Ecological research produces a tremendous amount of data, but the diversity in scales and topics covered and the ways in which studies are carried out result in large numbers of small, idiosyncratic data sets using heterogeneous terminologies. Such heterogeneity can be attributed, in part, to a lack of standards for acquiring, organizing and describing data. Here, we propose a terminological resource, a Thesaurus Of Plant characteristics (TOP), whose aim is to harmonize and formalize concepts for plant characteristics widely used in ecology. 2. TOP concentrates on two types of plant characteristics: traits and environmental associations. It builds on previous initiatives for several aspects: 1) characteristics are designed following the entity-quality model (a characteristic is modelled as the “Quality” of an “Entity” ”) used in the context of Open Biological Ontologies; 2) whenever possible, the Entities and Qualities are taken from existing terminology standards, mainly the PO (Plant Ontology) and PATO (Phenotypic Quality Ontology) ontologies, and 3) whenever a characteristic already has a definition, if appropriate, it is reused and referenced. The development of TOP, which complies with semantic web principles, was carried out through the involvement of experts from both the ecology and the semantics research communities. Regular updates of TOP are planned, based on community feedback and involvement. 3. TOP provides names, definitions, units, synonyms and related terms for about 700 plant characteristics. TOP is available online (www.top-thesaurus.org), and can be browsed using an alphabetical list of characteristics, a hierarchical tree of characteristics, a faceted and a free-text search, and through an Application Programming Interface. 4. Harmonizing definitions of concepts, as proposed by TOP, forms the basis for better integration of data across heterogeneous data sets and terminologies, thereby increasing the potential for data reuse. It also allows enhanced scientific synthesis. TOP therefore has the potential to improve research and communication not only within the field of ecology, but also in related fields with interest in plant functioning and distribution

Summary1. The integrative research field of biodiversity-ecosystem functioning (BEF) requires close collaboration between researchers from different disciplines working on different scales in time, space as well as taxon resolution. Data can describe anything from abiotic ecosystem components, to organisms, parts of organisms, genetic information or element stocks and flows. Researchers prefer the convenience of spreadsheets for data preparation, which can lead to isolated data sets that are diverse in structure and follow diverging naming conventions. 2. BEFdata (https://github.com/befdata/befdata) is a new, open source web platform for the upload, validation and storage of data from a formatted Excel workbook. Metadata can be downloaded in Ecological Metadata Language (EML). BEFdata allows the harmonization of naming conventions by generating category lists from the primary data, which can be reviewed and managed via the Excel workbook or directly on the platform. BEFdata provides a secure environment during ongoing analysis; project members can only access primary data from other researchers after the acceptance of a data request. 3. Due to its generic database schema, BEFdata platforms can be used for any research domain working with tabular data. It supports the compilation of coherent data sets at the level of the primary data, allowing researchers to explicitly model correlation structures across data sets for synthesis. The EML export enables efficient publishing of data in global repositories.

Topic Maps are the international industry standard for semantic information integration. Appropriate means for Topic Map exchange are crucial for its success as integration technology. Topic Map exchange bases on the governing Subject Equality decision approach, the decision whether two Subject Proxies indicate identical Subjects. This paper discusses the 'absence of shared vocabularies' in the context of these decisions. Thereby, a differentiation between Referential and Structuralist Subject Equality decision approaches is introduced. All existing approaches to Topic Map exchange base on the TMDM. This implies a Referential Subject Equality decision approach and bound to a concrete Subject Map Disclosure (SMD) ontology and Subject Map (SM) vocabulary. This paper introduces a Structuralist Subject Equality decision approach which is called SIM. It allows the exchange of Topic Maps in the absence of a shared SM ontology and SM vocabulary. The challenge in an exampleWithin a cooking peer-to-peer network remote peers exchange recipes documented as Topic Maps 1 . To collect information, peers send Topics which represent the Subjects of interest to remote peers. In the cooking network a Subject might be 'roasted lamb loin'. The remote peers check the availability of information about this Subject and respond with an according Topic Map Fragment. Afterwards, the requesting peer integrates all remote recipes about roasting lamb loins into its local recipe collection.This works fine if all peers made agreements about how to describe lamb cuts correctly. What happens if a remote peer uses the term lamb saddle instead? Or roasted lamb leg chops? The resulting meals are identical, but the requesting peers will never receive their recipes from distance. This shows that two critical points arise, if semantic agreements are not made by all peers logging into the network: How to request knowledge from remote peers if shared vocabularies are not available? How to integrate (merge) the received information into the local Topic Map?The solution proposed in this paper allows peers to interact in networks without having the overhead of centrally enforced vocabularies. Our solution detects 1 To avoid ambiguities all terminology concerning Topic Map Technologies is capitalised.

Since Porter’s work on competitive strategies in the 1980s, the concept of competitive intelligence has become part of the management mainstream. Currently, two big shifts are challenging the state of the art. On the one hand there is the rise of the ubiquitous servicification in all industries which makes the existing methods for product-oriented industries outdated. On the other hand there is the rise of big data (volume, velocity, variety). Both shifts are driving the development towards interactive competitive intelligence systems. The authors introduced a framework for interactive competitive intelligence systems which overcome the sequential water-fall processes which are current CI practice. In the introduced framework they combine the concept of Key Intelligence Topics (KIT) with the concept of (boundary) objects from interaction theory. The authors demonstrated with examples within their “IP Industry Base” how interactive CI for service-oriented sectors can be implemented. The resulting vector-based representations of the companies’ service profiles allow the user to visualize, compare, retrieve and analyse companies in a constructive and scalable way.

Topic Maps are means for representing sophisticated, conceptual indexes of any information collection for the purpose of semantic information integration. To properly fulfil this purpose, the generation of Topic Maps has to base on a solid theory. This paper proposes the Observation Principle as the theoretical fundament of a future scientific discipline Topic Maps Engineering. SemanticTalk generates sophisticated, conceptual indexes of speech streams in real-time. Reflecting the Observation Principle, this paper describes how these indexes are created, how they are represented as Topic Maps and how they can be used for semantic information integration purposes.