Ontologies and Software Modeling: Potentials, Experience and Challenges

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Ontologies and software modeling are related in several ways. Ontologies can be used to define modeling languages and integrate with model-driven engineering (MDE). However, using ontologies like OWL to fully represent modeling languages has challenges, as OWL does not support all needed concepts like collections. While early work has integrated ontologies and MDE at both the language and model levels, many open challenges remain around areas like requirements engineering, software processes, and developing domain-specific modeling languages.

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Ontologies and Software Modeling: Potentials, Experience and Challenges Dragan Ga šević Athabasca University Email: dgasevic@acm.org

Topics to discuss about ,[object Object],[object Object],[object Object],[object Object],[object Object]

[object Object],[object Object],[object Object]

Semantic Web ,[object Object],[object Object],[object Object]

What is an ontology? ,[object Object],[object Object],[object Object],[object Object],[object Object]

Semantic Web ,[object Object],[object Object]

Ontology languages enable reasoning! Not ontologies per se.

Model-Driven Engineering ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Metamodels and Models ,[object Object],[object Object],[object Object],[object Object],[object Object]

Ontologies are models & OWL is a modeling language

Modeling spaces ,[object Object],Everything is in the real world! We just use a convention to put some things in layers

Relations among Modeling Spaces ,[object Object],[object Object],[object Object],[object Object],[object Object]

Relations among Modeling Spaces ,[object Object],[object Object],[object Object],[object Object]

Relations among Modeling Spaces Parallel MS Orthogonal MS

Technical Spaces ,[object Object],[object Object],[object Object],[object Object]

Semantic Web Technical Space ,[object Object]

Integrating MDE and ontologies ,[object Object],[object Object],[object Object],[object Object]

Ontology Definition Metamodel ,[object Object],[object Object],[object Object],[object Object]

ODM Tooling ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

ODM Tooling ,[object Object],Not really mature yet for OWL

ODM Tooling ,[object Object],[object Object],[object Object],[object Object]

Ontologies for metamodeling ,[object Object],[object Object],[object Object]

Ontologies for metamodeling ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Is OWL enough? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[Milan o vi ć et al, 2007]

Is OWL enough? ,[object Object],[object Object],[object Object],[object Object]

[object Object],Feature Models in OWL [Wang et al, 2007]

[object Object],[object Object],[object Object],[object Object],Feature Models in OWL

TwoUse ,[object Object],[Parreiras et al, 2008]

Should OWL and UML/MOF be one language? [Atkinson, 2005] OWL and UML/MOF will be one language

Capturing Requirements ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Assisting in Software Process ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[Mohabbati et al, 2009] [ Bagheri et al, 2009; Boskovic et al, 2009]

Developing DS(M)L ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Conclusion ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Language variety identification aims at labelling texts in a native language (e.g. Spanish, Portuguese, English) with its specific variation (e.g. Ar- gentina, Chile, Mexico, Peru, Spain; Brazil, Portugal; UK, US). In this work we propose a low dimensionality representation (LDR) to address this task with five different varieties of Spanish: Argentina, Chile, Mexico, Peru and Spain. We compare our LDR method with common state-of-the-art representations and show an increase in accuracy of ∼35%. Furthermore, we compare LDR with two reference distributed representation models. Experimental results show com- petitive performance while dramatically reducing the dimensionality — and in- creasing the big data suitability — to only 6 features per variety. Additionally, we analyse the behaviour of the employed machine learning algorithms and the most discriminating features. Finally, we employ an alternative dataset to test the robustness of our low dimensionality representation with another set of similar languages.

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Over the last five years we have built mbeddr, a collection of integrated languages and language extensions built with the Jetbrains MPS language workbench. mbeddr consists of 81 languages, incl. IDE support, 34 of them C extensions. The mbeddr languages use a wide variety of notations – textual, tabular, symbolic and graphical – and the C extensions are modular; new extensions can be added without changing the existing implementation of C. mbeddr’s development has spanned roughly 15 person years so far, and the tool is used in practice and continues to be developed. This makes mbeddr a case study in language engineering of non-trivial size and complexity. In this keynote, I look back at mbeddr’s development to identify what worked well and what are still challenges in language engineering with a modern language workbench. I will address five areas: language modularity, notational freedom and projectional editing, mechanisms for managing complexity, performance and scalability issues and the consequences for the development process. I will close by highlighting a set of issues which need further research and engineering.

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This paper presents a set of linguistically informed and motivated multilingual alignments -- the CLUE4Translation Alignments -- covering several categories of multiwords and phrasal units, which constitute important challenges to high quality machine translation. The alignments comprise all possible word combinations between English, French, Portuguese, and Spanish parallel texts of the common test set of the Europarl corpus. The gold collection of the manually annotated alignments -- the Gold-CLUE-Translation -- is constituted of 400 sentences aligned according to previously proposed guidelines -- CLUE4Translation Alignment Guidelines -- for each language pair, resulting in a set of 2,400 alignments. The alignments were performed with the support of a new alignment tool -- CLUE-Aligner -- developed to facilitate the alignment of the translation units in the bitexts, including the alignment of non-contiguous multiwords and phrasal translation units. The Gold CLUE4Translation, the CLUE-Aligner, and the CLUE4Translation Alignment Guidelines are publicly available.

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Ontologies are one of the core foundations of the Semantic Web. To participate in Semantic Web projects, domain experts need to be able to understand the ontologies involved. Visual notations can provide an overview of the ontology and help users to understand the connections among entities. However, the users first need to learn the visual notation before they can interpret it correctly. Controlled natural language (CNL) representation would be readable right away and might be preferred in case of complex axioms, however, the structure of the ontology would remain less apparent. We propose to combine ontology visualizations with contextual ontology verbalizations of selected ontology (diagram) elements, displaying CNL explanations of OWL axioms corresponding to the selected visual notation elements. Thus, the domain experts will benefit from both the high-level overview provided by the graphical notation and the detailed CNL explanations of particular elements in the diagram.

Semantic Rules Representation in Controlled Natural Language in FluentEditor

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Abstract. The purpose of this paper is to present a way of representation of semantic rules (SWRL) in controlled natural language (English) in order to facilitate understanding the rules by humans interacting with a machine. The rule representation is implemented in FluentEditor – ontology editor with controlled natural language (CNL). The representation can be used in a lot of domains where people interact with machines and use specialized interfaces to define knowledge in a system (semantic knowledge base), e.g. representing medical knowledge and guidelines, procedures in crisis management or in management of any coordination processes. Such knowledge bases are able to support decision making in any discipline provided there is a knowledge stored in a proper semantic way.

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Model Driven Engineering (MDE) is increasingly gaining acceptance in the development of software systems as a mean to leverage abstraction and render business logic resilient to technological changes. Coordinated collections of models and modeling languages are used to describe applications on different abstraction levels and from different perspectives. In general, both models and metamodels are not preserved from the evolutionary pressure which inevitably affects almost any artifacts, possibly causing a cascade of adaptations which severely affects the modeling languages or the model population. This talk analyzes the different kinds of co-adaptations which are required, distinguishing among co-evolution in the large and in the small. In particular, the coupling between models and metamodels implies that when a metamodel undergoes a modification, the conforming models require to be accordingly co-adapted. Analogously, whenever a new version of a model is produced, the generated application may require an explicit adaptation of the generated artifacts, especially when specific assets are not directly reflected by the models and transformations, as for instance when dealing with serialized objects or with page content which is persistently stored in a database.

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W3C’s Semantic Web intents a common framework that allows data to be shared and reused across application and enterprise. The semantic web and its related technologies are the main directions of future web development where machine-processable information which supports user tasks. Ontologies are playing the vital role in Semantic Web. Researches on Ontology engineering had pointed out that an effective ontology application development methodology with integrated tool support is mandatory for its success. . Potential benefits are there to ontology engineering in making the toolset of Model Driven Architecture applicable to ontology modeling. Since Software and Ontology engineering are two complimentary branches, the scope of extension of the well proven methodologies and UML based modeling approaches used in software engineering to ontology engineering can bridge the gap between the engineering branches. This research paper is an attempt to suggest an exclusive hybrid methodology for ontology development from existing matured software engineering. Philosophical and engineering aspects of the newly derived methodology have been described clearly An attempt has been made for the application of proposed methodology with protégé editor. The full-fledged implementation of an domain ontology and its validation is the future research direction.

DODDLE-OWL: A Domain Ontology Construction Tool with OWL

Takeshi Morita

In this paper, we propose a domain ontology construction tool with OWL. The advantage of our tool is focusing the quality refinement phase of ontology construction. Through interactive support for refining the initial ontology, OWL-Lite level ontology, which consists of taxonomic relationships (defined as classes) and non-taxonomic relationships (defined as properties), is constructed effectively. The tool also provides semi-automatic generation of the initial ontology using domain specific documents and general ontologies.

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Recent advancements in the field of natural language processing have markedly enhanced the capability of machines to comprehend human language. However, as language models progress, they require continuous architectural enhancements and different approaches to text processing. One significant challenge stems from the rich diversity of languages, each characterized by its distinctive grammar resulting ina decreased accuracy of language models for specific languages, especially for low-resource languages. This limitation is exacerbated by the reliance of existing NLP models on rigid tokenization methods, rendering them susceptible to issues with previously unseen or infrequent words. Additionally, models based on word and subword tokenization are vulnerable to minor typographical errors, whether they occur naturally or result from adversarial misspellings. To address these challenges, this paper presents the utilization of a recently proposed free-tokenization method, such as Cannine, to enhance the comprehension of natural language. Specifically, we employ this method to develop an Arabic-free tokenization language model. In this research, we will precisely evaluate our model’s performance across a range of eight tasks using Arabic Language Understanding Evaluation (ALUE) benchmark. Furthermore, we will conduct a comparative analysis, pitting our free-tokenization model against existing Arabic language models that rely on sub-word tokenization. By making our pre-training and fine-tuning models accessible to the Arabic NLP community, we aim to facilitate the replication of our experiments and contribute to the advancement of Arabic language processing capabilities. To further support reproducibility and open-source collaboration, the complete source code and model checkpoints will be made publicly available on our Huggingface1 . In conclusion, the results of our study will demonstrate that the free-tokenization approach exhibits comparable performance to established Arabic language models that utilize sub-word tokenization techniques. Notably, in certain tasks, our model surpasses the performance of some of these existing models. This evidence underscores the efficacy of free-tokenization in processing the Arabic language, particularly in specific linguistic contexts.

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