Legal ontologies over time: A systematic mapping study
هستی شناسی های قانونی با گذشت زمان: یک مطالعه نگاشت سیستماتیک-2019
Over the last 30 years, AI & Law has provided breakthroughs in studies involving case-based reasoning, rule-based reasoning, information retrieval and, most recently, conceptual models for knowledge repre- sentation and reasoning, known as Legal Ontologies. Ontologies have been widely used by legal prac- titioners, scholars, and lay people in a variety of situations, such as simulating legal actions, semantic search and indexing, and to keep up-to-date with the continual change of laws and regulations. Given the high number of legal ontologies produced, the need to summarize this research realm through a well-defined methodological procedure is urgent need. This study presents the results of a systematic mapping of the literature, aiming at categorizing legal ontologies along certain dimensions, such as pur- pose, level of generality, underlying legal theories, among other aspects. The reasons to carry out a sys- tematic mapping are twofold: in addition to explaining the maturation of the area over recent decades, it helps to avoid the old problem of reinventing the wheel. Through organizing and classifying what has already been produced, it is possible to realize that the development of legal ontologies can rise to the level of reusability where prefabricated models might be coupled with new and more complex ontologies for practical law.
Keywords: Legal ontology| Systematic mapping study | Legal expert system | Legal theory | Semantic web
Using ontology-based clustering to understand the push and pull factors for British tourists visiting a Mediterranean coastal destination
استفاده از خوشه بندی مبتنی بر هستی شناسی جهت درک عوامل فشاری و کششی برای گردشگران بریتانیایی بازدید کننده از یک مقصد ساحل مدیترانه-2018
This paper studies why British tourists decide to travel to a particular destination in a Catalan region. The analysis is based on a survey that includes open-ended questions. First, we propose the operationalization of the concepts of motivation and meaning as push–pull factors when choosing a destination. Second, an ontology-based clustering method is presented, which makes it possible to analyse these qualitative factors from a semantic perspective to obtain tourist segments. A benchmark confirms that the segmentation obtained is better than that generated using classic clustering methods The results show that different meanings can be associated with any single place.
keywords: Data mining| Tourism motivations| Destination meaning| Ontologies| Qualitative data tourism geography
Semantic hyper-graph-based knowledge representation architecture for complex product development
معماری نمایندگی دانش مبتنی بر گرافیک معنایی برای توسعه محصول پیچیده-2018
More and more manufacturing companies are facing challenges in knowledge refining and reusing in stage of product development. To resolve this problem and make the knowledge convenient for acquisition, machine understandable and human-understandable, this paper proposes a framework of semantic hyper-graph-based knowledge representation to support the knowledge sharing for the product development. A case study of car headlamp development is given to validate the feasibility and effectiveness of the proposed method. The results bring out that it can help engineers to rapidly and accurately acquire knowledge. In future research, the knowledge recommendation service based on product development process should be considered.
Keywords: Product development knowledge ، Knowledge representation ، Knowledge service ، XML topic map ، Ontology
An ontology for numerical design of experiments processes
هستی شناسی برای طراحی عددی فرایندهای آزمایشات-2018
Numerical Designs of Experiments (NDoE) are used in a product development process to optimize the product. A NDoE may combine a costly numerical model and numerous experiments. The NDoE process consequently becomes very expensive. However, some methods and algorithms were developed to shorten the NDoE process, as sensitivity analysis, surrogate modelling and adaptive DoE. Because of their complexity, advanced expert knowledge or a long preparation step is required to optimally choose and configure all of these methods, in order to run the most efficient NDoE process. To answer this issue, a knowledge management approach is proposed in this paper. It capitalizes and reuses knowledge about NDoE process. This solution is proposed because of the lack in term of models and standardized processes for this specific NDoE application. An ontology was developed to manage, share and reuse knowledge and enable queries for information retrieval in a database. The database lists every NDoE processes executed. Then, the knowledge is analysed by a decision-support system to help designers to choose the best configuration.
Keywords: Ontology ، Design of experiments ، Simulation data management
Aspect ontology based review exploration
هستی شناسی جنبه برمبنای بررسی و مرور-2018
User feedback in the form of customer reviews, blogs, and forum posts is an essential feature of e-commerce. Users often read online product reviews to get an insight into the quality of various aspects of a product. Besides, users have different aspect preferences, and they look for reviews that contain relevant information regarding their preferred aspect(s). However, as reviews are unstructured and voluminous, it becomes exhaustive and laborious for users to find relevant reviews. Lack of domain knowledge about various aspects and sub-aspects of a product, and how they are related to each other, also add to the problem. Although this information could be there in product reviews, it is not easy for users to spot it instantly from the reviews. This paper seeks to address the above problems and presents two novel algorithms that summarize product reviews, and provides an interactive search interface, similar to popular faceted navigation. We solve the problem by creating an aspect ontology tree with high aspect extraction precision.
keywords: Electronic commerce |Review exploration |Opinion mining |Aspect ontology
How do data come to matter? Living and becoming with personal data
چگونه داده ها مهم می شوند؟ زندگی و تبدیل شدن به داده های شخصی-2018
Humans have become increasingly datafied with the use of digital technologies that generate information with and about their bodies and everyday lives. The onto-epistemological dimensions of human–data assemblages and their relationship to bodies and selves have yet to be thoroughly theorised. In this essay, I draw on key perspectives espoused in feminist materialism, vital materialism and the anthropology of material culture to examine the ways in which these assemblages operate as part of knowing, perceiving and sensing human bodies. I draw particularly on scholarship that employs organic metaphors and concepts of vitality, growth, making, articulation, composition and decomposition. I show how these metaphors and concepts relate to and build on each other, and how they can be applied to think through humans’ encounters with their digital data. I argue that these theoretical perspectives work to highlight the material and embodied dimensions of human–data assemblages as they grow and are enacted, articulated and incorporated into everyday lives.
Keywords: Digital data | epistemology | ontology | personal data | social theory | sociomaterial
Developing an integrated framework for using data mining techniques and ontology concepts for process improvement
توسعه چارچوب یکپارچه برای استفاده از تکنیک های داده کاوی و مفاهیم هستی شناسی برای بهبود فرایند-2018
Process, as an important knowledge resource, must be effectively managed and improved. The main prob lems are the large number of processes, their specific features, and the complicated relationships between them, which all lead to the increase in complexity and create a high-dimensionality problem. Traditional process management systems are unable to manage and improve processes with a high volume of data. Data mining techniques, however, can be employed to identify valuable patterns. With the aid of these patterns, suggestions for process improvement can be presented. Further, process ontology can be ap plied to share the process patterns between people, facilitate the process understanding, and develop the reusability of the extracted patterns for process improvement. This study presents a combined three-part, five-stage framework of data mining, process improve ment, and process ontology. To evaluate the applicability and effectiveness of the proposed framework, a real process dataset is applied. Two clustering and classification techniques are used to discover valuable patterns as the process ontology. The output of these two techniques can be considered as the recom mendations for improving the processes. The proposed framework can be exploited to support process improvement methodologies in organizations.
Keywords: Data mining ، Process improvement ، Ontology ، Classification ، Clustering
Deriving human activity from geo-located data by ontological and statistical reasoning
استخراج فعالیت های انسانی از داده های جغرافیایی بوسیله هستی شناسی واستدلال آماری-2018
Every day, billions of geo-referenced data (e.g., mobile phone data records, geo-tagged social media, gps records, etc.) are generated by user activities. Such data provides inspiring insights about human activities and behaviors, the discovery of which is important in a variety of domains such as social and economic development, urban planning, and health prevention. The major challenge in those areas is that inter preting such a big stream of data requires a deep understanding of context where each activity occurs. In this study, we use a geographical information data, OpenStreetMap (OSM) to enrich such context with possible knowledge. We build a combined logical and statistical reasoning model for inferring human ac tivities in qualitative terms in a given context. An extensive validation of the model is performed using separate data-sources in two different cities. The experimental study shows that the model is proven to be effective with a certain accuracy for predicting the context of human activity in mobile phone data records.
Keywords: Ontology ، Spatial data ، Human activity recognition ، Knowledge management
Ontologies for transportation research: A survey
هستی شناسی برای تحقیقات حمل و نقل: یک نظرسنجی-2018
Transportation research relies heavily on a variety of data. From sensors to surveys, data supports day-to-day operations as well as long-term planning and decision-making. The challenges that arise due to the volume and variety of data that are found in transportation research can be effectively addressed by ontologies. This opportunity has already been recognized – there are a number of existing transportation ontologies, however the relationship between them is unclear. The goal of this work is to provide an overview of the opportunities for ontologies in transpor tation research and operation, and to present a survey of existing transportation ontologies to serve two purposes: (1) to provide a resource for the transportation research community to aid in understanding (and potentially selecting between) existing transportation ontologies; and (2) to identify future work for the development of transportation ontologies, by identifying areas that may be lacking.
Keywords: Transportation ontology ، Knowledge representation ، Reasoning ، Interoperability ، Formal logic ، Semantic Web
A decision support system based on ontology and data mining to improve design using warranty data
یک سیستم پشتیبانی تصمیم بر اساس هستی شناسی و داده کاوی به منظور بهبود است طراحی با استفاده از داده های گارانتی-2018
Analysis of warranty based big data has gained considerable attention due to its potential for improving the quality of products whilst minimizing warranty costs. Similarly, customer feedback information and warranty claims, which are commonly stored in warranty databases might be analyzed to improve quality and reliability and reduce costs in areas, including product development processes, advanced product design, and manu facturing. However, three challenges exist, firstly to accurately identify manufacturing faults from these multiple sources of heterogeneous textual data. Secondly, accurately mapping the identified manufacturing faults with the appropriate design information and thirdly, using these mappings to simultaneously optimize costs, design parameters and tolerances. This paper proposes a Decision Support System (DSS) based on novel integrated stepwise methodologies including ontology-based text mining, self-organizing maps, reliability and cost opti mization for identifying manufacturing faults, mapping them to design information and finally optimizing design parameters for maximum reliability and minimum cost respectively. The DSS analyses warranty databases which collect the warranty failure information from the customers in a textual format. To extract the hidden knowledge from this, an ontology-based text mining based approach is adopted. A data mining based approach using Self Organizing Maps (SOM) has been proposed to draw information from the warranty database and to relate it to the manufacturing data. The clusters obtained using SOM are analyzed to identify the critical regions, i.e., sections of the map where maximum defects occur. Finally, to facilitate the correct implementation of design parameter changes, the frequency and type of defects analyzed from warranty data are used to identify areas where improvements have resulted in the greatest reliability for the lowest cost.
Keywords: Ontology ، Self-Organizing Maps ، Warranty data ، Text mining ، Decision support