ABSTRACT Machine reading comprehension has been a research focus in natural language processing and intelligence engineering. However, there is a lack of models and datasets for the MRC tasks in the anti-terrorism domain. Moreover, current research lacks the ability to embed accurate background knowledge and provide precise answers. To address these two problems, this paper first builds a text corpus and testbed that focuses on the anti-terrorism domain in a semi-automatic manner. Then, it proposes a knowledge-based machine reading comprehension model that fuses domain-related triples from a large-scale encyclopedic knowledge base to enhance the semantics of the text. To eliminate knowledge noise that could lead to semantic deviation, this paper uses a mixed mutual attention mechanism among questions, passages, and knowledge triples to select the most relevant triples before embedding their semantics into the sentences. Experiment results indicate that the proposed approach can achieve a 70.70% EM value and an 87.91% F1 score, with a 4.23% and 3.35% improvement over existing methods, respectively.

Temporal information is pervasive and crucial in medical records and other clinical text, as it formulates the development process of medical conditions and is vital for clinical decision making. However, providing a holistic knowledge representation and reasoning framework for various time expressions in the clinical text is challenging. In order to capture complex temporal semantics in clinical text, we propose a novel Clinical Time Ontology (CTO) as an extension from OWL framework. More specifically, we identified eight time-related problems in clinical text and created 11 core temporal classes to conceptualize the fuzzy time, cyclic time, irregular time, negations and other complex aspects of clinical time. Then, we extended Allen's and TEO's temporal relations and defined the relation concept description between complex and simple time. Simultaneously, we provided a formulaic and graphical presentation of complex time and complex time relationships. We carried out empirical study on the expressiveness and usability of CTO using real-world healthcare datasets. Finally, experiment results demonstrate that CTO could faithfully represent and reason over 93% of the temporal expressions, and it can cover a wider range of time-related classes in clinical domain.

With the rapid growth of the linked data on the Web, the quality assessment of the RDF data set becomes particularly important, especially for the quality and accessibility of the linked data. In most cases, RDF data sets are shared online, leading to a high maintenance cost for the quality assessment. This also potentially pollutes Internet data. Recently blockchain technology has shown the potential in many applications. Using the blockchain storage quality assessment results can reduce the centralization of the authority, and the quality assessment results have characteristics such as non-tampering. To this end, we propose an RDF data quality assessment model in a decentralized environment, pointing out a new dimension of RDF data quality. We use the blockchain to record the data quality assessment results and design a detailed update strategy for the quality assessment results. We have implemented a system DCQA to test and verify the feasibility of the quality assessment model. The proposed method can provide users with better cost-effective results when knowledge is independently protected.