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D.J Hani Mary Shenih* et al. /International Journal of Pharmacy & Technology ISSN: 0975-766X CODEN: IJPTFI Research Article Available Online through www.ijptonline.com SURVEY OF WEB CONTENT MINING AND RELATION EXTRACTION TECHNIQUES D.J Hani Mary Shenihaa, A.Ezil Sam Lenib a Research Scholar, Department of Computer Science and Engineering,Sathyabama University, Chennai, TamilNadu 600119, India. b Professor & Head,Department of Computer Science and Engineering,SRR Engineering College, Padur, Chennai 603103, India. Email:[email protected] Received on: 20.10.2016 Accepted on: 25.11.2016 Abstract In the World Wide Web, there are different types of semantic relations which belong to diverse entities. As years passed World Wide Web has became overloaded with information and it became hard to retrieve according to need. This problem is solved using web mining techniques. The web contains structured, unstructured, semi structured and multimedia data, semantic relations should be known in all relation extraction techniques. Traditional relation extraction methods require predefined relations and relation specific human tagged examples. Minimally supervised novel extraction method uses multiple source relations to learn a relational classifier for a target relation. Handling entities that are not related with multiple semantic relations is a challenge. This paper analyzes various techniques available in web content mining and supervised semantic relation extraction. Finally some techniques should be proposed to handle the entities not related with multiple semantic relations. Keywords: Semantic relation extraction; Web mining; Supervised novel extraction. 1. Introduction Web mining is integration of information gathered by traditional data mining technologies and the information gathered over World Wide Web by various methods and techniques. Web mining is an important application of data mining. Web mining is used to extract knowledge from web data such as web documents, document usage log of websites found between the hyperlinks. Web mining can be represented using two different approaches. They are process centric view and data centric view 1,2. IJPT| Dec-2016 | Vol. 8 | Issue No.4 | 22996-23009 Page 22996 D.J Hani Mary Shenih* et al. /International Journal of Pharmacy & Technology Process centric view illustrates web mining as a sequence of tasks and data centric view defines the type of web data used during mining process. Based on the type of data mined, web mining can be categorized into three broad and distinct categories. They are Web Structure mining, Web Content mining, Web usage mining. Each category of Web mining has its own distinct functionalities. Web content mining is a technique of extracting useful data, integrating knowledge and information available from the content of the web page. It can also be used for scanning and mining of text, graph and pictures from the web page to verify the significance of the search query. There are two views for web content mining such as data base view and information retrieval view3,4. In a database view, there will a better querying and information management on the web. In this view, the mining always tries to conjecture the web site structure and is used to convert website to a database. In case of information retrieval view, semi structured and unstructured data is used. In semi structured data, the structure of the hyperlink found between documents and the HTML structures found in the web documents are used for representation. Web structure mining is to spawn structural summing up about the WebPages and websites. It mainly focuses on the inter document level structure of the hyperlinks in web pages and websites. The relationship and similarity among various websites and WebPages are generated using web structure mining. There is more relationship with web content mining because the web sites and web documents have links and they use primary data or real data on the web. HITS and page rank are two important techniques used in Web structure mining. Web usage mining allows the anthology of Web admittance information for Web pages. The information about path leading to accessed web page are collected without human intervention into access logs through a web server. The output data describes the pattern of usage of WebPages including IP address, time and date of access including the page references in accessing web pages and websites. 2. Web Content Mining5,6 Has proposed a mechanism for detecting adult account in twitter using iterative social based classifier. Adult twitter accounts frequently connected with ordinary accounts as well as post countless ordinary entities, which makes the graph filled with noisy links. A novel graph based classification technique called Iterative Social based Classifier (ISC) is used to address the problem which is challenging to the noisy links. Large scale real world Twitter data are used for evaluation which showed that ISC can attain acceptable concert in adult account detection by labeling small numeral of IJPT| Dec-2016 | Vol. 8 | Issue No.4 | 22996-23009 Page 22997 D.J Hani Mary Shenih* et al. /International Journal of Pharmacy & Technology twitter accounts which are popular This method can be implemented in twitter by changing keywords and can be applied to other social networks. 7 Has proposed a mechanism for measuring semantic similarity between words using four word co-occurrence measures. Here page counts and snippets are retrieved from a web search engine. The semantic relation extracted between the words is done with the help of lexical pattern extraction algorithm. Support Vector Machines (SVM) is used for finding the optimal combination of clusters of lexical patterns and co occurrence measures which are based on page counts. The values of Precision, recall and F-score shows that the proposed method has best results comparing with other techniques for measuring semantic similarity between words. 7 Has proposed a social network extraction system called POLYPHONET.This system make use of a number of superior technique to dig out relations of persons, detect groups of persons, get hold of keywords for a person. Integrated systems are created to reduce the related methods into simple pseudo codes using Google. Several new algorithms are developed for social networking to formulate extraction scalable, to get hold of and make use of person-to-word relations, mining to classify relations into categories, and every unit is implemented in POLYPHONET. The limitation is, repetition of extraction of relations and entities identification are performed to gain a good accurate social network. 2.1 Web Content Mining Techniques Web content mining can be used in four types of data available in the web page. They are Unstructured, Structured, Semi structured and Multimedia data. There are many web content mining techniques. Figure 1 illustrates the various web content mining techniques available. Web Content mining mines many useful information like text, image, audio, video, metadata, hyperlinks and extracts many useful information. 2.1.1 Unstructured Mining Unstructured data indicates the information which has no predefined data. Usually, this unstructured information is typically text heavy which contain data such as dates, numbers and facts as well. As there is no predefined structure, it is quite difficult to understand using our traditional programs compared to data stored in databases. Some of the techniques used for unstructured mining are topic tracking, information extraction, summarization, clustering, and categorization and information visualization. There are many tools for implementing unstructured mining techniques. IJPT| Dec-2016 | Vol. 8 | Issue No.4 | 22996-23009 Page 22998 D.J Hani Mary Shenih* et al. /International Journal of Pharmacy & Technology 2.1.2 Structured Mining Structured data refers to information which is included in the database and searching can be done easily by straight forward search operations or with the help of search engine algorithms. Techniques used are WebCrawler8, wrapper generation, page content mining. The following Figure 1 illustrates various web content mining techniques in unstructured, Structured, Semi structured and Multimedia Web Content mining. Figure-I. 2.1.3 Semi-structured and multimedia Mining Semi structured data is another form of structured data that does not confirm with the formal structure of the data models coupled with relational databases or other forms of data tables. There are no tags or other markers to separate semantic elements and put into effect of the hierarchies of records and fields within the data. Techniques of semi structured data are top down extraction, web data extraction language and using Object Exchange Model (OEM).OEM is used for storing relevant information’s which are extracted from semi structured data. This extracted information from semi structured data is embedded into a group of useful information. In OEM there is no necessity to describe the structure of the object in advance. Multimedia data consist of a combination of variety of media types such as audio, text, animation IJPT| Dec-2016 | Vol. 8 | Issue No.4 | 22996-23009 Page 22999 D.J Hani Mary Shenih* et al. /International Journal of Pharmacy & Technology and video. Techniques used in mining multimedia data are SKICAT, Colour histogram matching, shot boundary detection and Multimedia miner. 2.1.4 Comparative Analysis of Web Content mining Techniques The following Table 1 illustrates the performance of web content mining in Adult account detection, Semantic similarity between words and Open information Extraction. The performance is represented by evaluating the precision and recall of the corresponding technique.[Table 1] Table 1. Comparision of Web Content Mining Techniques. Method Adult Account detection Semantic Similarity between words POLYPHONET Technique Precision Recall URL 092 0.17 Text Gnet Mine ICA Trust Rank ISC WebJaccard 0.62 0.39 0.62 0.37 0.78 0.59 0.41 0.61 0.58 0.23 0.89 0.71 Web Overlap Web Dice WEbPMI Sahmi Chen No Clust Four Word Co-occurrence Evaluation of questionnaire using Co-author class 0.59 0.58 0.26 0.63 0.47 0.79 0.85 78.5 0.68 0.71 0.42 0.66 0.62 0.80 0.87 53.6 Evaluation of questionnaire using Lab class Evaluation of questionnaire using Project class Evaluation of questionnaire using Conf class 55.6 28.3 20.3 20.0 39.9 41.3 2.2 Web Content Mining Tools There are many effective commercial and open source web content mining tools available. These web content mining tools help to download the essential information to the users by collecting the perfect and appropriate information.Brief overview of commercial and open source web content mining tools are given as follows. 1) Automation Anywhere9, is an intellectual computerization software to automatically perform business & IT processes, together with web data extraction and screen scraping. IJPT| Dec-2016 | Vol. 8 | Issue No.4 | 22996-23009 Page 23000 D.J Hani Mary Shenih* et al. /International Journal of Pharmacy & Technology 2) Mozenda, is More-Zenful-Data, web content mining, software which is used for circulating extracting and storing data by setting agents by users. 3) Screen Scraper, is a freely downloadable software that permits user to scrape and format unstructured, structured data from websites. 4) Web info extractor is a web content mining tool for extracting content and it is used for monitoring the content updates. 5) Web content extractor is an authoritative and simple tool for data retrieval and web scrapping. 2.2.1Comparison of Web Content Mining Tools There are many commercial and open source tools available, and it is highly difficult to compare the effectiveness and efficiency of tools. The Table 2 gives the comparison of 5 web content mining tools. The comparison is performed based on the performance on unstructured and structured web data, data recording and the user friendliness of the software. [Table 2] Table 2. Web Content Mining Tools Comparison. Tool Web content extractor Automation any where Web info extractor Mozenda Screen scrapper Structured data extraction Tasks Un Structured User friendly data extraction Data recording Yes Yes No No Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes No No No No 3. Relation Extraction A relation extraction is a strategy of classifying and detecting semantic relationship mentioned in a set of entities. This is done typically from a text or XML documents. This method is very much comparable with information extraction. The main difference between information extraction and relation extraction is information extraction performs the operation of removing repeated relations. Relation extraction is used for extracting relevant relations and facts where as information retrieval selects relevant document.Parse Trees can be used for representing semantic relation extraction10. 3.1 Relation Extraction Techniques There are various relation extraction techniques available. They are generally classified as Supervised Relation IJPT| Dec-2016 | Vol. 8 | Issue No.4 | 22996-23009 Page 23001 D.J Hani Mary Shenih* et al. /International Journal of Pharmacy & Technology Extraction, Unsupervised Relation Extraction, Distant Supervision, Semi Supervised Relation Extraction and Semantic relation Extraction. 3.1.1 Supervised Relation Extraction Supervised Relation extraction is extraction of information given as a classification task. A supervised system for relation extraction has three steps: 1. Data representation for labelled examples 2. Train a classification model as the relation detector/ classifier 3. Apply the model as the relation extractor on the unseen relation mentions. The classification task can be performed with the help of various classifiers. Rich structural depiction similar to parse trees can be formed by giving input to the classifiers. Based on the nature of input given to the classifier training, supervised approach is classified in to two types. They are feature based methods and kernel based methods. In Feature based methods a set of semantics and syntactic features are extracted from the text. After extraction, extracted features act as a decision parameter for deciding the various entities in the sentences are related or not. Contents of the syntactic features which are extracted are the types of two entities, entities themselves, number of words found in between entities, sequence of words found between entities and path in the parse tree containing the two entities. The paths linking the two entities are included in the semantic indication in the dependency parse. The extracted syntactic and semantic features are given as feature vector to the classifier for classification and training. Kernel based methods are used in string kernel based relation extraction which are used in the context of text classification. When we compute relation extracting among two strings the string kernel calculated the similarity based on number of sub sequences which are common to both of the strings. If we find more number of subsequence’s we conclude the two strings computed has more similarity and other wise those strings has less similarity. Every string given can be mapped to a higher dimensional space in which each dimension match up to the occurrence or nonexistence of a meticulous subsequence. Word sequences near the entities are represented for relation extraction objects under parse trees or questions containing the entities. 3.1.2 Unsupervised Relation Extraction Unsupervised relation extraction techniques gather pairs of co-occurring entities as relation instances, extract features for instances and apply unsupervised clustering techniques and the major relations of a corpus are found. This method IJPT| Dec-2016 | Vol. 8 | Issue No.4 | 22996-23009 Page 23002 D.J Hani Mary Shenih* et al. /International Journal of Pharmacy & Technology depends on tagging a predefined set of dispute types, such as person, organization, and location, in advance. proposes several generative models, largely similar to LDA , for relation extraction. 3.1.3 Distant Supervision Distant Supervision (DS) is one of the important training relation extractor without using labeled data 11. According to whether the argument pair is listed in the target relational tables in a knowledge base (KB) by labelling relation mentioned in the source corpus the examples for training are generated automatically. This method significantly reduces human efforts for relation extraction. 3.1.4 Semi Supervised Relation Extraction Semi supervised approaches are technique based on bootstrapping which result in the detection of huge numeral of relations and patterns.In relation extraction tasks there is availability of large amount of unlabelled data. There are fewer amounts of labeled data, because it is too expensive so boot strapping method is advantageous in creating a large quantity of labeled data. Yarowsky, 1995 and Blum & Mitchell, 1998 has proposed algorithms for semi supervised relation extraction. Concept of both the algorithms is the weak learners output is used as training data for next iteration.Cotraining method proposed by blum and mitchell in 1998 is a example for weakly supervised paradigm which learns from a huge volume of unlabelled data using separate, but using many views of data and a small set of labeled data. 3.1.5 Semantic Relation Extraction Semantic relation is the fundamental relation sandwiched between two concepts expressed by phrases or words. It is mainly used in text summarization, question answering, text to image generation; textual entailment etc.Semantic analytics is the use of ontologies to analyze content in web resources. This field of research combines text analytics and semantic web technologies like RDF. 3.1.6 Data sets for Relation Extraction The entity relation extraction needs to be evaluated with the help of data sets. The evaluation of the relation extraction depends upon the kind of the dataset used and method applied in relation extraction. There are different ways for relation extraction for supervised and unsupervised relation extraction methods.Wikipedia has become a popular data source for semantic relation extraction mechanism.Wikepedia contains many hyperlink entities in which most of the pages are used IJPT| Dec-2016 | Vol. 8 | Issue No.4 | 22996-23009 Page 23003 D.J Hani Mary Shenih* et al. /International Journal of Pharmacy & Technology for relation extraction. Wikepedia is also a rich source of data’s in which relations can be extracted for hyperlinked documents12.A Message understanding conference13 has started a program by DARPA to make possible to investigate various information extraction techniques. There are two primary tasks for evaluation. They are Named Entity Recogonisation (NER) and co reference resolution. Most familiarly used data sets are MUC,ACE,MEDLINE and YAGO. 3.1.7Evaluation of Supervised Relation Extraction For evaluating supervised relation extraction the problem is evaluated as a classification problem. There are three metrics used for relation extraction of supervised methods. They are Precision, Recall and F-measure14. These metrics are given as follows. 1) Precision In information retrieval mechanism positive predictive value called as precision which is the division of retrieved instances that are relevant. It can be computed at a given cut-off rank, making an allowance for only the top most results returned by the system. Such a measure is called precision at nether Precision metric for supervised relation extraction is defined as follows. Number of correctly extracted entity relations Precision (P) = Total number of extracted entity relations 2) Recall Recall is computed as the fraction of the relevant instances that are retrieved. It is the ratio of the total number of relevant records retrieved to the total number of relevant records in the database. The recall metric for supervised relation extraction is given as follows. Number of correctly extracted entity relations Recall (R) = Actual number of extracted entity relations 3) F-measure To provide single measurement for a system we combine precision and recall. That measure is called as F-measure. The weighted harmonic mean of precision and recall, Computation of the F-measure is given as follows. IJPT| Dec-2016 | Vol. 8 | Issue No.4 | 22996-23009 Page 23004 D.J Hani Mary Shenih* et al. /International Journal of Pharmacy & Technology F- Measure (F1) = 2 * Precision * Recall Precision + Recall In15 computation process is done by a random allocation of 100 instances which are categorized into three groups. In the first group 60 instances are categorized into training instances by having the source relation R, second group with 10 instances for training by having R as target relation and the last group with 30 instances as the instances for testing. For each target type of relation there are 1,140 training instances for the source relation and 10 instances for training the relation at target. By using pattern selection based on relation independency in entropy the 1,000 patterns which are top ranked are selected as relation independent patterns and the rest of the patterns are considered as relation specific patterns. In the precision, recall and F measure are calculated for the target relation scoring the value of Precision as 86.47,recall as 51.78 and F-measure as 62.77.here the macro average calculation is done with 20 relation types. 3.1.8 Comparative Analysis of Relation Extraction Techniques. Relation extraction from the web can be done using various methods and techniques. Some of the relation extraction techniques compared are minimally supervised relation extraction, [Table 3] Motif based Relation extraction technique and Relation Extraction from text16. Extracted relations are evaluated based on supervised relation extraction evaluation techniques such as Precision and Recall. There are various techniques used in each method of relation extraction. In the method of relation extraction from text techniques such as logic based, short path kernel and sub tree kernel techniques are used to extract the relations from text and evaluated using precision and recall. Motif based relation extraction method was used to extract the relations in Wikipedia hyperlinks using various data sets such as data mining, computer network, data structure. Euclidean geometry, classical mechanics ,micro biology and wine as given in Table 3 and Precision and recall values are computed separately for each data set. The following Table 3 explains about the comparison of various relation extraction techniques16 , 17. IJPT| Dec-2016 | Vol. 8 | Issue No.4 | 22996-23009 Page 23005 D.J Hani Mary Shenih* et al. /International Journal of Pharmacy & Technology Table 3. Relation Extraction Techniques Comparison. Method Technique Precision Recall Minimally supervised Minimally Supervised novel Relation Extraction Relation Extraction Relation Extraction from Text 86.47 51.78 Logic based 68.2 42.3 Shortest path kernel 65.5 53.8 Sub Tree Kernel 67.1 35 Wikipedia using Data mining 0.893 0.323 0.826 0.395 Data 0.884 0.498 Euclidean 0.898 0.501 Classical 0.864 0.459 Wikipedia using Microbiology 0.801 0.352 0.826 0.472 Dataset Wikipedia using Computer Network Dataset Wikipedia Motif based Relation Extraction using structure Dataset Wikipedia using geometry Dataset Wikipedia using mechanics Dataset Dataset Wikipedia using Wine Dataset 3.2 Relation Extraction Tools Relation Extraction Tools are used for extracting the relations many open source and commercial tools are available for relation extraction. Some of the tools available are DIPRE, Snowball, KnowItAll, TextRunner 18.These tools are explained as follows. 1. Text Runner, used for extracting more relations, broader sets of facts, with the reflecting order of magnitude 2. Dual Iterative Pattern Expansion (DIPRE)19 , used for extracting structural relation from the huge collection of HTML documents. 3. Snowball , is used for generating patterns, extracting tuples form various text documents. 4. KnowItAll is for automating the tedious mechanism of extracting huge volume of facts from web. 3.2.1Comparison of Relation Extraction Tools IJPT| Dec-2016 | Vol. 8 | Issue No.4 | 22996-23009 Page 23006 D.J Hani Mary Shenih* et al. /International Journal of Pharmacy & Technology There various Relation Extraction tools available for extracting the relations from huge volume of text or from the web. Each Relation extraction tool has its own specific functionality. The tools DIPRE, Snowball, KnowItAll and Text Runner are compared with various features such as Initial seed, predefine relation, External NLP tools, relation types, Language dependent and classifier. There are various binary and unary relation types.knowItAll has both binary as well as unary relation types.All others have only binary relation types.The following Table 4 illustrates the comparison of various relation extraction tools. Table 4. Relation Extraction Tools Comparison. DIPRE Snowball KnowItAll Text Runner Initial Seed Yes Yes Yes No Predefine Yes Yes Yes No No Yes:NER Yes:NP Yes: dependency chunker parser,NPChunker Relation External NLP Tools Relation Binary Binary Unary/Binary Binary No Yes Yes Yes Exact Pattern Matching with Naive Bayes Self Supported Matching similarity function Classifier binary classifier Types Language dependent Classifier 4. Conclusion After comparing and analyzing the existing techniques, available in supervised relation extraction we found that cost of the training is more and there is a great challenge of handling entities that are not related to each other and to handle entities with multiple semantic relations. A relation extraction can adapt relation extraction system that has to be trained to extract some specific new relation types. It can be used in domain based semantic relationship extraction and social network extraction. This paper provides an overview of the web content mining techniques and relation extraction techniques for supervised relation extraction method. IJPT| Dec-2016 | Vol. 8 | Issue No.4 | 22996-23009 Page 23007 D.J Hani Mary Shenih* et al. /International Journal of Pharmacy & Technology References 1. Johnson F, Gupta SK. Web Content Mining Techniques a Survey, International Journal of Computer Applications. 2012, 47(11), pp. 44-49. 2. Srivatsa T, Desikan P, Kumar V. 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