Sharing Semantic Resources

The Semantic Web is an extension of the current Web in which information, so far created for human consumption, becomes machine readable, “enabling computers and people to work in cooperation”. To turn into reality this vision several challenges are still open among which the most important is to share meaning formally represented with ontologies or more generally with semantic resources. This Semantic Web long-term goal has many convergences with the activities in the field of Human Language Technology and in particular in the development of Natural Language Processing applications where there is a great need of multilingual lexical resources. For instance, one of the most important lexical resources, WordNet, is also commonly regarded and used as an ontology. Nowadays, another important phenomenon is represented by the explosion of social collaboration, and Wikipedia, the largest encyclopedia in the world, is object of research as an up to date omni comprehensive semantic resource. The main topic of this thesis is the management and exploitation of semantic resources in a collaborative way, trying to use the already available resources as Wikipedia and Wordnet. This work presents a general environment able to turn into reality the vision of shared and distributed semantic resources and describes a distributed three-layer architecture to enable a rapid prototyping of cooperative applications for developing semantic resources

Semantic Enrichment of Ontology Mappings

Schema and ontology matching play an important part in the field of data integration and semantic web. Given two heterogeneous data sources, meta data matching usually constitutes the first step in the data integration workflow, which refers to the analysis and comparison of two input resources like schemas or ontologies. The result is a list of correspondences between the two schemas or ontologies, which is often called mapping or alignment. Many tools and research approaches have been proposed to automatically determine those correspondences. However, most match tools do not provide any information about the relation type that holds between matching concepts, for the simple but important reason that most common match strategies are too simple and heuristic to allow any sophisticated relation type determination. Knowing the specific type holding between two concepts, e.g., whether they are in an equality, subsumption (is-a) or part-of relation, is very important for advanced data integration tasks, such as ontology merging or ontology evolution. It is also very important for mappings in the biological or biomedical domain, where is-a and part-of relations may exceed the number of equality correspondences by far. Such more expressive mappings allow much better integration results and have scarcely been in the focus of research so far. In this doctoral thesis, the determination of the correspondence types in a given mapping is the focus of interest, which is referred to as semantic mapping enrichment. We introduce and present the mapping enrichment tool STROMA, which obtains a pre-calculated schema or ontology mapping and for each correspondence determines a semantic relation type. In contrast to previous approaches, we will strongly focus on linguistic laws and linguistic insights. By and large, linguistics is the key for precise matching and for the determination of relation types. We will introduce various strategies that make use of these linguistic laws and are able to calculate the semantic type between two matching concepts. The observations and insights gained from this research go far beyond the field of mapping enrichment and can be also applied to schema and ontology matching in general. Since generic strategies have certain limits and may not be able to determine the relation type between more complex concepts, like a laptop and a personal computer, background knowledge plays an important role in this research as well. For example, a thesaurus can help to recognize that these two concepts are in an is-a relation. We will show how background knowledge can be effectively used in this instance, how it is possible to draw conclusions even if a concept is not contained in it, how the relation types in complex paths can be resolved and how time complexity can be reduced by a so-called bidirectional search. The developed techniques go far beyond the background knowledge exploitation of previous approaches, and are now part of the semantic repository SemRep, a flexible and extendable system that combines different lexicographic resources. Further on, we will show how additional lexicographic resources can be developed automatically by parsing Wikipedia articles. The proposed Wikipedia relation extraction approach yields some millions of additional relations, which constitute significant additional knowledge for mapping enrichment. The extracted relations were also added to SemRep, which thus became a comprehensive background knowledge resource. To augment the quality of the repository, different techniques were used to discover and delete irrelevant semantic relations. We could show in several experiments that STROMA obtains very good results w.r.t. relation type detection. In a comparative evaluation, it was able to achieve considerably better results than related applications. This corroborates the overall usefulness and strengths of the implemented strategies, which were developed with particular emphasis on the principles and laws of linguistics

Challenges in building domain ontology for minority languages

The development of domain ontology is important in building a list of vocabulary whereas the process of sharing and reusing this knowledge management can be accomplished easily. This paper presents the challenges that arise in the ontology development area by focusing on one domain concept. Domain concept here can be transversed from different disciplines, such as agricultural, medicine, human-anatomy, and automotive. The assessments on the challenges vary among numerous ontology projects. The challenges can be influenced by the use of minority languages as the local resources since these languages are resource constrained compared to languages such as English that are rich in resource availability. Apart from that, minority languages tend to have issues concerning different morphological structures and grammatical structures. Numbers of existing ontologies for different disciplines had been produced in English language but little has been done for indigenous languages such as Iban. The main contribution here resides in the ontology development itself, which emphasise on the best means for a beginner to design, develop and deploy the ontology. Research based on the previous work and possible solution is presented in this paper

Telemedicine framework using case-based reasoning with evidences

Telemedicine is the medical practice of information exchanged from one location to another through electronic communications to improve the delivery of health care services. This research article describes a telemedicine framework with knowledge engineering using taxonomic reasoning of ontology modeling and semantic similarity. In addition to being a precious support in the procedure of medical decision-making, this framework can be used to strengthen significant collaborations and traceability that are important for the development of official deployment of telemedicine applications. Adequate mechanisms for information management with traceability of the reasoning process are also essential in the fields of epidemiology and public health. In this paper we enrich the case-based reasoning process by taking into account former evidence-based knowledge. We use the regular four steps approach and implement an additional (iii) step: (i) establish diagnosis, (ii) retrieve treatment, (iii) apply evidence, (iv) adaptation, (v) retain. Each step is performed using tools from knowledge engineering and information processing (natural language processing, ontology, indexation, algorithm, etc.). The case representation is done by the taxonomy component of a medical ontology model. The proposed approach is illustrated with an example from the oncology domain. Medical ontology allows a good and efficient modeling of the patient and his treatment. We are pointing up the role of evidences and specialist's opinions in effectiveness and safety of care

User-defined semantics for the design of IoT systems enabling smart interactive experiences

© The Author(s) 2020. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.Automation in computing systems has always been considered a valuable solution to unburden the user. Internet of Things (IoT) technology best suits automation in different domains, such as home automation, retail, industry, and transportation, to name but a few. While these domains are strongly characterized by implicit user interaction, more recently, automation has been adopted also for the provision of interactive and immersive experiences that actively involve the users. IoT technology thus becomes the key for Smart Interactive Experiences (SIEs), i.e., immersive automated experiences created by orchestrating different devices to enable smart environments to fluidly react to the final users’ behavior. There are domains, e.g., cultural heritage, where these systems and the SIEs can support and provide several benefits. However, experts of such domains, while intrigued by the opportunity to induce SIEs, are facing tough challenges in their everyday work activities when they are required to automate and orchestrate IoT devices without the necessary coding skills. This paper presents a design approach that tries to overcome these difficulties thanks to the adoption of ontologies for defining Event-Condition-Action rules. More specifically, the approach enables domain experts to identify and specify properties of IoT devices through a user-defined semantics that, being closer to the domain experts’ background, facilitates them in automating the IoT devices behavior. We also present a study comparing three different interaction paradigms conceived to support the specification of user-defined semantics through a “transparent” use of ontologies. Based on the results of this study, we work out some lessons learned on how the proposed paradigms help domain experts express their semantics, which in turn facilitates the creation of interactive applications enabling SIEs.Peer reviewedFinal Published versio

Exploiting Semantics from Widely Available Ontologies to Aid the Model Building Process

This dissertation attempts to address the changing needs of data science and analytics: making it easier to produce accurate models opening up opportunities and perspectives for novices to make sense of existing data. This work aims to incorporate semantics of data in addressing classical machine learning problems, which is one way to tame the deluge of data. The increased availability of data and the existence of easy-to-use procedures for regression and classification in commodity software allows anyone to search for correlations amongst a large set of variables with scant regard of their meaning. Consequently, people tend to use data indiscriminately, leading to the practice of data dredging. It is easy to use sophisticated tools to produce specious models, which generalize poorly and may lead to wrong conclusions. Despite much effort having been placed on advancing learning algorithms, current tools do little to shield people from using data in a semantically lax fashion. By examining the entire model building process and supplying semantic information derived from high-level knowledge in the form of an ontology, the machine can assist in exercising discretion to help the model builder avoid the pitfalls of data dredging. This work introduces a metric, called conceptual distance, to incorporate semantic information into the model building process. The conceptual distance is shown to be practically computed from large-scale existing ontologies. This metric is exploited in feature selection to enable a machine to take semantics of features into consideration when choosing them to build a model. Experiments with ontologies and real world datasets show the comparable performance of this metric in selecting a feature subset to the traditional data-driven measurements, in spite of using only labels of features, not the associated measures. Further, a new end-to-end model building process is developed by using the conceptual distance as a guideline to explore an ontological structure and retrieve relevant features automatically, making it convenient for a novice to build a semantically pertinent model. Experiments show that the proposed model building process can help a user to produce a model with performance comparable to that built by a domain expert. This work offers a tool to help the common man battle the hazard of data dredging that comes from the indiscriminate use of data. The tool results in models with improved generalization and easy to interpret, leading to better decisions or implications

ONTOMOTIF: ONTOLOGI PENCARIAN INFORMASI KENDARAAN BERMOTOR

Ontologi sebagai tulang punggung web semantik diperlukan pada berbagai domain. Ontologi yang didesain secara khusus untuk menangani pencarian informasi kendaraan bermotor sejauh ini belum ditemukan. Paper ini membahas pengembangan ontologi pencarian informasi kendaraan bermotor (OntoMotif). Selain menjelaskan spesifikasi kendaraan, OntoMotif juga mengakomodasi kepentingan penyedia jasa penjualan dan persewaan kendaraan bermotor dalam mempromosikan produknya. Tahapan-tahapan metode pengembangan OntoMotif mengacu pada Ontology Development 101. Dari hasil pengujian, dapat dibuktikan bahwa OntoMotif mampu menjawab kebutuhan pencari informasi yang menggunakan berbagai macam kalimat tanya. Dengan OntoMotif, query yang dibutuhkan untuk mendapatkan informasi relatif lebih sederhana. Oleh karena itu, OntoMotif dapat dijadikan sebagai salah satu acuan dalam rangka penyediaan data kendaraan bermotor dan propertinya yang sering kali ditanyakan dalam kehidupan sehari-hari

Dynamics in Logistics

This open access book highlights the interdisciplinary aspects of logistics research. Featuring empirical, methodological, and practice-oriented articles, it addresses the modelling, planning, optimization and control of processes. Chiefly focusing on supply chains, logistics networks, production systems, and systems and facilities for material flows, the respective contributions combine research on classical supply chain management, digitalized business processes, production engineering, electrical engineering, computer science and mathematical optimization. To celebrate 25 years of interdisciplinary and collaborative research conducted at the Bremen Research Cluster for Dynamics in Logistics (LogDynamics), in this book hand-picked experts currently or formerly affiliated with the Cluster provide retrospectives, present cutting-edge research, and outline future research directions