To make deep web data accessible, harvesters have a crucial role. Targeting dierent domains and websites enhances the need of a general-purpose harvester which can be applied to dierent settings and situations. To develop such a harvester, a large number of issues should be addressed. To have all inuential elements in one big picture, a new concept, called harvestability factor ( HF ), is introduced in this paper. The HF is dened as an attribute of a website ( HF W) or a harvester (HF H) representing the extent to which the website can be harvested or the harvester can harvest. The comprising elements of these factors are dierent websites' or harvesters' features. These elements are gathered from literature or introduced through the authors' experiments. In addition to enabling designers of evaluating where they products stand from the harvesting perspective, the HF can act as a framework for designing harvesters. Designers can dene the list of features and prioritize their implementations. To validate the eectiveness of HF in practice, it is shown how the HFs0 elements can be applied in categorizing deep websites and how this is useful in designing a harvester. To validate the HF H as an evaluation metric, it is shown how it can be calculated for the harvester implemented by the authors. The results show that the developed harvester works pretty well for the targeted test set by a score of 14.783 of 15.
Nowadays, in an information-thirsty environment, the deep web concept receives
lots of attention. The content hidden behind web forms which is invisible or
hidden to general search engines like Google or Yahoo is dened as deep web
[
Contributions As the main contribution, a new concept, called Harvestability Factor (HF ) is introduced. This concept enables websites’ and harvesters’ designers of evaluating where they products stand in the harvesting point of view. Through this concept, we also put all the important elements in harvesting deep websites in one big picture. This denes a framework for designing general deep web harvesters trying to cover all the features from dierent aspects in designing a harvester. Some of these factors are mentioned in the literature and the others are discovered through the experiments by the authors. Having all these important features in one big picture, we evaluate the inuence of each of them on harvesting. This helps creating an implementation strategy for harvester. Dening the importance of each feature helps prioritizing features implementations. Sections In Section 2, the harvestability factor ( HF ) is introduced. The Section 4 introduces the elements of HF W which are also applied for categorizing deep websites. In this section, all the features of deep websites aecting harvesting process are introduced and deep websites are categorized accordingly. In Section 5, the HF elements for a harvester are dened. All the requirements for designing a general purpose deep web harvester from general requirements to detailed ones are also discussed. The dierent approaches applied in literature to meet these requirements are also explored. Having mentioned all the necessary requirements, in Section 6, as a sample of such a general deep web harvester, the designed harvester by the authors of this paper is introduced and both HF as a design framework and an evaluation metric are validated. Finally, in Section 7, the conclusions drawn from this work are discussed and future work is suggested. 2
To formalize all important issues in accessing deep web data, a new factor is introduced in this paper as Harvestability F actor (HF ). Although in a harvesting process, the roles of both harvester and website are intertwined, separate denitions are required by website and harvester designers for better understanding of harvesting processes. Hence, the HF is dened as an attribute of a website or a harvester representing the extent to which the website can be harvested ( HF W) or the harvester can harvest ( HF H).
As it is shown in Formula 2.1, the HF of a given harvester ( h) is dened by applying function sum of multiplying harvester performances for each one of websites’ features discussed in Section 4 (percentage of harvester failure shown by F a) by the importance of that feature. In this formula, Crf and Cof represent the importance of a feature. n is the number of features and k represents the number of general features. The Crf represents how critical is the feature for harvesting and the Cof represents how often this feature is used in the targeted domain. In this formula, general requirements are represented by GF j . This is discussed more in Section 5. 3
In this paper, two issues are targeted by introducing the HF ; HF as a harvester
design framework and HF as an evaluation metric for websites and harvesters.
Since the introduction of deep web, there has been several attempts to give access
to this part of web and improve the existing approaches [
Crfi
k Cofi )) + X(hGFj ) j=1
In Formula 2.2, the HF W is dened for a website by considering its features discussed in Section 4. In this formula, given a website, for each one of its features, the average performance of harvesters and the importance of the feature are multiplied. n is the number of features and m is the number of harvesters considered which can be also one. In this formula, wpfi represents the absence or presence of the feature in the website.
n HF W (w) = X(1 i=1 (wpfi
m (1=m X(F afj )) Crfi )) j=1
Assigning accurate values to the weights and features mentioned in these two formulas is beyond the scope of this paper and considered as a feature work. However, in Section (6), using simple approaches to assign values to these parameters, it is shown how these formulas can help in evaluating harvesters and websites. In this paper, it is tried to cover all aspects of the introduced HF elements; business domain, harvesting goal, harvester features and websites features to give a design harvester guideline. (2.1) (2.2) possible without having all the eecting factors in one big picture. Introducing the HF is the rst step in this direction. The HF helps to study not only the harvesters but also the targeted domain and the websites features in the design process.
Evaluate/Compare harvesters Small amount of work has been done in comparing and analyzing web harvesting tools. The related studies in literature such as the work in [11,12] focus mainly on a limited number of aspects such as capability in dealing with dierent data formats, capability to record the extracted data, user friendliness, price in market, export formats, ability to manage the anonymous scraping and multi-threading. However, in this study, in addition to these features, a more detailed set of features are introduced. Despite other works, this paper provides a mechanism to produce an evaluation number to each harvester considering a wide range of features from general requirements to detailed capabilities. 4
In this section, dierent features of websites which are related to harvesting processes are studied. The roles of each feature as dening element of a website HF are also mentioned. Each of these features could be also applied for categorizing deep websites from the harvesting perspective. In the extended version of this paper [13], the more detailed descriptions of these elements could be found. 4.1
A number of techniques applied in developing and designing web sites and web pages create challenges for harvesters. These techniques are usually applied to add interactivity to web pages as well as for improving site navigation. In following, there is a list of such techniques.
Embedded Scripting Languages in HTML Pages Embedded scripts in
HTML pages can make content in layers either shown or hidden based on a user
action, or change in a state. They can even build HTTP requests to ll out and
submit a form dynamically. Managing HTML layers, performing redirections,
dynamically generating navigations like pop-up menus and creating hidden
anchors are some of the issues which could be caused by client-side scripts [
Complex URL Redirections For reasons such as similar or moved domains,
manipulating search engines or visitors, or URL shortening, URLs are redirected.
This means dierent responses are given to browser request which results in
browser showing a dierent page. These redirections could happen automatically
or manually. It is easier for harvesters to deal with redirections handled on server
side unless it is a redirection loop which does not load any page at the end or it
is a redirect chain which might take longer time to have the nal page returned
[
Applets or Flash code If Flash or Applet is used for designing whole website, it is almost impossible for harvesters to access its content without running expensive analysis over each item. Nowadays, websites designers avoid these practices in order to make sure their sites are on good terms with crawlers. If only the welcoming page is designed by Flash or Applet, it becomes easier for harvesters. If they are used for advertisements, they can be ignored.
Frames There are also some issues such as frames which can create diculties for harvesting processes. Detecting the right frame which contains the page content in a multi-frame page is one of the problems created by such issues. HTML Coding Practices For harvesters which rely on tags, attributes, and also presentation features, HTML code practices become highly important. Having bad-written HTML code (like not closed tags) might cause problems in analyzing page HTML tree and therefore incapability of harvester to extract data. Lacking well-dened ID, class, and other explanatory attributes for items could also make diculties for harvesters and make them prone to mistakes. Being consistent in coding practices for all pages and data items is also important. For example, if there is IDs for items, it should be the case for all of them or at least a dened set of items (like dierent categories). In some cases, data from the same category, even with the same presentation template have small dierences in the HTML code behind them. This might mislead harvesters. 4.2
Search Policies Query Interfaces There are a number of dierent web interfaces classied as keyword-based, form-like, browsing, and a combination of them [15]. Each one of these interfaces creates a dierent set of requirements for harvester. For example, in a form-like search interface, information on attribute-value bindings or accessing predened lists of values for attributes could be of great help for harvesters to decide on which queries to send to the search engine. Detecting interfaces and recognizing dierent features of web forms could be help harvesters. Knowing that query interface provides dierent search options like searching by keyword, industry domain, region, or time helps harvester to act eciently. Indexing Policies In case of having search feature in a website, it becomes important to know about the indexing policies. For example, with stop-words indexed in a web site, sending a stop-word query is one of the most reliable options to have a response. Also, if there is no limitation on browsing through search results, sending only one stop-word results in a high coverage. In addition to indexing policies regarding stop words, it is important to know which parts of data are indexed. For example, having only titles indexed makes great dierence in dening next queries with having whole text of detailed pages indexed. This is the case in generating queries based on most frequent words in visited pages. Search Queries and Algorithms In response to a query posed to a search engine, websites do not necessarily follow the same principles. In some cases, stop-words are removed from search queries, query phrases are treated in dierent ways (considered as AND phrase or OR phrase), or number of returned results shown to user is limited. There might be even dierences on additional information provided in reply to a query, such as statistics on search results and number of found related answers. There are also websites which put a limitation on the number of queries a client can send.
Navigation In most of websites, a query is sent, search results are displayed and by following each one of those returned results, a detailed page is represented. However, there are situations in which this is not the case. In some websites, in return to a query, a list of categories related to that query are displayed. Following each one of those categories might end up in another subcategory. This makes it dicult for harvester to realize which returned page is a category or actually a detailed page.
Security, Privacy and Legal Policies Answering this question should be one of the rst steps in harvesting process: is it legal to access data, store it and present it to users?. It is also important to note that if login is required by website to access data. Considering website’s terms of service to follow the privacy policies is also important. In some websites, the Robots Exclusion Protocol is applied which gives instructions about the site to web robots in a le named Robots.txt. In case of the existence of such a le and depending on how strict it is asked to be followed, necessary concerns should be considered. Not all the websites welcome bots (harvesters, or crawlers) with open arms. Having recognized bots through trac monitoring, bot identity declaration, or real person declaration techniques like a CAPTCHA, websites can use various measures to stop or slow them down. Blocking an IP address, disabling web service API, commercial anti-bot services, or using application rewalls are some of these measures. It is also important to note other privacy policies of the website like policy on disclosing aggregate information for analytical purposes by owners of website. 4.3
Type and Format of Residing Data in Data Sources The content of a
deep website could be categorized in two dierent groups [
Data Layout How data is represented in web pages aects the harvesters relying on presentation features of data. Dierent data types in a website could be presented in dierent ways. Even data items of a same category could be presented dierently based on their features. If these dierences in presentation are not known to harvester, it will use the same algorithm to extract all data. This might result in extracting none or undesired information. Structural variations on data presentation must be also tolerated by harvesters and treated accordingly. If the data is represented in a structured way like lists or tables or it is represented in text or a combination of both, harvester should treat them differently. It is also important if a data item has elds represented as nested data on pages; for example, comments or scores information. This poses dierent requirements on extracting and storage of information.
Data Type Formats Including ontologies and text-patterns in the process of extracting data from detailed pages makes it important to investigate how they can aect the harvesting process. Committing to one ontology and following same patterns for same concepts like dd-mm-yyyy format for all dates mentioned on the website could aect the conguration and design of the harvester. Also, for example, if the mentioned address format on the website is not the same for all addresses mentioned in the website, it can have a great eect on the harvester conguration.
Information of a Data Item is Scattered in Dierent Pages Usually, the queries are sent to search engine, returned results are followed and data about desired items is extracted. However, this is not always the case. In some cases data of a interesting data item is scattered in website. In a more common way, general data is presented in the page navigated through search results. However, more detailed information is provided in some other links which is accessible (only) through this detailed page (you need to go to the detailed page and then browse through the tabs or links to access the information you want). Finding these links and extracting information from them could be a challenge for harvesters.
Providing Semantic Annotations (Meta data) The pages may include meta data or semantic markups and annotations. The annotations might be embedded in the pages or organized into a semantic layer [16] stored and managed separately from the web pages. Data schema and instructions from this layer can be retrieved by harvesters before scraping the pages.
Website Content Language Dealing with the language of the targeted website is one of the abilities that the harvesters should have. Some of the approaches applied in harvesters are based on parsing the content of web pages like data patterns. Having this in mind, it should be noted that dealing with Chinese language needs dierent congurations than English or the Farsi languages. Having dierent languages in the targeted websites will cause diculties for these harvesters. 5
As mentioned in the Introduction Section, designing a deep web access approach is highly aected by business domains, websites, and the harvesting goals. In the previous section, the features of a website aecting the HF is mentioned. In dening the harvestability factor for a harvester, in addition to the ability of the harvester in dealing with each one of the mentioned website features in the previous section, there are also a number of general requirements which should be met by the harvester. These two set of features help us in dening the HF for a harvester. Knowing about the techniques and methods applied in each harvester helps in deciding about the harvester performs for each one of the elements. Therefore, in this section, a subsection is dedicated to dening these methods and techniques. 5.1
Every general purpose harvester, despite the dierences among its goal, and the domain and websites it is targeting, should meet a set of requirements important in all harvesting processes. Being automatic or running with minimal conguration is one of these requirements. Being scalable (applicable to a large number of websites), independent (of business domain, technology, and etc), ecient (with the least possible number of queries, harvests the most possible amount of data), easy to use (conguration and run settings should be easy for users to perform), and resilient to changes both on website content and presentation are other general requirements which should be followed by a harvester. With these features, a harvester should be able to ll in forms eciently and automatically, extract data/entities from the returned results pages, and store the extracted data 2 [15]. 2 In [15], two more steps are also considered for a harvester; discovering deep web sources of interest, and presenting extracted data to users and providing them with posing query mechanisms.
For all these steps considered in a deep web harvesting process, having an automatic error/change detection helps to improve harvesting process. This enables the harvester of doing an uninterrupted harvest as it becomes capable of detecting and resolving issues like IP based blocking, website failures, and etc. The harvester should be capable of providing rm guaranties about the exhaustive coverage of the harvested part of the Web. Size estimation of deep websites [?] and also dening a stop condition for harvesting process could help in reaching this goal. In monitoring entities on the Web, it becomes highly important if the harvester could be able to keep the data up-to-date. This needs harvesters being capable of detecting new and deleted entities on the Web. While fullling these high level requirements, the harvester should be also capable of harvesting the dierent categories of websites mentioned in Section 4. There are dierent approaches to meet these requirements in literature. In the following section, these approaches are introduced. 5.2
To access data behind web forms, a wide range of harvesters are suggested in
literature [
In HTML-based harvesters, the harvester relies on a set of dierent features
of document HTML code [17]. To analyze the HTML structure of the pages,
the document is translated into a parsing tree. This could be done by using
browser controls like Internet Explorer to parse webpages into Data Object
Model (DOM) trees. Then, by running a number of pre-dened extraction
rules on the tree, the data is extracted.
2. Harvesters based on Natural Language Processing (NLP) techniques
In these harvesters [18,19], NLP techniques such as ltering, part-of-speech
tagging, and lexical semantic tagging are applied to build relationships
between phrases and sentences. From these extracted relationships, a number
of extraction rules can be derived. These rules are based on syntactic and
semantic constraints and help to identify the relevant information within a
document.
3 This categorization is introduced in [
These harvesters [20] rely on a given set of training examples to derive a number of extraction rules. In these techniques, rather than relying on linguistic constraints found in the page, rules are based on features of the structure of the pieces of data. 4. Modeling-based harvesters
In modeling-based harvesters [21,22], a data model is dened. In this data model, a number of objects, their properties and relationships are dened. Based on this data model and its modeling primitives, points of interest are located in Web pages. 5. Ontology-based harvesters
In these harvesters [23], the extraction process is based on the data and not the presentation structure. These harvesters need a specic domain ontology. Through domain ontologies, concepts relevant to a particular topic or area of interest are dened and available for harvesters. The ontology-based harvesters use these ontologies to locate ontology’s constants present in the page and to construct objects associated with them. 6. Computer vision based harvester
These harvesters use computer vision techniques in addition to techniques from machine learning to analyze webpages. In these harvesters, the main goal is to identify and extract information from web pages by interpreting them visually as a human being does. Some of these approaches use also the visual features on the deep Web pages [24]. 7. Harvesters based on a combination of previous categories. For example, in a harvester based on HTML structure, applying machine learning techniques could help in having more precise extraction results. 6
As mentioned in Section 1, the HF can be used for evaluating a harvester in harvesting and a website in being harvested. It was also discussed that this factor can work as a design framework. To validate these claims, a collection of deep websites is studied considering the HF elements. The developed harvester by authors of this paper, as an example eort for developing a general purpose harvester, is applied on the test set. 6.1
To create the test set for illustrating how deep websites can be categorized based on the HF elements and how this can be used in designing a general purpose harvester, a set of websites from the list of top-100 job vacancy websites by [25]. In the selection of websites from this list, the ones including job boards are considered. To extend this test set, a set of Dutch job vacancy websites are also considered. For each of these websites, all the elements of HF are studied. To examine the harvester performance on each one of the categories, the harvester is applied on the websites. 6.2
The developed harvester is a HTML-based harvester which automates loading of pages in a browser. These features help to resolve the challenges caused by some of the websites’ features mentioned in Section 4. For example, to enable the harvester of implementing embedded scripts in HTML pages, the techniques for automating browsers are applied. Also, for selecting the points of interests, HTML-based techniques are considered. These features also help the harvester to meet the general requirements mentioned in Subsection 5.1 like automation, scalability, independency, eciency, and being easy to use. For eciency purposes, dierent query generation mechanism could be applied to have the most amount of data harvested with the least possible number of posed queries. The conguration is limited to entering the template, and XPaths for points of interests. There is also no need to enter a data model for data storage. Given these congurations for each website, high scalability level can be achieved. Domainindependency is also highly achieved through using only HTML-based techniques which also makes it language-independent. 6.3
HF as a Design Framework Through this study, it is shown how these websites are categorized by applying the HF elements and how this can guide the design and implementation of a harvester. Having studied the set of deep websites and prioritizing the features of deep websites, By applying this harvester on this set of websites, it is shown how these features are eective on harvesting processes in practice.
Results Studying this set of websites from the domain of job vacancies brings a number of facts into light. If we assume this set of websites represents the job vacancies domain, the results can guide the design process by emphasizing on the elements of HF faced more frequently. As it can be seen in Table 6.1, embedded scripts, query interfaces, data layouts, and in-persistent data patterns need further attention during the harvester design process.
Being based on browsers enables our harvester to overcome some of the challenges caused by embedded scripting languages in HTML pages. This is perfectly valid when there is no change of content based on user interaction with the page. However, presence of scripts changing the content based on user interaction or change of browser or time makes it more dicult for the harvester. Simulating user actions or changes in the page environment and comparing the generated result page with the previous version of the page should be performed to be capable of harvesting the page presented to users. This part is not included in our current version of harvester. However, it is worth mentioning that this type of scripts was not faced in our test collection. So, it was reasonable to postpone the implementation of this feature.
The second most common HF element in the test set is detecting query interfaces. In all the cases, our harvester could detect the template and query the Harvestability Factor’s
Element Embedded Script in HTML
4.1
Applet / Flash 4.1 Data Layout (dierent
layouts) 4.3
Navigation (not straight-forward) 4.2 Search Policies (limited
search results) 4.2 Indexing Policies (not
stopwords) 4.2 HTML Coding Practices (not
persistent) 4.1 Security / Privacy / Legal Policies 4.2
URL Redirection 4.1 Residing Data (text, no
structure) 4.3 Session Management 4.1
Query Interface Type 4.2 Persistent Data Patterns (not persistent) 4.3
Multi-frames 4.1 search engines. The other common faced feature is having dierent data layouts. This is resolved in our harvester by making it possible to dene dierent page templates for each website. However, this might pose a lot of time and eort during conguration phase if there are a large number of page templates used for showing data. In the HTML-based approaches, data can be extracted also based on the content. Therefore, if the data patterns are consistent, a high quality data extraction is still possible through the techniques applied by our harvester. Among the websites in the test set, 15 percent of the websites have limitation on browsing the number of viewed search results. To resolve this problem, dierent query generation mechanisms are applied which allow ecient harvesting of deep website. The harvester can also detect if stopwords are indexed or not and send the next queries accordingly. These meet two other common HF elements mentioned in Table 6.1.
Among the samples, it was observed that users are asked to enable the cookies for the website. This technique is becoming more frequently used by web developers. Therefore, harvesters should accordingly be able to recognize and resolve it. To resolve other session management techniques, keeping the session information and tracking the navigation path to the page are useful. In a not straight-forward search navigation website, which results in more steps than going through search, browsing results page, and viewing the detailed page, the developed harvester could work successfully. This was provided that there are only two types of page templates; search results page, and detailed page templates. The harvester can distinguish only these two types.
As it can be seen in Table 6.1, for some of the HF elements, no websites in the test set were found. This might be due to the specications of the test domain. For example, the application of techniques like Applet or Flash could be seen more frequently in domains like Graphics or Music industries and not so often in job vacancy domain. The same applies to requiring credentials to view job vacancies which is unacceptable in business models of these companies. It is also worth mentioning that dening some of these elements in HF for a website is time-consuming and sometimes hard. Persistent coding practices is one of those elements. It is time-consuming to study a website if it follows a persistent coding paradigm unless you face an exception.
Validation of Harvestability Factor as a Website/Harvester Evaluator In this part, the ability of the HF in evaluating the harvestability of a website or a harvester is discussed. As mentioned in Section 2, assigning values to weights and features in the HF W Formula is beyond the scope of this paper. However, to show how this could be benecial, we use a simple method to assign these numbers. All the values are assigned with probabilities. We assign the percentage of feature occurrence in the test set as the Cof . The F af values are calculated through F af = 1 successratef formula. The successratef are assigned by the authors of this paper based on the capability of the developed harvester in resolving the problems caused by each feature. Introducing a more accurate method for these values will be studied as future work. The ( Crf ) values are assigned based on the authors experience and the results observed during experiments. They represent how inuential is the corresponding feature in the whole harvesting process. We assign them with four values; Highly Critical (1) , Very Critical (0.75) , Critical (0.5), Eective not Critical (0.25) , and no eect (0) . Having assigned all the present parameters in the formula with values, the HF H can be calculated. The best score is the result of having all the features equal to 1 which results in 15. Our developed harvester score is 14.783. In the following line, it is shown how this number is calculated. This high number tells us that the harvester works well for this targeted domain. Having nine features absent in the websites tested in this domain gives a high advantage to this harvester. Of course, by having average numbers for each parameter of the HF formula, the harvester performance can be tested generally. In this part, we only show the HF of the harvester for a limited test set. 15000 H F180H00()D)+ev(e1lop(ed110H40 arv15e00s0ter)110=00 )9)++((11 ((1210160000 1501050000 170010100)0))+) +(1(1 ( 102(012040 1705017050 150001)20)00+))(1= 1(41:027083 7
As discussed in Section 6, the elements of the introduced harvestability factor can categorize deep websites based on their features which are important in harvesting process. This enables the owners of deep websites and website designers of evaluating where their products stand from harvesting point of view. This helps them to decide about which measures to take in order to follow their policies whether it is increasing access or limiting it. For harvester designers, the harvestabiltiy factor acts not only as an evaluation metric of how well the harvester can behave in practice dealing with dierent websites, it also behaves as a framework for designing deep web harvesters. The HF provides designer with a thorough list of requirements they should meet and also helps to prioritize the features to be addressed and included in the harvester. Categorizing deep websites based on their harvestabiltiy factors makes it feasible to understand the importance of dierent websites’ features. This helps to prioritize the features to be addressed and included in the harvester.
Having the HF as a comparison metric for dierent deep web harvesters is another advantage of this introduced concept. To show how this can be applied, we tested the formula for our own developed harvester on a predened set of job vacancy websites. To enable the measuring, we applied simple methods in assigning values. The importance of each element was judged in a combination of author experience and expertise with the frequency of usage of that element among the test set websites. Having more than half of the elements absent among the websites gave an advantage to the harvester to get a high score. Of course, this shows that harvester would work very well for this set. However, judging its performance for a bigger or dierent domain needs new values for each parameter in the formula. This is realized if the average values for each parameter in the formula are assigned. This enables developers to decide what to include in a harvester and predict the outcome in accurate numbers even before running the harvester on the target websites.
For the next step, we will study how to assign more accurate values automatically. This means having websites classied based on the introduced elements and judging about the importance of each element in a more automatic approach. Also, having average values to calculate the performance of a harvester in more general domains is in the list of our future work. As another future work, we aim at using the HF in guiding us in developing a more general deep web harvester. Using the studies performed in this paper and extending them to a bigger test set will help us in deciding on the features our deep web harvester should include and prioritizing their developments. 8
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