On the Potential of Artificial Intelligence Chatbots for Data Exploration of Federated Bioinformatics Knowledge Graphs

In this paper, we present work in progress on the role of artificial intelligence (AI) chatbots, such as ChatGPT, in facilitating data access to federated knowledge graphs. In particular, we provide examples from the field of bioinformatics, to illustrate the potential use of Conversational AI to describe datasets, as well as generate and explain (federated) queries across datasets for the benefit of domain experts

IfcWoD, Semantically Adapting IFC Model Relations into OWL Properties

In the context of Building Information Modelling, ontologies have been identified as interesting in achieving information interoperability. Regarding the construction and facility management domains, several IFC (Industry Foundation Classes) based ontologies have been developed, such as IfcOWL. In the context of ontology modelling, the constraint of optimizing the size of IFC STEP-based files can be leveraged. In this paper, we propose an adaptation of the IFC model into OWL which leverages from all modelling constraints required by the object-oriented structure of IFC schema. Therefore, we do not only present a syntactic but also a semantic adaptation of the IFC model. Our model takes into consideration the meaning of entities, relationships, properties and attributes defined by the IFC standard. Our approach presents several advantages compared to other initiatives such as the optimization of query execution time. Every advantage is defended by means of practical examples and benchmarks.Comment: In proceedings of the 32nd CIB W78 Conference on Information Technology in Construction, Oct 2015, Eindhoven, Netherland

A Federated Approach for Interoperating AEC/FM Ontologies

International audienceOver the last few years, the benefits of applying ontologies (semantic graph modelling) for Architecture, Engineering, Construction and Facility Management (AEC/FM) industry have been recognized by several researchers and industry stakeholders. One of the main motivations is because it eases AEC data manipulation and representation. However, a research question that still remains open is how to take advantage of semantic web technologies to interoperate the AEC/FM and other ontologies in a flexible and dynamical way in order to solve data structure heterogeneity problem. Because of this, we propose in this paper to apply a rule-based federated architecture to answer this research question

A Rule Based System for Semantical Enrichment of Building Information Exchange

International audienceIn the area of building construction and management, the dematerial-ization of data and processes has been a global issue for the past 10 years. Go-ing beyond the geometric representation of a building, Building Information Modeling (BIM) is an approach that aims at integrating into one single system heterogeneous data and processes from different actors. Such integration is a complex and fastidious task. The implementation of the related processes for data querying, retrieval or modification is not less difficult. To tackle this prob-lem, we have developed a novel approach based on Semantic Web technolo-gies. In doing so, we have defined an ontology inspired on IFC standard for rep-resenting building information. On top of this ontology, we have defined and implemented a set of SWRL rules. The paper at hand describes these rules and their application in the context of building information handling (notably by means of IFC files

Federating and querying heterogeneous and distributed Web APIs and triple stores

Today's international corporations such as BASF, a leading company in the crop protection industry, produce and consume more and more data that are often fragmented and accessible through Web APIs. In addition, part of the proprietary and public data of BASF's interest are stored in triple stores and accessible with the SPARQL query language. Homogenizing the data access modes and the underlying semantics of the data without modifying or replicating the original data sources become important requirements to achieve data integration and interoperability. In this work, we propose a federated data integration architecture within an industrial setup, that relies on an ontology-based data access method. Our performance evaluation in terms of query response time showed that most queries can be answered in under 1 second

Querying and reasoning over large scale building data sets: an outline of a performance benchmark

International audienceThe architectural design and construction domains work on a daily basis with massive amounts of data. Properly managing, exchanging and exploiting these data is an ever ongoing challenge in this domain. This has resulted in large semantic RDF graphs that are to be combined with a significant number of other data sets (building product catalogues, regulation data, geometric point cloud data, simulation data, sensor data), thus making an already huge dataset even larger. Making these big data available at high performance rates and speeds and into the correct (intuitive) formats is therefore an incredibly high challenge in this domain. Yet, hardly any benchmark is available for this industry that (1) gives an overview of the kind of data typically handled in this domain; and (2) that lists the query and reasoning performance results in handling these data. In this article, we therefore present a set of available sample data that explicates the scale of the situation, and we additionally perform a query and reasoning performance benchmark. This results not only in an initial set of quantitative performance results, but also in recommendations in implementing a web-based system relying heavily on large semantic data. As such, we propose an initial benchmark through which new upcoming data management proposals in the architectural design and construction domains can be measured