Ontology-based data integration

About: Ontology-based data integration is a research topic. Over the lifetime, 11065 publications have been published within this topic receiving 216888 citations.

Human Language Technologies

Abstract: A tension exists between the increasingly rich semantic models in knowledge management systems and the continuing prevalence of human language materials in large organisations. The process of tying semantic models and natural language together is referred to as semantic annotation, which may also be char- acterized as the dynamic creation of bidirectional relationships between ontologies and unstructured and semi-structured documents. Information extraction (IE) takes unseen texts as input and produces fixed-format, unambiguous data as output. It involves processing text to identify selected infor- mation, such as particular named entities or relations among them from text docu- ments. Named entities include people, organizations, locations and so on, while relations typically include physical relations (located, near, part-whole, etc.), per- sonal or social relations (business, family, etc.), and membership (employ-staff, member-of-group, etc.). Ontology-based information extraction (OBIE) can be adapted specifically for semantic annotation tasks. An important difference between traditional IE and OBIE is the latter's closely coupled use of an ontology as one of the system's resources - the ontology serves not only as a schema or list of classifications in the output, but also as input data - its structure affects the training and tagging processes. We present here two ontology-based developments for information extrac- tion. OBIE experiments demonstrate clearly that the integration of ontologies as a knowledge source within HLT applications leads to improved perform- ance. Another important finding is that computational efficiency of the under- lying machine learning methods is especially important for HLT tasks, as the system may need to train hundreds of classifiers depending on the size of the ontology.

Reasoning with a Network of Aligned Ontologies

Abstract: In the context of the Semantic Web or semantic peer to peer systems, many ontologies may exist and be developed independently. Ontology alignments help integrating, mediating or reasoning with a system of networked ontologies. Though different formalisms have already been defined to reason with such systems, they do not consider ontology alignments as first class objects designed by third party ontology matching systems. Correspondences between ontologies are often asserted from an external point of view encompassing both ontologies. We study consistency checking in a network of aligned ontologies represented in Integrated Distributed Description Logics ( IDDL ). This formalism treats local knowledge (ontologies) and global knowledge (inter-ontology semantic relations, i.e. alignments) separately by distinguishing local interpretations and global interpretation so that local systems do not need to directly connect to each other. We consequently devise a correct and complete algorithm which, although being far from tractacle, has interesting properties: it is independent from the local logics expressing ontologies by encapsulating local reasoners. This shows that consistency of a IDDL system is decidable whenever consistency of the local logics is decidable. Moreover, the expressiveness of local logics does not need to be known as long as local reasoners can handle at least $\mathcal{ALC}$.

On Ontology, ontologies, Conceptualizations, Modeling Languages, and (Meta)Models.

Abstract: In philosophy, the term ontology has been used since the 17th century to refer both to a philosophical discipline (Ontology with a capital “O”), and as a domain-independent system of categories that can be used in the conceptualization of domain-specific scientific theories. In the past decades there has been a growing interest in the subject of ontology in computer and information sciences. In the last few years, this interest has expanded considerably in the context of the Semantic Web and MDA (Model-Driven Architecture) research efforts, and due to the role ontologies are perceived to play in these initiatives. In this paper, we explore the relations between Ontology and ontologies in the philosophical sense with domain ontologies in computer science. Moreover, we elaborate on formal characterizations for the notions of ontology, conceptualization and metamodel, as well as on the relations between these notions. Additionally, we discuss a set of criteria that a modeling language should meet in order to be considered a suitable language to model phenomena in a given domain, and present a systematic framework for language evaluation and design. Furthermore, we argue for the importance of ontology in both philosophical senses aforementioned for designing and evaluating a suitable general ontology representation language, and we address the question whether the so-called Ontology Web languages can be considered as suitable general ontology representation languages. Finally, we motivate the need for two complementary classes of modeling languages in Ontology Engineering addressing two separate sets of concerns.

Mining e-Learning Domain Concept Map from Academic Articles

Abstract: Recent research has demonstrated the important of ontology and its applications. For example, while designing adaptive learning materials, designers need to refer to the ontology of a subject domain. Moreover, ontology can show the whole picture and core knowledge about a subject domain. Research from literature also suggested that graphical representation of ontology can reduce the problems of information overload and learning disorientation for learners. However, ontology constructions used to rely on domain experts in the past; it is a time consuming and high cost task. Ontology creation for emerging new domains like e-Learning is even more challenging. The aim of this paper is to construct e-Learning domain concept maps, an alternative form of ontology, from academic articles. We adopt some relevant journal articles and conferences papers in e-Learning domain as data sources, and apply text-mining techniques to automatically construct concept maps for e-Learning domain. The constructed concept maps can provide a useful reference for researchers, who are new to e- Leaning field, to study related issues, for teachers to design adaptive courses, and for learners to understand the whole picture of e-Learning domain knowledge.