Brigitte Mathiak

TheSoz: A SKOS representation of the thesaurus for the social sciences

The Thesaurus for the Social Sciences TheSoz is a Linked Dataset in SKOS format, which serves as a crucial instrument for information retrieval based on e.g. document indexing or search term recommendation. Thesauri and similar controlled vocabularies build a linking bridge for datasets from the Linked Open Data cloud. In this article the conversion process of the TheSoz to SKOS is described including the analysis of the original dataset and its structure, the mapping to adequate SKOS classes and properties, and the technical conversion. In order to create a semantically full representation of TheSoz in SKOS, extensions based on SKOS-XL had to be defined. These allow the modeling of special relations like compound equivalences and terms with ambiguities. Additionally, mappings to other datasets and the appliance of the TheSoz are presented. Finally, limitations and modeling issues encountered during the creation process are discussed.

Identifying references to datasets in publications

Research data and publications are usually stored in separate and structurally distinct information systems. Often, links between these resources are not explicitly available which complicates the search for previous research. In this paper, we propose a pattern induction method for the detection of study references in full texts. Since these references are not specified in a standardized way and may occur inside a variety of different contexts --- i.e., captions, footnotes, or continuous text --- our algorithm is required to induce very flexible patterns. To overcome the sparse distribution of training instances, we induce patterns iteratively using a bootstrapping approach. We show that our method achieves promising results for the automatic identification of data references and is a first step towards building an integrated information system.

Web-based multi-view visualizations for aggregated statistics

With the rise of the open data movement a lot of statistical data has been made publicly available by governments, statistical offices and other organizations. First efforts to visualize are made by the data providers themselves. Data aggregators go a step beyond: they collect data from different open data repositories and make them comparable by providing data sets from different providers and showing different statistics in the same chart. Another approach is to visualize two different indicators in a scatter plot or on a map. The integration of several data sets in one graph can have several drawbacks: different scales and units are mixed, the graph gets visually cluttered and one cannot easily distinguish between different indicators. Our approach marks a combination of (1) the integration of live data from different data sources, (2) presenting different indicators in coordinated visualizations and (3) allows adding user visualizations to enrich official statistics with personal data. Each indicator gets its own visualization, which fits best for the individual indicator in case of visualization type, scale, unit etc. The different visualizations are linked, so that related items can easily be identified by using mouse over effects on data items.

Object Property Matching utilizing the Overlap between Imported Ontologies

Large scale Linked Data is often based on relational databases and thereby tends to be modeled with rich object properties, specifying the exact relationship between two objects, rather than a generic is-a or part-of relationship. We study this phenomenon on government issued statistical data, where a vested interest exists in matching such object properties for data integration. We leverage the fact that while the labeling of the properties is often heterogeneous, e.g. ex1:geo and ex2:location, they link to individuals of semantically similar code lists, e.g. country lists. State-of-the-art ontology matching tools do not use this effect and therefore tend to miss the possible correspondences. We enhance the state-of-the-art matching process by aligning the individuals of such imported ontologies separately and computing the overlap between them to improve the matching of the object properties. The matchers themselves are used as black boxes and are thus interchangeable. The new correspondences found with this method lead to an increase of recall up to 2.5 times on real world data, with only a minor loss in precision.

VIZGR - Combining Data on a Visual Level

In this paper we present a novel method to connect data on the visualization level. In general, visualizations are a dead end, when it comes to reusability. Yet, users prefer to work with visualizations as evidenced by WYSIWYG editors. To enable users to work with their data in a way that is intuitive to them, we have created Vizgr. Vizgr.com offers basic visualization methods, like graphs, tag clouds, maps and time lines. But unlike normal data visualizations, these can be re-used, connected to each other and to web sites. We offer a simple opportunity to combine diverse data structures, such as geo-locations and networks, with each other by a mouse click. In an evaluation, we found that over 85 % of the participants were able to use and understand this technology without any training or explicit instructions.

Vizgr: Linking Data in Visualizations

Working with data can be very abstract without a proper visualization. Yet, once the data is visualized, it presents a dead end, so the user has to return to the data level to make enrichments. With Vizgr (vizgr.org), we offer an elegant simplification to this workflow by giving the opportunity to enrich the data in the visualization itself. Data, e.g. statistical data, data entered by the user, from DBpedia or other data sources, can be visualized by graphs, tag clouds, on maps and in timelines. The data points can be connected with each other, with data in other visualizations and any web address, regardless of the source. It allows users to make data presentations without changing to the data level, once the data is in the system. In an evaluation, we found that over 85% of the participants were able to use and understand this technology without any training or explicit instructions.

VisNavi: citation context visualization and navigation

The process of retrieving information for literature review purposes differs from traditional web information retrieval. Literature reviews differentiate between the weightiness of the retrieved data segments. For example, citations and their accompanying information, such as cited author, citation context etc., are a very important consideration when searching for relevant information in literature. However, this information is integrated into a scientific paper, in rich interrelationships, making it very complicated for standard search systems to present and track them efficiently. In this paper, we demonstrate a system, VisNavi, in the form of a visualized star-centered approach that introduces the rich citation interrelationships to the searchers in an effective and navigational appearance.

How to accelerate the process of designing domain ontologies based on XML schemas

Domain ontologies and XML Schemas serve to describe domain data models although they follow different modelling goals. By lifting the syntactic level of XML documents and validating XML Schemas to the semantic level of OWL ontologies and their RDF representations in an automatic way, all the information located in the XML Schemas of the domains can be reused by ontology engineers and domain experts to design domain ontologies from scratch. As this approach supports all components of the XML Schema metamodel, it is ensured that unexceptionally any XML Schema can be converted into a generated ontology. As structures of generated ontologies might be quite complex, domain ontologies can be inferred automatically by means of SWRL rules. Saved time and effort can then be used to add domain-specific semantic information, not covered by underlying XML Schemas, to the domain ontologies.

Towards Finding Animal Replacement Methods

Protecting animal rights and reducing animal suffering in experimentation is a globally recognized goal in science. Yet numbers have been rising, especially in basic research. While most scientists agree that they would prefer to use less invasive methods, studies have shown that current information systems are not equipped to support the search for alternative methods. In this paper, we outline our investigations into the problem. We look into supervised and semi-supervised methods and outline ways to remedy the problem. We learned that machine assisted methods can identify the documents in question, but they are not perfect yet and in particular the question about gathering sufficient training data is unsolved.

Supporting Literature Review by Searching, Visualizing and Navigating Related Papers

Citations and their accompanying information such as citation context is a very important consideration when searching for relevant information in literature. With literature continually expanding, obtaining this information in an efficient and effective way, using a standard search tool becomes a very laborious task. In addition, scientific articles found in the literature employ rich interrelationships e.g., citation and their accompanying information making it very difficult for standard search applications to present these interrelationships to the searcher in an efficient manner. In order to alleviate this intensive task, we have designed a tool VisNavi (Visualization and Navigation) that supports scientific researchers in their literature review, in a form of a visualized star-center approach that, for the current treated paper places a citing author at its center and the richly embedded interrelationships are spun around it. The star design enables researchers to gain a clearer insight by interactively exploring these rich interrelationships along with their accompanying information. We designed a human judgment experiment to obtain a human rating on the functionality of the tool. We then cast the human rating as a reference point to improve the tools design.