dbpediaspotlight

Mentors: Student: wmaroy

<p>This project is about integrating RML in the Dbpedia extraction framework. Dbpedia is derived from Wikipedia infoboxes using the extraction framework and mappings defined using the wikitext syntax. A next step would be replacing the wikitext defined mappings with RML. To accomplish this, adjustments will have to be made to the extraction framework.</p>

Mentors: Student: Aditya Nambiar

<p>DBpedia currently maintains a mapping between Wikipedia info-box properties to the DBpedia ontology, since several similar templates exist to describe the same type of info-boxes. The aim of the project is to enrich the existing mapping and possibly correct the incorrect mapping's using Wikidata.</p> <p>Several wikipedia pages use Wikidata values directly in their infoboxes. Hence by using the mapping between Wikidata properties and DBpedia Ontology classes along with the info-box data across several such wiki pages we can collect several such mappings. The first phase of the project revolves around using various such wikipedia templates , finding their usages across the wikipedia pages and extracting as many mappings as possible.</p> <p>In the second half of the project we use machine learning techniques to take care of any accidental / outlier usage of Wikidata mappings in wikipedia. At the end of the project we will be able to obtain a correct set of mapping which we can use to enrich the existing mapping.</p>

Mentors: Student: Kunal Jha

<p>DBpedia is one of the most extensive and most widely used knowledge base in over 125 languages. DBpedia Lookup is a tool that allows The DBpedia Lookup is a web service that allows users to obtain various DBpedia URIs for a given label (keywords/anchor texts). The service provides two different types of search APIs, namely, Keyword Search and Prefix Search. The lookup service currently returns the query results in XML (default) and JSON formats and works on English language. It is based on a Lucene Index providing a weighted label lookup, which combines string similarity with a relevance ranking in order to find the most relevant matches for a given label. As a part of the GSOC 2016, I am going to implement an improvised Lucene search module by generating indexes from SPARQL end points and RDF dumps. This module will also take care of the surface forms of the labels and will be an independent module that can be used on any RDF knowledge base.</p>

Mentors: Student: wojtuch

<p>DBpedia, a crowd- and open-sourced community project extracting the content from Wikipedia, stores this information in a huge RDF graph. DBpedia Spotlight is a tool which delivers the DBpedia resources that are being mentioned in the document.</p> <p>Using DBpedia Spotlight to extract and disambiguate Named Entities from Wikipedia articles and then applying a topic modelling algorithm (e.g. LDA) with URIs of DBpedia resources as features would result in a model, which is capable of describing the documents with the proportions of the topics covering them. But because the topics are also represented by DBpedia URIs, this approach could result in a novel RDF hierarchy and ontology with insights for further analysis of the emerged subgraphs.</p> <p>The direct implication and first application scenario for this project would be utilizing the inference engine in DBpedia Spotlight, as an additional step after the document has been annotated and predicting its topic coverage.</p>

Mentors: Student: Vincent Bohlen

<p>In modern times the amount of information published on the internet is growing to an immeasurable extent. Humans are no longer able to gather all the available information by hand but are more and more dependent on machines collecting relevant information automatically. This is why automatic information extraction and in especially automatic event extraction is important. In this project I will implement a system for event extraction using Classification and Rule-based Event Extraction. The underlying data for both approaches will be identical. I will gather wikipedia articles and perform a variety of NLP tasks on the extracted texts. First I will annotate the named entities in the text using named entity recognition performed by DBpedia Spotlight. Additionally I will annotate the text with Frame Semantics using FrameNet frames. I will then use the collected information, i.e. frames, entities, entity types, with the aforementioned two different methods to decide if the collection is an event or not.</p>