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1 Introduction and Motivation

Web archives are a unique source of data reflecting rapid evolution of the digital world. Recently, an increasing interest in using the data stored within the Web archives for research purposes has been observed in several disciplines such as history or digital sociology [14]. For example, as discussed in [6], Web archives can be an important source for communication and media history and within historiography in general.

Unfortunately, existing Web archives are very difficult to use since most often only a URL based access is provided. Researchers are typically interested in a few relevant Web sites regarding a given topic, domain or time frame to be selected for manual analysis. Finding such relevant data for a specific purpose within the Web archive is still very challenging. This is mainly attributed to the very large size of the data coupled with the lack of efficient tools for annotation, search and exploration. Indexing existing Web archives, which often contain hundreds of terabytes of data, is difficult and hence, full-text search capabilities are rarely available, not to mention more sophisticated semantic content analytics.

In this context, we look at different ways to obtain relevant documents from a Web archive efficiently and analyse the role of URLs of the archived documents towards this goal. The advantage of using URLs is twofold: First, the URLs of the archived documents can be retrieved from the archive efficiently, using CDX files (i.e. standard index files that contain URLs and additional metadata, such as mime type and capture dates). Second, URLs can contain important hints about the document content. In this context, related work on URL analytics on the Web shows that URLs can provide accurate estimates of the document language [3], location relevance [2] and topic classification [9].

In this paper we analyse the applicability and precision of Named Entity Extraction (NER) in the context of URLs. Furthermore, we analyse the distribution of the extracted named entities within the URLs in the Popular German Web dataset - a subset of the Internet Archive data covering popular domains in the “.de” top level domain over a period of 12 years. This analysis confirms the precision of NER in the context of URL analytics and helps to better understand which domains can be efficiently accessed using such light-weight annotation methods for a Web archive. Our results demonstrate that state-of-the-art NER tools, such as Stanford NERFootnote 1, can achieve high precision (up to 85 % in our dataset) if applied to the URLs after performing sufficient pre-processing and post-filtering described in this paper. We also observed that the number of extracted entities differs significantly across the domain categories and along the temporal dimension. In some years, the dataset contains dominant domains - i.e. the domains within a domain category that contribute the majority of captures in the specific year. In most cases, the variations in the extraction results can be explained by the varying number of captures from such dominant domains as well as by the entity-rich URLs in such domains (i.e. the domains like dblp.uni-trier.de - an open computer science bibliography, where the URLs typically contain named entities of the type person, dict.tu-chemnitz.de - a dictionary domain, that frequently presents entities of miscellaneous type).

Overall, our results confirm that NER is a useful method of semantic URL analytics and can provide precise results and high coverage in several domain categories.

2 The Popular German Web: A Dataset Description

The dataset used in this study is referred to as “Popular German Web ”. This dataset is a subset of the ‘.de’ top-level domain (tld), as it has been archived by the Internet ArchiveFootnote 2 and provided to us in the context of the ALEXANDRIAFootnote 3 project. This dataset comprises the most prominent domains in 17 categories from 2000 to 2012 selected according to the Alexa rankingFootnote 4.

Terminology: A URL (uniform resource locator) identifies a Web resource (for example a Web page) and specifies its location on the Web. Over time, the content of the Web page under any given URL may change. Therefore, Web archives often re-assessed the URLs after a period of time to collect new content. In the following we refer to a particular copy of the URL assessed at a certain time and stored in the archive as a capture. This way, a Web archive can possess several captures of the URL, whereas each capture can be uniquely identified through the URL and the date.

These captures are stored as CDX files that contain meta information about the crawls in a space-separated format with one line per capture, i.e. one snapshot of one URL at a given time. The corresponding line in the CDX file presents the structure illustrated by Fig. 1. We consider “original url” as the input for NER, “timestamp” provides the exact time when the URL was crawled and “status code” tells us whether or not a successful response was returned. The other fields are ignored since they do not represent useful information for our analysis.

Fig. 1.
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CDX structure

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2.1 Domain Extraction

There is an interest in these domains as they are impacting most users of the Web today and at the same time they have the biggest impact on upcoming research. The selection of domains was taken from different categories on Amazon’s Alexa. In order to match our dataset we fetched only websites from those categories on that comprise German websitesFootnote 5. In addition to the 15 top categories, we picked news and universities as two sub-categories that seemed particularly relevant to analyze separately in our study.

As the available dataset only included domains under the German tld ‘.de’, we filtered out all websites on Alexa’s list that are German but use a different tld. Out of the remaining, we extracted up to 100 from the top of every category. The final state of this ranking, which is analyzed here, was retrieved on July 10th, 2014 at 09:26 CET.

2.2 Dataset Cleaning and Pre-Processing

Following the domain extraction we performed a few cleaning steps at the URL level to filter out malformed URLs as well as those that are inappropriate for our analysis:

  • In this analysis we focus on the html content of the Web archive. Therefore, we discarded all captures that do not represent the html content (identified by .html or .htm extensions in the URLs).

  • We discarded all captures of the URLs that never returned a successful status code (i.e. a status code starting with “2”, according to the official HTTP status codes).

Fig. 2.
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URL processing pipeline

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Figure 2 illustrates the sequence of steps applied to pre-process the URLs, determine their language and, finally, to extract semantic information such as named entities. Regarding the URL tokenization, we considered only words extracted from the URL string also excluding special characters and numbers, based on a simple regular expression.

  • Pre-processing: From the pre-processing perspective, we consider only the URL path, discarding the extension. We exclude other parts such as domain name, parameters and numbers (e.g. port number, session id, etc.) from further consideration. These fields are not expected to contain semantic information specific to the particular Web document.

  • Stop Words Removal: In next step we detect stop words within the parsed URLs and eliminate them. In order to determine stop words, we randomly sample URLs and manually select the most frequent terms extracted from that sample. This is required, as no pre-defined stop word lists for URLs exists. Short terms (i.e. the terms with the length less than three characters) are also discarded in this step. Stop words filtering is particularly important to increase precision of the language detection performed in the next step.

  • Language Detection: We apply an n-gram language detection model to the remaining tokens in the URLs to detect the language of the URL and to select the correct language configuration for the extractors applied in the next step.

  • Named Entity Extraction: We apply Stanford NER to extract named entities mentioned in the preprocessed URLs. The language-specific named entity extractor (German or English) is selected based on the determined URL language.

2.3 Dataset Statistics

Ultimately, we obtained a dataset consisting of 17 categories with today’s popular domains from the ‘.de’ top level domain, as presented in Table 1. The resulting Popular German Web dataset covers 1,444 domains with more than 320 million captures in total. Table 1 presents the number of domains and captures in each domain category as well as the percentage of captures from which we could extract named entities using our method. As we can observe, the highest coverage of captures containing entities is attributed to the education category with 73 %, followed by the regional and sports categories with around 40 %. The dominating domains for the education category were stayfriends.de and wer-weiss-was.de, with 20–50 % of captures dependent on the year. These domains typically contain entity-rich URLs that explains the high percentage of entities extracted from this category. For many other domain categories the coverage of captures containing named entities exceeds 20 %. Overall, we can say that our method can efficiently produce annotations for a significant number of captures in many domain categories.

Table 1. The Popular German Web dataset details: the number of domains and captures per category.
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In order to obtain a better understanding of the temporal dimension of the dataset, we analyse the distribution of captures over time. Figure 3 illustrates the overall distribution of the number of captures per year in the time period from 2000 to 2012 normalized by the total number of captures in the Popular German Web dataset, where X axis represent the years and Y axis the percentage of captures.

This graph illustrates that the number of captures in the dataset is rapidly increasing over time, in particular starting from 2007, although at some points (2005–2006 and 2009) temporary decreases in the data collection rate can be observed. The total number of captures are more equally distributed for the majority of years per domains, where spiegel.de appears at the first position from 2001 to 2012, representing 7.72 % of all captures per year, on average. The university domain tu-berlin.de dominates the crawl by representing 47.30 % of all captures in 2000.

Fig. 3.
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Overall distribution of the number of captures per year.

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Figure 4 shows the distribution of all captures through five selected domain categories including shopping, sports, business, news and universities normalized by the total number of captures. As we can observe, various domain categories have different dynamics within the archive: Whereas the proportion of the news cites is relatively high and is only slowly increasing over time, the proportion of shopping cites increased rapidly in recent years. In contrast, the proportion of the universities cites is slowly decreasing, in particular starting from 2007.

Fig. 4.
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Number of captures within selected domain categories.

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Even the total number of captures of spiegel.de (a german news domain) exceeds total captures from top universities domains (e.g. uni-leipzig.de and cert.uni-stuttgart.de) from 2002 to 2003, the overall number of captures from universities is greater than news for those years. In this period the majority of domains belongs to universities (140) and only 40 belongs to news. From 2008 to 2011, captures from shopping (532 domains) are more frequent followed by news (136 domains) and business (588 domains).

Overall Fig. 4 shows the behavior of captures within five selected categories, where the quantity of captures in general is growing, except for universities starting in 2007. We also observed several business domains from 2008 to 2011, but the majority of captures belongs to shopping in this period.

3 URL Analytics

In this section we describe the results of the URL analysis we obtained by applying the pipeline described in Sect. 2.2 to the Popular German Web dataset described in Sect. 2.3. The goal of the analysis is two-fold: First, we evaluate the precision of the named entity extraction method for URLs proposed in this paper to confirm its effectiveness; Second, we would like to better understand the domain coverage and the temporal coverage of the proposed method while applied to our dataset. In this section we present the evaluation results of the method and statistics we collected while applying the method to the Popular German Web dataset.

3.1 Language Detection Statistics

In order to detect the language of a URL, we applied state-of-the-art techniques to language detection using n-grams [3]. The URL pre-processing described in Sect. 2.2, such as URL splitting and removal of URL-specific stop words makes it possible to apply the n-gram analysis on the relevant part of the URL only and to increase precision of the language detection. The stop words typical for the URLs are identified using a random sample from the whole URL collection and a manually identified frequency threshold.

According to the results of the language detection analysis, 52.89 % of the URLs in our Popular German Web dataset are in German, 27.96 % in English and 19.14 % in other languages. After applying the URL pre-processing, we obtained 89 % of precision for language detection. We measure this precision choosing a random sample of 100 URLs and manually checking the returning language.

3.2 Precision of the Named Entity Recognition for URLs

In this section we describe our evaluation results regarding the NER precision applied to the German and the English URLs in the Popular German Web dataset.

State-of-the-art Named Entity Recognition (NER) techniques are language dependent. Therefore, we restrict the NER processing to the URLs detected as German and English and apply language-specific configurations of the Stanford NER to these URLs. With this restriction, we cover more than 80 % of the URLs in the Popular German Web dataset (s. above).

In order to evaluate precision of Stanford NER on this dataset, we manually chose a random sample of 100 URLs out of those that have been detected to contain entities. Initially, the average precision of Stanford NER on this set reached 60 % for the German and 56 % for the English configuration of the entity extractor. This precision can be further increased by the simple post-processing, including two steps:

  1. 1.

    Removal of the entities with long labels (i.e. the labels containing more than 2 terms). Our manual examination has shown that in many cases such long labels result from the extraction errors.

  2. 2.

    Removal of the entities that rarely occur in the URLs (the number of the URLs detected to contain the entity is less than 3 in the entire dataset). According to our observations, entities extracted from very few URLs are often incorrect as opposed to the entities that are repeatedly observed in URLs.

After applying these corrections and re-evaluating the results, we observed the precision increase to 85 % for the German and to 82 % for the English extractor on this dataset.

Table 2. The most frequent named entities of type “location” and “person” in the urls of the Popular German Web dataset.
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3.3 Domain and Temporal Coverage of NER

In this section we summarize the extraction results to better understand the domain coverage and the temporal coverage of the proposed method for this dataset.

Whereas some domains and domain categories possess entity-rich URLs, others do not. In addition, the number of URLs that contain entities in the Web archive can vary along the temporal dimension. Therefore, in addition to the evaluation of the precision of the extraction method, it is important to better understand the domain coverage of NER applied to the URLs in the Web archive. This analysis can help to better understand which parts of the Web archive can be made accessible using the proposed light-weight named entity annotation.

Overall 42,547,734 captures containing named entities have been identified by the extractor. The frequencies of the named entities extracted from the URLs of these captures range from 2,301,917 to 3. We decided a limit of 3 as a way to maximize the NER precision, which is one of the post-processing steps described above.

The majority of the extracted entities are of type “location”, followed by the type “person”. The most frequent locations are local to Germany, whereas the person names are in many cases internationally known celebrities. Table 2 presents the most frequent entities of types “location” and “person” extracted from the Popular German Web dataset and their frequencies (i.e. the number of captures).

As an example of entities extracted from URLs, the Table 3 illustrates some URLs containing entities. Up to two different entities could be extracted from those URLs. Entities of type location as Berlin and Prenzlauer-Berg were found in the same URL and persons as Franz Maget (german politician) and Katja Kessler (german journalist).

Table 3. URLs containing entities
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Distribution of Entities by Domain Category. Figure 5 illustrates the distribution of captures containing entities in several selected domain categories.

Fig. 5.
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Captures having entities within categories

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In this figure we normalized the total number of entities by the total number of captures per year for each specific category where X axis represent the years and Y axis the percentage of captures with entities. We focus on those categories as they indicate interesting patterns with respect to the temporal dimension. We can observe that between 2001 and 2006 the distribution remains overall stable while after 2006 several peaks can be observed.

In the universities category a first significant increase can be observed between 2004 and 2005. By looking into the data it turns out that the domain uni-leipzig.de dominates the crawl representing 19.81 % of the captures in 2005. In 2006 the dblp.uni-trier.de, a popular computer science bibliography, dominates the captures regarding universities representing 42.73 %.

Continuing the analysis for universities, in 2007 the same domain (dblp.uni-trier.de) represents only 6.48 % of the total number of captures, and in that year all domains are more equally distributed. From 2008 to 2011 the dict.tu-chemnitz.de - a dictionary domain - dominates the collection and then gradually decreases from 33.26 % to 26.46 % of the total number of captures, respectively. In 2012 dblp.uni-trier.de dominates again, but only represents 12.47 % of the captures.

A first significant increase of entities in news can be observed in 2007. The detailed analysis shows that in 2006 and 2007 the news category was dominated by spiegel.de followed by openpr.de, a press release portal. The portal uses the titles of the news articles as names for their html pages. Since the number of crawled pages from this domain significantly increases from 200k pages in 2006 to 700k pages in 2007 we assume that the number of title-based URLs in the archive also increased in this period.

The sports category shows a significant increase from 2007 to 2010 due to a particular domain about transfer market of soccer players - transfermarkt.de.

Crawled pages from this domain increased from 500 k in 2007 to 1.5 million in 2010, thus we expect that more players’ names were mentioned and more entities of type person and location were found. The quantity of captures from such domain decreased in following years, therefore we expect that less entities were extracted, as shown by the graph.

Regarding business, the majority of captures belong to postbank.de - a postal bank domain in Germany, which increased from 680 k in 2008 to 1.1 million in 2011 and it constitutes an entity-rich domain for type location.

Overall Fig. 5 reveals a number of peaks from 2006 onwards. In the above example the reason was always the domination of a certain site. We assume that this is also the case for most of the other peaks that we observed in previous analyses.

Distribution of Entities by Type. Figure 6 illustrates the distribution of entity types through the years where the X axis represent the years and the Y axis the portion for each entity type we extracted, normalized by the total number of entities.

As mentioned above, the most common entities are of type location followed by the types person, and organization. In previous sections, we showed that while the amount of captures significantly increased starting in 2006, many entity-rich sites increased as well (e.g. postbank.de, openpr.de, transfermarkt.de). Thus the number of entity types also increases, as illustrated by Fig. 6, starting in 2006. Since the majority of domains contains entity-rich URLs of type location, such type represent the most frequent one from all entities.

Fig. 6.
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Entities types retrieved through years

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4 Related Work

The information contained in URLs has been analysed already in previous work, however it is the first time that entities have been extracted using only the information within URLs, the majority of current work rely on classification tasks and do not consider entity extraction in URLs. There are several works that classify the content of a document only based on its URL. Baykan et al. conducted an extensive set of URL features and classification methods to detect the topic of a Web document [4]. They report a precision of around 0.86 and a recall between 0.36 and 0.4 on a multi-class topic classification using a combination of character n-grams of length 4-8. In their datasets they have about \(32\,\%\) “empty URLs”, i.e. URLs whose tokens or n-grams did not occur in the training set, which bounds the recall achievable using this data. Similarly, Kan and Thi discuss the applicability of URLs for general classification tasks [11]. They also consider sequential features that take the typically hierarchical nature of URLs into account.

Special applications of URL classification are the detection of the document language [5], genre [1] or locational relevance [2]. In web crawling systems the information in URLs is used to detect duplicates [12] or documents containing relevant types of information [8, 10]. Furthermore, URLs have been used to detect malicious content [15] as well as online advertising [13].

A related field considers the anchor texts of links to a document. Similarly to URL classification, this approach allows a performance comparable to content-based methods on many task at a lower processing cost. For example, anchor-text based document ranking is significantly better for site finding tasks than a ranking using the content of the document [7]. However, these methods require the availability of anchor texts, whereas the URL of a document is always available. Furthermore, anchor texts need to be extracted and collected across the document collection, which has a higher cost especially for large document collections. Therefore we consider only URL based methods in this work.

5 Discussion

In this paper we presented our work on URL analytics towards providing efficient semantic annotations to large-scale Web archives. Our results demonstrate that named entity recognition techniques can be effectively applied to URLs of the Web documents in order to provide an efficient way of initial document annotation. Especially the years 2006 onwards provide useful information as the number of URLs providing entities has increased ever since. For observing and analyzing longer periods, news and shopping domains turn out to be more useful, while dominating sites have a lower impact. In the future work we plan to further analyse term extraction techniques for the URLs and to combine these techniques with light-weight content annotations to incrementally increase annotation coverage while maintaining scalability and efficiency of the annotation process as well as detecting temporal expressions still using the URL.

Understanding the dinamic of entities over time is important since we need to know which part of our dataset has most promising entities. Therefore, when a more specific search is needed, we should previously check which domains or domains categories had the most entities and generate a subset on this specific domains, instead of considering the entire archive, which sometimes is computationally unfeasible. The information we extracted from URLs can be further analysed in the document content, as where in the HTML page (title, paragraphs, etc.) the same entities or temporal expressions can be detected. This URL level analysis can support further keyword-based search algorithms in our web archive, also providing an overview of potential entity-rich domains (as those showed in last sections).