Dennis Diefenbach

Core techniques of question answering systems over knowledge bases: a survey

The Semantic Web contains an enormous amount of information in the form of knowledge bases (KB). To make this information available, many question answering (QA) systems over KBs were created in the last years. Building a QA system over KBs is difficult because there are many different challenges to be solved. In order to address these challenges, QA systems generally combine techniques from natural language processing, information retrieval, machine learning and Semantic Web. The aim of this survey is to give an overview of the techniques used in current QA systems over KBs. We present the techniques used by the QA systems which were evaluated on a popular series of benchmarks: Question Answering over Linked Data. Techniques that solve the same task are first grouped together and then described. The advantages and disadvantages are discussed for each technique. This allows a direct comparison of similar techniques. Additionally, we point to techniques that are used over WebQuestions and SimpleQuestions, which are two other popular benchmarks for QA systems.

Trill: A reusable Front-End for QA systems

The Semantic Web contains an enormous amount of information in the form of knowledge bases. To make this information available to end-users many question answering (QA) systems over knowledge bases were created in the last years. Their goal is to enable users to access large amounts of structured data in the Semantic Web by bridging the gap between natural language and formal query languages like SPARQL.

WDAqua-core0: A Question Answering Component for the Research Community

We describe and present a new Question Answering (QA) component that can be easily used by the QA research community.

Introducing Feedback in Qanary: How Users Can Interact with QA Systems

Providing a general and efficient Question Answering system over Knowledge Bases (KB) has been studied for years. Most of the works concentrated on the automatic translation of a natural language question into a formal query. However, few works address the problem on how users can interact with Question Answering systems during this translation process. We present a general mechanism that allows users to interact with Question Answering systems. It is built on top of Qanary, a framework for integrating Question Answering components. We show how the mechanism can be applied in a generalized way. In particular, we show how it can be used when the user asks ambiguous questions.

A Scalable Approach for Computing Semantic Relatedness using Semantic Web Data

Computing semantic relatedness is an essential operation for many natural language processing (NLP) tasks, such as Entity Linking (EL) and Question Answering (QA). It is still challenging to find a scalable approach to compute the semantic relatedness using Semantic Web data. Hence, we present for the first time an approach to pre-compute the semantic relatedness between the instances, relations, and classes of an ontology, such that they can be used in real-time applications.

WDAqua-core1: A Question Answering service for RDF Knowledge Bases

In the last two decades a new part of the web grew significantly, namely the Semantic Web. It contains many Knowledge Bases (KB) about different areas like music, books, publications, live science and many more. Question Answering (QA) over KBs is seen as the most promising approach to bring this data to end-users. We describe WDAqua-core1, a QA service for querying RDF knowledge-bases. It is multilingual, it supports different RDF knowledge bases and it understands both full natural language questions and keyword questions.

On the scalability of the QA System WDAqua-core1

Scalability is an important problem for Question Answering (QA) systems over Knowledge Bases (KBs). Current KBs easily contain hundreds of millions of triples and all these triples can potentially contain the information requested by the user.

PageRank and Generic Entity Summarization for RDF Knowledge Bases

Ranking and entity summarization are operations that are tightly connected and recurrent in many different domains. Possible application fields include information retrieval, question answering, named entity disambiguation, co-reference resolution, and natural language generation. Still, the use of these techniques is limited because there are few accessible resources. PageRank computations are resource-intensive and entity summarization is a complex research field in itself.