In order to reach an acceptable level of confidence in the quality of a software product, testing of the software is paramount. To obtain "good" quality software it is essential to rely on "good" test cases. To define the criteria for what make up for a "good" test case is not a trivial task. Over the past 15 years, a short list of publications have presented criteria for "good" test cases but without ranking them based on their importance. This paper presents a non-exhaustive and non-authoritative tentative list of 15 criteria and a ranking of their relative importance. A number of the criteria come from previous publications but also from discussions with our industrial partners. The ranking is based on results collected via a questionnaire that was sent out to a limited number of randomly chosen respondents in the Swedish software industry. This means that the results are more indicative than conclusive.
This deliverable specifies use cases based on bioinformatics research carried out by members ofA2. The use cases involve the use of rules to reason over ontologies and pathways (Dresden,Edinburgh, Paris, Linköping) and rules to specify workflows to integrate bioinformatics data (Lisbon, Skövde, Jena, Bucharest). The use cases are designed as a reference point to foster the take up of A2 use cases by I-work packages. Most notably, many of the use cases specify the need for querying and reactivity with languages like Xcerpt (I4), Erus (I5) and Prova (I5). The use cases range from basic research applications to fully deployed software with an international user base.
Bioinformatics is an important application area for semantic web technologies as much of the data is online and accessible in XML format, as some sites already support web services, and as ontologies are widely used to annotate data. In this deliverable, we give a survey over 18 of the most important bioinformatics resources and discuss their availability and accessibility, which are two of the main criteria for these resources to act as bases for later demonstrators.
Ontologies are an important technology for the Semantic Web and many ontologies have already been developed. Many ontologies also contain overlapping information and to be able to use them together effectively, we need to align them. Some of the current alignment techniques use information about the structure of the ontologies, but they have not produced good results in evaluations. We propose an approach where, in contrast to the other approaches, structural information is used as a filtering method in the alignment process. We evaluate the approach in terms of quality and performance.
The built environment is heavily dependent on wasteful linear economic models and needs to transition to the circular economy (CE). One of the key enablers of CE is Digital Product Passports (DPPs). However, determining the necessary information and selecting suitable technologies remains to be challenging in practical implementations. This research aims to present a framework for implementing DPPs using Knowledge Graphs (KGs). A literature review was conducted to identify the key components of the framework. The result shows that the key elements encompass use cases identification, data collection, modelling, integration, governance, access and querying, and maintenance and updating.
As building information modeling (BIM) gains popularity in the architecture, engineering, and construction (AEC) industry, manufacturers are required to distribute their product specifications in digital product models. Currently, manufacturers mainly employ proprietary formats, such as BIM objects supplemented by PDF documents to represent their product data descriptions. However, these formats do not support flexible automated product search and data integration. This paperdescribes the use of Semantic Web technologies in combination with BIM-based visual programming language (VPL) to automatically integrate product data from external databases. To facilitate data integration, we introduced a method to semantically represent product data linked with the CEN/TS 17623:2021 standard using ontologies in web ontology language (OWL). The study has focused on the use case of a manufacturer of lighting products. Results show that building designers are able to execute a more efficient product search that satisfies their query requirements and returns suitable products of their choice from the manufacturer's database based on their requests. This approach eliminates the time-consuming and error-prone process of manually entering product data into BIM software.
Ontologies are being used nowadays in many areas. Within the bioinformatics area there are a number of bio-ontologies that cover different aspects in molecular biology. Many of these ontologies contain overlapping information. In applications using multiple ontologies it is therefore of interest to be able to merge these ontologies. In this paper we describe a prototype implementation of an ontology merging tool for DAML+OIL ontologies. The tool generates suggestions for merging roles and concepts and for is-a relationships between concepts. We also evaluate the quality of the suggestions using well-known bio-ontologies
Developing ontologies is not an easy task and often the resulting ontologies are not consistent or strucurally complete. Such ontologies, although often useful, also lead to problems when used in semantically-enabled applications. Wrong conclusions may be derived or valid conclusions may be missed. To deal with this problem we may want to repair the ontologies. In this demo we present a system that supports the repair of the is-a hierarchy in ontologies. We have developed a tool that, given missing is-a relations, generates and recommends relevant ways to repair the is-a structure of the ontology and that allows a domain expert to do the repair in a semi-automatic way.
Developing ontologies is not an easy task and often the resulting ontologies are not consistent or complete. Such ontologies, although often useful, also lead to problems when used insemantically-enabled applications. Wrong conclusions may be derived or valid conclusions may be missed. To deal with this problem we may want to repair the ontologies. Up to date most work has been performed on finding and repairing the semantic defects such as unsatisfiable concepts and inconsistent ontologies. In this paper we tackle the problem of repairing modeling defects and in particular, the repairing of structural relations (is-a hierarchy) in the ontologies. We study the case where missing is-arelations are given. We define the notion of a structural repair and develop algorithms to compute repairing actions that would allow deriving the missing is-a relations in the repaired ontology. Further, we define preferences between repairs. We also look at how we can use external knowledge to recommend repairing actions to a domain expert. Further, we discuss an implemented prototype and its use as well as an experiment using the ontologies of the Anatomy track of the Ontology Alignment Evaluation Initiative.
New experimental methods allow researchers within molecular and systems biology to rapidly generate larger and larger amounts of data. This data is often made publicly available on the Internet and although this data is extremely useful, we are not using its full capacity. One important reason is that we still lack good ways to connect or integrate information from different resources. One kind of resource is the over 1000 data sources freely available on the Web. As most data sources are developed and maintained independently, they are highly heterogeneous. Information is also updated frequently. Other kinds of resources that are not so well-known or commonly used yet are the ontologies and the standards. Ontologies aim to define a common terminology for a domain of interest. Standards provide a way to exchange data between data sources and tools, even if the internal representations of the data in the resources and tools are different. In this chapter we argue that ontological knowledge and standards should be used for integration of data. We describe properties of the different types of data sources, ontological knowledge and standards that are available on the Web and discuss how this knowledge can be used to support integrated access to multiple biological data sources. Further, we present an integration approach that combines the identified ontological knowledge and standards with traditional information integration techniques. Current integration approaches only cover parts of the suggested approach. We also discuss the components in the model on which much recent work has been done in more detail: ontology-based data source integration, ontology alignment and integration using standards. Although many of our discussions in this chapter are general we exemplify mainly using work done within the REWERSE working group on Adding Semantics to the Bioinformatics Web.
Ontologies are an important technology for the Semantic Web. In different areas ontologies have already been developed and many of these ontologies contain overlapping information. Often we would therefore want to be able to use multiple ontologies and thus the ontologies need to be aligned. Currently, there exist a number of systems that support users in aligning ontologies, but not many comparative evaluations have been performed. In this paper we present a general framework for aligning ontologies where different alignment strategies can be combined. Further, we exemplify the use of the framework by describing a system (SAMBO) that is developed according to this framework. Within this system we have implemented some already existing alignment algorithms as well as some new algorithms. We also show how the framework can be used to experiment with combinations of strategies. This is a first step towards defining a framework that can be used for comparative evaluations of alignment strategies. For our tests we used several well-known bio-ontologies.
Ontologies are an important technology for the Semantic Web. In different areas ontologies have already been developed and many of these ontologies contain overlapping information. Often we would therefore want to be able to use multiple ontologies. To obtain good results, we need to find the relationships between terms in the different ontologies, i.e. we need to align them. Currently, there exist a number of systems that support users in aligning ontologies, but not many comparative evaluations have been performed and there exists little support to perform such evaluations. However, the study of the properties, the evaluation and comparison of the alignment strategies and their combinations, would give us valuable insight in how the strategies could be used in the best way. In this paper we propose the KitAMO framework for comparative evaluation of ontology alignment strategies and their combinations and present our current implementation. We evaluate the implementation with respect to performance. We also illustrate how the system can be used to evaluate and compare alignment strategies and their combinations in terms of performance and quality of the proposed alignments. Further, we show how the results can be analyzed to obtain deeper insights into the properties of the strategies.
Ontologies arebeing used nowadays in many areas. Within eacharea there are a number of ontologies, each with their own focus,that containoverlapping information. In applications using multiple ontologiesit istherefore of interest to be able to merge these ontologies. In thispaper wedescribe a prototype implementation of SAMBO, an ontology mergetool forDAML+OIL ontologies. The tool generates suggestions for mergingconceptsand relations and for creating is-a relationships between concepts.Weevaluate different strategies for the generation of suggestions. Wealsocompare our tool with the ontology merge tools Protégé-2000 withPROMPTand Chimaera in terms of the quality of suggestions and the time ittakes tomerge ontologies using these tools.
In recent years many biomedical ontologies, including anatomy ontologies, have been developed. Many of these ontologies contain overlapping information and often we would want to be able to use multiple ontologies. This requires finding the relationships between terms in the different ontologies, i.e. we need to align them. Sometimes we also want to merge ontologies into a new one. In this chapter we give an overview of current ontology alignment and merging systems. We focus on systems that compute similarities between terms in the different ontologies. We present a general framework for these kind of systems and discuss the existing strategies. We also present such a system (SAMBO) and discuss its use using anatomy ontologies. Further, we take a first step in dealing with the problem of using the best alignment algorithms for the ontologies we want to align. We present and illustrate the use of a framework and a tool (KitAMO) for comparative evaluation of ontology alignment strategies and their combinations.
Due to the recent explosion of the amount of on-line accessible biomedical data and tools, finding and retrieving the relevant information is not an easy task. The vision of a Semantic Web for life sciences alleviates these difficulties. A key technology for the Semantic Web is ontologies. In recent years many biomedical ontologies have been developed and many of these ontologies contain overlapping information. To be able to use multiple ontologies they have to be aligned or merged. In this paper we propose a framework for aligning and merging ontologies. Further, we developed a system for aligning and merging biomedical ontologies (SAMBO) based on this framework. The framework is also a first step towards a general framework that can be used for comparative evaluations of alignment strategies and their combinations. In this paper we evaluated different strategies and their combinations in terms of quality and processing time and compared SAMBO with two other systems.
Biological ontologies define the basic terms and relations in biological domains and are being used among others, as community reference, as the basis for interoperability between systems, and for search, integration, and exchange of biological data. In this chapter we present examples of biological ontologies and ontology-based knowledge, show how biological ontologies are used and discuss some important issues in ontology engineering.
This article describes a base system for ontology alignment, SAMBO, and an extension, SAMBOdtf. We present their results for the benchmark, anatomy and FAO tasks in the 2008 Ontology Alignment Evaluation Initiative. For the benchmark and FAO tasks SAMBO uses a strategy based on string matching as well as the use of a thesaurus. It obtains good results in many cases. For the anatomy task SAMBO uses a combination of string matching and the use of domain knowledge. This combination performed well in former evaluations using other anatomy ontologies. SAMBOdtf uses the same strategies but, in addition, uses an advanced filtering technique that augments recall while maintaining a high precision.
In this paper we propose and evaluate new strategies for aligning ontologies based on text categorization of literature using support vector machines-based text classifiers, and compare them with existing literature-based strategies. We also compare and combine these strategies with linguistic strategies.
Semisolid casting has emerged as an attractivealternative to conventional casting methods due to its potentialto yield superior mechanical properties, reduce environmentalpollution, and decrease production costs. However, optimizingprocess parameters and controlling the casting process remainschallenging. Process control largely relies on human expertise,associated with significant time and cost expenditures. Inresponse, this study presents a third-circle research project toinvestigate the correlation between the casting process and thesolidification process. The study proposes leveraging AI technologyto digitize the entire process control, thereby increasing thereliability and stability of cast products’ quality. The researchwill focus on understanding the key factors influencing thecasting process and developing an AI-based decision supportsystem to aid in process parameter selection and optimization.The outcomes of this study are expected to contribute to thedevelopment of more reliable and efficient semisolid castingprocesses.
Resource-efficient manufacturing is a foundation for sustainable and circular manufacturing. Semi-solid processing typically reduces material loss and improves productivity but generally requires a better understanding and control of the solidification of the cast material. Thermal analysis is commonly used in high-pressure die casting (HPDC) processes to determine casting process parameters, such as liquidus and solidus temperatures. However, this method is inadequate for semi-solid casting processes because the eutectic temperature is also a crucial parameter for successful semi-solid casting. This study explores the feasibility of using machine learning and artificial neural networks to predict fundamental values in Al-Si alloy casting. The Thermo-Calc 2022 software Scheil-Gulliver calculation function was used to generate the training and the test datasets, which included features such as melting temperature, alpha aluminium solidification temperature, eutectic temperature, and the solid fraction amounts at eutectic temperature. The results show that both models have a symmetric mean absolute percentage error (SMAPE) of less than 2 % with temperature prediction, with the machine learning model achieving a better accuracy of less than 1 %. A case study comparing practical measurements with prediction results is also discussed, demonstrating the potential of AI methods for predicting semi-solid casting processes.
One important aim within systems biology is to integrate disparate pieces of information, leading to discovery of higher-level knowledge about important functionality within living organisms. This makes standards for representation of data and technology for exchange and integration of data important key points for development within the area. In this article, we focus on the recent developments within the field. We compare the recent updates to the three standard representations for exchange of data SBML, PSI MI and BioPAX. In addition, we give an overview of available tools for these three standards and a discussion on how these developments support possibilities for data exchange and integration.
Semantic Role Labeling (SRL) plays an important role in different text mining tasks. The development of SRL systems for the biomedical area is frustrated by the lack of large-scale domain specific corpora that are annotated with semantic roles. In our previous work, we proposed a method for building FramenNet-like corpus for the area using domain knowledge provided by ontologies. In this paper, we present a framework for supporting the method and the system which we developed based on the framework. In the system we have developed the algorithms for selecting appropriate concepts to be translated into semantic frames, for capturing the information that describes frames from ontology terms, and for collecting example sentence using ontological knowledge.
Due to the explosion of the amount of biomedical data, knowledge and tools that are often publicly available over the Web, a number of difficulties are experienced by biomedical researchers. For instance, it is difficult to find, retrieve and integrate information that is relevant to their research tasks. Ontologies and the vision of a Semantic Web for life sciences alleviate these difficulties. In recent years many biomedical ontologies have been developed and many of these ontologies contain overlapping information. To be able to use multiple ontologies they have to be aligned or merged. A number of systems have been developed for aligning and merging ontologies and various alignment strategies are used in these systems. However, there are no general methods to support building such tools, and there exist very few evaluations of these strategies. In this thesis we give an overview of the existing systems. We propose a general framework for aligning and merging ontologies. Most existing systems can be seen as instantiations of this framework. Further, we develop SAMBO (System for Aligning and Merging Biomedical Ontologies) according to this framework. We implement different alignment strategies and their combinations, and evaluate them in terms of quality and processing time within SAMBO. We also compare SAMBO with two other systems. The work in this thesis is a first step towards a general framework that can be used for comparative evaluations of alignment strategies and their combinations.
The amount of biomedical information that is disseminated over the Web increases every day. This rich resource is used to find solutions to challenges across the life sciences. The Semantic Web for life sciences shows promise for effectively and efficiently locating, integrating, querying and inferring related information that is needed in daily biomedical research. One of the key technologies in the Semantic Web is ontologies, which furnish the semantics of the Semantic Web. A large number of biomedical ontologies have been developed. Many of these ontologies contain overlapping information, but it is unlikely that eventually there will be one single set of standard ontologies to which everyone will conform. Therefore, applications often need to deal with multiple overlapping ontologies, but the heterogeneity of ontologies hampers interoperability between different ontologies. Aligning ontologies, i.e. identifying relationships between different ontologies, aims to overcome this problem. A number of ontology alignment systems have been developed. In these systems various techniques and ideas have been proposed to facilitate identification of alignments between ontologies. However, there still is a range of issues to be addressed when we have alignment problems at hand. The work in this thesis contributes to three different aspects of identification of high quality alignments: 1) Ontology alignment strategies and systems. We surveyed the existing ontology alignment systems, and proposed a general ontology alignment framework. Most existing systems can be seen as instantiations of the framework. Also, we developed a system for aligning biomedical ontologies (SAMBO) according to this framework. We implemented various alignment strategies in the system. 2) Evaluation of ontology alignment strategies. We developed and implemented the KitAMO framework for comparative evaluation of different alignment strategies, and we evaluated different alignment strategies using the implementation. 3) Recommending optimal alignment strategies for different applications. We proposed a method for making recommendations.
Since there is no standard naming convention for genes and gene products, gene symbol disambiguation (GSD) has become a big challenge when mining biomedical literature. Several GSD methods have been proposed based on MEDLINE references to genes. However, nowadays gene databases, e.g. Entrez Gene, provide plenty of information about genes, and many biomedical ontologies, e.g. UMLS Metathesaurus and Semantic Network, have been developed. These knowledge sources could be used for disambiguation, in this paper we propose a method which relies on information about gene candidates from gene databases, contexts of gene symbols and biomedical ontologies. We implement our method, and evaluate the performance of the implementation using BioCreAtIvE II data sets.
In this chapter, we present that ontologies, as a formal representation of domain knowledge, can instruct us and ease all the tasks in building domain corpus annotated with semantic roles. We have built such a corpus for biological transport events using Gene Ontology. Then we report on a word-chunking approach for identifying semantic roles of biomedical predicates describing transport events. The results show that the system performance varies between different roles and the performance was not improved for all roles by introducing domain specific features
The work presented in this paper demonstrates an evaluation procedure for a real-life application ontology, coming from the avionics domain. The focus of the evaluation has specifically been on three ontology quality features, namely usability, correctness and applicability. In the paper, the properties of the three features are explained in the context of the application domain, the methods and tools used for the evaluation of the features are presented, and the evaluation results are presented and discussed. The results indicate that the three quality features are significant in the evaluation of our application ontology, that the proposed methods and tools allow for the evaluation of the three quality features and that the inherent quality of the application ontology can be confirmed.
Today XML is a common format supporting interoperabilityand information exchange between systems in the modeling and simulationeld. Although XML enables systems to agree on a common syntaxand understand the exchanged information, systems can misinterpretthem due to their dierent conceptualizations of the domain of interest.In this paper, we present a framework for automatic translation ofXML simulation models which follow the High Level Architecture (HLA) object model template specication, into OWL ontologies. In OWL ontologiesthe semantics of information is formally dened. It provides thebasis for interoperability and information exchange between simulationsystems on semantic level.
This paper presents an ontology which has been developed to represent the requirements of a software component pertaining to an embedded system in the avionics industry. The ontology was built based on the software requirements documents and was used to support advanced methods in the subsequent stages of the software development process. In this paper it is described theprocess that was used to build the ontology. Two pertinent quality measures that were applied to the ontology, i.e. usability and applicability, are also described, as well as the methods used to evaluate the quality measures and the result of these evaluations.
In recent years many biomedical ontologies have been developed and many of these ontologies contain overlapping information. To be able to use multiple ontologies they have to be aligned. In this paper we propose strategies for aligning ontologies based on life science literature. We propose a basic algorithm as well as extensions that take the structure of the ontologies into account. We evaluate the strategies and compare them with strategies implemented in the alignment system SAMBO. We also evaluate the combination of the proposed strategies and the SAMBO strategies.
Semantic Role Labeling (SRL) plays a key role in many NLP applications. The development of SRL systems for the biomedical domain is frustrated by the lack of large domain-specific corpora that are labeled with semantic roles. Corpus development has been very expensive and time-consuming. In this paper we propose a method for building frame-based corpus on the basis of domain knowledge provided by ontologies. We believe that ontologies, as a structured and semantic representation of domain knowledge, can instruct and ease the tasks in building the corpora. In the paper we present a corpus built by using the method. We compared it to BioFrameNet, and examined the gaps between the semantic classification of the target words in the domain-specific corpus and in FrameNet and Prop-Bank/VerbNet.
Semantic Role Labeling plays a key role in many text mining applications. The development of SRL systems for the biomedical domain is frustrated by the lack of large domain specific corpora that are labeled with semantic roles. In this paper we proposed a method for building corpus that are labeled with semantic roles for the domain of biomedicine. The method is based on the theory of frame semantics, and uses domain knowledge provided by ontologies. By using the method, we have built a corpus for transport events strictly following the domain knowledge provided by GO biological process ontology. We compared one of our frames to a BioFrameNet frame. We also examined the gaps between the semantic classification of the target words in this domain-specific corpus and in FrameNet and PropBank/VerbNet data. The successful corpus construction demonstrates that ontologies, as a formal representation of domain knowledge, can instruct us and ease all the tasks in building this kind of corpus. Furthermore, ontological domain knowledge leads to well-defined semantics exposed on the corpus, which will be very valuable in text mining applications.
In different areas ontologies have been developed and many of these ontologies contain overlapping information. Often we would therefore want to be able to use multiple ontologies. To obtain good results, we need to find the relationships between terms in the different ontologies, i.e. we need to align them. Currently, there already exist a number of different alignment strategies. However, it is usually difficult for a user that needs to align two ontologies to decide which of the different available strategies are the most suitable. In this paper we propose a method that provides recommendations on alignment strategies for a given alignment problem. The method is based on the evaluation of the different available alignment strategies on several small selected pieces from the ontologies, and uses the evaluation results to provide recommendations. In the paper we give the basic steps of the method, and then illustrate and discuss the method in the setting of an alignment problem with two well-known biomedical ontologies. We also experiment with different implementations of the steps in the method.
This article describes a system for ontology alignment, SAMBO, and presents its results for the benchmark and anatomy tasks in the 2007 Ontology Alignment Evaluation Initiative. For the benchmark task we have used a strategy based on string matching as well as the use of a thesaurus, and obtained good results in many cases. For the anatomy task we have used a combination of string matching and the use of domain knowledge. This combination performed well in former evaluations using other anatomy ontologies.