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.

Entity and relation linking are the core tasks in knowledge base question answering (KBQA). They connect natural language questions with triples in the knowledge base. In most studies, researchers perform these two tasks independently, which ignores the interplay between the entity and relation linking. To address the above problems, some researchers have proposed a framework for joint entity and relation linking based on feature joint and multi-attention. In this paper, based on their method, we offer a candidate set generation expansion model to improve the coverage of correct candidate words and to ensure that the correct disambiguation objects exist in the candidate list as much as possible. Our framework first uses the initial relation candidate set to obtain the entity nodes in the knowledge graph related to this relation. Second, the filtering rule filters out the less-relevant entity candidates to obtain the expanded entity candidate set. Third, the relation nodes directly connected to the nodes in the expanded entity candidate set are added to the initial relation candidate set. Finally, a ranking algorithm filters out the less-relevant relation candidates to obtain the expanded relation candidate set. An empirical study shows that this model improves the recall and correctness of the entity and relation linking for KBQA. The candidate set expansion method based on entity–relation interaction proposed in this paper is highly portable and scalable. The method in this paper considers the connections between question subgraphs in knowledge graphs and provides new ideas for the candidate set expansion.

Large-scale language models have demonstrated robust language understanding and generation capabilities, enabling them to tackle various complex natural language processing tasks. However, for domain-specific tasks like healthcare that require specialized expertise, relying solely on large language models for dialogue generation is insufficient. Moreover, this paper aims to improve the performance of models in medical conversations and enhance the interpretability of the intermediary processes. It argues that leveraging diverse knowledge and agent-based architecture can significantly address the challenges. We introduce an agent-based adaptive medical dialogue service (AMDS) for personalized healthcare. This service utilizes large language models as its cognitive core and integrates medical knowledge extracted from knowledge graph and process knowledge. Extensive experiments show that AMDS outperforms baselines in multi-turn medical dialogue generation tasks.

In the development of the semantic web, blank nodes (also called anonymous nodes or anonymous resources) are a significant factor in the data redundancy. Blank nodes are RDF nodes of the graphs which are not URI identifies. And they are convenient for those resources which are complex and not URI identifies but have property structures. It is right because blank nodes have no URI identifies that different people may create different blank nodes for the same anonymous resources which caused the huge information redundancy. A method is proposed in this paper, first, according to the features of blank nodes, detect the blank nodes, and then, dictionary the triples of the RDF graph, for blank nodes, expressed in negative, which is convenient to query the triples containing blank nodes. Then according to the mining rules of linked data, all the S-Models (The triples set which uses as subject), O-Model (The triples set which use o as object) and B-Blanks (Blank node collection) of the RDF graph can be constructed. Traverse the B - Blanks collection, and remove the redundancy of SB-Model (The triples set which use blank node b as subject) and OB-Model (The triples set which use blank node b as object). Experimental results show that the proposed blank node detection method is very efficient. And the efficiency of compression and storage is improved based on the processed RDF file. Experiments show that the use of dictionary-based correlation data to detect and remove the blank node greatly improves the operating efficiency. In this paper, the detection of the blank nodes based on the correlation data is only based on the representation of the blank nodes in the triplet. It can not detect the blank nodes for the data in the common RDF chart format. The blank nodes based on the correlation data For now, only for the simple RDF data model, the SOBM algorithm proposed in this paper can not remove the blank nodes well. So the next work mainly includes: (1) Perfecting the method of detecting blank nodes to better support RDF data format of various structures; (2) Perfecting the algorithm of removing blank nodes to make it better to deal with Blank node chain problem.