Semantic similarity measurement based on knowledge mining: an artificial neural net approach

Li, Wenwen; Raskin, R.; Goodchild, M. F.

doi:10.1080/13658816.2011.635595

Cited by 48 publications

(34 citation statements)

References 18 publications

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“…This involves model validation and calibration in the back-end and a statistical analysis feature in the front-end of our GUI. In addition, as more data and services are being produced, we will develop a spatial search tool that utilizes advanced semantic search technology [49][50][51][52][53][54][55][56] to allow users to quickly identify the resources in need. Meanwhile, we will also develop an intuitive and user-friendly interface to manage the spatial analytical workflow and intermediate results generated through it.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

A Geospatial Cyberinfrastructure for Urban Economic Analysis and Spatial Decision-Making

Goodchild

et al. 2013

IJGI

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Urban economic modeling and effective spatial planning are critical tools towards achieving urban sustainability. However, in practice, many technical obstacles, such as information islands, poor documentation of data and lack of software platforms to facilitate virtual collaboration, are challenging the effectiveness of decision-making processes. In this paper, we report on our efforts to design and develop a geospatial cyberinfrastructure (GCI) for urban economic analysis and simulation. This GCI provides an operational graphic user interface, built upon a service-oriented architecture to allow (1) widespread sharing and seamless integration of distributed geospatial data; (2) an effective way to address the uncertainty and positional errors encountered in fusing data from diverse sources; (3) the decomposition of complex planning questions into atomic spatial analysis tasks and the generation of a web service chain to tackle such complex problems; and (4) capturing and representing provenance of geospatial data to trace its flow in the modeling task. The Greater Los Angeles Region serves as the test bed. We expect this work to contribute to effective spatial policy analysis and decision-making through the adoption of advanced GCI and to broaden the application coverage of GCI to include urban economic simulations.

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Section: Conclusion and Discussionmentioning

confidence: 99%

A Geospatial Cyberinfrastructure for Urban Economic Analysis and Spatial Decision-Making

Goodchild

et al. 2013

IJGI

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“…They have an extraordinary ability to obtain patterns from complex or inexact data and their nonlinearity allows them to fit the data better [21], [22]. It is this ability that makes them ideal for searching the semantic web as many ontologies exist and finding links across billions of documents is challenging.…”

Section: B Artificial Neural Networkmentioning

confidence: 99%

“…Multiple Layer Feed-Forward Neural Networks (MLFFN) are also popular because of their ability to model complex relationships between output and input data [22]. However, they are often over trained as they adopt a trial-and-error approach to seek possible values of parameters for convergence of the global optimum [24].…”

Section: B Artificial Neural Networkmentioning

confidence: 99%

“…It is this ability that makes them ideal for searching the semantic web as many ontologies exist and finding links across billions of documents is challenging. Once the network is learning it becomes self-organized and over time the results can be improved to provide a greater level of accuracy [22]. ANNs are composed of three layers: 1) an input layer of nodes, 2) one or more hidden layers that capture the nonlinearity that occurs within the data and 3) an output layer [21], [23], [24].…”

Section: B Artificial Neural Networkmentioning

confidence: 99%

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Knowledge extraction using probabilistic reasoning: An artificial neural network approach

Dobbins

Fergus

2015

2015 International Joint Conference on Neural Networks (IJCNN)

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Abstract-The World Wide Web (WWW) has radically changed the way in which we access, generate and disseminate information. Its presence is felt daily and with more internetenabled devices being connected the web of knowledge is growing. We are now moving into era where the WWW is capable of 'understanding' the actual/intended meaning of our content. This is being achieved by creating links between distributed data sources using the Resource Description Framework (RDF). In order to find information in this web of interconnected sources, complex query languages are often employed, e.g. SPARQL. However, this approach is limited as exact query matches are often required. In order to overcome this challenge, this paper presents a probabilistic approach to searching RDF documents. The developed algorithm converts RDF data into a matrix of features and treats searching as a machine learning problem. Using a number of artificial neural network algorithms, a successfully developed prototype has been developed that demonstrates the applicability of the approach. The results illustrate that the Voted Perceptron classifier (VPC), perceptron linear classifier (PERLC) and random neural network classifier (RNNC) performed particularly well, with accuracies of 100%, 98% and 93% respectively.

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“…This strategy is also known as ontology matching or alignment and requires the calculation of the semantic similarity between different LULC class definitions. Typical methods include geometric [24], feature-based [25,26], network [27], alignment [28] and information-theoretic [29] modeling. Because a land type definition is usually given in a text block, text analysis techniques were also explored for extracting the spatial and aspatial attributes of an LULC class to enrich the formal definition in the LULC ontology [30].…”

Section: Progress In Enhancing the Semantic Interoperability Of Existmentioning

confidence: 99%

An Integrated Software Framework to Support Semantic Modeling and Reasoning of Spatiotemporal Change of Geographical Objects: A Use Case of Land Use and Land Cover Change Study

Zhou

2016

IJGI

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Abstract:Evolving Earth observation and change detection techniques enable the automatic identification of Land Use and Land Cover Change (LULCC) over a large extent from massive amounts of remote sensing data. It at the same time poses a major challenge in effective organization, representation and modeling of such information. This study proposes and implements an integrated computational framework to support the modeling, semantic and spatial reasoning of change information with regard to space, time and topology. We first proposed a conceptual model to formally represent the spatiotemporal variation of change data, which is essential knowledge to support various environmental and social studies, such as deforestation and urbanization studies. Then, a spatial ontology was created to encode these semantic spatiotemporal data in a machine-understandable format. Based on the knowledge defined in the ontology and related reasoning rules, a semantic platform was developed to support the semantic query and change trajectory reasoning of areas with LULCC. This semantic platform is innovative, as it integrates semantic and spatial reasoning into a coherent computational and operational software framework to support automated semantic analysis of time series data that can go beyond LULC datasets. In addition, this system scales well as the amount of data increases, validated by a number of experimental results. This work contributes significantly to both the geospatial Semantic Web and GIScience communities in terms of the establishment of the (web-based) semantic platform for collaborative question answering and decision-making.

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Semantic similarity measurement based on knowledge mining: an artificial neural net approach

Cited by 48 publications

References 18 publications

A Geospatial Cyberinfrastructure for Urban Economic Analysis and Spatial Decision-Making

A Geospatial Cyberinfrastructure for Urban Economic Analysis and Spatial Decision-Making

Knowledge extraction using probabilistic reasoning: An artificial neural network approach

An Integrated Software Framework to Support Semantic Modeling and Reasoning of Spatiotemporal Change of Geographical Objects: A Use Case of Land Use and Land Cover Change Study

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