Using the KISO's Framework to Integrate Knowledge Sources

In complicated application environment such as CIMS (Computer Integrated Manufacture System) enterprise, it will bring great benefits to integrate distributed knowledge sources. But the difficulties of knowledge sharing and reuse seriously encumbrance the implementation of knowledge integration. In this paper, we describe how a framework of knowledge-integrated system based on ontology (KISO) can be used to support integrating distributed knowledge sources.

Soft Computing: Fuzzy Logic Approach in Wireless Sensors Networks

The recent growth of communication and sensor technology results in the enlargement of a new attractive and challenging area-wireless sensor networks (WSNs). A network comprising of several minute wireless sensor nodes which are organized in a dense manner is called as a Wireless Sensor Network (WSN). Every node estimates the state of its surroundings in this network. The estimated results are then converted into the signal form in order to determine the features related to this technique after the processing of the signals. It’s high computational environment with limited and controlled broadcast range, processing, as well as limited energy. The embedded soft computing approach in wireless sensor networks is suggested. This approach means a grouping of embedded fuzzy logic and neural networks models for information processing in complex environment with unsure, rough, fuzzy measuring data. It is generalization of soft computing concept for the embedded, distributed, adaptive systems.

A Knowledge Graph-Based Deep Learning Framework for Efficient Content Similarity Search of Sustainable Development Goals Data

Sustainable development denotes the enhancement ofliving standards in the present without compromising future generations'resources.Sustainable Development Goals(SDGs)quantify the accomplishment of sustainable development and pave the way for a world worth living in for future generations.Scholars can contribute to the achievement of the SDGs by guiding the actions of practitioners based on the analysis of SDG data,as intended by this work.We propose a framework of algorithms based on dimensionality reduction methods with the use of Hilbert Space Filling Curves(HSFCs)in order to semantically cluster new uncategorised SDG data and novel indicators,and efficiently place them in the environment of a distributed knowledge graph store.First,a framework of algorithms for insertion of new indicators and projection on the HSFC curve based on their transformer-based similarity assessment,for retrieval of indicators and loadbalancing along with an approach for data classification of entrant-indicators is described.Then,a thorough case study in a distributed knowledge graph environment experimentally evaluates our framework.The results are presented and discussed in light of theory along with the actual impact that can have for practitioners analysing SDG data,including intergovernmental organizations,government agencies and social welfare organizations.Our approach empowers SDG knowledge graphs for causal analysis,inference,and manifold interpretations of the societal implications of SDG-related actions,as data are accessed in reduced retrieval times.It facilitates quicker measurement of influence of users and communities on specific goals and serves for faster distributed knowledge matching,as semantic cohesion of data is preserved.

Knowledge Graph Embedding for Hyper-Relational Data

Knowledge graph representation has been a long standing goal of artificial intelligence. In this paper,we consider a method for knowledge graph embedding of hyper-relational data, which are commonly found in knowledge graphs. Previous models such as Trans（E, H, R） and CTrans R are either insufficient for embedding hyper-relational data or focus on projecting an entity into multiple embeddings, which might not be effective for generalization nor accurately reflect real knowledge. To overcome these issues, we propose the novel model Trans HR, which transforms the hyper-relations in a pair of entities into an individual vector, serving as a translation between them. We experimentally evaluate our model on two typical tasks—link prediction and triple classification.The results demonstrate that Trans HR significantly outperforms Trans（E, H, R） and CTrans R, especially for hyperrelational data.

Hastily formed knowledge networks and distributed situation awareness for collaborative robotics

In the context of collaborative robotics,distributed situation awareness is essential for supporting collective intelligence in teams of robots and human agents where it can be used for both individual and collective decision support.This is particularly important in applications pertaining to emergency rescue and crisis management.During operational missions,data and knowledge are gathered incrementally and in different ways by heterogeneous robots and humans.We describe this as the creation of Hastily Formed Knowledge Networks(HFKNs).The focus of this paper is the specification and prototyping of a general distributed system architecture that supports the creation of HFKNs by teams of robots and humans.The information collected ranges from low-level sensor data to high-level semantic knowledge,the latter represented in part as RDF Graphs.The framework includes a synchronization protocol and associated algorithms that allow for the automatic distribution and sharing of data and knowledge between agents.This is done through the distributed synchronization of RDF Graphs shared between agents.High-level semantic queries specified in SPARQL can be used by robots and humans alike to acquire both knowledge and data content from team members.The system is empirically validated and complexity results of the proposed algorithms are provided.Additionally,a field robotics case study is described,where a 3D mapping mission has been executed using several UAVs in a collaborative emergency rescue scenario while using the full HFKN Framework.