An Ontology-based Automation System

This paper presents an ontology-based approach to benefit automatic fertilization management for citrus orchards located in mountainous region. The core of the fertilization approach is the citrus fertilization ontology, which covers knowledge about citrus fertilizers and fertilization application. Specially, our approach can provide not only the yearly fertilization quantities of required pure nitrogen, phosphorus, and potassium according to their disease symptoms, but also the suitable fertilizing recommendations for the citrus orchards with different soil properties. The current version of the ontology (ver. 2.9.10) contains 103 classes, 34 properties, 800 instances, which are defined by 3056 RDF triples and is evaluated by using 90 competency questions. Furthermore, we run experiments with our proposal targeting at four citrus orchards in Chongqing, and compare its outputs with the reference values advised by the agri-professionals of citrus planting.

A Context-independent Ontological Linked Data Alignment Approach to Instance Matching

Linking data by finding matching instances in different datasets requires considering many characteristics, such as structural heterogeneity, implicit knowledge, and URI (Uniform Resource Identifier)-oriented identification. The authors propose a context-independent approach to align Linked data through an alignment process based on the ontological model’s components and considering data’s multidimensionality. The researchers experimented with the proposed approach against two methods for aligning linked data in two datasets and evaluated precision, recall, and f-measure metrics. The authors also conducted a case study in a real scenario considering a Brazilian publication dataset on computers and education. This study’s results indicate that the proposed approach overcomes the other methods (regarding the precision, recall, and f-measure metrics), requiring less work when changing the dataset domain. This work’s main contributions include enabling real datasets to be semi-automatically linked, presenting an approach capable of calculating resource similarity.

An Open-Ended Web Knowledge Retrieval Framework for the Household Domain With Explanation and Learning Through Argumentation

The authors present a knowledge retrieval framework for the household domain enhanced with external knowledge sources that can argue over the information that it returns and learn new knowledge through an argumentation dialogue. The framework provides access to commonsense knowledge about household environments and performs semantic matching between entities from the web knowledge graph ConceptNet, using semantic knowledge from DBpedia and WordNet, with the ones existing in the knowledge graph. They offer a set of predefined SPARQL templates that directly address the ontology on which their knowledge retrieval framework is built and querying through SPARQL. The framework also features an argumentation component, where the user can argue against the answers of the knowledge retrieval component of the framework under two different scenarios: the missing knowledge scenario, where an entity should be in the answers, and the wrong knowledge scenario, where an entity should not be in the answers. This argumentation dialogue can end up in learning a new piece of knowledge when the user wins the dialogue.

An Agent-Based Social Simulation for Citizenship Competences and Conflict Resolution Styles

The development of citizenship competences plays an important role in a complex system like society. Thus, to analyze how such competences impact other contexts is a great challenge because this kind of study involves the work with people and the use of variables that depend on human behaviors. In this sense, many studies have highlighted the advantage of using simulation systems and tools. In particular, the agent-based social simulation field relies upon the Semantic Web to manage knowledge representation in social scenarios. This study focuses on how citizenship competences impact conflict resolution. Moreover, a simulation model in which citizens interact to resolve conflicts by considering citizenship competences and conflict resolution styles is also introduced. It was developed in NetLogo together with an extension that connects it with the ontology of competences. Results show that the higher interactions of citizens-conflicts, the higher level of citizenship competences, and the number of conflicts solved is higher when using citizenship competences.

Deep Embedding Learning with Auto-Encoder for Large-Scale Ontology Matching

Ontology matching is an efficient method to establish interoperability among heterogeneous ontologies. Large-scale ontology matching still remains a big challenge for its long time and large memory space consumption. The actual solution to this problem is ontology partitioning which is also challenging. This paper presents DeepOM, an ontology matching system to deal with this large-scale heterogeneity problem without partitioning using deep learning techniques. It consists on creating semantic embeddings for concepts of input ontologies using a reference ontology, and use them to train an auto-encoder in order to learn more accurate and less dimensional representations for concepts. The experimental results of its evaluation on large ontologies, and its comparison with different ontology matching systems which have participated to the same test challenge, are very encouraging with a precision score of 0.99. They demonstrate the higher efficiency of the proposed system to increase the performance of the large-scale ontology matching task.

Hybrid Firefly-Ontology based Clustering Algorithm for Analyzing Tweets to Extract Causal Factors

Social media especially Twitter has become ubiquitous among people where they express their opinions on various domains. This paper presents a Hybrid Firefly – Ontology-based Clustering (FF-OC) algorithm which attempts to extract factors impacting a major public issue that is trending. In this research work, the issue of food price rise and disease which was trending during the time of the investigation is considered. The novelty of the algorithm lies in the fact that it clusters the association rules without any prior knowledge. The findings from the experimentation suggest different factors impacting the rise of price in food items and diseases such as diabetes, flu, zika virus. The empirical results show the significant improvement when compared with Artificial Bees Colony, Cuckoo Search Algorithm, Particle Swarm Optimization, and Ant Colony Optimization based clustering algorithms. The proposed method gives an improvement of 81% in terms of DB index, 79% in terms of silhouette index, 85% in terms of C index when compared to other algorithms.

An Improved Structural-Based Ontology Matching Approach Using Similarity Spreading

Increasing number of ontologies demand the interoperability between them in order to gain accurate information. the ontology heterogeneity also makes the interoperability process even more difficult. These scenarios let the development of effective and efficient ontology matching. The existing ontology matching systems are mainly focusing with subject derivatives of the concern domain. Since ontologies are represented as data model in structured format, In this paper, a new modified model of similarity spreading for ontology mapping is proposed. In this approach the mapping mainly involves with node clustering based on edge affinity and then the graph matching is achieved by applying coefficient similarity propagation. This process is carried out by iterative manner and at the end the similarity score is calculated for iteration. This model is evaluated in terms of precision, recall and f-measure parameters and found that it outperforms well than its similar kind of systems.