94 research outputs found
Querying Geometric Figures Using a Controlled Language, Ontological Graphs and Dependency Lattices
Dynamic geometry systems (DGS) have become basic tools in many areas of
geometry as, for example, in education. Geometry Automated Theorem Provers
(GATP) are an active area of research and are considered as being basic tools
in future enhanced educational software as well as in a next generation of
mechanized mathematics assistants. Recently emerged Web repositories of
geometric knowledge, like TGTP and Intergeo, are an attempt to make the already
vast data set of geometric knowledge widely available. Considering the large
amount of geometric information already available, we face the need of a query
mechanism for descriptions of geometric constructions.
In this paper we discuss two approaches for describing geometric figures
(declarative and procedural), and present algorithms for querying geometric
figures in declaratively and procedurally described corpora, by using a DGS or
a dedicated controlled natural language for queries.Comment: 14 pages, 5 figures, accepted at CICM 201
Exchange of Geometric Information Between Applications
The Web Geometry Laboratory (WGL) is a collaborative and adaptive e-learning
Web platform integrating a well known dynamic geometry system. Thousands of
Geometric problems for Geometric Theorem Provers (TGTP) is a Web-based
repository of geometric problems to support the testing and evaluation of
geometric automated theorem proving systems.
The users of these systems should be able to profit from each other. The TGTP
corpus must be made available to the WGL user, allowing, in this way, the
exploration of TGTP problems and their proofs. On the other direction TGTP
could gain by the possibility of a wider users base submitting new problems.
Such information exchange between clients (e.g. WGL) and servers (e.g. TGTP)
raises many issues: geometric search - someone, working in a geometric problem,
must be able to ask for more information regarding that construction; levels of
geometric knowledge and interest - the problems in the servers must be
classified in such a way that, in response to a client query, only the problems
in the user's level and/or interest are returned; different aims of each tool -
e.g. WGL is about secondary school geometry, TGTP is about formal proofs in
semi-analytic and algebraic proof methods, not a perfect match indeed;
localisation issues, e.g. a Portuguese user obliged to make the query and
process the answer in English; technical issues-many technical issues need to
be addressed to make this exchange of geometric information possible and
useful.
Instead of a giant (difficult to maintain) tool, trying to cover all, the
interconnection of specialised tools seems much more promising. The challenges
to make that connection work are many and difficult, but, it is the authors
impression, not insurmountable.Comment: In Proceedings ThEdu'17, arXiv:1803.0072
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Automatic message annotation and semantic interface for context aware mobile computing
This thesis was submitted for the degree of Docter of Philosophy and awarded by Brunel University.In this thesis, the concept of mobile messaging awareness has been investigated by designing and implementing a framework which is able to annotate the short text messages with context ontology for semantic reasoning inference and classification purposes. The annotated metadata of text message keywords are identified and annotated with concepts, entities and knowledge that drawn from ontology without the need of learning process and the proposed framework supports semantic reasoning based messages awareness for categorization purposes. The first stage of the research is developing the framework of facilitating mobile communication with short text annotated messages (SAMS), which facilitates annotating short text message with part of speech tags augmented with an internal and external metadata. In the SAMS framework the annotation process is carried out automatically at the time of composing a message. The obtained metadata is collected from the device’s file system and the message header information which is then accumulated with the message’s tagged keywords to form an XML file, simultaneously. The significance of annotation process is to assist the proposed framework during the search and retrieval processes to identify the tagged keywords and The Semantic Web Technologies are utilised to improve the reasoning mechanism. Later, the proposed framework is further improved “Contextual Ontology based Short Text Messages reasoning (SOIM)”. SOIM further enhances the search capabilities of SAMS by adopting short text message annotation and semantic reasoning capabilities with domain ontology as Domain ontology is modeled into set of ontological knowledge modules that capture features of contextual entities and features of particular event or situation. Fundamentally, the framework SOIM relies on the hierarchical semantic distance to compute an approximated match degree of new set of relevant keywords to their corresponding abstract class in the domain ontology. Adopting contextual ontology leverages the framework performance to enhance the text comprehension and message categorization. Fuzzy Sets and Rough Sets theory have been integrated with SOIM to improve the inference capabilities and system efficiency. Since SOIM is based on the degree of similarity to choose the matched pattern to the message, the issue of choosing the best-retrieved pattern has arisen during the stage of decision-making. Fuzzy reasoning classifier based rules that adopt the Fuzzy Set theory for decision making have been applied on top of SOIM framework in order to increase the accuracy of the classification process with clearer decision. The issue of uncertainty in the system has been addressed by utilising the Rough Sets theory, in which the irrelevant and indecisive properties which affect the framework efficiency negatively have been ignored during the matching process.The Ministry of Higher Education and Scientific Research (IRAQ
Automatic message annotation and semantic interface for context aware mobile computing
In this thesis, the concept of mobile messaging awareness has been investigated by designing and implementing a framework which is able to annotate the short text messages with context ontology for semantic reasoning inference and classification purposes. The annotated metadata of text message keywords are identified and annotated with concepts, entities and knowledge that drawn from ontology without the need of learning process and the proposed framework supports semantic reasoning based messages awareness for categorization purposes. The first stage of the research is developing the framework of facilitating mobile communication with short text annotated messages (SAMS), which facilitates annotating short text message with part of speech tags augmented with an internal and external metadata. In the SAMS framework the annotation process is carried out automatically at the time of composing a message. The obtained metadata is collected from the device’s file system and the message header information which is then accumulated with the message’s tagged keywords to form an XML file, simultaneously. The significance of annotation process is to assist the proposed framework during the search and retrieval processes to identify the tagged keywords and The Semantic Web Technologies are utilised to improve the reasoning mechanism. Later, the proposed framework is further improved “Contextual Ontology based Short Text Messages reasoning (SOIM)”. SOIM further enhances the search capabilities of SAMS by adopting short text message annotation and semantic reasoning capabilities with domain ontology as Domain ontology is modeled into set of ontological knowledge modules that capture features of contextual entities and features of particular event or situation. Fundamentally, the framework SOIM relies on the hierarchical semantic distance to compute an approximated match degree of new set of relevant keywords to their corresponding abstract class in the domain ontology. Adopting contextual ontology leverages the framework performance to enhance the text comprehension and message categorization. Fuzzy Sets and Rough Sets theory have been integrated with SOIM to improve the inference capabilities and system efficiency. Since SOIM is based on the degree of similarity to choose the matched pattern to the message, the issue of choosing the best-retrieved pattern has arisen during the stage of decision-making. Fuzzy reasoning classifier based rules that adopt the Fuzzy Set theory for decision making have been applied on top of SOIM framework in order to increase the accuracy of the classification process with clearer decision. The issue of uncertainty in the system has been addressed by utilising the Rough Sets theory, in which the irrelevant and indecisive properties which affect the framework efficiency negatively have been ignored during the matching process.EThOS - Electronic Theses Online ServiceMinistry of Higher Education and Scientific Research (Iraq)GBUnited Kingdo
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SUPPORTING ENGINEERING DESIGN OF ADDITIVELY MANUFACTURED MEDICAL DEVICES WITH KNOWLEDGE MANAGEMENT THROUGH ONTOLOGIES
Medical environments pose a substantial challenge for engineering designers. They combine significant knowledge demands with large investment for new product development and severe consequences in the case of design failure. Engineering designers must contend with an often-chaotic environment to which they have limited access and familiarity, a user base that is difficult to engage and highly diverse in many attributes, and a market structure that often pits stakeholders against one another. As medical care in general moves towards personalized models and surgical tools towards less invasive options emerging manufacturing technologies in additive manufacturing offer significant potential for the design of highly innovative medical devices. At the same time however these same technologies also introduce yet more challenges to the design process.
This dissertation presents a knowledge-based approach to addressing the existing and emerging challenges of medical device design. The approach aims to address these challenges using knowledge captured in a suite of modular ontologies modeling knowledge domains that must be considered in medical device design. These include ontologies for understanding clinical context, human factors, regulation, enterprise, and manufacturability. Together these ontologies support design ideation, knowledge capture, and design verification. These ontologies are subsequently used to formulate a comprehensive knowledge framework for medical device design, and to enable an innovative design process. Case studies analyzing the design of surgical tools in several medical specialties are used to assess the capabilities of this approach
A semantic and agent-based approach to support information retrieval, interoperability and multi-lateral viewpoints for heterogeneous environmental databases
PhDData stored in individual autonomous databases often needs to be combined and
interrelated. For example, in the Inland Water (IW) environment monitoring domain,
the spatial and temporal variation of measurements of different water quality indicators
stored in different databases are of interest. Data from multiple data sources is more
complex to combine when there is a lack of metadata in a computation forin and when
the syntax and semantics of the stored data models are heterogeneous. The main types
of information retrieval (IR) requirements are query transparency and data
harmonisation for data interoperability and support for multiple user views. A
combined Semantic Web based and Agent based distributed system framework has
been developed to support the above IR requirements. It has been implemented using
the Jena ontology and JADE agent toolkits. The semantic part supports the
interoperability of autonomous data sources by merging their intensional data, using a
Global-As-View or GAV approach, into a global semantic model, represented in
DAML+OIL and in OWL. This is used to mediate between different local database
views. The agent part provides the semantic services to import, align and parse
semantic metadata instances, to support data mediation and to reason about data
mappings during alignment. The framework has applied to support information
retrieval, interoperability and multi-lateral viewpoints for four European environmental
agency databases.
An extended GAV approach has been developed and applied to handle queries that can
be reformulated over multiple user views of the stored data. This allows users to
retrieve data in a conceptualisation that is better suited to them rather than to have to
understand the entire detailed global view conceptualisation. User viewpoints are
derived from the global ontology or existing viewpoints of it. This has the advantage
that it reduces the number of potential conceptualisations and their associated
mappings to be more computationally manageable. Whereas an ad hoc framework
based upon conventional distributed programming language and a rule framework
could be used to support user views and adaptation to user views, a more formal
framework has the benefit in that it can support reasoning about the consistency,
equivalence, containment and conflict resolution when traversing data models. A
preliminary formulation of the formal model has been undertaken and is based upon
extending a Datalog type algebra with hierarchical, attribute and instance value
operators. These operators can be applied to support compositional mapping and
consistency checking of data views. The multiple viewpoint system was implemented
as a Java-based application consisting of two sub-systems, one for viewpoint
adaptation and management, the other for query processing and query result
adjustment
New Fundamental Technologies in Data Mining
The progress of data mining technology and large public popularity establish a need for a comprehensive text on the subject. The series of books entitled by "Data Mining" address the need by presenting in-depth description of novel mining algorithms and many useful applications. In addition to understanding each section deeply, the two books present useful hints and strategies to solving problems in the following chapters. The contributing authors have highlighted many future research directions that will foster multi-disciplinary collaborations and hence will lead to significant development in the field of data mining
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