With the continuous growth of the Linked Data Cloud, adequate methods to efficiently explore semantic data are increasingly required. Faceted browsing is an established technique for exploratory search. Users are given an overview of a collection's attributes that can be used to progressively refine their filter criteria and delve into the data. However, manual facet predefinition is often inappropriate for at least three reasons: Firstly, heterogeneous and large scale knowledge graphs offer a huge number of possible facets. Choosing among them may be virtually impossible without algorithmic support. Secondly, knowledge graphs are often constantly changing, hence, predefinitions need to be redone or adapted. Finally, facets are generally applied to only a subset of resources (e.g., search query results). Thus, they have to match this subset and not the knowledge graph as a whole. Precomputing facets for each possible subset is impractical except for very small graphs. We present our approach for automatic facet generation and selection over knowledge graphs. We propose methods for (1) candidate facet generation and (2) facet ranking, based on metrics that both judge a facet in isolation as well as in relation to others. We integrate those methods in an overall system workflow that also explores indirect facets, before we present the results of an initial evaluation.

Biodiversity is the assortment of life on earth covering evolutionary, ecological, biological, and social forms. To preserve life in all its variety and richness, it is imperative to monitor the current state of biodiversity and its change over time and to understand the forces driving it. This need has resulted in numerous works being published in this field. With this, a large amount of textual data (publications) and metadata (e.g. dataset description) has been generated. To support the management and analysis of these data, two techniques from computer science are of interest, namely Named Entity Recognition (NER) and Relation Extraction (RE). While the former enables better content discovery and understanding, the latter fosters the analysis by detecting connections between entities and, thus, allows us to draw conclusions and answer relevant domain-specific questions. To automatically predict entities and their relations, machine/deep learning techniques could be used. The training and evaluation of those techniques require labelled corpora. In this paper, we present two gold-standard corpora for Named Entity Recognition (NER) and Relation Extraction (RE) generated from biodiversity datasets metadata and abstracts that can be used as evaluation benchmarks for the development of new computer-supported tools that require machine learning or deep learning techniques. These corpora are manually labelled and verified by biodiversity experts. In addition, we explain the detailed steps of constructing these datasets. Moreover, we demonstrate the underlying ontology for the classes and relations used to annotate such corpora.

Faceted Browsing is a widespread approach for exploratory search. Without requiring an in-depth knowledge of the domain, users can narrow down a resource set until it fits their need. An increasing amount of data is published either directly as Linked Data or is at least annotated using concepts from the Linked Data Cloud. This allows identifying commonalities and differences among resources beyond the comparison of mere string representations of metadata. As the size of data repositories increases, so does the range of covered domains and the number of properties that can provide the basis for a new facet. Manually predefining suitable facet collections becomes impractical. We present our initial work on automatically creating suitable facets for a semantically annotated set of resources. In particular, we address two problems arising with automatic facet generation: (1) Which facets are applicable to the current set of resources and (2) which reasonably sized subset provides the best support to users?