Heterogeneous Knowledge Representation Research Articles

Enhancing awareness of industrial robots in collaborative manufacturing

The diffusion of Human-Robot Collaborative cells is prevented by several barriers. Classical control approaches seem not yet fully suitable for facing the variability conveyed by the presence of human operators beside robots. The capabilities of representing heterogeneous knowledge representation and performing abstract reasoning are crucial to enhance the flexibility of control solutions. To this aim, the ontology SOHO (Sharework Ontology for Human-Robot Collaboration) has been specifically designed for representing Human-Robot Collaboration scenarios, following a context-based approach. This work brings several contributions. This paper proposes an extension of SOHO to better characterize behavioral constraints of collaborative tasks. Furthermore, this work shows a knowledge extraction procedure designed to automatize the synthesis of Artificial Intelligence plan-based controllers for realizing flexible coordination of human and robot behaviors in collaborative tasks. The generality of the ontological model and the developed representation capabilities as well as the validity of the synthesized planning domains are evaluated on a number of realistic industrial scenarios where collaborative robots are actually deployed.

A multi-view heterogeneous knowledge learning method for personalized POI recommendation

Existing POI recommendation methods often fail to capture the fine-grained preferences of users and face the challenge of modeling multiple relationships. Moreover, knowledge graph-based recommendation methods are limited in storing dynamic user trajectories, making them unsuitable for POI recommendation scenarios. In this paper, we propose a Multi-View Heterogeneous Knowledge learning model that utilizes techniques for heterogeneous knowledge representation learning and multi-view context modeling. Our model comprehensively models user preferences and the relationships between users and POIs by utilizing information from users’ visiting sequences and POI attributes knowledge graph. Specifically, we design a heterogeneous knowledge embedding method to learn the representation of users and POIs using POI attribute knowledge and users’ visiting sequences. Additionally, we constructed a user trajectory similarity graph and a POI attribute similarity graph to explore potential relations between users and between POIs. The former measures the similarity of user behaviors based on user visit sequences, and the latter quantifies the similarity between different POIs through a novel feature mapping method. Finally, we propose a multi-view hybrid learning method that combines unsupervised and supervised learning paradigms to model complex relationships, improving the overall recommendation performance. Extensive experiments on real-world datasets validate the effectiveness of our method.

Meta-Context Ontology for Self-Adaptive Mobile Web Service Discovery in Smart Systems

Self-adaptive mobile web service (MWS) discovery in Smart Systems depends on heterogeneous context-sensitive attributes that are composed dynamically to satisfy the user’s MWS request despite the constantly changing environment. Several ontologies are developed to deal with the issues of heterogeneous knowledge representation in smart systems. Unfortunately, these ontologies are mostly inexpressive, unextendible, constricted, and slightly fragmented due to a lack of socially focused ontology development procedures. In this paper, a context ontology for self-adaptive MWS discovery in smart systems is proposed to provide a better representation of the heterogeneous context for smart systems and facilitate the delivery of MWS. The lightweight unified process for ontology building (UPON Lite) is adopted for ontology development. The ontology is experimentally evaluated in protégé using real-world smart systems from the healthcare and agriculture domain. Consequently, the ontology was found to be more expressive, extensible, and support self-adaptive MWS discovery in Smart Systems.

A framework for merging ontologies in the context of smart factories

Current trends, such as individualization, increasing complexity, and specialization, require digitization in engineering and production. However, digitization by itself often leads to so-called data silos, which cannot be leveraged effectively when designing and operating smart factories due to the heterogeneity of the information available. This paper presents a framework for (semi-)automatically merging the highly reusable terminological components of production ontologies in an a posteriori way. The framework combines translations, domain-specific vocabularies, and inconsistency checks with syntactic, terminological, and structural analyses to integrate knowledge representations formalized in the Web Ontology Language. Integrating heterogeneous knowledge representations in the production domain can improve support systems for engineers, increase awareness of interdependencies, and enable unambiguous communication in smart factories.

An Ontology for Human-Robot Collaboration

The diffusion of Human-Robot Collaborative cells is prevented by some barriers. Classical control approaches seem not yet fully suitable for facing variability conveyed by the presence of human operators beside robots. Heterogeneous knowledge representation capabilities and abstract reasoning are crucial to enhance flexibility of control solutions. This work presents SOHO (Sharework Ontology for Human Robot Collaboration), a novel ontology specifically designed for Human-Robot Collaboration. The paper describes the pursued context-based approach, the novelty of the designed ontology with respect to the state of the art and shows its validity in a realistic Human-Robot Collaboration scenario.

Heterogeneous knowledge representation integrating connectionist and symbolic computation

Heterogeneous knowledge representation allows the combination of several knowledge representation techniques. For instance connectionist and symbolic systems are two different computational paradigms and knowledge representations. Unfortunately, the integration of different paradigms and knowledge representations is not easy and very often is informal. In this paper, we propose a formal approach to integrate these two paradigms where as a symbolic system we consider a (logic) rule-based system. The integration is operated at language level between neural networks and rule languages. The formal model that allows the integration is based on constraint logic programming and provides an integrated framework to represent and process heterogeneous knowledge. In order to achieve this we define a new language that allows us to express and model in a natural and intuitive way the above issues together with the operational semantics.

Heterogeneous knowledge representation: integrating connectionist and symbolic computation

Heterogeneous knowledge representation allows combination of several knowledge representation techniques. For instance, connectionist and symbolic systems are two different computational paradigms and knowledge representations. Unfortunately, the integration of different paradigms and knowledge representations is not easy and very often is informal. In this paper, we propose a formal approach to integrate these two paradigms where as a symbolic system we consider a (logic) rule-based system. The integration is operated at language level between neural networks and rule languages. The formal model that allows the integration is based on constraint logic programming and provides an integrated framework to represent and process heterogeneous knowledge. In order to achieve this we define a new language that allows expression and modelling in a natural and intuitive way the above issues together with the operational semantics .

Heterogeneous knowledge representation using a finite automaton and first order logic: a case study in electromyography

In a certain number of situations, human cognitive functioning is difficult to represent with classical artificial intelligence structures. Such a difficulty arises in the polyneuropathy diagnosis which is based on the spatial distribution, along the nerve fibres, of lesions, together with the synthesis of several partial diagnoses. Faced with this problem while building up an expert system (NEUROP), we developed a heterogeneous knowledge representation associating a finite automaton with first order logic. A number of knowledge representation problems raised by the electrophysiological test features are examined in this study and the expert system architecture allowing such a knowledge modeling is laid out.