Search

In recent years, knowledge graphs (KGs) have been widely applied in various domains for different purposes. The semantic model of KGs can represent knowledge through a hierarchical structure based on classes of entities, their properties, and their relationships. The construction of large KGs can enable the integration of heterogeneous information sources and help Artificial Intelligence (AI) systems be more explainable and interpretable. This systematic review examines a selection of recent publications to understand how KGs are currently being used in eXplainable AI systems. To achieve this goal, we design a framework and divide the use of KGs into four categories: extracting features, extracting relationships, constructing KGs, and KG reasoning. We also identify where KGs are mostly used in eXplainable AI systems (pre-model, in-model, and post-model) according to the aforementioned categories. Based on our analysis, KGs have been mainly used in pre-model XAI for feature and relation extraction. They were also utilised for inference and reasoning in post-model XAI. We found several studies that leveraged KGs to explain the XAI models in the healthcare domain. [ABSTRACT FROM AUTHOR]

The majority of available datasets in open government data are statistical. They are widely published by various governments to be used by the public and data consumers. However, most open government data portals do not provide the five-star Linked Data standard datasets. The published datasets are isolated from one another while conceptually connected. This paper constructs a knowledge graph for the disease-related datasets of a Canadian government data portal, Nova Scotia Open Data. We leveraged the Semantic Web technologies to transform the disease-related datasets into Resource Description Framework (RDF) and enriched them with semantic rules. An RDF data model using the RDF Cube vocabulary was designed in this work to develop a graph that adheres to best practices and standards, allowing for expansion, modification and flexible re-use. The study also discusses the lessons learned during the cross-dimensional knowledge graph construction and integration of open statistical datasets from multiple sources. [ABSTRACT FROM AUTHOR]

Building trust and transparency in healthcare can be achieved using eXplainable Artificial Intelligence (XAI), as it facilitates the decision-making process for healthcare professionals. Knowledge graphs can be used in XAI for explainability by structuring information, extracting features and relations, and performing reasoning. This paper highlights the role of knowledge graphs in XAI models in healthcare, considering a state-of-the-art review. Based on our review, knowledge graphs have been used for explainability to detect healthcare misinformation, adverse drug reactions, drug-drug interactions and to reduce the knowledge gap between healthcare experts and AI-based models. We also discuss how to leverage knowledge graphs in pre-model, in-model, and post-model XAI models in healthcare to make them more explainable. [ABSTRACT FROM AUTHOR]

Automatic topic labelling aims to generate coherent, interpretable, and meaningful labels to facilitate the interpretation of various topics within a corpus of documents. Typically, we represent a topic as a list of terms and documents ranked according to probability. In our research, we employ Top2Vec for topic modelling. We propose a novel three-phase, zero-shot topic labelling framework leveraging the ConceptNet knowledge graph (a comprehensive semantic network of words and phrases) and pre-trained language models as external sources of information. The first phase enriches the top n words of a given topic (based on probability) by expanding their neighbourhood in ConceptNet, bridging missing connections and information gaps, and subsequently yielding a semantically enhanced set of potential labels through the generation of a sub-graph. The second phase constructs a neighbourhood graph (organized as a subgraph of ConceptNet) for each candidate label, evaluating each node's semantic similarity to the topic and retaining the best sub-graph according to semantic similarity. In this phase, a one-word label is extracted from the final graph, concisely representing the topic. In the third phase, we use the language model to derive the final labels by taking the optimal graph as input. In the third phase, we utilize the language model to derive the sentence and summary labels using the optimal graph as input. These additional labels offer more comprehensive and contextually rich topic representations, facilitating more profound understanding and interpretation. By harnessing the power of knowledge graphs and language models, our framework expands the knowledge beyond the topic documents, optimizing the discovered topics with more representative terms while preserving the topic information. The proposed zero-shot approach (employing pre-trained language models and the ConceptNet knowledge graph without additional training) alleviates computational burdens. It reduces the cognitive and interpretative load on end-users by generating three types of labels for each topic: a one-word, sentence, and summary. Experimental results demonstrate that our model significantly surpasses unsupervised baselines and traditional topic labelling models while remaining competitive with supervised baselines in topic labelling performance. • Novel zero-shot topic labelling framework integrating ConceptNet and language models. • First to generate three label types: word, sentence, and summary for topics. • Pioneering use of knowledge graph and language model blend for topic labelling. • Quantitative analysis of our topic labelling's efficacy on diverse datasets. [Display omitted] [ABSTRACT FROM AUTHOR]