The traditional entity extraction problem lies in the ability of extracting named entities from plain text using natural language processing techniques and intensive training from large document collections. Examples of named entities include organisations, people, locations, or dates. There are many research activities involving named entities; we are interested in entity ranking in the field of information retrieval. In this paper, we describe our approach to identifying and ranking entities from the INEX Wikipedia document collection. Wikipedia offers a number of interesting features for entity identification and ranking that we first introduce. We then describe the principles and the architecture of our entity ranking system, and introduce our methodology for evaluation. Our preliminary results show that the use of categories and the link structure of Wikipedia, together with entity examples, can significantly improve retrieval effectiveness.
A wrapper is a tool that extracts information (entities or values) from a document, or a set of documents, with a purpose of reusing information in another system. A lot of research has been carried out in this field by the database community, mostly in relation to querying heterogeneous databases @cite_24 @cite_21 @cite_15 @cite_5 . More recently, wrappers have also been built to extract information from web pages with different applications in mind, such as product comparison, reuse of information in virtual documents, or building experimental data sets. Most web wrappers are either based on scripting languages @cite_24 @cite_21 that are very close to current XML query languages, or use wrapper induction @cite_15 @cite_5 that learn rules for extracting information.
@cite_24 @cite_5 @cite_15 @cite_21
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Extracting data from Web pages using wrappers is a fundamental problem arising in a large variety of applications of vast practical interests. There are two main issues relevant to Web-data extraction, namely wrapper generation and wrapper maintenance. In this paper, we propose a novel schema-guided approach to the problem of automatic wrapper maintenance. It is based on the observation that despite various page changes, many important features of the pages are preserved, such as syntactic patterns, annotations, and hyperlinks of the extracted data items. Our approach uses these preserved features to identify the locations of the desired values in the changed pages, and repair wrappers correspondingly by inducing semantic blocks from the HTML tree. Our intensive experiments on real Web sites show that the proposed approach can effectively maintain wrappers to extract desired data with high accuracies. The proliferation of online information sources has led to an increased use of wrappers for extracting data from Web sources. While most of the previous research has focused on quick and efficient generation of wrappers, the development of tools for wrapper maintenance has received less attention. This is an important research problem because Web sources often change in ways that prevent the wrappers from extracting data correctly. We present an efficient algorithm that learns structural information about data from positive examples alone. We describe how this information can be used for two wrapper maintenance applications: wrapper verification and reinduction. The wrapper verification system detects when a wrapper is not extracting correct data, usually because the Web source has changed its format. The reinduction algorithm automatically recovers from changes in the Web source by identifying data on Web pages so that a new wrapper may be generated for this source. To validate our approach, we monitored 27 wrappers over a period of a year. The verification algorithm correctly discovered 35 of the 37 wrapper changes, and made 16 mistakes, resulting in precision of 0.73 and recall of 0.95. We validated the reinduction algorithm on ten Web sources. We were able to successfully reinduce the wrappers, obtaining precision and recall values of 0.90 and 0.80 on the data extraction task. We study the problem of automatic repairing of wrappers for Web information providers. Majority of Web wrappers use "hooks'' or "landmarks'' to find and extract relevant information from Web pages and such wrappers often become inoperable when the page structure is changed. The solution we propose in this paper extends conventional forward wrappers with alternative classifiers built using content features of extracted information and wrappers processing pages backward. We report some preliminary results of the information extraction recovery and wrapper repairing for a set of real Web provider changes. Information distributed through the Web keeps growing faster day by day, and for this reason, several techniques for extracting Web data have been suggested during last years. Often, extraction tasks are performed through so called wrappers, procedures extracting information from Web pages, e.g. implementing logic-based techniques. Many fields of application today require a strong degree of robustness of wrappers, in order not to compromise assets of information or reliability of data extracted.