Konrad Höffner

LinkedGeoData: A core for a web of spatial open data

The Semantic Web eases data and information integration tasks by providing an infrastructure based on RDF and ontologies. In this paper, we contribute to the development of a spatial Data Web by elaborating on how the collaboratively collected OpenStreetMap data can be interactively transformed and represented adhering to the RDF data model. This transformation will simplify information integration and aggregation tasks that require comprehensive background knowledge related to spatial features such as ways, structures, and landscapes. We describe how this data is interlinked with other spatial data sets, how it can be made accessible for machines according to the Linked Data paradigm and for humans by means of several applications, including a faceted geo-browser. The spatial data, vocabularies, interlinks and some of the applications are openly available in the LinkedGeoData project.

Towards an open question answering architecture

Billions of facts pertaining to a multitude of domains are now available on the Web as RDF data. However, accessing this data is still a difficult endeavour for non-expert users. In order to meliorate the access to this data, approaches imposing minimal hurdles to their users are required. Although many question answering systems over Linked Data have being proposed, retrieving the desired data is still significantly challenging. In addition, developing and evaluating question answering systems remains a very complex task. To overcome these obstacles, we present a modular and extensible open-source question answering framework. We demonstrate how the framework can be used by integrating two state-of-the-art question answering systems. As a result our evaluation shows that overall better results can be achieved by the use of combination rather than individual stand-alone versions.

Survey on challenges of Question Answering in the Semantic Web

Semantic Question Answering (SQA) removes two major access requirements to the Semantic Web: the mastery of a formal query language like SPARQL and knowledge of a specific vocabulary. Because of the complexity of natural language, SQA presents difficult challenges and many research opportunities. Instead of a shared effort, however, many essential components are redeveloped, which is an inefficient use of researcher’s time and resources. This survey analyzes 62 different SQA systems, which are systematically and manually selected using predefined inclusion and exclusion criteria, leading to 72 selected publications out of 1960 candidates. We identify common challenges, structure solutions, and provide recommendations for future systems. This work is based on publications from the end of 2010 to July 2015 and is also compared to older but similar surveys.

AskNow: A Framework for Natural Language Query Formalization in SPARQL

Natural Language Query Formalization involves semantically parsing queries in natural language and translating them into their corresponding formal representations. It is a key component for developing question-answering QA systems on RDF data. The chosen formal representation language in this case is often SPARQL. In this paper, we propose a framework, called AskNow, where users can pose queries in English to a target RDF knowledge base e.g.i?źDBpedia, which are first normalized into an intermediary canonical syntactic form, called Normalized Query Structure NQS, and then translated into SPARQL queries. NQS facilitates the identification of the desire or expected output information and the user-provided input information, and establishing their mutual semantic relationship. At the same time, it is sufficiently adaptive to query paraphrasing. We have empirically evaluated the framework with respect to the syntactic robustness of NQS and semantic accuracy of the SPARQL translator on standard benchmark datasets.

LinkedSpending: OpenSpending becomes Linked Open Data

There is a high public demand to increase transparency in government spending. Open spending data has the power to reduce corruption by increasing accountability and strengthens democracy because voters can make better informed decisions. An informed and trusting public also strengthens the government itself because it is more likely to commit to large projects. OpenSpending.org is a an open platform that provides public finance data from governments around the world. In this article, we present its RDF conversion LinkedSpending which provides more than five million planned and carried out financial transactions in 627 data sets from all over the world from 2005 to 2035 as Linked Open Data. This data is represented in the RDF Data Cube vocabulary and is freely available and openly licensed.

Keyword Query Expansion on Linked Data Using Linguistic and Semantic Features

Effective search in structured information based on textual user input is of high importance in thousands of applications. Query expansion methods augment the original query of a user with alternative query elements with similar meaning to increase the chance of retrieving appropriate resources. In this work, we introduce a number of new query expansion features based on semantic and linguistic inferencing over Linked Open Data. We evaluate the effectiveness of each feature individually as well as their combinations employing several machine learning approaches. The evaluation is carried out on a training dataset extracted from the QALD question answering benchmark. Furthermore, we propose an optimized linear combination of linguistic and lightweight semantic features in order to predict the usefulness of each expansion candidate. Our experimental study shows a considerable improvement in precision and recall over baseline approaches.

Towards question answering on statistical linked data

As an increasing amount of statistical data is published as linked data, intuitive ways of satisfying information needs and getting new insights out of the data become more and more important. Question answering systems provide such an intuitive interface by translating natural language queries into SPARQL, which is the native query language of RDF knowledge bases. Statistical data, however, is structurally very different from other data and cannot be queried using existing approaches. We analyze the particularities of statistical data represented in the RDF Data Cube Vocabulary in relation to question answering and sketch a new question answering algorithm on statistical data. In order to estimate typical user questions, a statistical question corpus is compiled and its elements are categorized.

CubeQA—Question Answering on RDF Data Cubes

Statistical data in the form of RDF Data Cubes is becoming increasingly valuable as it influences decisions in areas such as health care, policy and finance. While a growing amount is becoming freely available through the open data movement, this data is opaque to laypersons. Semantic Question Answering (SQA) technologies provide intuitive access via free-form natural language queries but general SQA systems cannot process RDF Data Cubes. On the intersection between RDF Data Cubes and SQA, we create a new subfield of SQA, called RDCQA. We create an RDQCA benchmark as task 3 of the QALD-6 evaluation challenge, to stimulate further research and enable quantitative comparison between RDCQA systems. We design and evaluate the domain independent CubeQA algorithm, which is the first RDCQA system and achieves a global \(F_1\) score of 0.43 on the QALD6T3-test benchmark, showing that RDCQA is feasible.

SAIM – One Step Closer to Zero-Configuration Link Discovery

Link discovery plays a central role in the implementation of the Linked Data vision. In this demo paper, we present SAIM, a tool that aims to support users during the creation of high-quality link specifications. The tool implements a simple but effective workflow to creating initial link specifications. In addition, SAIM implements a variety of state-of-the-art machine-learning algorithms for unsupervised, semi-supervised and supervised instance matching on structured data. We demonstrate SAIM by using benchmark data such as the OAEI datasets.

User Interface for a Template Based Question Answering System

As an increasing amount of RDF data is published as Linked Data, intuitive ways of accessing this data become more and more important. Natural language question answering approaches have been proposed as a good compromise between intuitiveness and expressiveness. We present a user interface for the template based question answering system which covers the full question answering pipeline and answers factual questions with a list of RDF resources. Users can ask full-sentence, English factual questions and get a list of resources which are then visualized using those properties which are expected to carry the most important information for the user. The available knowledge bases are (1) DBpedia for general domain question answering and (2) Oxford real estate for housing searches. However, the system is easily extensible to other knowledge bases.