Question: What knowledge graphs do they extract adequate semantic structures from?
Evidence: From the definitions of `explainable AI' and `context understanding,' in the previous section, we can derive the following corollary:
The explainability of AI algorithms is related to how context is processed, computationally, based on the machine's perceptual capabilities and on the external knowledge resources that are available.
Along this direction, the remainder of this chapter explores two concrete scenarios of context understanding, realized by neuro-symbolic architectures|i.e., hybrid AI frameworks that instruct machine perception (based on deep neural networks) with knowledge graphs. These examples were chosen to illustrate the general applicability of neuro-symbolism and its relevance to contemporary research problems.
Specifically, section SECREF3 considers context understanding for autonomous vehicles: we describe how a knowledge graph can be built from a dataset of urban driving situations and how this knowledge graph can be translated into a continuous vector-space representation. This embedding space can be used to estimate the semantic similarity of visual scenes by using neural networks as powerful, non-linear function approximators. Here, models may be trained to make danger assessments of the visual scene and, if necessary, transfer control to the human in complex scenarios. The ability to make this assessment is an important capability for autonomous vehicles, when we consider the negative ramifications for a machine to remain invariant to changing weather conditions, anomalous behavior of dynamic obstacles on the road (e.g., other vehicles, pedestrians), varied lighting conditions, and other challenging circumstances. We suggest neuro-symbolic fusion as one solution and, indeed, our results show that our embedding space preserves the semantic properties of the conceptual elements that make up visual scenes.
In section SECREF17, we describe context understanding for language tasks. Here, models are supplied with three separate modalities: external commonsense knowledge, unstructured textual context, and a series of answer candidates. In this task, models are tested on their ability to fuse together these disparate sources of information for making the appropriate logical inferences. We designed methods to extract adequate semantic structures (i.e., triples) from two comprehensive commonsense knowledge graphs, ConceptNet BIBREF6 and Atomic BIBREF7, and to inject this external context into language models. In general, open-domain linguistic context is useful for different tasks in Natural Language Processing (NLP), including: information-extraction, text-classification, extractive and abstractive summarization, and question-answering (QA). For ease of quantitative evaluation, we consider a QA task in section SECREF17. In particular, the task is to select the correct answer from a pool of candidates, given a question that specifically requires commonsense to resolve. For example, the question, If electrical equipment won't power on, what connection should be checked? is associated with `company', `airport', `telephone network', `wires', and `freeway'(where `wires' is the correct answer choice). We demonstrate that our proposed hybrid architecture out-performs the state-of-the-art neural approaches that do not utilize structured commonsense knowledge bases. Furthermore, we discuss how our approach maintains explainability in the model's decision-making process: the model has the joint task of learning an attention distribution over the commonsense knowledge context which, in turn, depends on the knowledge triples that were conceptually most salient for selecting the correct answer candidate, downstream. Fundamentally, the goal of this project is to make human interaction with chatbots and personal assistants more robust. For this to happen, it is crucial to equip intelligent agents with a shared understanding of general contexts, i.e., commonsense. Conventionally, machine commonsense had been computationally articulated using symbolic languages|Cyc being one of the most prominent outcomes of this approach BIBREF8. However, symbolic commonsense representations are neither scalable nor comprehensive, as they depend heavily on the knowledge engineering experts that encode them. In this regard, the advent of deep learning and, in particular, the possibility of fusing symbolic knowledge into sub-symbolic (neural) layers, has recently led to a revival of this AI research topic.
We designed methods to extract adequate semantic structures (i.e., triples) from two comprehensive commonsense knowledge graphs, ConceptNet [22] and Atomic [37], and to inject this external context into language models. I
Answer: ConceptNet and Atomic.