Magnus Knuth

WhoKnows? Evaluating linked data heuristics with a quiz that cleans up DBpedia

Purpose – Linking Open Data (LOD) provides a vast amount of well structured semantic information, but many inconsistencies may occur, especially if the data are generated with the help of automated methods. Data cleansing approaches enable detection of inconsistencies and overhauling of affected data sets, but they are difficult to apply automatically. The purpose of this paper is to present WhoKnows?, an online quiz that generates different kinds of questionnaires from DBpedia data sets.Design/methodology/approach – Besides its playfulness, WhoKnows? has been developed for the evaluation of property relevance ranking heuristics on DBpedia data, with the convenient side effect of detecting inconsistencies and doubtful facts.Findings – The original purpose for developing WhoKnows? was to evaluate heuristics to rank LOD properties and thus, obtain a semantic relatedness between entities according to the properties by which they are linked. The presented approach is an efficient method to detect popular prop...

DBpedia ontology enrichment for inconsistency detection

In recent years the Web of Data experiences an extraordinary development: an increasing amount of Linked Data is available on the World Wide Web (WWW) and new use cases are emerging continually. However, the provided data is only valuable if it is accurate and without contradictions. One essential part of the Web of Data is DBpedia, which covers the structured data of Wikipedia. Due to its automatic extraction based on Wikipedia resources that have been created by various contributors, DBpedia data often is error-prone. In order to enable the detection of inconsistencies this work focuses on the enrichment of the DBpedia ontology by statistical methods. Taken the enriched ontology as a basis the process of the extraction of Wikipedia data is adapted, in a way that inconsistencies are detected during the extraction. The creation of suitable correction suggestions should encourage users to solve existing errors and thus create a knowledge base of higher quality.

RISQ! Renowned Individuals Semantic Quiz: a Jeopardy like quiz game for ranking facts

In 2011 the IBM Computer Watson was beating its human opponents in the American TV quiz show Jeopardy!. However, the questions for the quiz have been developed by a team of human authors. Authoring questions is a difficult task, because in a Jeopardy! game the questions should be neither too easy nor too hard and should fit the general scope of knowledge of the audience and players. Linked Open Data (LOD) provides huge amounts of information that is growing daily. Yet, there is no ranking that determines the importance of LOD facts, as e. g. by querying LOD for movies starring a distinct actor provides numerous answers, whereas it cannot be answered, which of the movies was the most important for this actor. To rank search results for semantic search various heuristics have been developed to cope with the problem of missing rank in the semantic web. This paper proposes a Jeopardy! like quiz game with questions automatically generated from LOD facts to gather ranking information for persons to provide a basis for the evaluation of semantic ranking heuristics.

Evaluating Query and Storage Strategies for RDF Archives

There is an emerging demand on efficiently archiving and (temporal) querying different versions of evolving semantic Web data. As novel archiving systems are starting to address this challenge, foundations/standards for benchmarking RDF archives are needed to evaluate its storage space efficiency and the performance of different retrieval operations. To this end, we provide theoretical foundations on the design of data and queries to evaluate emerging RDF archiving systems. Then, we instantiate these foundations along a concrete set of queries on the basis of a real-world evolving dataset. Finally, we perform an empirical evaluation of various current archiving techniques and querying strategies on this data. Our work comprises -- to the best of our knowledge -- the first benchmark for querying evolving RDF data archives.

Evaluating entity summarization using a game-based ground truth

In recent years, strategies for Linked Data consumption have caught attention in Semantic Web research. For direct consumption by users, Linked Data mashups, interfaces, and visualizations have become a popular research area. Many approaches in this field aim to make Linked Data interaction more user friendly to improve its accessibility for non-technical users. A subtask for Linked Data interfaces is to present entities and their properties in a concise form. In general, these summaries take individual attributes and sometimes user contexts and preferences into account. But the objective evaluation of the quality of such summaries is an expensive task. In this paper we introduce a game-based approach aiming to establish a ground truth for the evaluation of entity summarization. We exemplify the applicability of the approach by evaluating two recent summarization approaches.

DBpedia Commons: Structured Multimedia Metadata from the Wikimedia Commons

The Wikimedia Commons is an online repository of over twenty-five million freely usable audio, video and still image files, including scanned books, historically significant photographs, animal recordings, illustrative figures and maps. Being volunteer-contributed, these media files have different amounts of descriptive metadata with varying degrees of accuracy. The DBpedia Information Extraction Framework is capable of parsing unstructured text into semi-structured data from Wikipedia and transforming it into RDF for general use, but so far it has only been used to extract encyclopedia-like content. In this paper, we describe the creation of the DBpedia Commons (DBc) dataset, which was achieved by an extension of the Extraction Framework to support knowledge extraction from Wikimedia Commons as a media repository. To our knowledge, this is the first complete RDFization of the Wikimedia Commons and the largest media metadata RDF database in the LOD cloud.

Data Cleansing Consolidation with PatchR

The Linking Open Data (LOD) initiative is turning large resources of publicly available structured data from various domains into interlinked RDF(S) facts to constitute the so-called “Web of Data”.

A semantic model for representing items in clinical trials

The specification of clinical items is a fundamental problem in the planning of a clinical trial. In this paper we present an approach that uses an RDF model in combination with a suite of OWL ontologies to formally describe the structural and conceptual composition of items. We argue that a Semantic Web data model is more flexible than traditional relational models and therefore more appropriate for specifying and managing data variables with regard to data validity and mappings to external medical terminologies.

Scheduling Refresh Queries for Keeping Results from a SPARQL Endpoint Up-to-Date (Short Paper)

Many datasets change over time. As a consequence, long-running applications that cache and repeatedly use query results obtained from a SPARQL endpoint may resubmit the queries regularly to ensure up-to-dateness of the results. While this approach may be feasible if the number of such regular refresh queries is manageable, with an increasing number of applications adopting this approach, the SPARQL endpoint may become overloaded with such refresh queries. A more scalable approach would be to use a middle-ware component at which the applications register their queries and get notified with updated query results once the results have changed. Then, this middle-ware can schedule the repeated execution of the refresh queries without overloading the endpoint. In this paper, we study the problem of scheduling refresh queries for a large number of registered queries by assuming an overload-avoiding upper bound on the length of a regular time slot available for testing refresh queries. We investigate a variety of scheduling strategies and compare them experimentally in terms of time slots needed before they recognize changes and number of changes that they miss.

Generating a linked soccer dataset

The provision of Linked Data about sporting results enables extensive statistics, while connections to further datasets allow enhanced and sophisticated analyses. Moreover, providing sports data as Linked Open Data may promote new applications, which are currently impossible due to the locked nature of todays proprietary sports databases. Though the sport domain is strongly underrepresented in the Linked Open Data Cloud. In this paper we present a dataset containing information about soccer entities crawled from heterogeneous sources and linked to related entities from the LOD cloud. To enable easy exploration and to illustrate the capabilities of the dataset a web interface is providing a structured overview and extensive statistics.