A major problem during development and maintenance of software is lack of quality documentation. Many programers have problems identifying which infor- mation is relevant for someone with no knowledge of the system and therefore write incomplete documentation. One way to get around these problems would be to use a tool that extracts information from both comments and the actual source code and presents the structure of the program visually. This thesis aims to design an XML-driven system for the extraction and pre- sentation of meta information about source code to that purpose. Relevant meta information in this case is, for example, which entities (classes, methods, variables, etc.) exist in the program and how they interact with each other. The result is a prototype implemented to manage two company developed lan- guages. The prototype demonstrates how the system can be implemented and show that the approach is scalable. The prototype is not suitable for commercial use due to its abstraction level, but with the help of qualified XML databases there are great possibilities to build a usable system using the same techniques in the future. / Ett stort problem under utvecklingen och underhållet av mjukvara är bristande dokumentation av källkoden. Många programmerare har svårt att identifiera vilken information som är viktig för någon som inte är insatt i systemet och skriver därför bristfällig dokumentation. Ett sätt att komma runt dessa problem skulle vara att använda verktyg som extraherar information från såväl kommentarer som faktisk källkod och presenterar programmets struktur påett tydligt och visuellt sätt. Det här examensarbetet ämnar att designa ett system för XML-driven extra- hering och presentation av metainformation om källkoden med just det syftet. Metainformationen som avses här är exempelvis vilka entiteter (klasser, metoder, variabler, mm.) som finns i källkoden samt hur dessa interagerar med varandra. Resultatet är en prototyp implementerad för att hantera tvåföretagsutvecklade språk. Prototypen demonstrerar hur systemet kan implementeras och visar att me- toden är skalbar. Prototypen är abstraktionsmässigt inte lämplig för kommersiellt bruk men med hjälp av kvalificerade XML-databaser finns det stora möjligheter att i framtiden bygga ett praktiskt användbart system baserat på samma tekniker.

This thesis focuses on entity and fact extraction from the web. Different knowledge representations and techniques for information extraction are discussed before the design for a knowledge extraction system, called WebKnox, is introduced. The main contribution of this thesis is the trust ranking of extracted facts with a self-supervised learning loop and the extraction system with its composition of known and refined extraction algorithms. The used techniques show an improvement in precision and recall in most of the matters for entity and fact extractions compared to the chosen baseline approaches.

The search for information about entities, such as people or movies, plays an increasingly important role on the Web. This information is still scattered across many Web pages, making it more time consuming for a user to find all relevant information about an entity. This thesis describes techniques to extract entities and information about these entities from the Web, such as facts, opinions, questions and answers, interactive multimedia objects, and events. The findings of this thesis are that it is possible to create a large knowledge base automatically using a manually-crafted ontology. The precision of the extracted information was found to be between 75–90 % (facts and entities respectively) after using assessment algorithms. The algorithms from this thesis can be used to create such a knowledge base, which can be used in various research fields, such as question answering, named entity recognition, and information retrieval.

The search for information about entities, such as people or movies, plays an increasingly important role on the Web. This information is still scattered across many Web pages, making it more time consuming for a user to find all relevant information about an entity. This thesis describes techniques to extract entities and information about these entities from the Web, such as facts, opinions, questions and answers, interactive multimedia objects, and events. The findings of this thesis are that it is possible to create a large knowledge base automatically using a manually-crafted ontology. The precision of the extracted information was found to be between 75–90 % (facts and entities respectively) after using assessment algorithms. The algorithms from this thesis can be used to create such a knowledge base, which can be used in various research fields, such as question answering, named entity recognition, and information retrieval.

This thesis focuses on entity and fact extraction from the web. Different knowledge representations and techniques for information extraction are discussed before the design for a knowledge extraction system, called WebKnox, is introduced. The main contribution of this thesis is the trust ranking of extracted facts with a self-supervised learning loop and the extraction system with its composition of known and refined extraction algorithms. The used techniques show an improvement in precision and recall in most of the matters for entity and fact extractions compared to the chosen baseline approaches.