0000000000178647
AUTHOR
Paulis Barzdins
Pini Language and PiniTree Ontology Editor: Annotation and Verbalisation for Atomised Journalism
We present a new ontology language Pini and the PiniTree ontology editor supporting it. Despite Pini language bearing lot of similarities with RDF, UML class diagrams, Property Graphs and their frontends like Google Knowledge Graph and Protege, it is a more expressive language enabling FrameNet-style natural language annotation for Atomised journalism use case.
Metamodel specialization based DSL for DL lifecycle data management
A new Domain Specific Language (DSL) based approach to Deep Learning (DL) lifecycle data management (LDM) is presented: a very simple but universal DL LDM tool, still usable in practice (called Core tool); and an advanced extension mechanism, that converts the Core tool into a DSL tool building framework for DL LDM tasks. The method used is based on the metamodel specialisation approach for DSL modeling tools introduced by authors.
Towards DSL for DL Lifecycle Data Management
A new method based on Domain Specific Language (DSL) approach to Deep Learning (DL) lifecycle data management tool support is presented: a very simple DL lifecycle data management tool, which however is usable in practice (it will be called Core tool) and a very advanced extension mechanism which in fact converts the Core tool into domain specific tool (DSL tool) building framework for DL lifecycle data management tasks. The extension mechanism will be based on the metamodel specialization approach to DSL modeling tools introduced by authors. The main idea of metamodel specialization is that we, at first, define the Universal Metamodel (UMM) for a domain and then for each use case define a …
RDF* Graph Database as Interlingua for the TextWorld Challenge
This paper briefly describes the top-scoring submission to the First TextWorld Problems: A Reinforcement and Language Learning Challenge. To alleviate the partial observability problem, characteristic to the TextWorld games, we split the Agent into two independent components: Observer and Actor, communicating only via the Interlingua of the RDF* graph database. The RDF* graph database serves as the “world model” memory incrementally updated by the Observer via FrameNet informed Natural Language Understanding techniques and is used by the Actor for the efficient exploration and planning of the game Action sequences. We find that the deep-learning approach works best for the Observer componen…