Domain Ontology Learning Research Articles

An SQL Domain Ontology Learning for Analyzing Hierarchies of Structures in Pre-Learning Assessment Agents

This paper presents the use of description logics (DL) in the definition and development of a Structured Query Language (SQL) domain ontology for a multi-agent based pre-assessment system. Description logics is a knowledge representation language for defining terms or classes, the relationships between classes, their instances, including individuals and literals. In a formal school curriculum, modules of learning are inter-dependent. So, teaching and learning follows an ordered sequence of learning from lower-level module(s) to higher-level ones. This process enables students to gain mastery of lower-level materials before moving up the ladder to higher-level learning. To describe an SQL ontology and its representation for a multi-agent based system application, this paper uses a description logic language to present the organization of learning modules into DesiredConcept $$ $$ , PrerequisiteConcept $$ $$ and LeafNodes $$ $$ as well as their associated relationships, namely, hasPrerequisite and hasKB between the learning modules. The paper thus presents a TBox and an Abox of a DL ontology and further transformation into a first-order predicate for a multi-agent based system that was implemented in Jason.

ADOL: a novel framework for automatic domain ontology learning

Ontology, as a semantic representation of a shared conceptualization, makes knowledge machine-readable and easy to spread. One of its typical applications is used to develop e-learning systems with Educational Ontology. Ontology can help students master knowledge architecture of required subjects and make scattered courseware more systematic. A big challenge is how to construct Educational Ontology to describe systematic knowledge of different subjects automatically. Currently, most of the ontologies are developed and extended manually, which requires the developers to possess certain professional knowledge and is time-consuming. In this paper, a framework to construct and extend Educational Ontology automatically is proposed.2 The proposed ontology learning framework, called ‘ADOL,’ can convert domain textbooks into a corresponding ontology automatically and efficiently. A case study on High School Physics shows that our approach is feasible and efficient.

Learning domain ontologies from engineering documents for manufacturing knowledge reuse by a biologically inspired approach

The evolution of any given product family in manufacturing enterprises follows the epicycles in product lifecycle development, thus generating many useful but unmanageable engineering documents. These documents record nearly all the information about product lifecycle and thus provide domain-specific concepts and significant relations for ontology extraction. A qualified ontology, in turn, facilitates the management and reuse of manufacturing knowledge. Consequently, this paper proposes a biologically inspired adaptive growth (BIAG) approach to learn domain ontologies (DO) from these documents. BIAG is modeled on the growth and development mechanisms of a tree, where genes and three key stages of tree lifecycle including seed formation, root development, and tree growth are reference for DO learning. In BIAG, axioms and constrains that serve the same function as genes of a tree are first generated by a semi-supervised learning algorithm, to control the construction process of DO; then three adaptive growth strategies inspired by the above three stages of tree lifecycle are proposed to extract DO from engineering documents. The results of the DO construction and application examples demonstrate the feasibility and effectiveness of BIAG, which could, therefore, be a good choice for DO construction towards manufacturing knowledge reuse.

Using online update of distributional semantics models for decision-making support for concepts extraction in the domain ontology learning task

Most of the information processed by computer systems is presented in the form of text corpuses. The number of such texts (as well as the corpus as a whole) only increases with time, and therefore the word processing tasks remain relevant to this day. Ontology allows to describe semantics using domain concepts and relations between them [1, 2]. In the ontology learning task, the ontology is dependent on quality of corpus which may not be readily available. There are different approaches to creating ontologies (including the use of different tools and frameworks). This paper discusses the use of word2vec (group of related models that are used to produce word embeddings) using online vocabulary update and extension of the original data corpus with additional training for the domain concepts extraction to automate the domain ontology creation.

Unsupervised Domain Ontology Learning from Text

Unsupervised Domain Ontology Learning from Text

Research and Application on Domain Ontology Learning Method Based on LDA

Research and Application on Domain Ontology Learning Method Based on LDA

OWL 2 learn profile: an ontology sublanguage for the learning domain.

Many experimental ontologies have been developed for the learning domain for use at different institutions. These ontologies include different OWL/OWL 2 (Web Ontology Language) constructors. However, it is not clear which OWL 2 constructors are the most appropriate ones for designing ontologies for the learning domain. It is possible that the constructors used in these learning domain ontologies match one of the three standard OWL 2 profiles (sublanguages). To investigate whether this is the case, we have analysed a corpus of 14 ontologies designed for the learning domain. We have also compared the constructors used in these ontologies with those of the OWL 2 RL profile, one of the OWL 2 standard profiles. The results of our analysis suggest that the OWL 2 constructors used in these ontologies do not exactly match the standard OWL 2 RL profile, but form a subset of that profile which we call OWL 2 Learn.

Learning Context-Sensitive Domain Ontologies from Folksonomies: A Cognitively Motivated Method

Ontology is the backbone of the Semantic Web, helping users search for relevant resources from the Web of linked data. The existing context-free mapping approach between tags and concepts fails to address the problems of social synonymy and social polysemy when ontologies are induced from folksonomies. The novel contributions of this paper are threefold. First, grounded in the cognitively motivated category utility measure, a novel basic-level concept mining algorithm is developed to construct semantically rich concept vectors to alleviate the problem of social synonymy. Second, contextual aspects of ontology learning are exploited via probabilistic topic modeling to address the problem of social polysemy. Third, a novel context-sensitive domain ontology learning algorithm that combines link- and content-based semantic analysis is developed to identify both taxonomic and associative relations among concepts. To the best of our knowledge, this is the first successful research that exploits a cognitively motivated method to learn context-sensitive domain ontologies from folksonomies. By using the Open Directory Project ontology as a benchmark, we examined the effectiveness of the proposed algorithms based on social annotations crawled from three different folksonomy sites. Our experimental results show that the proposed ontology learning system significantly outperforms the best baseline system by 13.83% in terms of taxonomic F-measure. The practical implication of our research is that high-quality ontologies are constructed with minimal human intervention to facilitate concept-driven retrieval of linked data and the knowledge-based interoperability among enterprises.

Ontology-Based Classification of Building Types Detected from Airborne Laser Scanning Data

Accurate information on urban building types plays a crucial role for urban development, planning, and management. In this paper, we apply Object-Based Image Analysis (OBIA) methods to extract buildings from Airborne Laser Scanner (ALS) data and investigate the possibility of classifying detected buildings into “Residential/Small Buildings”, “Apartment Buildings”, and “Industrial and Factory Building” classes by means of domain ontology and machine learning techniques. The buildings objects are classified using exclusively the information computed from the ALS data. To select the relevant features for predicting the classes of interest, the Random Forest classifier has been applied. The ontology-based classification yielded convincing results for the “Residential/Small Buildings” class (F-Measure 97.7%), whereas the “Apartment Buildings” and “Industrial and Factory Buildings” classes achieved less accurate results (F-Measure 60% and 51%, respectively).

Lightweight domain ontology learning from texts: graph theory-based approach using Wikipedia

Ontology engineering is the backbone of the semantic web. However, the construction of formal ontologies is a tough exercise which requires time and heavy costs. Ontology learning is thus a solution for this requirement. Since texts are massively available everywhere, making up of experts' knowledge and their know-how, it is of great value to capture the knowledge existing within such texts. Our approach is thus the kind of research work that answers the challenge of creating concepts' hierarchies from textual data taking advantage of the Wikipedia encyclopaedia to achieve some good-quality results. This paper presents a novel approach which essentially uses plain text Wikipedia instead of its categorical system and works with a simplified algorithm to infer a domain taxonomy from a graph.

What Do RDF and SPARQL bring to Big Data Projects?

Big DataVol. 1, No. 1 Original ArticlesWhat Do RDF and SPARQL bring to Big Data Projects?Bob DuCharmeBob DuCharmeSearch for more papers by this authorPublished Online:13 Feb 2013https://doi.org/10.1089/big.2012.0004AboutSectionsView articleView Full TextPDF/EPUB ToolsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View articleFiguresReferencesRelatedDetailsCited byBig Data Organization Challenge5 June 2022The LRA Workbench: an IDE for efficient REST API composition through linked metadata14 September 2021 | Journal of Big Data, Vol. 8, No. 1Ontological Formalisation of Mathematical Equations for Phenomic Data Exploitation21 July 2021The Importance of Big Data Metadata in Crisis ManagementKnowledge-Oriented Tool Complex Processing Databases of Scientific Publications1 December 2020 | Control Systems and Computers, No. 5 (289)An SQL Domain Ontology Learning for Analyzing Hierarchies of Structures in Pre-Learning Assessment Agents13 October 2020 | SN Computer Science, Vol. 1, No. 6Some Features of the Construction of Ontological Domain Models1 July 2019 | Control Systems and Computers, No. 3 (281)Semantics-based sensitive topic diffusion detection framework towards privacy aware online social networks1 March 2018 | Cluster Computing, Vol. 22, No. S1RegenBase: a knowledge base of spinal cord injury biology for translational research7 April 2016 | Database, Vol. 2016An Overview of Cloud Computing and Deployment of Big Data Analytics in the Cloud1 March 2014 | International Journal of Scientific Research in Science, Engineering and Technology Volume 1Issue 1Mar 2013 InformationCopyright 2013, Mary Ann Liebert, Inc.To cite this article:Bob DuCharme.What Do RDF and SPARQL bring to Big Data Projects?.Big Data.Mar 2013.38-41.http://doi.org/10.1089/big.2012.0004Published in Volume: 1 Issue 1: February 13, 2013PDF download

WebAnnot: a learner's dedicated web-based annotation tool

In this paper, we propose a model of semantic annotation suitable for learners. It is used to implement a web annotation tool WebAnnot which enables the learners to annotate their pedagogical documents with graphical and semantic annotations. The annotation semantics is built using both generic and learning domain ontologies. WebAnnot provides two annotation functions: manual and pattern-based ones. WebAnnot is implemented as a Firefox add-on, using semantic web technologies and languages. We present the results of an evaluation study of the proposed model conducted with computer science undergraduate students, regarding their annotation experience using WebAnnot.

A New Method for Knowledge and Information Management Domain Ontology Graph Model

A new ontology learning model called domain ontology graph (DOG) is proposed in this paper. There are two key components in the DOG, i.e., the definition of the ontology graph and the ontology learning process. The former defines the ontology and knowledge conceptualization model from the domain-specific text documents; the latter offers the necessary method of semiautomatic domain ontology learning and generates the corresponding ontology graphs. Two kinds of ontological operations are also defined based on the proposed DOG, i.e., document ontology graph generation and ontology-graph-based text classification. The simulation studies focused upon Chinese text data are used to demonstrate the potential effectiveness of our proposed strategy. This is accomplished by generating DOGs to represent the domain knowledge and conducting the text classifications based on the generated ontology graph. The experimental results show that the new method can produce significantly better classification accuracy (e.g., with 92.3% in f-measure) compared with other methods (such as 86.8% in f-measure for the term-frequency-inverse-document-frequency approach). The high performance demonstrates that our presented ontological operations based on the ontology graph knowledge model are effectively developed.

Realization of Adaptive Learning Content Based on Learning Object and Domain Ontology

With the evolution of information technology, E-learning has spread rapidly. Traditional E-learning has proved not fit requirement of modern education, new trend of developing the adaptive and customized E-learning system draws more and more attentions from researchers and practitioners. In this paper, we proposed an E-learning system which can provide adaptive content according to different situations of user. To achieve the adaptive E-learning system, two new technologies have been adopted by the system: learning object and domain ontology. Using these two technologies the system can provide intelligent learning content based on the user’s context, knowledge’s context. The paper also presents a scenario to demonstrate the step of production of adaptive learning content.

CRCTOL: A semantic-based domain ontology learning system

Domain ontologies play an important role in supporting knowledge-based applications in the Semantic Web. To facilitate the building of ontologies, text mining techniques have been used to perform o...

CRCTOL: A semantic‐based domain ontology learning system

AbstractDomain ontologies play an important role in supporting knowledge‐based applications in the Semantic Web. To facilitate the building of ontologies, text mining techniques have been used to perform ontology learning from texts. However, traditional systems employ shallow natural language processing techniques and focus only on concept and taxonomic relation extraction. In this paper we present a system, known as Concept‐Relation‐Concept Tuple‐based Ontology Learning (CRCTOL), for mining ontologies automatically from domain‐specific documents. Specifically, CRCTOL adopts a full text parsing technique and employs a combination of statistical and lexico‐syntactic methods, including a statistical algorithm that extracts key concepts from a document collection, a word sense disambiguation algorithm that disambiguates words in the key concepts, a rule‐based algorithm that extracts relations between the key concepts, and a modified generalized association rule mining algorithm that prunes unimportant relations for ontology learning. As a result, the ontologies learned by CRCTOL are more concise and contain a richer semantics in terms of the range and number of semantic relations compared with alternative systems. We present two case studies where CRCTOL is used to build a terrorism domain ontology and a sport event domain ontology. At the component level, quantitative evaluation by comparing with Text‐To‐Onto and its successor Text2Onto has shown that CRCTOL is able to extract concepts and semantic relations with a significantly higher level of accuracy. At the ontology level, the quality of the learned ontologies is evaluated by either employing a set of quantitative and qualitative methods including analyzing the graph structural property, comparison to WordNet, and expert rating, or directly comparing with a human‐edited benchmark ontology, demonstrating the high quality of the ontologies learned.

Domain Ontology Learning from the Web

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A knowledge acquisition methodology to ontology construction for information retrieval from medical documents

: Vast amounts of medical information reside within text documents, so that the automatic retrieval of such information would certainly be beneficial for clinical activities. The need for overcoming the bottleneck provoked by the manual construction of ontologies has generated several studies and research on obtaining semi-automatic methods to build ontologies. Most techniques for learning domain ontologies from free text have important limitations. Thus, they can extract concepts so that only taxonomies are generally produced although there are other types of semantic relations relevant in knowledge modelling. This paper presents a language-independent approach for extracting knowledge from medical natural language documents. The knowledge is represented by means of ontologies that can have multiple semantic relationships among concepts.

A methodology for constructing of philosophy ontology based on philosophical texts

An ontology is a crucial factor for the success of the Semantic Web and other knowledge-based systems in terms of share and reuse of domain knowledge. However, there are a few concrete ontologies within actual knowledge domains including learning domains. In this paper, we develop an ontology which is an explicit formal specification of concepts and semantic relations among them in philosophy. We call it a philosophy ontology. Our philosophy is a formal specification of philosophical knowledge including knowledge of contents of classical texts of philosophy. We propose a methodology, which consists of detailed guidelines and templates, for constructing text-based ontology. Our methodology consists of 3 major steps and 14 minor steps. To implement the philosophy ontology, we develop an ontology management system based on Topic Maps. Our system includes a semi-automatic translator for creating Topic Map documents from the output of conceptualization steps and other tools to construct, store, retrieve ontologies based on Topic Maps. Our methodology and tools can be applied to other learning domain ontologies, such as history, literature, arts, and music.

Learning Domain Ontologies from Document Warehouses and Dedicated Web Sites

We present a method and a tool, OntoLearn, aimed at the extraction of domain ontologies from Web sites, and more generally from documents shared among the members of virtual organizations. OntoLearn first extracts a domain terminology from available documents. Then, complex domain terms are semantically interpreted and arranged in a hierarchical fashion. Finally, a general-purpose ontology, WordNet, is trimmed and enriched with the detected domain concepts. The major novel aspect of this approach is semantic interpretation, that is, the association of a complex concept with a complex term. This involves finding the appropriate WordNet concept for each word of a terminological string and the appropriate conceptual relations that hold among the concept components. Semantic interpretation is based on a new word sense disambiguation algorithm, called structural semantic interconnections.