Description Logic Queries Research Articles

Capability knowledge base query to allocate process resources for master recipe formulation

Abstract We present a method for matching the required capabilities of general recipes with available resources to formulate master recipes. Process elements extracted from BatchML XML files are used to decompose the required capabilities and match them with the capabilities offered by components at distinct levels. A capability OWL ontology with properties aids in querying and matching capabilities with resources, enabling validation via description logic (DL) queries post-inference and integration into the master recipe. DL queries and matching within a knowledge base demonstrate AI-based knowledge processing for production systems.

Enhancing Drug-Related Data Exchange: Advanced Technical Support for Interoperability.

This paper describes the technical support services developed to facilitate the usage of the Canadian Drug Ontology (OCRx). These services include a lookup service named PaperRx, which retrieves OCRx drug entities using description logic queries (DLqueries) conducted within OCRx's classified structure. Additionally, the services feature a SPARQL endpoint, an OWL file downloader, and a RESTful Application Programming Interface. This suite of services supports the application of the Findable, Accessible, Interoperable, and Reusable (FAIR) principles to OCRx.

Improving the interoperability of drugs terminologies: Infusing local standardization with an international perspective

Objectives:The objective of this study is to describe how OCRx (Canadian Drug Ontology) has been built to address the dual need for local drug information integration in Canada and alignment with international standards requirements. Methods:This paper delves into (i) the implementation efforts to meet the Identification of Medicinal Product (IDMP) requirements in OCRx, alongside the ontology update strategy, (ii) the structure of the ontology itself, (iii) the alignment approach with several reference Knowledge Organization Systems, including SNOMED CT, RxNorm, and the list of “Code Identifiant de Spécialité” (CIS-Code), and (iv) the look-up services developed to facilitate its access and utilization. Results:Each OCRx release contains two distinct versions: the full and the up-to-date version. The full version encompasses all drugs with a DIN code sanctioned by Health Canada, while the up-to-date version is limited to drugs currently marketed in Canada. In the last release of OCRx, the full version comprises 162,400 classes; meanwhile, the up-to-date version consists of 36,909 classes. In terms of mappings with OCRx, substances in RxNorm and SNOMED CT fall below 40%, registering at 37% and 22% respectively. Meanwhile, mappings for CIS-Code achieve coverage of 61%. The strength mappings are notably low for RxNorm at 40% and for CIS-code at 28%. This affects the mapping of clinical drugs, which are predominantly alignable through post-coordinated expressions: 56% for RxNorm, 80% for SNOMED CT, and 35% for CIS-Code. The main support service of OCRx is a look-up service known as PaperRx that displays OCRx’s entities based on description logic queries (DL-queries) performed through the classified structure of OCRx. The look-up services also contain a SPARQL endpoint, an OCRx OWL file downloader, and a RESTful API. Discussion:The OCRx ontology demonstrates a significant effort towards integrating Canadian drug information with international standards. However, there are areas for improvement. In the future, our focus will be on refining the structure of OCRx for better classification capability and improvement of dosage conversion. Additionally, we aim to harness OCRx in constructing an ontology-based annotator, setting our sights on its deployment in real-world data integration scenarios.

Ontology-based detection and identification of complex event of illegal parking using SPARQL and description logic queries

Dynamic monitoring and management of traffic is a significant challenge which requires a mobile surveillance system which is becoming feasible with the evolution of 5G and related technologies. With the advent of 5G, huge amount of data can be transferred over long distance. Furthermore, the evolution of Multi-access Edge Computing (MEC) offers the processing near the edge, resulting in quick response which can cater to mobile surveillance. The mobile surveillance can be performed through a vehicle (Unmanned Aerial Vehicle (UAV)/mobile vehicle) which collects the multi-modal data and sends to nearest edge server over the 5G network. The server sends response quickly to the control center from where the appropriate action can be taken to mitigate its effect on the individuals in real-time. Furthermore, the analysis of multi-modal data captured by surveillance systems must be integrated and efficiently represented. Therefore, in this work, an ontology-based approach is proposed by integrating unstructured video data and structured sensor data for detecting and identifying the complex event of illegal parking. Identifying illegally parked vehicles needs dynamic and strict monitoring; which require the deployment of surveillance systems across the entire city. However, installing a video camera at each place is not feasible. Therefore, the surveillance thorough mobile vehicle is presented to identify illegal parked vehicle for traffic management over the 5G network. In addition, an ontology is developed to represent and reason over the roadside video data while integrating it with sensor data to form a knowledge graph. The knowledge graph retrieves semantic information about the events using SPARQL queries and Description Logics (DL) queries. Thus, a complete video is not required and the extracted information is stored in Resource Description Framework (RDF) format to reduce space requirement significantly. The approach is evaluated by recognizing the complex events of detecting illegally parked vehicles on a real-time dataset recorded using the experimental setup. Furthermore, the framework represents the surveillance video data of parked vehicles captured using the camera installed in a mobile vehicle to cover the road, including the vehicle information location, position, time, etc., as a knowledge graph that can be used later for further analysis.

A strategy for archives metadata representation on CIDOC-CRM and knowledge discovery

This paper presents a strategy for the semantic migration of Portuguese National Archives records into CIDOC-CRM standard, an ontology developed for museums, within the context of the EPISA project. The approach to automatically populate the CIDOC-CRM is based on Mapping Description Rules to semantically translate the archives descriptive information into CIDOC-CRM representation. The compliance of the CIDOC-CRM model recommendations guarantees that the populated CIDOC-CRM ontology of archives descriptive information verifies interoperability, and could be linked and integrated with other populated CIDOC-CRM ontologies. In the information modelling, requirements on the mapping representation, due to the intent of interpreting natural language text to automatically extract information of metadata text fields and to interpret natural language queries, are taken into account. To automatically interpret the Mapping Description Rules, OWL API was used to obtain the set of assertions that represents the information in the target ontology and two datasets are available with some migration examples. The exploration of the knowledge representation is done through some Description Logic queries to highlight the advantages of having this new representation of the National Archives. The evaluation of the resulting representation can be done automatically proving its correctness for the metadata that has a direct representation in CIDOC-CRM.

Expressivity of Planning with Horn Description Logic Ontologies

State constraints in AI Planning globally restrict the legal environment states. Standard planning languages make closed-domain and closed-world assumptions. Here we address open-world state constraints formalized by planning over a description logic (DL) ontology. Previously, this combination of DL and planning has been investigated for the light-weight DL DL-Lite. Here we propose a novel compilation scheme into standard PDDL with derived predicates, which applies to more expressive DLs and is based on the rewritability of DL queries into Datalog with stratified negation. We also provide a new rewritability result for the DL Horn-ALCHOIQ, which allows us to apply our compilation scheme to quite expressive ontologies. In contrast, we show that in the slight extension Horn-SROIQ no such compilation is possible unless the weak exponential hierarchy collapses. Finally, we show that our approach can outperform previous work on existing benchmarks for planning with DL ontologies, and is feasible on new benchmarks taking advantage of more expressive ontologies.

OnTARi: an ontology for factors influencing therapy adherence to rehabilitation

BackgroundAdherence and motivation are key factors for successful treatment of patients with chronic diseases, especially in long-term care processes like rehabilitation. However, only a few patients achieve good treatment adherence. The causes are manifold. Adherence-influencing factors vary depending on indications, therapies, and individuals. Positive and negative effects are rarely confirmed or even contradictory. An ontology seems to be convenient to represent existing knowledge in this domain and to make it available for information retrieval.MethodsFirst, a manual data extraction of current knowledge in the domain of treatment adherence in rehabilitation was conducted. Data was retrieved from various sources, including basic literature, scientific publications, and health behavior models. Second, all adherence and motivation factors identified were formalized according to the ontology development methodology METHONTOLOGY. This comprises the specification, conceptualization, formalization, and implementation of the ontology “Ontology for factors influencing therapy adherence to rehabilitation” (OnTARi) in Protégé. A taxonomy-oriented evaluation was conducted by two domain experts.ResultsOnTARi includes 281 classes implemented in ontology web language, ten object properties, 22 data properties, 1440 logical axioms, 244 individuals, and 1023 annotations. Six higher-level classes are differentiated: (1) Adherence, (2) AdherenceFactors, (3) AdherenceFactorCategory, (4) Rehabilitation, (5) RehabilitationForm, and (6) RehabilitationType. By means of the class AdherenceFactors 227 adherence factors, thereof 49 hard factors, are represented. Each factor involves a proper description, synonyms, possibly existing acronyms, and a German translation. OnTARi illustrates links between adherence factors through 160 influences-relations. Description logic queries implemented in Protégé allow multiple targeted requests, e.g., for the extraction of adherence factors in a specific rehabilitation area.ConclusionsWith OnTARi, a generic reference model was built to represent potential adherence and motivation factors and their interrelations in rehabilitation of patients with chronic diseases. In terms of information retrieval, this formalization can serve as a basis for implementation and adaptation of conventional rehabilitative measures, taking into account (patient-specific) adherence factors. OnTARi also enables the development of medical assistance systems to increase motivation and adherence in rehabilitation processes.

An Ontology-Based Information Extraction System for Organic Farming

In the existing farming system, information is obtained manually, and most times, farmers act based on their discretion. Sometimes, farmers rely on information from experts and extension officers for decision making. In recent times, a lot of information systems are available with relevant information on organic farming practices; however, such information is scattered in different context, form, and media all over the internet, making their retrieval difficult. The use of ontology with the aid of a conceptual scheme makes the comprehensive and detailed formalization of any subject domain possible. This study is aimed at acquiring, storing, and providing organic farming-based information available to current and intending software developer who may wish to develop applications for farmers. It employs information extraction (IE) and ontology development techniques to develop an ontology-based information extraction (OBIE) system called ontology-based information extraction system for organic farming (OBIESOF). The knowledge base was built using protégé editor; Java was used for the implementation of the ontology knowledge base with the aid of the high-level application programming language for working web ontology language application program interface (OWL API). In contrast, HermiT was used to checking the consistencies of the ontology and for submitting queries in order to verify their validity. The queries were expressed in description logic (DL) query language. The authors tested the capability of the ontology to respond to user queries by posing instances of the competency questions from DL query interface. The answers generated by the ontology were promising and serve as positive pointers to its usefulness as a knowledge repository.

Ontology Learning Approaches to Provide Domain-Specific Knowledge Base

Abstract Ontology learning techniques are based on extensive basic knowledge, from unstructured data to structured data. Creating an ontology manually is undoubtedly a time consuming process. In addition, manually-curated background knowledge is a scarce resource for many domains. Ontology learning is a relatively new research area that draws from related fields. However, a number of approaches are still rising up and, what is more, they vary and evolve across different aspects. Therefore, the purpose of this article is to provide comprehensive knowledge about selected groups of methods, tools and approaches supporting ontology. Presenting different approaches to learning ontology and including them in one publicly available summary, aims to provide an overview of the most appropriate methods, tools and approaches to building ontologies from various sources. Besides, the proposed solution in this article will expand the already offered ontology of learning methods with additional, more detailed functions. In addition to the domain ontology offered, this is another goal of this article. The correctness of the proposed ontology was verified by the constructed competence questions and implemented using Description Logic query mechanism.

MODEL ONTOLOGI VARIETAS KELAPA SAWIT SEBAGAI DASAR SISTEM BERBASIS PENGETAHUAN RISET PERKEBUNAN

Knowledge is an important asset that is easily lost. This loss process has a significant impact on plantation research organizations whose research takes a long time. This research aimed to develop an ontology model for recording knowledge about oil palm varieties. In order to save knowledge, in this research nine oil palm varieties were modeled into ontology using the Web Ontology Language (OWL). Any information in oil palm varieties can be sorted and grouped into classes, object properties, data properties, or data expressions. The relationship between data in oil palm varieties can be well represented using ontology. Based on the results from HermiT reasoners, the ontologies with 43 classes are consistent. Information in the ontology of oil palm varieties can be easily rediscovered using the description logic query (DL-Query). The model produced from this research can be used as an initial model in designing knowledge management systems and ontology-based oil palm expert systems.Keywords: ontology, OWL, oil palm seed, knowledge management

Ontology-Based Approach to Social Data Sentiment Analysis: Detection of Adolescent Depression Signals.

BackgroundSocial networking services (SNSs) contain abundant information about the feelings, thoughts, interests, and patterns of behavior of adolescents that can be obtained by analyzing SNS postings. An ontology that expresses the shared concepts and their relationships in a specific field could be used as a semantic framework for social media data analytics.ObjectiveThe aim of this study was to refine an adolescent depression ontology and terminology as a framework for analyzing social media data and to evaluate description logics between classes and the applicability of this ontology to sentiment analysis.MethodsThe domain and scope of the ontology were defined using competency questions. The concepts constituting the ontology and terminology were collected from clinical practice guidelines, the literature, and social media postings on adolescent depression. Class concepts, their hierarchy, and the relationships among class concepts were defined. An internal structure of the ontology was designed using the entity-attribute-value (EAV) triplet data model, and superclasses of the ontology were aligned with the upper ontology. Description logics between classes were evaluated by mapping concepts extracted from the answers to frequently asked questions (FAQs) onto the ontology concepts derived from description logic queries. The applicability of the ontology was validated by examining the representability of 1358 sentiment phrases using the ontology EAV model and conducting sentiment analyses of social media data using ontology class concepts.ResultsWe developed an adolescent depression ontology that comprised 443 classes and 60 relationships among the classes; the terminology comprised 1682 synonyms of the 443 classes. In the description logics test, no error in relationships between classes was found, and about 89% (55/62) of the concepts cited in the answers to FAQs mapped onto the ontology class. Regarding applicability, the EAV triplet models of the ontology class represented about 91.4% of the sentiment phrases included in the sentiment dictionary. In the sentiment analyses, “academic stresses” and “suicide” contributed negatively to the sentiment of adolescent depression.ConclusionsThe ontology and terminology developed in this study provide a semantic foundation for analyzing social media data on adolescent depression. To be useful in social media data analysis, the ontology, especially the terminology, needs to be updated constantly to reflect rapidly changing terms used by adolescents in social media postings. In addition, more attributes and value sets reflecting depression-related sentiments should be added to the ontology.

Backwards State-space Reduction for Planning in Dynamic Knowledge Bases

In this paper we address the problem of planning in rich domains, where knowledge representation is a key aspect for managing the complexity and size of the planning domain. We follow the approach of Description Logic (DL) based Dynamic Knowledge Bases, where a state of the world is represented concisely by a (possibly changing) ABox and a (fixed) TBox containing the axioms, and actions that allow to change the content of the ABox. The plan goal is given in terms of satisfaction of a DL query. In this paper we start from a traditional forward planning algorithm and we propose a much more efficient variant by combining backward and forward search. In particular, we propose a Backward State-space Reduction technique that consists in two phases: first, an Abstract Planning Graph P is created by using the Abstract Backward Planning Algorithm (ABP), then the abstract planning graph P is instantiated into a corresponding planning graph P by using the Forward Plan Instantiation Algorithm (FPI). The advantage is that in the preliminary ABP phase we produce a symbolic plan that is a pattern to direct the search of the concrete plan. This can be seen as a kind of informed search where the preliminary backward phase is useful to discover properties of the state-space that can be used to direct the subsequent forward phase. We evaluate the effectiveness of our ABP+FPI algorithm in the reduction of the explored planning domain by comparing it to a standard forward planning algorithm and applying both of them to a concrete business case study.

Answering Continuous Description Logic Queries

During the last years, mobile computing has been the focus of many research efforts, due mainly to the ever-growing use of mobile devices. In this context, there is a need to manage dynamic data, such as location data or other data provided by sensors. As an example, the continuous processing of location-dependent queries has been the subject of thorough research. However, there is still a need of highly expressive ways of formulating queries, augmenting in this way the systems' answer capabilities. Regarding this issue, the modeling power of Description Logics (DLs) and the inferring capabilities of their attached reasoners could fulfill this new requirement. The main problem is that DLs are inherently oriented to model static knowledge, that is, to capture the nature of the modeled objects, but not to handle changes in the property values (which requires a full ontology reclassification), as it is common in mobile computing environments (e.g., the location is expected to vary continually). In this paper, the authors present a novel approach to process continuous queries that combines 1) the DL reasoning capabilities to deal with static knowledge, with 2) the efficient data access provided by a relational database to deal with volatile knowledge. By marking at modeling time the properties that are expected to change during the lifetime of the queries, the authors'system is able to exploit both the results of the classification process provided by a DL reasoner, and the low computational costs of a database when accessing changing data (mobile environments, semantic sensors, etc.), following a two-step continuous query processing that enables us to handle continuous DL queries efficiently. Experimental results show the feasibility of the authors' approach.

Developing a semantically rich ontology for the biobank-administration domain.

BackgroundBiobanks are a critical resource for translational science. Recently, semantic web technologies such as ontologies have been found useful in retrieving research data from biobanks. However, recent research has also shown that there is a lack of data about the administrative aspects of biobanks. These data would be helpful to answer research-relevant questions such as what is the scope of specimens collected in a biobank, what is the curation status of the specimens, and what is the contact information for curators of biobanks. Our use cases include giving researchers the ability to retrieve key administrative data (e.g. contact information, contact's affiliation, etc.) about the biobanks where specific specimens of interest are stored. Thus, our goal is to provide an ontology that represents the administrative entities in biobanking and their relations. We base our ontology development on a set of 53 data attributes called MIABIS, which were in part the result of semantic integration efforts of the European Biobanking and Biomolecular Resources Research Infrastructure (BBMRI). The previous work on MIABIS provided the domain analysis for our ontology. We report on a test of our ontology against competency questions that we derived from the initial BBMRI use cases. Future work includes additional ontology development to answer additional competency questions from these use cases.ResultsWe created an open-source ontology of biobank administration called Ontologized MIABIS (OMIABIS) coded in OWL 2.0 and developed according to the principles of the OBO Foundry. It re-uses pre-existing ontologies when possible in cooperation with developers of other ontologies in related domains, such as the Ontology of Biomedical Investigation. OMIABIS provides a formalized representation of biobanks and their administration. Using the ontology and a set of Description Logic queries derived from the competency questions that we identified, we were able to retrieve test data with perfect accuracy. In addition, we began development of a mapping from the ontology to pre-existing biobank data structures commonly used in the U.S.ConclusionsIn conclusion, we created OMIABIS, an ontology of biobank administration. We found that basing its development on pre-existing resources to meet the BBMRI use cases resulted in a biobanking ontology that is re-useable in environments other than BBMRI. Our ontology retrieved all true positives and no false positives when queried according to the competency questions we derived from the BBMRI use cases. Mapping OMIABIS to a data structure used for biospecimen collections in a medical center in Little Rock, AR showed adequate coverage of our ontology.

Description logic based quantifier restriction and query of an OWL ontology

It is important to represent the knowledge residing on WWW in a uniform manner understandable by both man and machine to make it semantic. Thus some taxonomy is needed to make representations of the web contents which can be machine readable and usable .This taxonomy can be thought of as ontology. This paper proposes to relate Description logic (DL) and Web ontology language (OWL) based Ontology and demonstrate how a OWL ontology quantifier description can enable DL query to generate inferences there by demonstrating the machine interpretability aspect of the ontology and thus render support to the Semantic web