32,468 research outputs found
G2PTL: A Pre-trained Model for Delivery Address and its Applications in Logistics System
Text-based delivery addresses, as the data foundation for logistics systems,
contain abundant and crucial location information. How to effectively encode
the delivery address is a core task to boost the performance of downstream
tasks in the logistics system. Pre-trained Models (PTMs) designed for Natural
Language Process (NLP) have emerged as the dominant tools for encoding semantic
information in text. Though promising, those NLP-based PTMs fall short of
encoding geographic knowledge in the delivery address, which considerably trims
down the performance of delivery-related tasks in logistic systems such as
Cainiao. To tackle the above problem, we propose a domain-specific pre-trained
model, named G2PTL, a Geography-Graph Pre-trained model for delivery address in
Logistics field. G2PTL combines the semantic learning capabilities of text
pre-training with the geographical-relationship encoding abilities of graph
modeling. Specifically, we first utilize real-world logistics delivery data to
construct a large-scale heterogeneous graph of delivery addresses, which
contains abundant geographic knowledge and delivery information. Then, G2PTL is
pre-trained with subgraphs sampled from the heterogeneous graph. Comprehensive
experiments are conducted to demonstrate the effectiveness of G2PTL through
four downstream tasks in logistics systems on real-world datasets. G2PTL has
been deployed in production in Cainiao's logistics system, which significantly
improves the performance of delivery-related tasks
Integrating building and urban semantics to empower smart water solutions
Current urban water research involves intelligent sensing, systems integration, proactive users and data-driven management through advanced analytics. The convergence of building information modeling with the smart water field provides an opportunity to transcend existing operational barriers. Such research would pave the way for demand-side management, active consumers, and demand-optimized networks, through interoperability and a system of systems approach. This paper presents a semantic knowledge management service and domain ontology which support a novel cloud-edge solution, by unifying domestic socio-technical water systems with clean and waste networks at an urban scale, to deliver value-added services for consumers and network operators. The web service integrates state of the art sensing, data analytics and middleware components. We propose an ontology for the domain which describes smart homes, smart metering, telemetry, and geographic information systems, alongside social concepts. This integrates previously isolated systems as well as supply and demand-side interventions, to improve system performance. A use case of demand-optimized management is introduced, and smart home application interoperability is demonstrated, before the performance of the semantic web service is presented and compared to alternatives. Our findings suggest that semantic web technologies and IoT can merge to bring together large data models with dynamic data streams, to support powerful applications in the operational phase of built environment systems
Technology Integration around the Geographic Information: A State of the Art
One of the elements that have popularized and facilitated the use of geographical information on a variety of computational applications has been the use of Web maps; this has opened new research challenges on different subjects, from locating places and people, the study of social behavior or the analyzing of the hidden structures of the terms used in a natural language query used for locating a place. However, the use of geographic information under technological features is not new, instead it has been part of a development and technological integration process. This paper presents a state of the art review about the application of geographic information under different approaches: its use on location based services, the collaborative user participation on it, its contextual-awareness, its use in the Semantic Web and the challenges of its use in natural languge queries. Finally, a prototype that integrates most of these areas is presented
Semantic Technologies for Manuscript Descriptions — Concepts and Visions
The contribution at hand relates recent developments in the area of the World Wide
Web to codicological research. In the last number of years, an informational extension
of the internet has been discussed and extensively researched: the Semantic Web. It
has already been applied in many areas, including digital information processing of
cultural heritage data. The Semantic Web facilitates the organisation and linking of
data across websites, according to a given semantic structure. Software can then process
this structural and semantic information to extract further knowledge. In the area
of codicological research, many institutions are making efforts to improve the online
availability of handwritten codices. If these resources could also employ Semantic
Web techniques, considerable research potential could be unleashed. However, data
acquisition from less structured data sources will be problematic. In particular, data
stemming from unstructured sources needs to be made accessible to SemanticWeb tools
through information extraction techniques. In the area of museum research, the CIDOC
Conceptual Reference Model (CRM) has been widely examined and is being adopted
successfully. The CRM translates well to Semantic Web research, and its concentration
on contextualization of objects could support approaches in codicological research.
Further concepts for the creation and management of bibliographic coherences and
structured vocabularies related to the CRM will be considered in this chapter. Finally, a
user scenario showing all processing steps in their context will be elaborated on
A Survey of Volunteered Open Geo-Knowledge Bases in the Semantic Web
Over the past decade, rapid advances in web technologies, coupled with
innovative models of spatial data collection and consumption, have generated a
robust growth in geo-referenced information, resulting in spatial information
overload. Increasing 'geographic intelligence' in traditional text-based
information retrieval has become a prominent approach to respond to this issue
and to fulfill users' spatial information needs. Numerous efforts in the
Semantic Geospatial Web, Volunteered Geographic Information (VGI), and the
Linking Open Data initiative have converged in a constellation of open
knowledge bases, freely available online. In this article, we survey these open
knowledge bases, focusing on their geospatial dimension. Particular attention
is devoted to the crucial issue of the quality of geo-knowledge bases, as well
as of crowdsourced data. A new knowledge base, the OpenStreetMap Semantic
Network, is outlined as our contribution to this area. Research directions in
information integration and Geographic Information Retrieval (GIR) are then
reviewed, with a critical discussion of their current limitations and future
prospects
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