Lightweight Ontology Research Articles

A Lightweight Ontology for Enterprise Architecture Mining of API Gateway Logs

A Lightweight Ontology for Enterprise Architecture Mining of API Gateway Logs

A derived information framework for a dynamic knowledge graph and its application to smart cities

In this work, we develop a derived information framework to semantically annotate how a piece of information can be obtained from others in a dynamic knowledge graph. We encode this using the notion of a “derivation” and capture its metadata with a lightweight ontology. We provide an agent template designed to monitor derivations and to standardise agents performing this and related operations. We implement both synchronous and asynchronous communication modes for agents interacting with the knowledge graph. When occurring in conjunction, directed acyclic graphs of derivations can arise, with changing data propagating through the knowledge graph by means of agents’ actions. While the framework itself is domain-agnostic, we apply it in the context of smart cities as part of the World Avatar project and demonstrate that it is capable of handling sequential events across different timescales. Starting from source information, the framework automatically populates derived data and ensures they remain up to date upon access for a potential flood impact assessment use case.

Selecting accepted assertions in partially ordered inconsistent DL-Lite knowledge bases

The problem of inconsistency handling in knowledge bases has received considerable research attention in several settings and has met many applications. Inconsistency usually stems from the fact that the information available for reasoning, query answering and decision-making tasks is obtained from different, scattered and potentially unreliable information sources. Performing these tasks is often computationally expensive, especially for knowledge bases in which the data is endowed with preferences and/or uncertainty. In the context of formal ontologies, enriching the assertions (i.e. the data pieces) with conceptual knowledge makes the query answering task harder computationally. Tractable results have been successfully established for the lightweight languages of the DL-Lite family. In this paper, we present two tractable methods for handling inconsistency in partially ordered lightweight ontologies. In essence, both methods are inspired from the well-known Intersection of ABox Repair (IAR) semantics, which proceeds by replacing the initial inconsistent knowledge base with the intersection of all of its maximally consistent sub-bases. The first method assumes a partial preorder over the knowledge base and identifies the accepted assertions, called ‘elected’, in polynomial time in data complexity. The second method captures uncertainty quantitatively in the framework of possibility theory and identifies the so-called ‘π-accepted’ assertions, also in polynomial time in data complexity. However, both characterisations require the conflict set to be readily available. This constitutes a serious limitation in frameworks and applications where the conflict set cannot be exhibited efficiently. In this paper, we propose a new and equivalent characterisation for each method. We show that the elected assertions and also the π-accepted assertions can be identified in polynomial time in the size of the dataset for lightweight ontologies.

A lightweight ontology for landmarks to assist rescue in mountainous areas

Abstract. When people are injured or lost in mountains during outdoor activities and when web-based locations are not available, they locate themselves by describing their environment, routes and activities. The description of their location is done using landmarks and spatial locations (e.g., “I am located in front of Punay Lake”, “I am near a protected area”). Landmarks used can be named (e.g., “Punay Lake”) or unnamed if the landmark has no name or if the victim does not know it (e.g., "area lake"). Landmarks are represented in geographic databases by name (if possible), type and geometry. To reduce the heterogeneity of landmark types present in oral language and geographic databases representing landmarks, and thus improve locating victims, our goal is to define a controlled vocabulary for landmarks. In this research, we present a lightweight ontology (i.e. ontology having generally less complexity and does not express formal constraints) of landmarks, named Landmark Ontology (OOR), describing landmark types. It is an application ontology, i.e. it is designed to support mountain rescue operations. The ontology construction is adapted from the SAMOD methodology for engineering ontology development and involves researchers and experts from mountain rescue teams. The construction of OOR is composed of four main phases: knowledge acquisition, conceptual formalisation, implementation, and testing. The implementation phase is carried out by an iterative and collaborative approach and using four formalised sources of knowledge (a landmark and a landform ontologies, and two other domain vocabularies), an un-formalised taxonomy of outdoor activities, and five authoritative and volunteered geographic information sources representing geographic data. The landmarks ontology contains 543 classes associated with 1739 labels: 1086 prefLabel (preferential label) in French and English, 321 altLabel (alternative label) in French, and 332 altLabel in English. The depth of the ontology varies from four for land cover, hydrological and land subdivision landmark types), to six for landform types, and eight for building types. Although the use of ontology is broader, in this paper we illustrate and test its use through three applications in the context of mountain rescue operations: semantic mapping, data instantiation and data matching.

Алгоритмы, методы и подходы к обезличиванию и обогащению данных, в том числе персональных

This article presents the results of a study of algorithms, methods and approaches to depersonalization and enrichment of data, including personal data. Among the types of data enrichment, demographic, geographic and behavioral data enrichments were considered, as well as statistical, semantic and pragmatic data enrichment algorithms were studied. In addition to data enrichment, categories of ontology enrichment were considered, namely expressive ontologies, lightweight ontologies such as taxonomies, and a category that includes works that use reasoning to partially replace traditional methods of knowledge extraction. Ontology enrichment is a broad area of research that can be divided into three categories of work devoted to extracting semantic knowledge from heterogeneous data. As a result of the analysis, it was found that data enrichment processes optimize sales, as well as reduce business costs, by saving finances through information management. The advantages and disadvantages of the considered approaches and methods of data enrichment and ontologies were presented. The main benefit of fortification is the increased value and accuracy of information that helps companies make important business decisions. The main disadvantage is the risk of growing redundant data, which can lead to incorrect analytics and, accordingly, to wrong business decisions, which in turn harms the business. The significance of the analysis is also presented – on the basis of the studies carried out, it is planned to form a technical proposal for creating the basic infrastructure of the project of the NTI Central Committee "Trusted Information Exchange Environment" for further research on the topic of data enrichment and depersonalization.

Integration of Ontology Transformation into Hidden Markov Model

The goal of this study is to suggest a method for turning an ontology into a hidden Markov model (HMM). Ontology properties (relationships between classes) and ontology classes are taken as HMM symbols and states, respectively. Knowledge is represented in many different fields using the central element of the Semantic Web dubbed ontology. The authors employed machine learning technologies like HMM to add knowledge to these ontologies or to extract knowledge from within them. The meaning obtained from ontologies is not described during this task. The ontology triples that were extracted using SPARQL queries are used in this paper to transform the ontology into an HMM in order to handle this semantic. The Pizza ontology has been used to implement this method, which is based on lightweight ontologies.

Analogical lightweight ontology of EU criminal procedural rights in judicial cooperation

This article describes the creation of a lightweight ontology of European Union (EU) criminal procedural rights in judicial cooperation. The ontology is intended to help legal practitioners understand the precise contextual meaning of terms as well as helping to inform the creation of a rule ontology of criminal procedural rights in judicial cooperation. In particular, we started from the problem that directives sometimes do not contain articles dedicated to definitions. This issue provided us with an opportunity to explore a phenomenon typically neglected in the construction of domain-specific legal ontologies. Whether classical definitions are present or absent, laws and legal sources in general are typically peppered with a number of hidden definitions (in the sense that they are not clearly marked out as such) as well as incomplete definitions, which may nevertheless help legal practitioners (and legal reasoning systems) to reason on the basis of analogy or teleology. In this article we describe the theoretical basis for building an analogical lightweight ontology in the framework of an EU project called CrossJustice. We present our methodology for collecting the data, extracting the data fields and creating the ontology with WebProtégé, followed by our conclusions and ideas for future work.

Conflict-Based Inconsistency-Tolerant Query Answering

In this paper, we place ourselves in the context of inconsistency-tolerant query answering over lightweight ontologies, which aims to query a set of conflicting facts using an ontology that represents generic knowledge about a particular domain. Existing inconsistency-tolerant semantics typically consist in selecting some of (maximal) consistent subsets of facts, called repairs. We explore a novel strategy to select the most relevant repairs based on the stratification of the assertional base into priority levels that we automatically induce from the ontology. We propose a method that exploits conflict statistical regularities between facts to induce an embedding, in which each fact is represented by a vector. Based on Euclidean distances between facts, we classify the assertions from the most reliable to the least important ones. We then use these distances to define relevant repairs. Interestingly enough, we show that the obtained repair is done in polynomial time.

PHDD: Corpus of Physical Health Data Disclosure on Twitter During COVID-19 Pandemic

Health-related information is considered as ‘highly sensitive’ by the European General Data Protection Regulations (GDPR) and determining whether a text document contains health-related information or not is of interest for both individuals and companies in a number of different scenarios. Although some efforts have been made to detect different categories of personal data in texts, including health information, the classification task by machines is still challenging. In this work, we aim to contribute to solving this challenge by building a corpus of tweets being shared in the current COVID-19 pandemic context. The corpus is called PHDD(Corpus of Physical Health Data Disclosure on Twitter During COVID-19 Pandemic) and contains 1,494 tweets which have been manually tagged by three taggers in three dimensions: health-sensitivity status, categories of health information, and subject of health history. Furthermore, a lightweight ontology called PTHI(Privacy Tags for Health Information), which reuses two other vocabularies, namely hl7 and dpv, is built to represent the corpus in a machine-readable format. The corpus is publicly available and can be used by NLP experts for implementation of techniques to detect sensitive health information in textual documents.

The euBusinessGraph ontology: A lightweight ontology for harmonizing basic company information

Company data, ranging from basic company information such as company name(s) and incorporation date to complex balance sheets and personal data about directors and shareholders, are the foundation that many data value chains depend upon in various sectors (e.g., business information, marketing and sales, etc.). Company data becomes a valuable asset when data is collected and integrated from a variety of sources, both authoritative (e.g., national business registers) and non-authoritative (e.g., company websites). Company data integration is however a difficult task primarily due to the heterogeneity and complexity of company data, and the lack of generally agreed upon semantic descriptions of the concepts in this domain. In this article, we introduce the euBusinessGraph ontology as a lightweight mechanism for harmonising company data for the purpose of aggregating, linking, provisioning and analysing basic company data. The article provides an overview of the related work, ontology scope, ontology development process, explanations of core concepts and relationships, and the implementation of the ontology. Furthermore, we present scenarios where the ontology was used, among others, for publishing company data (business knowledge graph) and for comparing data from various company data providers. The euBusinessGraph ontology serves as an asset not only for enabling various tasks related to company data but also on which various extensions can be built upon.

Structuring E-Brainstorming to Better Support Innovation Processes

Innovation is a key instrument to start a transformational process based on collaboration. It is fundamental for organisations and institutions to have well defined strategies. In this context, brainstorming sessions - and e-brainstorming tools - are effective techniques to put together and associate draft ideas. Yet, in many cases, those ideas and associations do not leave enough digital footprints, are no further used or are lost. This paper introduces the use of Social and Semantic Web technologies to support e-brainstorming. In particular, we present a lightweight ontology to structure e-brainstorming sessions, and the enrichment of existing e-brainstorming tools to do so.

Educing knowledge from text: semantic information extraction of spatial concepts and places

Abstract. A growing body of geospatial research has shifted the focus from fully structured to semistructured and unstructured content written in natural language. Natural language texts provide a wealth of knowledge about geospatial concepts, places, events, and activities that needs to be extracted and formalized to support semantic annotation, knowledge-based exploration, and semantic search. The paper presents a web-based prototype for the extraction of geospatial entities and concepts, and the subsequent semantic visualization and interactive exploration of the extraction results. A lightweight ontology anchored in natural language guides the interpretation of natural language texts and the extraction of relevant domain knowledge. The approach is applied on three heterogeneous sources which provide a wealth of spatial concepts and place names.

A user-centric metadata model to foster sharing and reuse of multidisciplinary datasets in environmental and life sciences

The recent technological advancements and emergence of the open data in environmental and life sciences are opening new research opportunities while creating new challenges around data management. They make available an unprecedented amount of data that can be exploited for studying complex phenomena. However, new challenges related to data management need to be addressed to ensure effective data sharing, discovery and reuse, especially when dealing with interdisciplinary research contexts. These issues are magnified in interdisciplinary context, by the fact that each discipline has its practices, e.g., specific formats and metadata standards. Moreover, the majority of current data management practices do not consider semantic heterogeneity existing among disciplines. For this reason, we introduce a flexible metadata model that describes the datasets of various disciplines using a common paradigm based on the observation concept. It provides a key vision for articulating the user point of view and underlying scientific domains. In this study, we therefore decide to mainly reuse the SOSA lightweight ontology (Sensor, Observation, Sample, and Actuator) to efficiently leverage others existing ontologies to improve datasets discovery and reuse coming from Earth and life observation. The main benefit of the proposed metadata model is that it extends the technical description, usually provided by existing metadata models, with the observation context description enabling the need of a user viewpoint. Moreover, following the FAIR principles, the metadata model specifies the semantics of its elements using ontologies and vocabularies, and reuses as much as possible ontological and terminological existing resources. We show the benefit and applicability of the model through a case study we identified as representative after interviewing researchers in environmental and life sciences.

Handling inconsistency in partially preordered ontologies: the Elect method

Abstract We focus on the problem of handling inconsistency in lightweight ontologies. We assume that the terminological knowledge base (TBox) is specified in DL-Lite and that the set of assertional facts (ABox) is partially preordered and may be inconsistent with respect to the TBox. One of the main contributions of this paper is the provision of an efficient and safe method, called Elect, to restore the consistency of the ABox with respect to the TBox. In the case where the assertional base is flat (i.e. no priorities are associated with the ABox) or totally preordered, we show that our method collapses with the well-known intersection ABox repair semantics and the non-defeated semantics, respectively. The semantic justification of the Elect method is obtained by first viewing a partially preordered ABox as a family of totally preordered ABoxes and then applying non-defeated inference to each of the totally preordered ABoxes. We introduce the notion of elected assertions which allows us to provide an equivalent characterization of the Elect method without explicitly generating all the totally preordered ABoxes. We show that computing the set of elected assertions is done in polynomial time with respect to the size of the ABox. The second part of the paper discusses how to go beyond the Elect method. In particular, we discuss to what extent the Elect method can be generalized to description logics that are more expressive than DL-Lite.

Toward a Knowledge-based Personalised Recommender System for Mobile App Development

Over the last few years, the arena of mobile application development has expanded considerably beyond the demand of the world's software markets. With the growing number of mobile software companies and the increasing sophistication of smartphone technology, developers have been establishing several categories of applications on dissimilar platforms. However, developers confront several challenges when undertaking mobile application projects. In particular, there is a lack of consolidated systems that can competently, promptly and efficiently provide developers with personalised services. Hence, it is essential to develop tailored systems that can recommend appropriate tools, IDEs, platforms, software components and other correlated artifacts to mobile application developers. This paper proposes a new recommender system framework comprising a robust set of techniques that are designed to provide mobile app developers with a specific platform where they can browse and search for personalised artifacts. In particular, the new recommender system framework comprises the following functions: (i) domain knowledge inference module: including various semantic web technologies and lightweight ontologies; (ii) profiling and preferencing: a new proposed time- aware multidimensional user modelling; (iii) query expansion: to improve and enhance the retrieved results by semantically augmenting users’ query; and (iv) recommendation and information filtration: to make use of the aforementioned components to provide personalised services to the designated users and to answer a user’s query with the minimum mismatches.

Semantic Correlation Rules as a Logic Layer between Content Management and Content Delivery

Semantic technologies have recently gained considerable influence and attention in the field of technical communication and information management. While metadata management is already a well-known field of content management technologies, its semantic extension addresses more recently, for example, problems of model-based product development and related content engineering processes. On the other hand, dynamic search technology and content delivery can benefit from semantic modelling by enhancing search functionalities or by integrating various data sources utilizing semantic mapping. In this evolving environment, we propose a logical layer of content correlations as so-called semantic correlation rules (SCR). This layer can be understood as an interface between content management systems, semantic modelling systems and content delivery portals. Semantic correlation rules serve as a light-weight ontology consisting primarily of untyped semantic relations between metadata classes. In doing so, class-to-class linking mechanisms can be implemented in content delivery and search environments while serving as a basis for the previously introduced microDoc concept

IoT-Stream: A Lightweight Ontology for Internet of Things Data Streams and Its Use with Data Analytics and Event Detection Services.

With the proliferation of sensors and IoT technologies, stream data are increasingly stored and analysed, but rarely combined, due to the heterogeneity of sources and technologies. Semantics are increasingly used to share sensory data, but not so much for annotating stream data. Semantic models for stream annotation are scarce, as generally, semantics are heavy to process and not ideal for Internet of Things (IoT) environments, where the data are frequently updated. We present a light model to semantically annotate streams, IoT-Stream. It takes advantage of common knowledge sharing of the semantics, but keeping the inferences and queries simple. Furthermore, we present a system architecture to demonstrate the adoption the semantic model, and provide examples of instantiation of the system for different use cases. The system architecture is based on commonly used architectures in the field of IoT, such as web services, microservices and middleware. Our system approach includes the semantic annotations that take place in the pipeline of IoT services and sensory data analytics. It includes modules needed to annotate, consume, and query data annotated with IoT-Stream. In addition to this, we present tools that could be used in conjunction to the IoT-Stream model and facilitate the use of semantics in IoT.

Intelligent Case-based System for Decision-making in the Electricity Sector

The energy is important for the economic development of nations; and one of the main challenges today. The Electrical Union develops two systems for the automatization of transmission and distribution process: the Integral System of Network Management (SIGERE) and the Integral Management System of the Electrical Industry Construction Enterprise (SIGECIE). The data collected facilitate and improve the efficiency in the operation, use, analysis, planning and management of the electricity networks. A weakness of the systems proposed is in the dissatisfaction with the queries carried out. Hence, the objective of the research is: to develop intelligent queries in real time, based on existing knowledge that facilitate decision making in the electrical energy transmission and distribution processes. In the research a case-based system is develop, that it’s based on the premise that similar previous problems will have similar solutions. The similarity between two cases is determined by the weighted sum of the distance of their traits and the calculation of the distance between traits was done according to its nature. The conceptual basis of the system is develop with a lightweight ontology which it is incorporated in the cases database as a trait. An intelligent real-time queries system for the Electric Union (SICUNE) was implemented, achieving the generation of automatic queries that allow the system to respond to any type of queries in real time. The experimental study showed the feasibility of the proposal

An Argumentation-Based Approach for Computing Inconsistency Degree in Possibilistic Lightweight Ontologies

Reasoning with inconsistent ontologies plays an important role in Semantic Web applications. An important feature of argument theory is that it is able to naturally handle inconsistencies in ontologies and allows a user to represent information in the form of an argument. In argumentation, given an inconsistent knowledge base, arguments compete to decide which are the accepted consequences. In this paper, we are interested in using the argumentation for the inconsistency degree of uncertain knowledge bases expressed in possibilistic DL-Lite (the key notion in reasoning from a possibilistic DL-Lite knowledge base) without going through the negative closure. In the present work, the terminological base is assumed to be fully certain and the uncertainty is only considered on the assertion based. We proved that it is coherent and feasible to use Dung’s abstract argumentation theory to compute the inconsistency degree and how argumentation semantics relate to the state of the art of handling inconsistency.

SOSA: A lightweight ontology for sensors, observations, samples, and actuators

The Sensor, Observation, Sample, and Actuator (SOSA) ontology provides a formal but lightweight general-purpose specification for modellingthe interaction between the entities involved in the acts of observation, actuation, and sampling. SOSA is the result of rethinking the W3C-XG Semantic Sensor Network (SSN) ontology based on changes in scope and target audience, technical developments, and lessons learned over the past years. SOSA also acts as a replacement of SSN’s Stimulus Sensor Observation (SSO) core. It has been developed by the first joint working group of the Open Geospatial Consortium (OGC) and the World Wide Web Consortium (W3C) on Spatial Data on the Web. In this work, we motivate the need for SOSA, provide an overview of the main classes and properties, and briefly discuss its integration with the new release of the SSN ontology as well as various other alignments to specifications such as OGC’s Observations and Measurements (O&M), Dolce-Ultralite (DUL), and other prominent ontologies. We will also touch upon common modelling problems and application areas related to publishing and searching observation, sampling, and actuation data on the Web. The SOSA ontology and standard can be accessed at https://www.w3.org/TR/vocab-ssn/.