121,527 research outputs found
Tailoring temporal description logics for reasoning over temporal conceptual models
Temporal data models have been used to describe how data can evolve in the context of temporal databases. Both the Extended Entity-Relationship (EER) model and the Unified Modelling Language (UML) have been temporally extended to design temporal databases. To automatically check quality properties of conceptual schemas various encoding to Description Logics (DLs) have been proposed in the literature. On the other hand, reasoning on temporally extended DLs turn out to be too complex for effective reasoning ranging from 2ExpTime up to undecidable languages. We propose here to temporalize the ‘light-weight’ DL-Lite logics obtaining nice computational results while still being able to represent various constraints of temporal conceptual models. In particular, we consider temporal extensions of DL-Lite^N_bool, which was shown to be adequate for capturing non-temporal conceptual models without relationship inclusion, and its fragment DL-Lite^N_core with most primitive concept inclusions, which are nevertheless enough to represent almost all types of atemporal constraints (apart from
covering)
A Survey of Location Prediction on Twitter
Locations, e.g., countries, states, cities, and point-of-interests, are
central to news, emergency events, and people's daily lives. Automatic
identification of locations associated with or mentioned in documents has been
explored for decades. As one of the most popular online social network
platforms, Twitter has attracted a large number of users who send millions of
tweets on daily basis. Due to the world-wide coverage of its users and
real-time freshness of tweets, location prediction on Twitter has gained
significant attention in recent years. Research efforts are spent on dealing
with new challenges and opportunities brought by the noisy, short, and
context-rich nature of tweets. In this survey, we aim at offering an overall
picture of location prediction on Twitter. Specifically, we concentrate on the
prediction of user home locations, tweet locations, and mentioned locations. We
first define the three tasks and review the evaluation metrics. By summarizing
Twitter network, tweet content, and tweet context as potential inputs, we then
structurally highlight how the problems depend on these inputs. Each dependency
is illustrated by a comprehensive review of the corresponding strategies
adopted in state-of-the-art approaches. In addition, we also briefly review two
related problems, i.e., semantic location prediction and point-of-interest
recommendation. Finally, we list future research directions.Comment: Accepted to TKDE. 30 pages, 1 figur
A semantic-based platform for the digital analysis of architectural heritage
This essay focuses on the fields of architectural documentation and digital representation. We present a research paper concerning the development of an information system at the scale of architecture, taking into account the relationships that can be established between the representation of buildings (shape, dimension, state of conservation, hypothetical restitution) and heterogeneous information about various fields (such as the technical, the documentary or still the historical one). The proposed approach aims to organize multiple representations (and associated information) around a semantic description model with the goal of defining a system for the multi-field analysis of buildings
Measuring Expert Performance at Manually Classifying Domain Entities under Upper Ontology Classes
Classifying entities in domain ontologies under upper ontology classes is a
recommended task in ontology engineering to facilitate semantic
interoperability and modelling consistency. Integrating upper ontologies this
way is difficult and, despite emerging automated methods, remains a largely
manual task.
Little is known about how well experts perform at upper ontology integration.
To develop methodological and tool support, we first need to understand how
well experts do this task. We designed a study to measure the performance of
human experts at manually classifying classes in a general knowledge domain
ontology with entities in the Basic Formal Ontology (BFO), an upper ontology
used widely in the biomedical domain.
We conclude that manually classifying domain entities under upper ontology
classes is indeed very difficult to do correctly. Given the importance of the
task and the high degree of inconsistent classifications we encountered, we
further conclude that it is necessary to improve the methodological framework
surrounding the manual integration of domain and upper ontologies
A semantic web approach for built heritage representation
In a built heritage process, meant as a structured system of activities
aimed at the investigation, preservation, and management of architectural
heritage, any task accomplished by the several actors involved in it is deeply
influenced by the way the knowledge is represented and shared. In the current
heritage practice, knowledge representation and management have shown several
limitations due to the difficulty of dealing with large amount of extremely heterogeneous
data. On this basis, this research aims at extending semantic web
approaches and technologies to architectural heritage knowledge management in
order to provide an integrated and multidisciplinary representation of the artifact
and of the knowledge necessary to support any decision or any intervention and
management activity. To this purpose, an ontology-based system, representing
the knowledge related to the artifact and its contexts, has been developed through
the formalization of domain-specific entities and relationships between them
Ontology modelling methodology for temporal and interdependent applications
The increasing adoption of Semantic Web technology by several classes of applications in recent years, has made ontology engineering a crucial part of application development. Nowadays, the abundant accessibility of interdependent information from multiple resources and representing various fields such as health, transport, and banking etc., further evidence the growing need for utilising ontology for the development of Web applications. While there have been several advances in the adoption of the ontology for application development, less emphasis is being made on the modelling methodologies for representing modern-day application that are characterised by the temporal nature of the data they process, which is captured from multiple sources. Taking into account the benefits of a methodology in the system development, we propose a novel methodology for modelling ontologies representing Context-Aware Temporal and Interdependent Systems (CATIS). CATIS is an ontology development methodology for modelling temporal interdependent applications in order to achieve the desired results when modelling sophisticated applications with temporal and inter dependent attributes to suit today's application requirements
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