We have developed Metis, a question-answering system that finds an answer by matching a question graph with the knowledge graphs. The question graph is obtained as a result of semantic analysis of a question sentence, the knowledge graphs are similarly analyzed from knowledge sentences retrieved from a database using keywords extracted from the question sentence. In retrieving such knowledge se...

In this paper, we give an overview of the framework of graph transformation units which provides syntactic and semantic means for analyzing, modeling, and structuring all kinds of graph processing and graph transformation.

We have developed a question-answering system Metis with natural-language interface. Metis generates the answer to a question by comparing the semantic graph of the question sentence with sentences discovered on the Internet as knowledge source. Specifically, we first get a set of semantic frames for the question sentence, as the output from a semantic analysis system, SAGE. Then we extract sev...

By taking the semantic object parsing task as an exemplar application scenario, we propose the Graph Long Short-Term Memory (Graph LSTM) network, which is the generalization of LSTM from sequential data or multidimensional data to general graph-structured data. Particularly, instead of evenly and fixedly dividing an image to pixels or patches in existing multi-dimensional LSTM structures (e.g.,...

The EU-funded project Lynx focuses on the creation of a knowledge graph for legal domain (Legal Knowledge Graph, LKG) and its use semantic processing, analysis enrichment documents from domain. This article describes cases covered in project, entire developed platform services that operate documents.

Graph databases and NoSQL databases, two very important topics in Big Data, have gained popularity in recent years due to their unique characteristics in their horizontally scale-out capability and flexible schema or schema-free design. The recent release of OWL-DBC , an adaptor between Oracle Spatial and Graph 2 and the TrOWL reasoner [2, 1], has built a tight integration between one of the le...

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Artificial intelligence relies on the use of semantic technologies to represent shared world humanity. The story how this came be is exemplified by Semantic Web standards Facebook “Like” button. In case button, a decentralized and open was used fuel accumulation personal data for advertising throughout entire Web. advent “Like button” shortly followed Google’s creation Google Knowledge Graph, p...

Recently, link prediction algorithms based on neural embeddings have gained tremendous popularity in the Semantic Web community, and are extensively used for knowledge graph completion. While algorithmic advances strongly focused efficient ways of learning embeddings, fewer attention has been drawn to different their performance robustness can be evaluated. In this work we propose an open-sourc...

The research presented in this paper builds on previous work that lead to the definition of a family of semantic relatedness algorithms that compute a proximity given as input a pair of concept labels. The algorithms depends on a semantic graph, provided as RDF data, and on a particular set of weights assigned to the properties of RDF statements (types of arcs in the RDF graph). The current res...