This work concentrates on the design and implementation of an automated support for data extraction from product descriptions. This system will be used for e-shop purposes. The work introduces present approaches to information extraction from HTML documents. It focuses chiefly at wrappers and methods for their induction. The visual approach to information extraction is also mentioned. System requirements and basic principles are described in the design part of the work. Next, a detailed description of a path tracing algorithm in document object model is explained. The last section of the work evaluates the results of experiments made with the implemented system.

Title: Structured Data Extraction from Unstructured Text Author: Bc. Peter Kóša Department: Department of Software Engineering Supervisor: Mgr. Martin Nečaský, Ph.D., Department of Software Engineering Abstract: In the last 20 years, there has been an ever-growing amount of information present on the Internet and in published texts. However, this information is often in a non-structured format and this causes various problems such as the inability to efficiently search in diverse collections of texts (medical reports, ads, etc.). To overcome these problems, we need efficient tools capable of automatic processing, extracting the important information and storing of these results in some form for later reuse. The purpose of this thesis is to compare existing solutions as well as to compare them with our solution, which was created in the scope of software project SemJob. The SemJob project is introduced and the reader can therefore obtain knowledge about its inner structure and workings. Keywords: structured data extraction, extraction rules, (semi)automatic wrapper induction

Title: Structured Data Extraction from Unstructured Text Author: Bc. Peter Kóša Department: Department of Software Engineering Supervisor: Mgr. Martin Nečaský, Ph.D., Department of Software Engineering Abstract: In the last 20 years, there has been an ever-growing amount of information present on the Internet and in published texts. However, this information is often in a non-structured format and this causes various problems such as the inability to efficiently search in diverse collections of texts (medical reports, ads, etc.). To overcome these problems, we need efficient tools capable of automatic processing, extracting the important information and storing of these results in some form for later reuse. The purpose of this thesis is to compare existing solutions as well as to compare them with our solution, which was created in the scope of software project SemJob. The SemJob project is introduced and the reader can therefore obtain knowledge about its inner structure and workings. Keywords: structured data extraction, extraction rules, ontologies, (semi)automatic wrapper induction

Lists of records in machine-printed documents contain much useful information. As one example, the thousands of family history books scanned, OCRed, and placed on-line by FamilySearch.org probably contain hundreds of millions of fact assertions about people, places, family relationships, and life events. Data like this cannot be fully utilized until a person or process locates the data in the document text, extracts it, and structures it with respect to an ontology or database schema. Yet, in the family history industry and other industries, data in lists goes largely unused because no known approach adequately addresses all of the costs, challenges, and requirements of a complete end-to-end solution to this task. The diverse information is costly to extract because many kinds of lists appear even within a single document, differing from each other in both structure and content. The lists' records and component data fields are usually not set apart explicitly from the rest of the text, especially in a corpus of OCRed historical documents. OCR errors and the lack of document structure (e.g. HMTL tags) make list content hard to recognize by a software tool developed without a substantial amount of highly specialized, hand-coded knowledge or machine learning supervision. Making an approach that is not only accurate but also sufficiently scalable in terms of time and space complexity to process a large corpus efficiently is especially challenging. In this dissertation, we introduce a novel family of scalable approaches to list discovery and ontology population. Its contributions include the following. We introduce the first general-purpose methods of which we are aware for both list detection and wrapper induction for lists in OCRed or other plain text. We formally outline a mapping between in-line labeled text and populated ontologies, effectively reducing the ontology population problem to a sequence labeling problem, opening the door to applying sequence labelers and other common text tools to the goal of populating a richly structured ontology from text. We provide a novel admissible heuristic for inducing regular expression wrappers using an A* search. We introduce two ways of modeling list-structured text with a hidden Markov model. We present two query strategies for active learning in a list-wrapper induction setting. Our primary contributions are two complete and scalable wrapper-induction-based solutions to the end-to-end challenge of finding lists, extracting data, and populating an ontology. The first has linear time and space complexity and extracts highly accurate information at a low cost in terms of user involvement. The second has time and space complexity that are linear in the size of the input text and quadratic in the length of an output record and achieves higher F1-measures for extracted information as a function of supervision cost. We measure the performance of each of these approaches and show that they perform better than strong baselines, including variations of our own approaches and a conditional random field-based approach.