Winnowing ontologies based on application use

The requirements of specific applications and services are often over estimated when ontologies are reused or built. This sometimes results in many ontologies being too large for their intended purposes. It is not uncommon that when applications and services are deployed over an ontology, only a few parts of the ontology are queried and used. Identifying which parts of an ontology are being used could be helpful to winnow the ontology, i.e., simplify or shrink the ontology to smaller, more fit for purpose size. Some approaches to handle this problem have already been suggested in the literature. However, none of that work showed how ontology-based applications can be used in the ontology-resizing process, or how they might be affected by it. This paper presents a study on the use of the AKT Reference Ontology by a number of applications and services,and investigates the possibility of relying on this usage information to winnow that ontology

Ontology Winnowing: A Case Study on the AKT Reference Ontology

Many ontologies are built for the main purpose of representing a domain, rather than to meet the requirements of a specific application. When applications and services are deployed over an ontology, it is sometimes the case that only few parts of the ontology are queried and used. Identifying which parts of an ontology are being used could useful for realising the necessary fragments of the ontology to run the applications. Such information could be used to winnow an ontology, i.e., simplifying or shrinking the ontology to smaller, more fit for purpose sizes. This paper presents a study on the use of the AKT Reference Ontology by a number of applications and services, and investigate the possibility of using this information to winnow that ontology

Ontology construction from online ontologies

One of the main hurdles towards a wide endorsement of ontologies is the high cost of constructing them. Reuse of existing ontologies offers a much cheaper alternative than building new ones from scratch, yet tools to support such reuse are still in their infancy. However, more ontologies are becoming available on the web, and online libraries for storing and indexing ontologies are increasing in number and demand. Search engines have also started to appear, to facilitate search and retrieval of online ontologies. This paper presents a fresh view on constructing ontologies automatically, by identifying, ranking, and merging fragments of online ontologies

Using background knowledge for ontology evolution

One of the current bottlenecks for automating ontology evolution is resolving the right links between newly arising information and the existing knowledge in the ontology. Most of existing approaches mainly rely on the user when it comes to capturing and representing new knowledge. Our ontology evolution framework intends to reduce or even eliminate user input through the use of background knowledge. In this paper, we show how various sources of background knowledge could be exploited for relation discovery. We perform a relation discovery experiment focusing on the use of WordNet and Semantic Web ontologies as sources of background knowledge. We back our experiment with a thorough analysis that highlights various issues on how to improve and validate relation discovery in the future, which will directly improve the task of automatically performing ontology changes during evolution

Ontology summarization: an analysis and an evaluation

Ontology summarization has been recognized as a very useful technique to facilitate ontology understanding and then support ontology reuse as a new or supplementing technique. A number of efforts have emerged lately that apply different criteria, addressing different features of ontology, to extract ontology summaries. However, those efforts are ad-hoc in that there lacks consensus on a number of issues fundamental to the development of the field, such as a definition for ontology summarization, use case scenarios etc. Also, there lack sufficient evaluations and analysis, e.g. comparison among them and with other similar techniques, to provide meaning guidelines for users of this technique. With the aim to provide solutions to those fundamental issues, in this work, we present an analysis of this technique and its approaches. With the help of an objective evaluation method, we investigate what features of ontology are important in ontology summarization

Evaluations of User-Driven Ontology Summarization

Ontology Summarization has been found useful to facilitate ontology engineering tasks in a number of different ways. Recently, it has been recognised as a means to facilitate ontology understanding and then support tasks like ontology reuse in ontology construction. Among the works in literature, not only distinctive methods are used to summarize ontology, also different measures are deployed to evaluate the summarization results. Without a set of common evaluation measures in place, it is not possible to compare the performance and therefore judge the effectiveness of those summarization methods. In this paper, we investigate the applicability of the evaluation measures from ontology evaluation and summary evaluation domain for ontology summary evaluation. Based on those measures, we evaluate the performances of the existing user-driven ontology summarization approaches

Towards a Canonical Method to Solve Patterns of Ontology Modeling Issues (9 Month Report)

This report presents a brief description of the different activities carried out in the field of ontology engineering. It identifies a lack of guidelines on how to address modeling issues during the ontology conceptualization phase, in the current methodologies to build ontologies from scratch. It describes an example scenario of an ontology modeling task and it proposes a possible solution inspired by folksonomy based systems and faceted classification. This is followed by a study of the difficulties found to adapt the IEEE Learning Object Metadata (LOM) standard to model an ontology fit for purpose in a specific university curricula domain. It also gives an example of a prototype for a potential next generation semantic web application and a brief summary of the main viewpoints that will characterize such applications. Finally it outlines possible paths of further research to address ontology modeling issues and it suggests looking at various sources for possible solutions (schemes of folksonomy and faceted classification, design principles of object-oriented and relational database applications, and ontology evaluation)

APPLICATIONS OF GRAPH THEORY FOR REUSE OF MODEL BASED SYSTEMS ENGINEERING DESIGN DATA

This dissertation contributes to systems engineering (SE) by introducing and demonstrating a novel graph-based design repository (GBDR) tool. GBDR enables engineers to leverage system design information from a heterogenous set of system models created using multiple model based systems engineering (MBSE) software tools as an integrated body of knowledge. Specifically, the research provides a set of approaches that allow the use of system models described in Systems Modeling Language and Lifecycle Modeling Language as an integrated body of design information. The coalesced body of system design information serves to support concept ideation and analysis within SE. The research accomplishes this by using a graph database to store system model information imported from digital artifacts created by MBSE tools and applying principles from graph theory and semantic web technologies to identify likely connections and equivalent concepts across system models, modeling languages, and metamodels. The research demonstrates that the presented tool can import, store, synthesize, search, display, distribute, and export information from multiple MBSE tools. As a practical demonstration, feasible subsystem design alternatives for a small unmanned aircraft system government reference architecture are identified from within a set of existing system models.OSD CAPECivilian, Office of the Secretary of DefenseApproved for public release. Distribution is unlimited

Evolva: a comprehensive approach to ontology evolution

Ontology evolution is increasingly gaining momentum in the area of Semantic Web research. Current approaches target the evolution in terms of either content, or change management, without covering both aspects in the same framework. Moreover, they are slowed down as they heavily rely on user input. We tackle the aforementioned issues by proposing Evolva, a comprehensive ontology evolution framework, which handles a complete ontology evolution cycle, and makes use of background knowledge for decreasing user input

A data mining approach to ontology learning for automatic content-related question-answering in MOOCs.

The advent of Massive Open Online Courses (MOOCs) allows massive volume of registrants to enrol in these MOOCs. This research aims to offer MOOCs registrants with automatic content related feedback to fulfil their cognitive needs. A framework is proposed which consists of three modules which are the subject ontology learning module, the short text classification module, and the question answering module. Unlike previous research, to identify relevant concepts for ontology learning a regular expression parser approach is used. Also, the relevant concepts are extracted from unstructured documents. To build the concept hierarchy, a frequent pattern mining approach is used which is guided by a heuristic function to ensure that sibling concepts are at the same level in the hierarchy. As this process does not require specific lexical or syntactic information, it can be applied to any subject. To validate the approach, the resulting ontology is used in a question-answering system which analyses students' content-related questions and generates answers for them. Textbook end of chapter questions/answers are used to validate the question-answering system. The resulting ontology is compared vs. the use of Text2Onto for the question-answering system, and it achieved favourable results. Finally, different indexing approaches based on a subject's ontology are investigated when classifying short text in MOOCs forum discussion data; the investigated indexing approaches are: unigram-based, concept-based and hierarchical concept indexing. The experimental results show that the ontology-based feature indexing approaches outperform the unigram-based indexing approach. Experiments are done in binary classification and multiple labels classification settings . The results are consistent and show that hierarchical concept indexing outperforms both concept-based and unigram-based indexing. The BAGGING and random forests classifiers achieved the best result among the tested classifiers