Mehul Bhatt

Semantic Completeness in Sub-ontology Extraction Using Distributed Methods

The use of ontologies lies at the very heart of the newly emerging era of Semantic Web. They provide a shared conceptual- ization of some domain that may be communicated between people and application systems. A common problem with web ontologies is that they tend to grow large in scale and complexity as a result of ever increasing information requirements. The resulting ontologies are too large to be used in their entirety by one application. Our previous work, M aterialized Ontology V iew E xtractor (MOVE), has addressed this problem by proposing a distributed architecture for the extraction/optimization of a sub-ontology from a large scale base ontology. The extraction process consists of a number of independent optimization schemes that cover various aspects of the optimization process. In this paper, we extend MOVE with a Semantic Complete- ness Optimization Scheme (SCOS), which addresses the issue of the semantic correctness of the resulting sub-ontology. Moreover, we utilize distributed methods to implement SCOS in a cluster environment. Here, a distributed memory architecture serves two purposes: (a). Facilitates the utilization of a cluster environment typical in business organizations, which is in line with our envisaged application of the proposed system and (b). Enhances the performance of the computationally extensive extraction process when dealing with massively sized realistic ontologies.

CLP(QS): a declarative spatial reasoning framework

We propose CLP(QS), a declarative spatial reasoning framework capable of representing and reasoning about high-level, qualitative spatial knowledge about the world. We systematically formalize and implement the semantics of a range of qualitative spatial calculi using a system of non-linear polynomial equations in the context of a classical constraint logic programming framework. Whereas CLP(QS) is a general framework, we demonstrate its applicability for the domain of Computer Aided Architecture Design. With CLP(QS) serving as a prototype, we position declarative spatial reasoning as a general paradigm open to other formalizations, reinterpretations, and extensions. We argue that the accessibility of qualitative spatial representation and reasoning mechanisms via the medium of high-level, logic-based formalizations is crucial for their utility toward solving real-world problems.

A distributed approach to sub-ontology extraction

The new era of semantic Web has enabled users to extract semantically relevant data from the Web. The backbone of the semantic Web is a shared uniform structure which defines how Web information is split up regardless of the implementation language or the syntax used to represent the data. This structure is known as an ontology. As information on the Web increases significantly in size, Web ontologies also tend to grow bigger, to such an extent that they become too large to be used in their entirety by any single application. This has stimulated our work in the area of sub-ontology extraction where each user may extract optimized sub-ontologies from an existing base ontology. Sub-ontologies are valid independent ontologies, known as materialized ontologies, that are specifically extracted to meet certain needs. Because of the size of the original ontology, the process of repeatedly iterating the millions of nodes and relationships to form an optimized sub-ontology can be very extensive. Therefore we have identified the need for a distributed approach to the extraction process. As ontologies are currently widely used, our proposed approach for distributed ontology extraction will play an important role in improving the efficiency of information retrieval.

Spatio-terminological inference for the design of ambient environments

We present an approach to assist the smart environment design process by means of automated validation of work-in-progress designs. The approach facilitates validation of not only the purely structural requirements, but also the functional requirements expected of a smart environment whilst keeping in mind the plethora of sensory and interactive devices embedded within such an environment. The approach, founded in spatio-terminological reasoning, is illustrated in the context of formal ontology modeling constructs and reasoners, industrial architecture data standards and state-of-the-art commercial design software.

MOVE: A Distributed Framework for Materialized Ontology View Extraction

AbstractThe use of ontologies lies at the very heart of the newly emerging era of semantic web. Ontologies provide a shared conceptualization of some domain that may be communicated between people and application systems. As information on the web increases significantly in size, web ontologies also tend to grow bigger, to such an extent that they become too large to be used in their entirety by any single application. Moreover, because of the size of the original ontology, the process of repeatedly iterating the millions of nodes and relationships to form an optimized sub-ontology becomes very computationally extensive. Therefore, it is imperative that parallel and distributed computing techniques be utilized to implement the extraction process. These problems have stimulated our work in the area of sub-ontology extraction where each user may extract optimized sub-ontologies from an existing base ontology. The extraction process consists of a number of independent optimization schemes that cover various aspects of the optimization process, such as ensuring consistency of the user-specified requirements for the sub-ontology, ensuring semantic completeness of the sub-ontology, etc. Sub-ontologies are valid independent ontologies, known as materialized ontologies, that are specifically extracted to meet certain needs. Our proposed and implemented framework for the extraction process, referred to as Materialized Ontology View Extractor (MOVE), has addressed this problem by proposing a distributed architecture for the extraction/optimization of a sub-ontology from a large-scale base ontology. We utilize coarse-grained data-level parallelism inherent in the problem domain. Such an architecture serves two purposes: (a) facilitates the utilization of a cluster environment typical in business organizations, which is in line with our envisaged application of the proposed system, and (b) enhances the performance of the computationally extensive extraction process when dealing with massively sized realistic ontologies. As ontologies are currently widely used, our proposed approach for distributed ontology extraction will play an important role in improving the efficiency of ontology-based information retrieval.

Qualitative Spatial and Temporal Reasoning: Emerging Applications, Trends, and Directions

The field of Qualitative Spatial and Temporal Representation and Reasoning (QSTR) has evolved as a specialised discipline within Artificial Intelligence (Allen, 1983; Freksa, 1991; van Beek, 1992; Ladkin & Maddux, 1994; Cohn & Renz, 2007; Renz & Nebel, 2007). Recent years have witnessed remarkable advances in some of the long-standing problems of the field, primarily pertaining to spatial calculi and model construction issues emanating from the founding premises and early work in the community (Ligozat, 1990; Guesgen & Hertzberg, 1993, 1988). Subsequently, major developments have accrued with new results about tractability of spatial calculi and characterisation of important subclasses of relations (e.g., Nebel & Bürckert, 1994; Bessière et al., 1996; Renz, 1999, 2007; Li et al., 2009) and explicit construction of models of one or more aspects of space (e.g., Freksa, 1992; Randell et al., 1992; Cohn et al., 1997; Bennett, 2001; de Weghe et al., 2005; Moratz, 2006). Similar to these works, which are situated within an Artificial Intelligence/Knowledge Representation (KR) context, many crucial advances have accrued from other communities concerned with the development of formalisms and algorithms for modelling and reasoning about spatial information, a prime example here being the domain of spatial information theory for Geography (and Geographic Information Systems (GIS)) (Egenhofer & Franzosa, 1991; Egenhofer & Mark, 1995).

Ontology driven semantic profiling and retrieval in medical information systems

We propose the application of a novel sub-ontology extraction methodology for achieving interoperability and improving the semantic validity of information retrieval in the medical information systems (MIS) domain. The system offers advanced profiling of a users field of specialization by exploiting the concept of sub-ontology extraction, i.e., each sub-ontology may subsequently represent a particular user profile. Semantic profiling of a users field of specialization or interest is necessary functionality in any medical domain information retrieval system; this is because the (structural and semantic) extent of information sources is massive and individual users are only likely to be interested in specific parts of the overall knowledge documents on the basis of their area of specialization. The prototypical system, OntoMOVE, has been specifically designed for application in the medical information systems domain. OntoMOVE utilizes semantic web standards like RDF(S) and OWL in addition to medical domain standards and vocabularies encompassed by the UMLS knowledge sources.

Ontological modelling of form and function for architectural design

Form, function and the relationship between the two serve a crucial role in design. Within architectural design, key aspects of the anticipated function of buildings, or of spatial environments in general, are supposed to be supported by their structural form, i.e., their shape, layout, or connectivity. Whereas the philosophy of form and function is a well-researched topic, the practical relations and dependencies between form and function are only known implicitly by designers and architects. Specifically, the formal modelling of structural forms and resulting artefactual functions within design and design assistance systems remains elusive.In our work, we aim at making these definitions explicit by ontologically modelling respective domain entities, their properties and related constraints. We interpret “structural form” and “artefactual function” by specifying modular ontologies and their interplay for the architectural design domain. A key aspect in our modelling approach is the use of formal conceptual requirements and qualitative spatial calculi as a link between the structural form of a design and the differing functional capabilities that it affords or leads to. We demonstrate how our ontological modelling reflects types of architectural form and function, and how it facilitates the conceptual modelling of requirement constraints in architectural design.

Modelling Dynamic Spatial Systems in the Situation Calculus

Abstract We propose and systematically formalise a dynamical spatial systems approach for the modelling of changing spatial environments. The formalisation adheres to the semantics of the situation calculus and includes a systematic account of key aspects that are necessary to realize a domain-independent qualitative spatial theory that may be utilised across diverse application domains. The spatial theory is primarily derivable from the all-pervasive generic notion of “qualitative spatial calculi” that are representative of differing aspects of space. In addition, the theory also includes aspects, both ontological and phenomenal in nature, that are considered inherent in dynamic spatial systems. Foundational to the formalisation is a causal theory that adheres to the representational and computational semantics of the situation calculus. This foundational theory provides the necessary (general) mechanism required to represent and reason about changing spatial environments and also includes an account of the key fundamental epistemological issues concerning the frame and the ramification problems that arise whilst modelling change within such domains. The main advantage of the proposed approach is that based on the structure and semantics of the proposed framework, fundamental reasoning tasks such as projection and explanation directly follow. Within the specialised spatial reasoning domain, these translate to spatial planning/re-configuration, causal explanation and spatial simulation. Our approach is based on the hypothesis that alternate formalisations of existing qualitative spatial calculi using high-level tools such as the situation calculus are essential for their utilisation in diverse application domains such as intelligent systems, cognitive robotics and event-based GIS.

Spatial Computing for Design—an Artificial Intelligence Perspective

The articulation of the Science of Design by Herbert Simon and the paradigmatic relevance of Artificial Intelligence in that context are closely intertwined topics: Simon elaborates the ‘Sciences of the Artificial’ in the context of the design of artefacts. Situated in this AI-centric view of design, we characterize “spatial computing for design” as a specialisation concerned with the development of the general representational and computational apparatus necessary for solving modelling and reasoning problems in spatial design. Several representation and reasoning problems are dis-cussed in the backdrop of relevant examples involving the formal modelling of structural form with respect to a desired/anticipated artefactual function. The discussion, although applicable to any spatial design activity, is grounded in the domain of assistive decision-support in the context of a conventional computer-aided architecture design workflow.