Norman Heino

An overview of the BIOASQ large-scale biomedical semantic indexing and question answering competition

BackgroundThis article provides an overview of the first BioASQ challenge, a competition on large-scale biomedical semantic indexing and question answering (QA), which took place between March and September 2013. BioASQ assesses the ability of systems to semantically index very large numbers of biomedical scientific articles, and to return concise and user-understandable answers to given natural language questions by combining information from biomedical articles and ontologies.ResultsThe 2013 BioASQ competition comprised two tasks, Task 1a and Task 1b. In Task 1a participants were asked to automatically annotate new PubMed documents with MeSH headings. Twelve teams participated in Task 1a, with a total of 46 system runs submitted, and one of the teams performing consistently better than the MTI indexer used by NLM to suggest MeSH headings to curators. Task 1b used benchmark datasets containing 29 development and 282 test English questions, along with gold standard (reference) answers, prepared by a team of biomedical experts from around Europe and participants had to automatically produce answers. Three teams participated in Task 1b, with 11 system runs. The BioASQ infrastructure, including benchmark datasets, evaluation mechanisms, and the results of the participants and baseline methods, is publicly available.ConclusionsA publicly available evaluation infrastructure for biomedical semantic indexing and QA has been developed, which includes benchmark datasets, and can be used to evaluate systems that: assign MeSH headings to published articles or to English questions; retrieve relevant RDF triples from ontologies, relevant articles and snippets from PubMed Central; produce “exact” and paragraph-sized “ideal” answers (summaries). The results of the systems that participated in the 2013 BioASQ competition are promising. In Task 1a one of the systems performed consistently better from the NLM’s MTI indexer. In Task 1b the systems received high scores in the manual evaluation of the “ideal” answers; hence, they produced high quality summaries as answers. Overall, BioASQ helped obtain a unified view of how techniques from text classification, semantic indexing, document and passage retrieval, question answering, and text summarization can be combined to allow biomedical experts to obtain concise, user-understandable answers to questions reflecting their real information needs.

Developing Semantic Web Applications with the OntoWiki Framework

In this paper, we introduce the OntoWiki Application Framework for developing Semantic Web applications with a strong emphasis on collaboration. After presenting OntoWiki as our main show case for the framework, we give both an architectural overview and a detailed view on the included components.We conclude this paper with a presentation of different use cases where the framework was strongly involved.

RDFS reasoning on massively parallel hardware

Recent developments in hardware have shown an increase in parallelism as opposed to clock rates. In order to fully exploit these new avenues of performance improvement, computationally expensive workloads have to be expressed in a way that allows for fine-grained parallelism. In this paper, we address the problem of describing RDFS entailment in such a way. Different from previous work on parallel RDFS reasoning, we assume a shared memory architecture. We analyze the problem of duplicates that naturally occur in RDFS reasoning and develop strategies towards its mitigation, exploiting all levels of our architecture. We implement and evaluate our approach on two real-world datasets and study its performance characteristics on different levels of parallelization. We conclude that RDFS entailment lends itself well to parallelization but can benefit even more from careful optimizations that take into account intricacies of modern parallel hardware.

RDFauthor: employing RDFa for collaborative knowledge engineering

In this paper we present RDFauthor, an approach for authoring information that adheres to the RDF data model. RDFauthor completely hides syntax as well as RDF and ontology data model difficulties from end users and allows to edit information on arbitrary RDFa-annotated web pages. RDFauthor extends RDFa with representations for provenance and update endpoint information. RDFauthor is based on extracting RDF triples from RDFa annotations and transforming the RDFa-annotated HTML view into an editable form by using a set of authoring widgets. As a result, every RDFa-annotated web page can be made easily writeable, even if information originates from different sources.

SCMS: semantifying content management systems

The migration to the Semantic Web requires from CMS that they integrate human- and machine-readable data to support their seamless integration into the Semantic Web. Yet, there is still a blatant need for frameworks that can be easily integrated into CMS and allow to transform their content into machine-readable knowledge with high accuracy. In this paper, we describe the SCMS (Semantic Content Management Systems) framework, whose main goals are the extraction of knowledge from unstructured data in any CMS and the integration of the extracted knowledge into the same CMS. Our framework integrates a highly accurate knowledge extraction pipeline. In addition, it relies on the RDF and HTTP standards for communication and can thus be integrated in virtually any CMS. We present how our framework is being used in the energy sector. We also evaluate our approach and show that our framework outperforms even commercial software by reaching up to 96% F-score.

EvoPat - pattern-based evolution and refactoring of RDF knowledge bases

Facilitating the seamless evolution of RDF knowledge bases on the Semantic Web presents still a major challenge. In this work we devise EvoPat - a pattern-based approach for the evolution and refactoring of knowledge bases. The approach is based on the definition of basic evolution patterns, which are represented declaratively and can capture simple evolution and refactoring operations on both data and schema levels. For more advanced and domain-specific evolution and refactorings, several simple evolution patterns can be combined into a compound one. We performed a comprehensive survey of possible evolution patterns with a combinatorial analysis of all possible before/after combinations, resulting in an extensive catalog of usable evolution patterns. Our approach was implemented as an extension for the OntoWiki semantic collaboration platform and framework.

Managing Web Content Using Linked Data Principles - Combining Semantic Structure with Dynamic Content Syndication

Despite the success of the emerging Linked Data Web, offering content in a machine-processable way and -- at the same time -- as a traditional Web site is still not a trivial task. In this paper, we present the OntoWiki-CMS -- an extension to the collaborative knowledge engineering toolkit OntoWiki for managing semantically enriched Web content. OntoWiki-CMS is based on OntoWiki for the collaborative authoring of semantically enriched Web content, vocabularies and taxonomies for the semantic structuring of the Web content and the OntoWiki Site Extension, a template and dynamic syndication system for representing the semantically enriched content as a Web site and the dynamic integration of supplementary content. OntoWiki-CMS facilitates and integrates existing content-specific content management strategies (such as blogs, bibliographic repositories or social networks). OntoWiki-CMS helps to balance between the creation of rich, stable semantic structures and the participatory involvement of a potentially large editor and contributor community. As a result semantic structuring of the Web content facilitates better search, browsing and exploration as we demonstrate with a use case.

When to Reach for the Cloud: Using Parallel Hardware for Link Discovery

With the ever-growing amount of RDF data available across the Web, the discovery of links between datasets and deduplication of resources within knowledge bases have become tasks of crucial importance. Over the last years, several link discovery approaches have been developed to tackle the runtime and complexity problems that are intrinsic to link discovery. Yet, so far, little attention has been paid to the management of hardware resources for the execution of link discovery tasks. This paper addresses this research gap by investigating the efficient use of hardware resources for link discovery. We implement the \(\mathcal{HR}^3\) approach for three different parallel processing paradigms including the use of GPUs and MapReduce platforms. We also perform a thorough performance comparison for these implementations. Our results show that certain tasks that appear to require cloud computing techniques can actually be accomplished using standard parallel hardware. Moreover, our evaluation provides break-even points that can serve as guidelines for deciding on when to use which hardware for link discovery.

OntoWiki mobile: knowledge management in your pocket

As comparatively powerful mobile computing devices are becoming more common, mobile web applications have started gaining in popularity. In this paper we present an approach for a mobile semantic collaboration platform based on the OntoWiki framework. It allows users to collect instance data, refine the structure of knowledge bases and browse data using hierarchical or faceted navigation on-the-go even without a present data connection. A crucial part of OntoWiki Mobile is the advanced replication and conflict resolution for RDF content. The approach for conflict resolution is based on a combination of distributed revision control strategies and the EvoPat method for data evolution and ontology refactoring. OntoWiki mobile is available as an HTML5 Web application and can be used in scenarios where semantically rich information has to be collected in field-conditions such as during biodiversity expeditions to remote areas.

Making the semantic data web easily writeable with RDFauthor

In this demo we present RDFauthor, an approach for authoring information that adheres to the RDF data model. RDFauthor completely hides syntax as well as RDF and ontology data model difficulties from end users and allows to edit information on arbitrary RDFa-annotated web pages. RDFauthor is based on extracting RDF triples from RDFa-annoted Web pages and transforming the RDFa-annotated HTML view into an editable form by using a set of authoring widgets. As a result, every RDFa-annotated web page can be made writeable, even if information originates from different sources.