Date: March 25, 2019

Speaker #1: Hans-Georg Fill, University of Fribourg, Switzerland

Abstract

Knowledge Blockchains revert to blockchain technologies for the decentralized storage of enterprise models in a tamper-resistant and irrevocable manner. Thereby, cryptographic technologies such as digital signatures and cryptographic hash functions are used for enabling the transparent monitoring of knowledge, the tracking of provenance, ownership, and relationships of knowledge in an organization, the establishing of delegation schemes for knowledge management, and for ensuring the existence of patterns in models via zero-knowledge proofs. In a recent position paper, it has been proposed to apply these concepts to ontologies. In this way, chains of trust could be added to decentrally stored ontologies for determining for example who has contributed which parts of an ontology and who is allowed to delegate rights to other persons. Furthermore, the use of zero-knowledge proofs permits to prove that certain concepts are contained in an ontology without revealing the actual content of the ontology, which may be beneficial for processing information in privacy-sensitive environments. The talk will conclude with a discussion of the current limitations and an outlook on further research.

Presentation: Slides are available here.

Speaker #2: Saeedeh Shekarpour, University of Dayton, OH

Abstract

Although there is an emerging trend towards generating embeddings for primarily unstructured data and, recently, for structured data, no systematic suite for measuring the quality of embeddings has been proposed yet. This deficiency is further sensed with respect to embeddings generated for structured data because there are no concrete evaluation metrics measuring the quality of the encoded structure as well as semantic patterns in the embedding space. In this paper, we introduce a framework containing three distinct tasks concerned with the individual aspects of ontological concepts: (i) the categorization aspect, (ii) the hierarchical aspect, and (iii) the relational aspect. Then, in the scope of each task, a number of intrinsic metrics are proposed for evaluating the quality of the embeddings. Furthermore, w.r.t. this framework, multiple experimental studies were run to compare the quality of the available embedding models. Employing this framework in future research can reduce misjudgment and provide greater insight about quality comparisons of embeddings for ontological concepts. We positioned our sampled data and code at https://github.com/alshargi/Concept2vec under GNU General Public License v3.0.