Poster & Demo

Ontologies and vocabularies are commonly used within Linked data cloud for typing individual objects. Due to their semantics, domain ontologies are used as a source of knowledge in diverse applications. Data mining phases are commonly enhanced by various forms of background knowledge, besides other things, to explain discovered patterns. In this poster paper we propose how to enhance a selection of proper explanations for data mining results based on ontologies. The approach of offering relevant explanations is described and demonstrated on an example. The poster paper is wrapped up with discussions, conclusions and future work.

This paper introduces the main viewpoints of a novel approach concerning onto-logical aspects of Medical Decision-Making Systems (MDMS) development. A Feed Forward Artificial Neural Networks (fANNs) based MDMS on has been extensively tested using real world patients’ clinical data in the field of Pulmonary Diseases (PDs) and has been developed as an application for Android Devices.

According to the Semantic web, future of internet would be a complex and huge global knowledge base, in which the Knowledge graphs can play a significant role in developing this emerging technology. A Knowledge graph is a collection of entities semantically connected which makes a contribution to tasks of both academia and industry.

Physician Review Websites allow users to subjectively evaluate self-experienced health services. As these are private impressions, users provide deep insights into their lives while sharing their experiences after a visit to their doctors. Thereby, users accidentally disclose information on the Internet, what poses a serious threat to users' privacy and may lead to unforeseeable consequences.

The demonstrator provides a novel semantic search approach for a business-to-business (B2B) platform. Users can search through product and ser-vice catalogues, manage orders, and trace the progress of order fulfillment. A network of ontologies annotates the product catalogues and related knowledge from multiple domains. The latter resides in a Triple Store.

Navigating and comprehending the legal text of web shops’ general terms and conditions is a burden for consumers. This poster abstract describes work-in-progress to design a visualization environment specifically addressing the needs of online shoppers. This environment highlights keywords of relevance (e.g., returning items), provides visual overview, and supports comparison of two texts.

In this poster, we will present attendees how the recent state-of-the-art Semantic Web tool SANSA could be used to tackle blockchain specific challenges. In particular, the poster will focus on the use case of CryptoKitties: a popular Ethereum-based online game where users are able to trade virtual kitty pets in a secure way.

Biological and medical researchers explore the mechanisms of living organisms and tend to gain a better understanding of underlying fundamental biological processes of life. To tackle such complex tasks they constantly need to gather and accumulate new knowledge by performing experiments and studying scientific literature.

Ontologies and vocabularies are commonly used within Linked data cloud for typing individual objects. Due to their semantics, domain ontologies are used as a source of knowledge in diverse applications. Data mining phases are commonly enhanced by various forms of background knowledge, besides other things, to explain discovered patterns.

We present a practical method for explaining deep learning- based text mining with ontology-based information. Our approach uses the recently proposed OntoSenticNet ontology for sentiment mining, and consists of a composite deep learning classifier for sentiment mining, en- dowed with an ontology-driven attention module. The attention module analyzes the attention the neural network pays to semantic labels as- signed to bigrams in input texts.