Web Data Extraction, Applications and Techniques: A Survey

Web Data Extraction is an important problem that has been studied by means of different scientific tools and in a broad range of applications. Many approaches to extracting data from the Web have been designed to solve specific problems and operate in ad-hoc domains. Other approaches, instead, heavily reuse techniques and algorithms developed in the field of Information Extraction. This survey aims at providing a structured and comprehensive overview of the literature in the field of Web Data Extraction. We provided a simple classification framework in which existing Web Data Extraction applications are grouped into two main classes, namely applications at the Enterprise level and at the Social Web level. At the Enterprise level, Web Data Extraction techniques emerge as a key tool to perform data analysis in Business and Competitive Intelligence systems as well as for business process re-engineering. At the Social Web level, Web Data Extraction techniques allow to gather a large amount of structured data continuously generated and disseminated by Web 2.0, Social Media and Online Social Network users and this offers unprecedented opportunities to analyze human behavior at a very large scale. We discuss also the potential of cross-fertilization, i.e., on the possibility of re-using Web Data Extraction techniques originally designed to work in a given domain, in other domains.Comment: Knowledge-based System

Web Data Extraction For Content Aggregation From E-Commerce Websites

Internetist on saanud piiramatu andmeallikas. Läbi otsingumootorite\n\ron see andmehulk tehtud kättesaadavaks igapäevasele interneti kasutajale. Sellele vaatamata on seal ikka informatsiooni, mis pole lihtsasti kättesaadav olemasolevateotsingumootoritega. See tekitab jätkuvalt vajadust ehitada aina uusi otsingumootoreid, mis esitavad informatsiooni uuel kujul, paremini kui seda on varem tehtud. Selleks, et esitada andmeid sellisel kujul, et neist tekiks lisaväärtus tuleb nad kõigepealt kokku koguda ning seejärel töödelda ja analüüsida. Antud magistritöö uurib andmete kogumise faasi selles protsessis.\n\rEsitletakse modernset andmete eraldamise süsteemi ZedBot, mis võimaldab veebilehtedel esinevad pooleldi struktureeritud andmed teisendada kõrge täpsusega struktureeritud kujule. Loodud süsteem täidab enamikku nõudeid, mida peab tänapäevane andmeeraldussüsteem täitma, milleks on: platvormist sõltumatus, võimas reeglite kirjelduse süsteem, automaatne reeglite genereerimise süsteem ja lihtsasti kasutatav kasutajaliides andmete annoteerimiseks. Eriliselt disainitud otsi-robot võimaldab andmete eraldamist kogu veebilehelt ilma inimese sekkumiseta. Töös näidatakse, et esitletud programm on sobilik andmete eraldamiseks väga suure täpsusega suurelt hulgalt veebilehtedelt ning tööriista poolt loodud andmestiku saab kasutada tooteinfo agregeerimiseks ning uue lisandväärtuse loomiseks.World Wide Web has become an unlimited source of data. Search engines have made this information available to every day Internet user. There is still information available that is not easily accessible through existing search engines, so there remains the need to create new search engines that would present information better than before. In order to present data in a way that gives extra value, it must be collected, analysed and transformed. This master thesis focuses on data collection part. Modern information extraction system ZedBot is presented, that allows extraction of highly structured data form semi structured web pages. It complies with majority of requirements set for modern data extraction system: it is platform independent, it has powerful semi automatic wrapper generation system and has easy to use user interface for annotating structured data. Specially designed web crawler allows to extraction to be performed on whole web site level without human interaction. \n\r We show that presented tool is suitable for extraction highly accurate data from large number of websites and can be used as a data source for product aggregation system to create new added value

Explication Search Results From Huge Amount Of Published Data

The Internet presents a huge amount of useful information which is usually formatted for its users, which makes it difficult to extract relevant data from various sources. Therefore, the availability of robust, flexible Information Extraction (IE) systems that transform the Web pages into program-friendly structures such as a relational database will become a great necessity. Search result record (SRR) is the result page obtained from web database (WDB) and these records are used to display the result for each query. Each SRR contain multiple data units which need to be label semantically for machine process able. In this paper we present the automatic annotation approach which involve three phases to annotate and display the result. In first phase the data units in result record are identified and aligned to different groups such that the data in same group have the same semantics. . This approach is highly effective. From the annotated search result, frequently used websites are identified by using apriority Algorithm which involve pattern mining.  In this paper, we present an automatic annotation approach that first aligns the data units on a result page into different groups such that the data in the same group have the same semantic. And then we assign labels to each of this group

Explication Search Results From Huge Amount Of Published Data

The Internet presents a huge amount of useful information which is usually formatted for its users, which makes it difficult to extract relevant data from various sources. Therefore, the availability of robust, flexible Information Extraction (IE) systems that transform the Web pages into program-friendly structures such as a relational database will become a great necessity. Search result record (SRR) is the result page obtained from web database (WDB) and these records are used to display the result for each query. Each SRR contain multiple data units which need to be label semantically for machine process able. In this paper we present the automatic annotation approach which involve three phases to annotate and display the result. In first phase the data units in result record are identified and aligned to different groups such that the data in same group have the same semantics. . This approach is highly effective. From the annotated search result, frequently used websites are identified by using apriority Algorithm which involve pattern mining.  In this paper, we present an automatic annotation approach that first aligns the data units on a result page into different groups such that the data in the same group have the same semantic. And then we assign labels to each of this group

Interpreting Pursuit Outcomes from Data Web Bases

The Internet presents a huge amount of useful information which is usually formatted for its users, which makes it difficult to extract relevant data from various sources. Therefore, the availability of robust, flexible Information Extraction (IE) systems that transform the Web pages into program-friendly structures such as a relational database will become a great necessity .The motivation behind such systems lies in the emerging need for going beyond the concept of “human browsing.”The World Wide Web is today the main “all kind of information” repository and has been so far very successful in disseminating information to humans[5]. The Web has become the preferred medium for many database applications, such as e-commerce and digital libraries. These applications store information in huge databases that user’s access, query, and update through the Web. Database-driven Web sites have their own interfaces and access forms for creating HTML pages on the fly. Web database technologies define the way that these forms can connect to and retrieve data from database servers.[3] In this paper, we present an automatic annotation approach that first aligns the data units on a result page into different groups such that the data in the same group have the same semantic. And then we assign labels to each of this group

Acquisition des contenus intelligents dans l’archivage du Web

Web sites are dynamic by nature with content and structure changing overtime; many pages on the Web are produced by content management systems (CMSs). Tools currently used by Web archivists to preserve the content of the Web blindly crawl and store Web pages, disregarding the CMS the site is based on and whatever structured content is contained in Web pages. We first present an application-aware helper (AAH) that fits into an archiving crawl processing chain to perform intelligent and adaptive crawling of Web applications, given a knowledge base of common CMSs. The AAH has been integrated into two Web crawlers in the framework of the ARCOMEM project: the proprietary crawler of the Internet Memory Foundation and a customized version of Heritrix. Then we propose an efficient unsupervised Web crawling system ACEBot (Adaptive Crawler Bot for data Extraction), a structure-driven crawler that utilizes the inner structure of the pages and guides the crawling process based on the importance of their content. ACEBot works intwo phases: in the offline phase, it constructs a dynamic site map (limiting the number of URLs retrieved), learns a traversal strategy based on the importance of navigation patterns (selecting those leading to valuable content); in the online phase, ACEBot performs massive downloading following the chosen navigation patterns. The AAH and ACEBot makes 7 and 5 times, respectively, fewer HTTP requests as compared to a generic crawler, without compromising on effectiveness. We finally propose OWET (Open Web Extraction Toolkit) as a free platform for semi-supervised data extraction. OWET allows a user to extract the data hidden behind Web formsLes sites Web sont par nature dynamiques, leur contenu et leur structure changeant au fil du temps ; de nombreuses pages sur le Web sont produites par des systèmes de gestion de contenu (CMS). Les outils actuellement utilisés par les archivistes du Web pour préserver le contenu du Web collectent et stockent de manière aveugle les pages Web, en ne tenant pas compte du CMS sur lequel le site est construit et du contenu structuré de ces pages Web. Nous présentons dans un premier temps un application-aware helper (AAH) qui s’intègre à une chaine d’archivage classique pour accomplir une collecte intelligente et adaptative des applications Web, étant donnée une base de connaissance deCMS courants. L’AAH a été intégrée à deux crawlers Web dans le cadre du projet ARCOMEM : le crawler propriétaire d’Internet Memory Foundation et une version personnalisée d’Heritrix. Nous proposons ensuite un système de crawl efficace et non supervisé, ACEBot (Adaptive Crawler Bot for data Extraction), guidé par la structure qui exploite la structure interne des pages et dirige le processus de crawl en fonction de l’importance du contenu. ACEBot fonctionne en deux phases : dans la phase hors-ligne, il construit un plan dynamique du site (en limitant le nombre d’URL récupérées), apprend une stratégie de parcours basée sur l’importance des motifs de navigation (sélectionnant ceux qui mènent à du contenu de valeur) ; dans la phase en-ligne, ACEBot accomplit un téléchargement massif en suivant les motifs de navigation choisis. L’AAH et ACEBot font 7 et 5 fois moins, respectivement, de requêtes HTTP qu’un crawler générique, sans compromis de qualité. Nous proposons enfin OWET (Open Web Extraction Toolkit), une plate-forme libre pour l’extraction de données semi-supervisée. OWET permet à un utilisateur d’extraire les données cachées derrière des formulaires Web

Post Processing Wrapper Generated Tables For Labeling Anonymous Datasets

A large number of wrappers generate tables without column names for human consumption because the meaning of the columns are apparent from the context and easy for humans to understand, but in emerging applications, labels are needed for autonomous assignment and schema mapping where machine tries to understand the tables. Autonomous label assignment is critical in volume data processing where ad hoc mediation, extraction and querying is involved. We propose an algorithm Lads for Labeling Anonymous Datasets, which can holistically label/annotate tabular Web document. The algorithm has been tested on anonymous datasets from a number of sites, yielding very promising results. We report here our experimental results on anonymous datasets from a number of sites e.g., music, movie, watch, political, automobile, synthetic obtained through different search engine such as Google, Yahoo and MSN. The comparative probabilities of attributes being candidate labels are presented which seem to be very promising, achieved as high as 98% probability of assigning good label to anonymous attribute. To the best of our knowledge, this is the first of its kind for label assignment based on multiple search engines\u27 recommendation. We have introduced a new paradigm, Web search engine based annotator which can holistically label tabular Web document. We categorize column into three types: disjoint set column (DSC), repeated prefix/suffix column (RPS) and numeric column (NUM). For labeling DSC column, our method rely on hit counts from Web search engine (e.g., Google, Yahoo and MSN). We formulate speculative queries to Web search engine and use the principle of disambiguation by maximal evidence to come up with our solution. Our algorithm Lads is guaranteed to work for the disjoint set column. Experimental results from large number of sites in different domains and subjective evaluation of our approach show that the proposed algorithm Lads works fairly well. In this line we claim that our algorithm Lads is robust. In order to assign label for the Disjoint Set Column, we need a candidate set of labels (e.g., label library) which can be collected on-the-fly from user SQL query variable as well as from Web Form label tag. We classify a set of homogeneous anonymous datasets into meaningful label and at the same time cluster those labels into a label library by learning user expectation and materialization of her expectation from a site. Previous work in this field rely on extraction ontologies, we eliminate the need for domain specific ontologies as we could extract label from the Web form. Our system is novel in the sense that we accommodate label from the user query variable. We hypothesize that our proposed algorithm Lads will do a good job for autonomous label assignment. We bridge the gap between two orthogonal research directions: wrapper generation and ontology generation from Web site (i.e., label extraction). We are NOT aware of any such prior work that address to connect these two orthogonal research for value added services such as online comparison shopping