Formalising Hypothesis Virtues in Knowledge Graphs: A General Theoretical Framework and its Validation in Literature-Based Discovery Experiments

We introduce an approach to discovery informatics that uses so called knowledge graphs as the essential representation structure. Knowledge graph is an umbrella term that subsumes various approaches to tractable representation of large volumes of loosely structured knowledge in a graph form. It has been used primarily in the Web and Linked Open Data contexts, but is applicable to any other area dealing with knowledge representation. In the perspective of our approach motivated by the challenges of discovery informatics, knowledge graphs correspond to hypotheses. We present a framework for formalising so called hypothesis virtues within knowledge graphs. The framework is based on a classic work in philosophy of science, and naturally progresses from mostly informative foundational notions to actionable specifications of measures corresponding to particular virtues. These measures can consequently be used to determine refined sub-sets of knowledge graphs that have large relative potential for making discoveries. We validate the proposed framework by experiments in literature-based discovery. The experiments have demonstrated the utility of our work and its superiority w.r.t. related approaches.

A Methodology for Empirical Analysis of LOD Datasets

CoCoE stands for Complexity, Coherence and Entropy, and presents an extensible methodology for empirical analysis of Linked Open Data (i.e., RDF graphs). CoCoE can offer answers to questions like: Is dataset A better than B for knowledge discovery since it is more complex and informative?, Is dataset X better than Y for simple value lookups due its flatter structure?, etc. In order to address such questions, we introduce a set of well-founded measures based on complementary notions from distributional semantics, network analysis and information theory. These measures are part of a specific implementation of the CoCoE methodology that is available for download. Last but not least, we illustrate CoCoE by its application to selected biomedical RDF datasets.

Distributional Framework for Emergent Knowledge Acquisition and its Application to Automated Document Annotation

The paper introduces a framework for representation and acquisition of knowledge emerging from large samples of textual data. We utilise a tensor-based, distributional representation of simple statements extracted from text, and show how one can use the representation to infer emergent knowledge patterns from the textual data in an unsupervised manner. Examples of the patterns we investigate in the paper are implicit term relationships or conjunctive IF-THEN rules. To evaluate the practical relevance of our approach, we apply it to annotation of life science articles with terms from MeSH (a controlled biomedical vocabulary and thesaurus).