Building Extraction from LIDAR Based Semantic Analysis

Extraction of buildings from LIDAR data has been an active research field in recent years. A scheme for building detection and reconstruction from LIDAR data is presented with an object-oriented method which is based on the buildings’ semantic rules. Two key steps are discussed: how to group the discrete LIDAR points into single objects and how to establish the buildings’ semantic rules. In the end, the buildings are reconstructed in 3D form and three common parametric building models (flat, gabled, hipped) are implemented.

Alzheimer’s Disease Diagnosis Based on a Semantic Rule-Based Modeling and Reasoning Approach

Alzheimer’s disease(AD)is a very complex disease that causes brain failure,then eventually,dementia ensues.It is a global health problem.99%of clinical trials have failed to limit the progression of this disease.The risks and barriers to detecting AD are huge as pathological events begin decades before appearing clinical symptoms.Therapies for AD are likely to be more helpful if the diagnosis is determined early before the final stage of neurological dysfunction.In this regard,the need becomes more urgent for biomarker-based detection.A key issue in understanding AD is the need to solve complex and high-dimensional datasets and heterogeneous biomarkers,such as genetics,magnetic resonance imaging(MRI),cerebrospinal fluid(CSF),and cognitive scores.Establishing an interpretable reasoning system and performing interoperability that achieves in terms of a semantic model is potentially very useful.Thus,our aim in this work is to propose an interpretable approach to detect AD based on Alzheimer’s disease diagnosis ontology(ADDO)and the expression of semantic web rule language(SWRL).This work implements an ontology-based application that exploits three different machine learning models.These models are random forest(RF),JRip,and J48,which have been used along with the voting ensemble.ADNI dataset was used for this study.The proposed classifier’s result with the voting ensemble achieves a higher accuracy of 94.1%and precision of 94.3%.Our approach provides effective inference rules.Besides,it contributes to a real,accurate,and interpretable classifier model based on various AD biomarkers for inferring whether the subject is a normal cognitive(NC),significant memory concern(SMC),early mild cognitive impairment(EMCI),late mild cognitive impairment(LMCI),or AD.

A NOVEL FRAMEWORK FOR SOCCER GOAL DETECTION BASED ON SEMANTIC RULE

Focusing on the problem of goal event detection in soccer videos,a novel method based on Hidden Markov Model(HMM) and the semantic rule is proposed.Firstly,a HMM for a goal event is constructed.Then a Normalized Semantic Weighted Sum(NSWS) rule is established by defining a new feature of shots,semantic observation weight.The test video is detected based on the HMM and the NSWS rule,respectively.Finally,a fusion scheme based on logic distance is proposed and the detection results of the HMM and the NSWS rule are fused by optimal weights in the decision level,obtaining the final result.Experimental results indicate that the proposed method achieves 96.43% precision and 100% recall,which shows the effectiveness of this letter.

Towards knowledge-based geovisualisation using Semantic Web technologies:a knowledge representation approach coupling ontologies and rules

Geovisualisation is a knowledge-intensive art in which both providers and users need to possess a wide range of knowledge.Current syntactic approaches to presenting visualisation information lack semantics on the one hand,and on the other hand are too bespoke.Such limitations impede the transfer,interpretation,and reuse of the geovisualisation knowledge.In this paper,we propose a knowledge-based approach to formally represent geovisualisation knowledge in a semantically-enriched and machine-readable manner using Semantic Web technologies.Specifically,we represent knowledge regarding cartographic scale,data portrayal and geometry source,which are three key aspects of geovisualisation in the contemporary web mapping era,coupling ontologies and semantic rules.The knowledge base enables inference for deriving the corresponding geometries and portrayals for visualisation under different conditions.A prototype system is developed in which geospatial linked data are used as underlying data,and some geovisualisation knowledge is formalised into a knowledge base to visualise the data and provide rich semantics to users.The proposed approach can partially form the foundation for the vision of web of knowledge for geovisualisation.

A Novel Deep Neural Network Compression Model for Airport Object Detection

A novel deep neural network compression model for airport object detection has been presented.This novel model aims at disadvantages of deep neural network,i.e.the complexity of the model and the great cost of calculation.According to the requirement of airport object detection,the model obtains temporal and spatial semantic rules from the uncompressed model.These spatial semantic rules are added to the model after parameter compression to assist the detection.The rules can improve the accuracy of the detection model in order to make up for the loss caused by parameter compression.The experiments show that the effect of the novel compression detection model is no worse than that of the uncompressed original model.Even some of the original model false detection can be eliminated through the prior knowledge.

A FORMAL MODEL FOR ANALYZING THE SCR-STYLE REQUIREMENTS SPECIFICATIONS

Based on SCR(Software Cost Reduction), this paper presents a formal mOdel analyzingSCR-style requirements- This model mainly apply state trans1ation rules, semantic computing rules and attributes to define formal sementics of a tabular notation in the SCR requirements method, and may automatically analyze requirements specifications to be specified by the SCR method. Combining with a simp1eexample, this paper introduces how to analyze consistency and completeness of requirements specifica-tlons.

Detecting Vicious Cycles in Urban Problem Knowledge Graph using Inference Rules

Urban areas have many problems,including homelessness,graffiti,and littering.These problems are influenced by various factors and are linked to each other;thus,an understanding of the problem structure is required in order to detect and solve the root problems that generate vicious cycles.Moreover,before implementing action plans to solve these problems,local governments need to estimate cost-effectiveness when the plans are carried out.Therefore,this paper proposed constructing an urban problem knowledge graph that would include urban problems’causality and the related cost information in budget sheets.In addition,this paper proposed a method for detecting vicious cycles of urban problems using SPARQL queries with inference rules from the knowledge graph.Finally,several root problems that led to vicious cycles were detected.Urban-problem experts evaluated the extracted causal relations.

τSQWRL:A TSQL2-Like Query Language for Temporal Ontologies Generated from JSON Big Data

Temporal ontologies allow to represent not only concepts,their properties,and their relationships,but also time-varying information through explicit versioning of definitions or through the four-dimensional perdurantist view.They are widely used to formally represent temporal data semantics in several applications belonging to different fields(e.g.,Semantic Web,expert systems,knowledge bases,big data,and artificial intelligence).They facilitate temporal knowledge representation and discovery,with the support of temporal data querying and reasoning.However,there is no standard or consensual temporal ontology query language.In a previous work,we have proposed an approach namedτJOWL(temporal OWL 2 from temporal JSON,where OWL 2 stands for"OWL 2 Web Ontology Language"and JSON stands for"JavaScript Object Notation").τJOWL allows(1)to automatically build a temporal OWL 2 ontology of data,following the Closed World Assumption(CWA),from temporal JSON-based big data,and(2)to manage its incremental maintenance accommodating their evolution,in a temporal and multi-schema-version environment.In this paper,we propose a temporal ontology query language forτJOWL,namedτSQWRL(temporal SQWRL),designed as a temporal extension of the ontology query language—Semantic Query-enhanced Web Rule Language(SQWRL).The new language has been inspired by the features of the consensual temporal query language TSQL2(Temporal SQL2),well known in the temporal(relational)database community.The aim of the proposal is to enable and simplify the task of retrieving any desired ontology version or of specifying any(complex)temporal query on time-varying ontologies generated from time-varying big data.Some examples,in the Internet of Healthcare Things(IoHT)domain,are provided to motivate and illustrate our proposal.