxonomy vacillated by QA system is Fact, definitional wh-type, sematically introverted cross-language search questions. QA systems include a question classifier unit that decides the nature of question and the nature of answer. Subsequent to the question analytics, the system uses numerous models that incorporate complex NLP procedures on a abridged sum of corpus or text. Consequently, a document retrieval part use search engines to recognize the credentials or snippets in the document set probably contain the answer. Then a filter pre decide on petite text portions that contain cords of the similar kind as the probable answer. Finally, an answer mining module ensures for further traces in the text, to decide if the answer entity can indeed answer the question. Search engines obtain appropriate information in complete document mining or link to data source. But to-day users want precise answers to his queries rather than a time consuming search through the list of documents. The solution to this problem is Question Answering.QA system are hybrid system which is optimization of various step in searching machines In short literature to QA statistical search answering machine like IBM Watson are latest development in QA system .
7.
PROPOSED SYSTEMProposed system consists of four dimension modeled search engines implementation 1) User interface mapping concept of search questions with ontology framework 2) Text analytics using n-grams for upgradable pattern mining machine
3) machine learning for time saving search, selection of proper data cluster for search question with Learning to rank perspective algorithm system “adha rank “
4) Decision support system with web CART for selecting proper methodologies ie selecting text analytics method or ranking algorithm etc.
The proposed system is hybrid QA system which models user question in context, concept search with answer mining using text analytics best method and machine learning makes trainable machine with decision support to formulate dynamic model for optimized performance.
8. ALPHA ANALYSIS AND DATA EXTRACTION
Starting the upshot of our knowledge seeks we documented the vital cram for this discovery review. We aggravated every upshot support, review on every concerned technique. We kept back to navigate the plan till familiarity five categorized papers on IR technology where studied to assessment of research questions. To acquire relevant knowledge starting each vital study, we intended and exploited an data mining model. The model integrate diverse meadow, every paper enlightened research area question with percentile support. The major architecture discover each vital research paper, encoding noteworthy information to an data extraction model, and an vary architecture in parallel checkered on and alienated data from a slight sampling set of crucial revise investigation. The methodology is simple with intelligent summary of every research paper .the research extraction model is form of n-rows and 10 columns with “research title”, “author name”, “abstract”, “algorithm”, “methodology” , “pros” , “cons” and “ tag mark” We subsequently extract the tag mark column to determined information for constancy, which strength reveal indulgent in paraphrase. To be certain, the stability about the determined in rank data is presented that is furnished by information extractor. At end, we included the data from the tag column to formulate new optimized methodologies essential to answer the question exploration posed in effective tally summary format.
Volume 3, Issue 5, May 2014
Page 427
9. RESULTS
In this research Para we break down concluding tag of 30 research papers on conceptual investigation.para is divided in two sub para each answering a explorative question under taken in research.
A.Why need of purgation of search engines to answering machines?
Most of the 30 papers added in are futuristic demonstrate a single technique optimization in IR system which are concluding remark to optimization of search machines .particularly five categorized paper by second data in parallel extraction help to formulate that there is need of answering machines with optimization of current technologies.focusing optimization of search engines at various levels.
B.how to build upgradable search engine machine with selective optimized method
Grasp of the illustrations suggest that search engine advancement has shown progressive growth to expert hybrid machines which are combination of several optimized techniques. The discovery map formulates a pictorial conceptual frame for building answering engines. In corporation of decision support formulates highly hybrid reasoning machine overcoming pit fall in search engine to produce answer engine.
10. DISCUSSION
Our ending reveals that there is implausible prospect for further research in answer engine. we acknowledge to near are two crucial array of prospect for future effort in Search engine advancement: (1) user center design and trial machines and (2) combination of varied IR techniques to produce hybrid machine(context identification machines)
Previously, we distinguished just one research article , that formulated research domain of search engine to QA system .in this last research Para we answer technological question in deployment of framework many prototype theory would model but ultimately only software ethics and SDLC model incorporating standalone,roboust error free architecture should be consider. Second technological question is language and platform selecting python is new platform with real object oriented design answer machines can be built in python with incorporating apache luence (open source) design. the last Para address designer issues.
Exploration of search engine and advanced IR techniques hand out a answer piece in conception and tending incorporation of deductive reasoning capability in search engines. This research review we illustrate an logical script tally that set Discovery Map in direction from search engines to answer machines (QA system. We scrutinize current state of art of systems. We famed 30 essential research articles as per our concept, and inspected articles to answer two searching questions. We depicted the techniques that renowned expert have used to IR system (search advancement), the concluding results drawn by research person .A concluding mark is search answering machines are future of search engine and development of QA system is road map in direction from search engines to answering machine .a large unexplored area in IR system remains which need to model highly humanized reasoning system incorporating world knowledge a future corridor for search technology.
Acknowledgment
The idea of research was presented by me with strongly technicality of sachin shinde knowledge and innovative technology formulation was done by research analyst, I express my gratitude to by thanking immense support of Prof S.P Medhane, and Prof .S.D.Joshi.
References
[1] www.googlescholar.com
[2] www.ieeeexplorer.com
[3] www.acm.com
[4] www.wikipedia.com
[5] R.baeza-yates and B.riberio-netro “modern information retrival” Addision wesely 2-11,1998.
[6] K.Aas and L.kikvil. “text categorization : A survey ,” Technical Report Raport NR 941,Norwegian Computing centre ,1999.
[7] Lofti A Zadeh “from search Engines to Question answering system:the problem of world knowledge,relevance,Deduction and percisiation” computational intelligence, international conference 9th fuzzy days in dortmund springer 2006
[8] Ning Zhong yuefeng Li,Sheng-Tang Wu “Effective pattern Discovery for text Mining” IEEE Transactions on knowledge and Data Engineering vol 24,No 1 Jan 2014.
[9] Annu Anna Lal,Anna Alphy “A variant Architecture Design for Intelligent Medical Search Engine(iMed) ,International journal of Advanced Research in computer and Communication Engineering April 2013.
Volume 3, Issue 5, May 2014
Page 428
[11]Ning zhong ,yuefeng Li and sheng –Tang Wu “effective pattern Discovery for text mining” IEEE Transactions onknowledge and data engineering Jan 2012.\
[12]V vapnik the nature of ststistical learningtheory new york springerverlag 1997
[13]We wang “simple analysis on research applications of machine learning,computer informationtechnology 2010
[14]Ricardo baeza yates berthier ribeiro neto “Modern information retrival: the concept and technology behind search” Adission and wesely 2013.
[15]Dheeraj Rajagopal “A Graph-Based Approach to Commonsense Concept Extraction and Semantic Similarity Detection
[16]Aditya Parameswaran “Towards The Web Of Concepts: Extracting Concepts from Large Datasets” 2010 , VLDB ‘10, September 13-17, 2010
[17]Hien nguyen ,Eugene ,santos J Y “hybrid model for capturing user intent seeking for information. [18]V vapnik ststistical theory new york :j wiley 1998
[19]Nhamo Mdzingwa “Data Mining with Oracle 10g using Clustering and Classification Algorithms xindongwu · Vipin Kumar · J. Ross Quinlan “Top 10 algorithms in data mining” Springer-Verlag London Limited 2007
[20]Zhang Chengzhi1, 2, Wu Dan3” Concept Extraction and Clustering for Topic Digital Library Construction ACM International Conference on Web Intelligence and Intelligent Agent Technology
[21]Kenneth wai-ting leung, wilfred ng “personalized concept-based clustering of search engine queries” ieee transactions on knowledge and data engineering, vol. 20, no. 11, november 2008
[22]Yakup Yildirim, Adnan Yazici”Automatic Semantic Content Extraction in Videos Using a Fuzzy Ontologyand Rule-BasedLudovicDenoyer “HMM-based Passage Models for Document Classification and Ranking Annual Colloquium on Information Retrieval, 2001
[23]Xiaohui Tao,yefeng Li,Ning Zhong,Richi nayak “Ontology mining for personalized web information gathering” dewey decimal classification
[24]Suresh S, Shobana M” Enhancing Information Retrieval using Concept- Based Mining Model with Feature Extraction and Clustering
[25]Ali Mohamed Nabil Allam and Mohamed Hassan Haggag. The question answer-ing systems: A survey. International Journal of Research and Reviews in Infor-mation Sciences (IJRRIS), 02(03), 2012.
[26]Poonam Gupta and Vishal Gupta. Article: A survey of text question answering techniques. International Journal of Computer Applications, 53(4):1–8, Septem-ber 2012. Published by Foundation of Computer Science, New York, USA
[27]Di Sciullo, A.M. “A reason to optimize information processing with a core property of natural language” ntelligent Software Methodologies, Tools and Techniques (SoMeT), 2013 IEEE 12th
[28]Qumsiyeh, R. ; Yiu-Kai Ng “Enhancing Web Search Using Query-Based Clusters and Labels” Web Intelligence (WI) and Intelligent Agent Technologies (IAT), 2013 IEEE/WIC/ACM
[29]Sankepally, A. ; Bin Zhou “An Aggregate Search Model for Web Search Engines: An Empirical Study” Web Intelligence (WI) and Intelligent Agent Technologies (IAT), 2013 IEEE/WIC/ACM
[30]Kamath, S.S. ; Piraviperumal, D. ; Meena, G. ; Karkidholi, S. ; Kumar, K. A semantic search engine for answering domain specific user queries Communications and Signal Processing (ICCSP), 2013 International Conference [31]Najmi, E., Hashmi, K., Khazalah, F., Malik, Z. “Intelligent semantic question answering system” Cybernetics
(CYBCONF), 2013 IEEE International Conference.
[32]Khokale, R. ; Atique, M. “Intelligent Natural Language Interface for web-based information retrieval” Information Communication and Embedded Systems (ICICES), 2013 International Conference
[33]Yong qing wang qingx li,review on studies and advances of machine learningapproaches,telkomnika international journal of elevtrical engineering 2014
[34]Christopher D. Manning, Prabhakar Raghavan, and Hinrich Schtze. Introduc-tion to Information Retrieval. Cambridge University Press, New York, NY, USA, 2008
[35]Dragomir R. Radev, Weiguo Fan, Hong Qi, Harris Wu, and Amardeep Grewal. Probabilistic question answering on the web. JASIST, 56(6):571–583, 2005
[36]M. Ramprasath and S. Hariharan. A survey on question answering system. Inter-national Journal of Research and Reviews in Information Sciences, 02(01), 2012.
[37]Deepak Ravichandran and Eduard H. Hovy. Learning surface text patterns for a question answering system. In ACL, pages 41–47, 2002.
[38]Hakan Sundblad. Question Classification in Question Answering Systems, June 2007.
