We face up to the challenge of explainability in Multimodal Artificial Intelligence (MMAI). At the nexus of neuroscience-inspired and quantum computing, interpretable and transparent spin-geometrical neural architectures for early fusion of large-scale, heterogeneous, graph-structured data are envisioned, harnessing recent evidence for relativistic quantum neural coding of (co-)behavioral states in the self-organizing brain, under competitive, multidimensional dynamics. The designs draw on a self-dual classical description – via special Clifford-Lipschitz operations – of spinorial quantum states within registers of at most 16 qubits for efficient encoding of exponentially large neural structures. Formally ‘trained’, Lorentz neural architectures with precisely one lateral layer of exclusively inhibitory interneurons accounting for anti-modalities, as well as their co-architectures with intra-layer connections are highlighted. The approach accommodates the fusion of up to 16 time-invariant interconnected (anti-)modalities and the crystallization of latent multidimensional patterns. Comprehensive insights are expected to be gained through applications to Multimodal Big Data, under diverse real-world scenarios.

In this paper, we describe and evaluate a Web service discovery framework using OWL-S advertisements, combined with the distinction between service and Web service of the WSMO Discovery Framework. More specifically, we follow the Web service discovery model, which is based on abstract and lightweight semantic Web service descriptions, using the Service Profile ontology of OWL-S. Our goal is to determine fast an initial set of candidate Web services for a specific request. This set can then be used in more fine-grained discovery approaches, based on richer Web service descriptions. Our Web service matchmaking algorithm extends object-based matching techniques used in Structural Case-based Reasoning, allowing 1) the retrieval of Web services not only based on subsumption relationships, but exploiting also the structural information of OWL ontologies and 2) the exploitation of Web services classification in Profile taxonomies, performing domain-dependent discovery. Furthermore, we describe how the typical paradigm of Profile input/output annotation with ontology concepts can be extended, allowing ontology roles to be considered as well. We have implemented our framework in the OWLS-SLR system, which we extensively evaluate and compare to the OWLS-MX matchmaker.

This paper presents PORSCE II, a system that combines planning and ontology concept relevance for automatically composing semantic web services. The presented approach includes transformation of the web service composition problem into a planning problem, enhancement with semantic awareness and relaxation and solution through external planners. The produced plans are visualized and their accuracy is assessed.

In this paper, we describe O-DEVICE, a memory-based knowledge-based system for reasoning and querying OWL ontologies by implementing RDF/OWL entailments in the form of production rules in order to apply the formal semantics of the language. Our approach is based on a transformation procedure of OWL ontologies into an object-oriented schema and the application of inference production rules over the generated objects in order to implement the various semantics of OWL. In order to enhance the performance of the system, we introduce a dynamic approach of generating production rules for ABOX reasoning and an incremental approach of loading ontologies. O-DEVICE is built over the CLIPS production rule system, using the object-oriented language COOL to model and handle ontology concepts and RDF resources. One of the contributions of our work is that we enable a well-known and efficient production rule system to handle OWL ontologies. We argue that although native OWL rule reasoners may process ontology information faster, they lack some of the key features that rule systems offer, such as the efficient manipulation of the information through complex rule programs. We present a comparison of our system with other OWL reasoners, showing that O-DEVICE can constitute a practical rule environment for ontology manipulation.