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Information Extraction from Biomedical Texts
Knoth, Petr ; Burget, Radek (referee) ; Smrž, Pavel (advisor)
Recently, there has been much effort in making biomedical knowledge, typically stored in scientific articles, more accessible and interoperable. As a matter of fact, the unstructured nature of such texts makes it difficult to apply knowledge discovery and inference techniques. Annotating information units with semantic information in these texts is the first step to make the knowledge machine-analyzable. In this work, we first study methods for automatic information extraction from natural language text. Then we discuss the main benefits and disadvantages of the state-of-art information extraction systems and, as a result of this, we adopt a machine learning approach to automatically learn extraction patterns in our experiments. Unfortunately, machine learning techniques often require a huge amount of training data, which can be sometimes laborious to gather. In order to face up to this tedious problem, we investigate the concept of weakly supervised or bootstrapping techniques. Finally, we show in our experiments that our machine learning methods performed reasonably well and significantly better than the baseline. Moreover, in the weakly supervised learning task we were able to substantially bring down the amount of labeled data needed for training of the extraction system.
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Methods of Information Extraction
Adamček, Adam ; Smrž, Pavel (referee) ; Kouřil, Jan (advisor)
The goal of information extraction is to retrieve relational data from texts written in natural human language. Applications of such obtained information is wide - from text summarization, through ontology creation up to answering questions by QA systems. This work describes design and implementation of a system working in computer cluster which transforms a dump of Wikipedia articles to a set of extracted information that is stored in distributed RDF database with a possibility to query it using created user interface.
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Extracting text data from the webpages
Mazal, Zdeněk ; Morský, Ondřej (referee) ; Fojtová, Lucie (advisor)
This work focus at data and especially text mining from Web pages, an overview of programs for downloading the text and ways of their extraction. It also contains an overview of the most frequently used programs for extracting data from internet. The output of this thesis is a Java program that can download text from a selection of servers and save them into xml le.
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Bulk extraction of public administration data to RDF
Pomykacz, Michal ; Svátek, Vojtěch (advisor) ; Mynarz, Jindřich (referee)
The purpose of this work was to deal with data extraction from various formats (HTML, XML, XLS) and transformation for further processing. As the data sources were used Czech public contracts and related code lists and classifications. Main goal was to implement periodic data extraction, RDF transformation and publishing the output in form of Linked Data using SPARQL endpoint. It was necessary to design and implement extraction modules for UnifiedViews tool as it was used for periodic extractions. Theoretical section of this thesis explains the principles of linked data and key tools used for data extraction and manipulation. Practical section deals with extractors design and implementation. Part describing extractor implementation shows methods for parsing data in various dataset formats and its transformation to RDF. The success of each extractor implementation is presented at the conclusion along with thought of usability in a real world.
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Mobile personal assistants
Techl, Jan ; Sigmund, Tomáš (advisor) ; Labský, Martin (referee)
This thesis focuses on analysis, definition and description of mobile personal assistants as a phenomenon emerging in past few years. Mobile personal assistants are first mentioned in the context of computational linguistics and information needs, which is one of the motivations to use them. Main interest of this thesis is an introduction of the core technologies for the natural language communication between the assistant and its user, followed by an introduction of host environments and possible usage. The thesis also presents the limitations and risks resulting from using them, which are in some ways affecting their usability. Beside the analysis the main focus is on the design and implementation of the natural language understanding (NLU) system, which can be used in particular personal assistant application. This system is implemented as a web service and consists of an annotation scheme with a set of components. The results show that the system architecture and tools used are suitable solution for the construction of a basic NLU system, which has been created and which is in the compliance with the requested parameters. It is still difficult task to achieve high precision, which depends on many factors including the amount of training data, which was very small in this case. However, the resulting application is a solid starting point for its further development and extensions.
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Extracting Structured Data from Czech Web Using Extraction Ontologies
Pouzar, Aleš ; Svátek, Vojtěch (advisor) ; Labský, Martin (referee)
The presented thesis deals with the task of automatic information extraction from HTML documents for two selected domains. Laptop offers are extracted from e-shops and free-published job offerings are extracted from company sites. The extraction process outputs structured data of high granularity grouped into data records, in which corresponding semantic label is assigned to each data item. The task was performed using the extraction system Ex, which combines two approaches: manually written rules and supervised machine learning algorithms. Due to the expert knowledge in the form of extraction rules the lack of training data could be overcome. The rules are independent of the specific formatting structure so that one extraction model could be used for heterogeneous set of documents. The achieved success of the extraction process in the case of laptop offers showed that extraction ontology describing one or a few product types could be combined with wrapper induction methods to automatically extract all product type offers on a web scale with minimum human effort.
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Extrakce informací z webových stránek pomoci extrakčních ontologií
Labský, Martin ; Berka, Petr (advisor) ; Strossa, Petr (referee) ; Vojtáš, Peter (referee) ; Snášel, Václav (referee)
Automatic information extraction (IE) from various types of text became very popular during the last decade. Owing to information overload, there are many practical applications that can utilize semantically labelled data extracted from textual sources like the Internet, emails, intranet documents and even conventional sources like newspaper and magazines. Applications of IE exist in many areas of computer science: information retrieval systems, question answering or website quality assessment. This work focuses on developing IE methods and tools that are particularly suited to extraction from semi-structured documents such as web pages and to situations where available training data is limited. The main contribution of this thesis is the proposed approach of extended extraction ontologies. It attempts to combine extraction evidence from three distinct sources: (1) manually specified extraction knowledge, (2) existing training data and (3) formatting regularities that are often present in online documents. The underlying hypothesis is that using extraction evidence of all three types by the extraction algorithm can help improve its extraction accuracy and robustness. The motivation for this work has been the lack of described methods and tools that would exploit these extraction evidence types at the same time. This thesis first describes a statistically trained approach to IE based on Hidden Markov Models which integrates with a picture classification algorithm in order to extract product offers from the Internet, including textual items as well as images. This approach is evaluated using a bicycle sale domain. Several methods of image classification using various feature sets are described and evaluated as well. These trained approaches are then integrated in the proposed novel approach of extended extraction ontologies, which builds on top of the work of Embley [21] by exploiting manual, trained and formatting types of extraction evidence at the same time. The intended benefit of using extraction ontologies is a quick development of a functional IE prototype, its smooth transition to deployed IE application and the possibility to leverage the use of each of the three extraction evidence types. Also, since extraction ontologies are typically developed by adapting suitable domain ontologies and the ontology remains in center of the extraction process, the work related to the conversion of extracted results back to a domain ontology or schema is minimized. The described approach is evaluated using several distinct real-world datasets.
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Ontology Learning and Information Extraction for the Semantic Web
Kavalec, Martin ; Berka, Petr (advisor) ; Štěpánková, Olga (referee) ; Snášel, Václav (referee)
The work gives overview of its three main topics: semantic web, information extraction and ontology learning. A method for identification relevant information on web pages is described and experimentally tested on pages of companies offering products and services. The method is based on analysis of a sample web pages and their position in the Open Directory catalogue. Furthermore, a modfication of association rules mining algorithm is proposed and experimentally tested. In addition to an identification of a relation between ontology concepts, it suggest possible naming of the relation.
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