scholarly journals Syntactic-semantic treebank for domain ontology creation

2015 ◽  
pp. 213-225
Author(s):  
Petya Osenova ◽  
Kiril Simov
Keyword(s):  
Domain Knowledge ◽  
Semantic Knowledge ◽  
Domain Ontology ◽  
Semantic Relations ◽  
Syntactic Structures ◽  
Linguistic Patterns ◽  
Ontology Building ◽  
The Creation ◽  
Syntactic Annotation

Syntactic-semantic treebank for domain ontology creationThis paper focuses on the creation of a domain treebank for the purposes of compiling a domain ontology. The domain treebank is viewed as a suitable resource for extracting of semantic relations from syntactic structures. First, the steps for ontology building are considered. Then, the processing over glossaries and standards is described with regard to their syntactic annotation. The utility of deriving semantic knowledge from the Treebank is also illustrated via the basic phrases. The idea is that the domain knowledge is represented in the domain data, but via treebanking more linguistic patterns can be extracted, which to be mapped to concepts and relations in a domain ontology.

scholarly journals From survey to semantic representation for Cultural Heritage: the 3D modeling of recurring architectural elements

ACTA IMEKO ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 98
Author(s):  
Valeria Croce ◽  
Gabriella Caroti ◽  
Andrea Piemonte ◽  
Marco Giorgio Bevilacqua
Keyword(s):  
Cultural Heritage ◽  
Semantic Representation ◽  
Laser Scanner ◽  
3D Models ◽  
Semantic Knowledge ◽  
Documentary Sources ◽  
Building Components ◽  
Integrated Programs ◽  
The Creation ◽  
Historical Sites

The digitization of Cultural Heritage paves the way for new approaches to surveying and restitution of historical sites. With a view to the management of integrated programs of documentation and conservation, the research is now focusing on the creation of information systems where to link the digital representation of a building to semantic knowledge. With reference to the emblematic case study of the Calci Charterhouse, also known as Pisa Charterhouse, this contribution illustrates an approach to be followed in the transition from 3D survey information, derived from laser scanner and photogrammetric techniques, to the creation of semantically enriched 3D models. The proposed approach is based on the recognition -segmentation and classification- of elements on the original raw point cloud, and on the manual mapping of NURBS elements on it. For this shape recognition process, reference to architectural treatises and vocabularies of classical architecture is a key step. The created building components are finally imported in a H-BIM environment, where they are enriched with semantic information related to historical knowledge, documentary sources and restoration activities.

Using Ontology as Scaffolding for Authoring Teaching Materials

2007 ◽  
pp. 203-221
Author(s):  
Jin-Tan Yang ◽  
Pao Ta Yu ◽  
Nian Shing Chen ◽  
Chun Yen Tsai ◽  
Chi-Chin Lee ◽  
...  
Keyword(s):  
Resource Description Framework ◽  
Domain Knowledge ◽  
Learning Objects ◽  
Domain Ontology ◽  
School Level ◽  
Future Research ◽  
Teaching Materials ◽  
Rich Description ◽  
Description Framework ◽  
Resource Description

The purpose of this study is to conduct teachers to author a teaching material by using visualized domain ontology as scaffolding. Based on a content repository management system (CRMS), mathematics ontology to support teachers for authoring teaching materials is developed. Although the domain ontology of mathematics at secondary school level in Taiwan provides structured vocabularies for describing domain content, those teachers who want to create a knowledge-rich description of domain knowledge, such as required by the “Semantic Web,” use ontology that turns out to provide only part of knowledge required. In this chapter, we examine problems related to capturing the learning resources or learning objects (LOs) on a CRMS. To construct ontology for a subset of mathematics course descriptions, the representation requirements by resource description framework/resource description framework schema (RDF/RDFS) was implemented. Furthermore, a visualized online authoring tool (VOAT) is designed for authoring teaching materials on the Web. Finally, discussion and future research are addressed.

Knowledge-Based Induction of Clinical Prediction Rules

2011 ◽  
pp. 350-375
Author(s):  
Mila Kwiatkowska ◽  
M. Stella Atkins ◽  
Les Matthews ◽  
Najib T. Ayas ◽  
C. Frank Ryan
Keyword(s):  
Domain Knowledge ◽  
Medical Knowledge ◽  
Data Sets ◽  
Clinical Prediction ◽  
Clinical Prediction Rules ◽  
Prediction Rules ◽  
Integrative Framework ◽  
Knowledge Based ◽  
The Creation ◽  
Definition Of

This chapter describes how to integrate medical knowledge with purely inductive (data-driven) methods for the creation of clinical prediction rules. It addresses three issues: representation of medical knowledge, secondary analysis of medical data, and evaluation of automatically induced predictive models in the context of existing knowledge. To address the complexity of the domain knowledge, the authors have introduced a semio-fuzzy framework, which has its theoretical foundations in semiotics and fuzzy logic. This integrative framework has been applied to the creation of clinical prediction rules for the diagnosis of obstructive sleep apnea, a serious and under-diagnosed respiratory disorder. The authors use a semio-fuzzy approach (1) to construct a knowledge base for the definition of diagnostic criteria, predictors, and existing prediction rules; (2) to describe and analyze data sets used in the data mining process; and (3) to interpret the induced models in terms of confirmation, contradiction, and contribution to existing knowledge.

Morphological Units: A Theoretical and Psycholinguistic Perspective

2020 ◽  
Author(s):  
Dominiek Sandra
Keyword(s):  
Lexical Processing ◽  
Syntactic Structure ◽  
Morphological Structure ◽  
Semantic Relations ◽  
Semantic Transparency ◽  
Relational Information ◽  
Syntactic Structures ◽  
Processing Level ◽  
Communicative Efficiency ◽  
Complex Words

Speakers can transfer meanings to each other because they represent them in a perceptible form. Phonology and syntactic structure are two levels of linguistic form. Morphemes are situated in-between them. Like phonemes they have a phonological component, and like syntactic structures they carry relational information. A distinction can be made between inflectional and lexical morphology. Both are devices in the service of communicative efficiency, by highlighting grammatical and semantic relations, respectively. Morphological structure has also been studied in psycholinguistics, especially by researchers who are interested in the process of visual word recognition. They found that a word is recognized more easily when it belongs to a large morphological family, which suggests that the mental lexicon is structured along morphological lines. The semantic transparency of a word’s morphological structure plays an important role. Several findings also suggest that morphology plays an important role at a pre-lexical processing level as well. It seems that morphologically complex words are subjected to a process of blind morphological decomposition before lexical access is attempted.

scholarly journals Robot Task Planning in Deterministic and Probabilistic Conditions Using Semantic Knowledge Base

2016 ◽  
Vol 7 (1) ◽  
pp. 56-77 ◽  
Author(s):  
Ahmed Abdulhadi Al-Moadhen ◽  
Michael Packianather ◽  
Rossitza Setchi ◽  
Renxi Qiu
Keyword(s):  
Domain Knowledge ◽  
Semantic Knowledge ◽  
Task Planning ◽  
Relational Models ◽  
Markov Logic ◽  
Plan Generation ◽  
Knowledge Based ◽  
Probabilistic Values ◽  
Robot Task ◽  
Semantic Knowledge Base

A new method is proposed to increase the reliability of generating symbolic plans by extending the Semantic-Knowledge Based (SKB) plan generation to take into account the amount of information and uncertainty related to existing objects, their types and properties, as well as their relationships with each other. This approach constructs plans by depending on probabilistic values which are derived from learning statistical relational models such as Markov Logic Networks (MLN). An MLN module is established for probabilistic learning and inference together with semantic information to provide a basis for plausible learning and reasoning services in support of robot task-planning. The MLN module is constructed by using an algorithm to transform the knowledge stored in SKB to types, predicates and formulas which represent the main building block for this module. Following this, the semantic domain knowledge is used to derive implicit expectations of world states and the effects of the action which is nominated for insertion into the task plan. The expectations are matched with MLN output.

scholarly journals UBioLab: a web-LABoratory for Ubiquitous in-silico experiments

2012 ◽  
Vol 9 (1) ◽  
pp. 12-31 ◽  
Author(s):  
E. Bartocci ◽  
M. R. Di Berardini ◽  
E. Merelli ◽  
L. Vito
Keyword(s):  
In Silico ◽  
Domain Knowledge ◽  
Semantic Knowledge ◽  
Semantic Heterogeneity ◽  
Web Based ◽  
Agent Based ◽  
Architectural Features ◽  
Physical Laboratory ◽  
Distributed Execution ◽  
Grid Nodes

Summary The huge and dynamic amount of bioinformatic resources (e.g., data and tools) available nowadays in Internet represents a big challenge for biologists -for what concerns their management and visualization- and for bioinformaticians -for what concerns the possibility of rapidly creating and executing in-silico experiments involving resources and activities spread over the WWW hyperspace. Any framework aiming at integrating such resources as in a physical laboratory has imperatively to tackle -and possibly to handle in a transparent and uniform way- aspects concerning physical distribution, semantic heterogeneity, co-existence of different computational paradigms and, as a consequence, of different invocation interfaces (i.e., OGSA for Grid nodes, SOAP for Web Services, Java RMI for Java objects, etc.). The framework UBioLab has been just designed and developed as a prototype following the above objective. Several architectural features -as those ones of being fully Web-based and of combining domain ontologies, Semantic Web and workflow techniques- give evidence of an effort in such a direction.The integration of a semantic knowledge management system for distributed (bioinformatic) resources, a semantic-driven graphic environment for defining and monitoring ubiquitous workflows and an intelligent agent-based technology for their distributed execution allows UBioLab to be a semantic guide for bioinformaticians and biologists providing (i) a flexible environment for visualizing, organizing and inferring any (semantics and computational) “type” of domain knowledge (e.g., resources and activities, expressed in a declarative form), (ii) a powerful engine for defining and storing semantic-driven ubiquitous in-silico experiments on the domain hyperspace, as well as (iii) a transparent, automatic and distributed environment for correct experiment executions.

scholarly journals An ontology-based knowledge management framework for a distributed water information system

2012 ◽  
Vol 15 (4) ◽  
pp. 1169-1188 ◽  
Author(s):  
Qing Liu ◽  
Quan Bai ◽  
Corne Kloppers ◽  
Peter Fitch ◽  
Qifeng Bai ◽  
...  
Keyword(s):  
Knowledge Management ◽  
Life Cycle ◽  
Domain Knowledge ◽  
Heterogeneous Systems ◽  
Semantic Knowledge ◽  
Knowledge Model ◽  
Provenance Information ◽  
Management Framework ◽  
Data Transparency ◽  
Multiple Domains

With the increasing complexity of hydrologic problems, data collection and data analysis are often carried out in distributed heterogeneous systems. Therefore it is critical for users to determine the origin of data and its trustworthiness. Provenance describes the information life cycle of data products. It has been recognised as one of the most promising methods to improve data transparency. However, due to the complexity of the information life cycle involved, it is a challenge to query the provenance information which may be generated by distributed systems, with different vocabularies and conventions, and may involve knowledge of multiple domains. In this paper, we present a semantic knowledge management framework that tracks and integrates provenance information across distributed heterogeneous systems. It is underpinned by the Integrated Knowledge model that describes the domain knowledge and the provenance information involved in the information life cycle of a particular data product. We evaluate the proposed framework in the context of two real-world water information systems.

Sign in / Sign up

Export Citation Format

Share Document