Extending the Foundational Model of Anatomy with Automatically Acquired Spatial Relations

Formal ontologies have made significant impact in bioscience over the last ten years. Among them, the Foundational Model of Anatomy Ontology (FMA) is the most comprehensive model for the spatio-structural representation of human anatomy. In the research project MEDICO we use the FMA as our main source of background knowledge about human anatomy. Our ultimate goals are to use spatial knowledge from the FMA (1) to improve automatic parsing algorithms for 3D volume data sets generated by Computed Tomography and Magnetic Resonance Imaging and (2) to generate semantic annotations using the concepts from the FMA to allow semantic search on medical image repositories. We argue that in this context more spatial relation instances are needed than those currently available in the FMA. In this publication we present a technique for the automatic inductive acquisition of spatial relation instances by generalizing from expert-annotated volume datasets

FLIP : functional-plus-logic programming on an integrated platform

In FLIP, a novel approach to the integration of relational and functional languages on the basis of abstract machines (in the context of the RELFUN language and implementation) is described. This integration is carried out for several reasons: to combine two declarative paradigms into a more expressive one, to allow existing software libraries in relational and functional (here LL, a COMMON LISP derivative) languages to be used together without the need of re-implementation, to speed up relational programs by transforming deterministic relations into functions, and to enhance the expressiveness of relational languages by new extra-logicals with the help of functions. The integration is performed on two levels: 1. on the abstract machine level (the WAM, the abstract machine behind most implementations of relational languages, and the LLAMA, an abstract machine especially designed for the efficient execution of LL, are coupled), and 2. on the source language level (LL functions are accessible from relations and vice versa). One of the major points of this work is the detection and transformation of deterministic relations (into LL functions), resulting in a speed-up factor of 2-4. For this, a theoretical foundation for deterministic relations and several intermediate representation languages for the transformation process are developed

Indexing PROLOG procedures into DAGs by heuristic classification

This paper first gives an overview of standard PROLOG indexing and then shows, in a step-by-step manner, how it can be improved by slightly extending the WAM indexing instruction set to allow indexing on multiple arguments. Heuristics are described that overcome the difficulty of computing the indexing WAM code. In order to become independent from a concrete WAM instruction set, an abstract graphical representation based on DAGs (called DAXes) is introduced. The paper includes a COMMON LISP listing of the main heuristics implemented; the algorithms were developed for RELFUN, a relational-plus-functional language, but can easily be used in arbitrary PROLOG implementations

Named Graphs as a Mechanism for Reasoning About Provenance

Named Graphs is a simple, compatible extension to the RDF abstract syntax that enables statements to be made about RDF graphs. This approach is in contrast to earlier attempts such as RDF reification, or knowledge-base specific extensions including quads and contexts. In this paper we demonstrate the use of Named Graphs and our experiences developing new kinds of semantic web application that build on Named Graphs for digital signatures, provenance, and semantic reasoning. We present a working example based on the Named Graphs for Jena (NG4J) API, from which we developed a semantic version control system for Software Engineering capable of reasoning about Named Graph-based provenance. We go on to discuss the implications of Named Graphs for Description Logics and semantic inference strategies

Ontology-Based Digital Twin Framework for Smart Factories

In modern smart factories we have multiple entities that interact with one another, such as worker-assistance system, robot collaboration and their corresponding software modules. To fa- cilitate seamless cooperation between those subsystems, it is beneficial that they all have access to one coherent environment model. Hence, we propose an ontology-based Digital Twin that al- lows semantic representation of all important parts of such a scenario. It allows uniform access for different application components such as intention recognition and robotic action planning. Furthermore, it provides information tailored to the needs of those different components, e.g., via different zoom levels and affordances

Querying Semantic Web Resources Using TRIPLE Views

Resources on the Semantic Web are described by metadata based on some formal or informal ontology. It is a common situation that casual users are not familiar with a domain ontology in detail. This makes it difficult for such users (or their user tools) to formulate queries to find the relevant resources. Users consider the resources in their specific context, so the most straightforward solution is to formulate queries in an ontology that corresponds to a user-specific view. We present an approach based on multiple views expressed in ontologies simpler than the domain ontology. This allows users to query heterogeneous data repositories in terms of multiple, relatively simple, view ontologies. Ontology developers can define such view ontologies and the corresponding mapping rules. These ontologies are represented in Semantic Web ontology languages such as RDFS, DAML+OIL, or OWL. We present our approach with examples from the e-learning domain using the Semantic Web query and transformation language TRIPLE

Techniques for organizational memory information systems

The KnowMore project aims at providing active support to humans working on knowledge-intensive tasks. To this end the knowledge available in the modeled business processes or their incarnations in specific workflows shall be used to improve information handling. We present a representation formalism for knowledge-intensive tasks and the specification of its object-oriented realization. An operational semantics is sketched by specifying the basic functionality of the Knowledge Agent which works on the knowledge intensive task representation. The Knowledge Agent uses a meta-level description of all information sources available in the Organizational Memory. We discuss the main dimensions that such a description scheme must be designed along, namely information content, structure, and context. On top of relational database management systems, we basically realize deductive object- oriented modeling with a comfortable annotation facility. The concrete knowledge descriptions are obtained by configuring the generic formalism with ontologies which describe the required modeling dimensions. To support the access to documents, data, and formal knowledge in an Organizational Memory an integrated domain ontology and thesaurus is proposed which can be constructed semi-automatically by combining document-analysis and knowledge engineering methods. Thereby the costs for up-front knowledge engineering and the need to consult domain experts can be considerably reduced. We present an automatic thesaurus generation tool and show how it can be applied to build and enhance an integrated ontology /thesaurus. A first evaluation shows that the proposed method does indeed facilitate knowledge acquisition and maintenance of an organizational memory

Relfun/X : an experimental prolog implementation of Relfun

Relfun/X is an experimental implementation of Relfun, a relational and functional language developed by Harold Boley at Kaiserslautern University. It is totally implemented in Prolog; additionally, the Relfun/X programs are compiled into Prolog programs (i.e. "consulted" analogously to the ordinary consulting scheme of Prolog). While Relfun/X does not provide all the features of the Lisp-based Relfun implementation, it is the first running version supporting Relfun´s multi-footed clauses

RELFUN guide : programming with relations and functions made easy

A practical description of relational/functional programming in RELFUN is given. The language constructs are introduced by a tutorial dialog. Builtins, primitives, and commands are explained. Examples are given on all aspects relevant to using the language