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Knowledge graphs have gained increasing popularity in the last decade in science and technology. However, knowledge graphs are currently relatively simple to moderate semantic structures that are mainly a collection of factual statements. Question answering (QA) benchmarks and systems were so far mainly geared towards encyclopedic knowledge graphs such as DBpedia and Wikidata. We present SciQA a scientific QA benchmark for scholarly knowledge. The benchmark leverages the Open Research Knowledge Graph (ORKG) which includes almost 170,000 resources describing research contributions of almost 15,000 scholarly articles from 709 research fields. Following a bottom-up methodology, we first manually developed a set of 100 complex questions that can be answered using this knowledge graph. Furthermore, we devised eight question templates with which we automatically generated further 2465 questions, that can also be answered with the ORKG. The questions cover a range of research fields and question types and are translated into corresponding SPARQL queries over the ORKG. Based on two preliminary evaluations, we show that the resulting SciQA benchmark represents a challenging task for next-generation QA systems. This task is part of the open competitions at the 22nd International Semantic Web Conference 2023 as the Scholarly Question Answering over Linked Data (QALD) Challenge.

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The paper proposes a new complex solution for automatic analysis and terms identification in regulatory and technical documentation (RTD). The task of terms identification in the documentation is one of the key issues in the digitalization dealing with the design and construction of buildings and structures. At the moment, the search and verification of RTD requirements is performed manually, which entails a significant number of errors. Automation of such tasks will significantly improve the quality of computer-aided design. The developed algorithm is based on such methods of natural language analysis as tokenization, search for lemmas and stems, analysis of stop words and word embeddings applied to tokens and phrases, part-of-speech tagging, syntactic annotation, etc. The experiments on the automatic extraction of terms from regulatory documents have shown great prospects of the proposed algorithm and its application for building knowledge graphs in the design domain. The recognition accuracy for 202 documents selected by experts was 79 % for the coincidence of names and 37 % for the coincidence of term identifiers. This is a comparable result with the known approaches to solving this problem. The results of the work can be used in computer-aided design systems based on Building information modeling (BIM) models, as well as to automate the examination of design documentation.