This paper introduces a novel framework for context-driven need detection in home care, with a strong emphasis on the role of Bidirectional Encoder Representations from Transformers (BERT) in providing nuanced contextual understanding. Our approach leverages BERT to process unstructured textual communications, which then guides probabilistic reasoning through the OwlMEBN Jena API, while dynamically enriching a knowledge graph represented using the Web Ontology Language (OWL). This process integrates multi-modal data via an adaptive weighted fusion of wearable sensor data, caregiver observations, and textual communications. Rigorous experiments, in a simulated environment utilizing real-world data from the e-SAAD platform, demonstrate a significant performance improvement, achieving an Area Under the Receiver Operating Characteristic curve (AUROC) of 0.93 and an F1-score of 0.87, significantly outperforming baseline methods. This enhanced performance underscores BERT’s crucial role in enabling more accurate and personalized home care services by providing deep contextualization of beneficiary needs, while the OwlMEBN framework manages the uncertainty and provides structured probabilistic inferences.

Social media platforms serve as massive repositories of textual data, reflecting diverse human interactions and preferences. However, the unstructured nature of this content poses significant challenges for extracting semantically rich insights. This paper introduces a novel methodology for the automated construction of knowledge graphs (KGs) from social media discourse, specifically focusing on Twitter tweets. Our approach synergistically integrates large language models (LLMs), specifically a fine-tuned BERT model, with an ontology-driven framework. First, we define a detailed ontology of online communication concepts. The pre-trained BERT model is then fine-tuned using a multi-task learning approach on a curated dataset of anonymized and segmented Twitter discussions, thereby aligning its semantic representations with the predefined ontology. The fine-tuned LLM is leveraged for several critical tasks including entity and relation extraction, sentiment analysis, intention classification and the inference of contextual information and discussion styles. Furthermore, a mechanism is introduced to infer inter-user relationships and shared interests using graph neural networks (GNNs), analyzing patterns in interaction and language use. This multi-faceted extracted and inferred data is subsequently employed to build a knowledge graph, stored and queried via the Neo4j graph database management system. This study presents several contributions such as the integration of a ontology with an LLM method and the innovative user relationship and shared interest extraction using graph neural networks. The proposed methodology was rigorously evaluated using a real-world dataset of Twitter discussions, showcasing its ability to capture semantic content, and elucidate inter-user relationships effectively and revealing shared interests of the users involved. Furthermore, an ablation study is included which further demonstrates each of the method component contribution and demonstrates the importance of such integrations. Our findings highlight the potential for various downstream applications such as in community structure analysis and sentiment analysis to improve information management within online social networks.

This paper introduces OntoMed-KGTransformer, a novel framework for integrating unstructured clinical text with structured medical knowledge graphs (KGs) and ontologies (specifically, UMLS). Our approach addresses key limitations in clinical decision support by combining: (1) an ontology-guided hierarchical attention mechanism that enforces domain-specific semantic constraints within a Transformer architecture; (2) a dynamic graph-centric tokenization strategy that bridges textual and KG representations; and (3) a dual-encoder architecture with ontology-driven contrastive learning. Evaluations on the MIMIC-III dataset and a custom Hetionet-derived KG demonstrate significant improvements over state-of-the-art baselines (e.g., KG-BERT, BioBERT) in diagnostic prediction, with a 15% relative increase in F1-score. Furthermore, the framework enhances interpretability through the extraction of clinically relevant knowledge paths, promoting trust and transparency in clinical decision-making.

Reasoning on an ontology is presently limited to the logical one. However, in the case of inconsistent knowledge or unreliable and incomplete information, it is difficult for a system to make a decision or measure the degree of truth of a hypothesis. Several approaches have been made and focused mainly on how to represent probabilistic information in ontologies and then use them for reasoning. Although, this requires a great effort for systems already designed and based on ontological or Bayesian knowledge bases. In these cases the users must integrate the probabilistic information of Bayesian networks (BN) in ontology manually. We propose a new approach to integrate the BN (more particularly Object-Oriented Bayesian Networks (OOBn)) with OWL by providing a Jena API (Application Programming Interface). OOBNs is an extension of the standard BN using the paradigm object. Our approach (i) uses the BN knowledge base for ontological enrichment, (ii) allows the adjustment of BN structures through structural and parametric learning based on ontological knowledge bases, (iii) integrate probabilistic information into SPARQL queries. In addition, we propose a solution in which we use a selection of instances according to the needs of the user described by a set of SPARQL requests.

Several areas have evolved with the evolution of technology. Telemedicine and home care are one of them. While there are many studies on home care, the overall problem of decision-making is not sufficiently addressed. In previous research, we proposed an ontological-based intervention system. Despite its abilities, reasoning on an ontology is currently limited to logic. However, in the case of inconsistent knowledge or unreliable and incomplete information, it is difficult for a system to make a decision or assess the degree of truth of a hypothesis. Several approaches have been made and focused mainly on how to represent probabilistic information in ontologies and then use them for reasoning. Although, this requires a great effort for systems already designed and based on ontological or Bayesian knowledge bases. In this article, we propose two approaches to combining probabilistic and logical reasoning within a home-based medical intervention system. The first, using probabilistic ontology PR-OWL. The second, providing a Jena API (Application Programming Interface).

During the first generation of home intervention and support, complaints were usually triggered by telephone call. In most situations needs where described orally; there were no sufficient information neither on the patient's condition nor on his medical history. Thanks to the advance of technology, especially systems based on data mining, artificial intelligence time is saved, and immediate service is offered. Although home intervention and care have been the subject of numerous studies, the resolution of the overall decision-making problem is not sufficiently developed. Due to the lack of information such as the state of health of a patient, the set of parameters characterizing the daily life habits of the person analyzed in parallel with the evolution of physiological and environmental parameters. In addition to that, it is necessary to take into consideration the Medical Core, his location, his profile not only his professional status, but also his capacities and skills, which are not explicitly described in his curriculum. Different studies and systems exist in literature. Each them takes into account only part of the parameters. Indeed, these studies face either the monitoring of daily activities, the monitoring of physiological data or other environmental part. Either they take into account the specific features of the medical core profile. Either these systems use probabilistic data mining, which involves many interactions with experts to interpret the data, or an expert system based on the rules of inference defined by medical experts. In addition, most do not use controlled vocabulary, which provides semantics to the system. This complicates information sharing and collaborative work. We proposed in a previous work an ontology-based solution [3], the goal is to help the collaborator to make a decision and to draw new information. as the complexity of the data increases, so does the need to deal with uncertainty. Several approaches to the representation and reasoning of uncertainty in the semantic web have emerged. This article is a study for the integration of uncertainty in e-SAAD ontologies expressed in Web ontology language.