Developing web crawlers for vertical search engines: a survey of the current research

Vertical search engines allow users to query for information within a subset of documents relevant to a pre-determined topic (Chakrabarti, 1999). One challenging aspect to deploying a vertical search engine is building a Web crawler that distinguishes relevant documents from non-relevant documents. In this research, we describe and analyze various methods to crawl relevant documents for vertical search engines, and we examine ways to apply these methods to building a local search engine. In a typical crawl cycle for a vertical search engine, the crawler grabs a URL from the URL frontier, downloads content from the URL, and determines the document’s relevancy to the pre-defined topic. If the document is deemed relevant, it is indexed and its links are added to the URL frontier. Two questions are raised in this process: how do we judge a document’s relevance, and how should we prioritize URLs in the frontier in order to reach the best documents first? To determine the relevancy of a document, we may hold on to a set of pre-determined keywords that we attempt to match in a crawled document’s content and metadata. Another possibility is to use relevance feedback, a mechanism where we train the crawler to spot relevant documents by feeding it training data. In order to prioritize links within the URL frontier, we can use a breadth-first crawler where we just index pages one level at a time, bridges which are pages that aren’t crawled but used to gather more links, reinforcement learning where the crawler is rewarded for reaching relevant pages, and decision trees where the priority given to a link depends on the quality of the parent page

The Corpus Expansion Toolkit: finding what we want on the web

This thesis presents the Corpus Expansion Toolkit (CET), a generally applicable toolkit that allows researchers to build domain-specific corpora from the web. The main purpose of the work presented in this thesis and the development of the CET is to provide a solution to discovering desired content on the web from possibly unknown locations or a poorly defined domain. Using an iterative process, the CET is able to solve the problem of discovering domain-specific online content and expand a corpus using only a very small number of example documents or characteristic phrases taken from the target domain. Using a human-in-the-loop strategy and a chain of discrete software components the CET also allows the concept of a domain to be iteratively defined using the very online resources used to expand the original corpus. The CET combines feature extraction, search, web crawling and machine learning methods to collected, store, filter and perform information extraction on collected documents. Using a small number of example ‘seed’ documents the CET is able to expand the original corpus by finding more relevant documents from the web and provide a number of tools to support their analysis. This thesis presents a case study-based methodology that introduces the various contributions and components of the CET through the discussion of five case studies covering a wide variety of domains and requirements that the CET has been applied. These case studies hope to illustrate three main use cases, listed below, where the CET is applicable: 1. Domain known – source known 2. Domain known – source unknown 3. Domain unknown – source unknown First, use cases where the sites for document collection are known and the topic of research is clearly defined. Second, instances where the topic of research is clearly defined but where to find relevant documents on the web is unknown. Third, the most extreme use case, where the domain is poorly defined or unknown to the researcher and the location of the information is also unknown. This thesis presents a solution that allows researchers to begin with very little information on a specific topic and iteratively build a clear conception of a domain and translate that to a computational system

Topic modelling of Finnish Internet discussion forums as a tool for trend identification and marketing applications

The increasing availability of public discussion text data on the Internet motivates to study methods to identify current themes and trends. Being able to extract and summarize relevant information from public data in real time gives rise to competitive advantage and applications in the marketing actions of a company. This thesis presents a method of topic modelling and trend identification to extract information from Finnish Internet discussion forums. The development of text analytics, and especially topic modelling techniques, is reviewed and suitable methods are identified from the literature. The Latent Dirichlet Allocation topic model and the Dynamic Topic Model are applied in finding underlying topics from the Internet discussion forum data. The discussion data collection with web scarping and text data preprocessing methods are presented. Trends are identified with a method derived from outlier detection. Real world events, such as the news about Finnish army vegetarian meal day and the Helsinki summit of presidents Trump and Putin, were identified in an unsupervised manner. Applications for marketing are considered, e.g. automatic search engine advert keyword generation and website content recommendation. Future prospects for further improving the developed topical trend identification method are proposed. This includes the use of more complex topic models, extensive framework for tuning trend identification parameters and studying the use of more domain specific text data sources such as blogs, social media feeds or customer feedback

Bootstrapping Web Archive Collections From Micro-Collections in Social Media

In a Web plagued by disappearing resources, Web archive collections provide a valuable means of preserving Web resources important to the study of past events. These archived collections start with seed URIs (Uniform Resource Identifiers) hand-selected by curators. Curators produce high quality seeds by removing non-relevant URIs and adding URIs from credible and authoritative sources, but this ability comes at a cost: it is time consuming to collect these seeds. The result of this is a shortage of curators, a lack of Web archive collections for various important news events, and a need for an automatic system for generating seeds. We investigate the problem of generating seed URIs automatically, and explore the state of the art in collection building and seed selection. Attempts toward generating seeds automatically have mostly relied on scraping Web or social media Search Engine Result Pages (SERPs). In this work, we introduce a novel source for generating seeds from URIs in the threaded conversations of social media posts created by single or multiple users. Users on social media sites routinely create and share narratives about news events consisting of hand-selected URIs of news stories, tweets, videos, etc. In this work, we call these posts Micro-collections, whether shared on Reddit or Twitter, and we consider them as an important source for seeds. This is because, the effort taken to create Micro-collections is an indication of editorial activity and a demonstration of domain expertise. Therefore, we propose a model for generating seeds from Micro-collections. We begin by introducing a simple vocabulary, called post class for describing social media posts across different platforms, and extract seeds from the Micro-collections post class. We further propose Quality Proxies for seeds by extending the idea of collection comparison to evaluation, and present our Micro-collection/Quality Proxy (MCQP) framework for bootstrapping Web archive collections from Micro-collections in social media

Retrieval, crawling and fusion of entity-centric data on the web

While the Web of (entity-centric) data has seen tremendous growth over the past years, take-up and re-use is still limited. Data vary heavily with respect to their scale, quality, coverage or dynamics, what poses challenges for tasks such as entity retrieval or search. This chapter provides an overview of approaches to deal with the increasing heterogeneity of Web data. On the one hand, recommendation, linking, profiling and retrieval can provide efficient means to enable discovery and search of entity-centric data, specifically when dealing with traditional knowledge graphs and linked data. On the other hand, embedded markup such as Microdata and RDFa has emerged a novel, Web-scale source of entitycentric knowledge. While markup has seen increasing adoption over the last few years, driven by initiatives such as schema.org, it constitutes an increasingly important source of entity-centric data on the Web, being in the same order of magnitude as the Web itself with regards to dynamics and scale. To this end, markup data lends itself as a data source for aiding tasks such as knowledge base augmentation, where data fusion techniques are required to address the inherent characteristics of markup data, such as its redundancy, heterogeneity and lack of links. Future directions are concerned with the exploitation of the complementary nature of markup data and traditional knowledge graphs. The final publication is available at Springer via http://dx.doi.org/ 10.1007/978-3-319-53640-8_1