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Integrating deep learning techniques, particularly language models (LMs), with knowledge representation techniques like ontologies has raised widespread attention, urging the need of a platform that supports both paradigms. Although packages such as OWL API and Jena offer robust support for basic ontology processing features, they lack the capability to transform various types of information within ontologies into formats suitable for downstream deep learning-based applications. Moreover, widely-used ontology APIs are primarily Java-based while deep learning frameworks like PyTorch and Tensorflow are mainly for Python programming. To address the needs, we present DeepOnto , a Python package designed for ontology engineering with deep learning. The package encompasses a core ontology processing module founded on the widely-recognised and reliable OWL API, encapsulating its fundamental features in a more "Pythonic" manner and extending its capabilities to incorporate other essential components including reasoning, verbalisation, normalisation, taxonomy, projection, and more. Building on this module, DeepOnto offers a suite of tools, resources, and algorithms that support various ontology engineering tasks, such as ontology alignment and completion, by harnessing deep learning methods, primarily pre-trained LMs. In this paper, we also demonstrate the practical utility of DeepOnto through two use-cases: the Digital Health Coaching in Samsung Research UK and the Bio-ML track of the Ontology Alignment Evaluation Initiative (OAEI). [ABSTRACT FROM AUTHOR]

Electronic commerce and finance are progressively supporting and including decentralized, shared and public ledgers such as the blockchain. This is reshaping traditional commercial activities by advancing them towards Decentralized Finance (DeFi) and Commerce 3.0, thereby supporting the latter's potential to outpace the hurdles of central authority controllers and lawgivers. The quantity and entropy of the information that must be sought and managed to become active participants in such a relentlessly evolving scenario are increasing at a steady pace. For example, that information comprises asset or service description, general rules of the game, and specific technologies involved for decentralization. Moreover, the relevant information ought to be shared among innumerable and heterogeneous stakeholders, such as producers, buyers, digital identity providers, valuation services, and shipment services, to just name a few. A clear semantic representation of such a complex and multifaceted blockchain-based e-Commerce ecosystem would contribute dramatically to make it more usable, namely more automatically accessible to virtually anyone wanting to play the role of a stakeholder, thereby reducing programmers' effort. However, we feel that reaching that goal still requires substantial effort in the tailoring of Semantic Web technologies, hence this article sets out on such a route and advances a stack of OWL 2 ontologies for the semantic description of decentralized e-commerce. The stack includes a number of relevant features, ranging from the applicable stakeholders through the supply chain of the offerings for an asset, up to the Ethereum blockchain, its tokens and smart contracts. Ontologies are defined by taking a behaviouristic approach to represent the various participants as agents in terms of their actions, inspired by the Theory of Agents and the related mentalistic notions. The stack is validated through appropriate metrics and SPARQL queries implementing suitable competency questions, then demonstrated through the representation of a real world use case, namely, the iExec marketplace. [ABSTRACT FROM AUTHOR]

Decentralised ledgers are gaining momentum following the interest of industries and people in smart contracts. Major attention is paid to blockchain applications intended for trading assets that exploit digital cryptographic certificates called tokens. Particularly relevant tokens are the non-fungible tokens (NFTs), namely, unique and non-replicable tokens used to represent the cryptographic counterpart of assets ranging from pieces of art through to licenses and certifications. A relevant consequence of the hard-coded nature of blockchains is the hardness of probing, in particular when advanced searchers involving the capabilities of the smart contracts or the assets digitised by NFTs are required. For this purpose, a formal representation for the operational semantics of smart contracts and of tokens has become particularly urgent, especially in economy and finance, where blockchains become increasingly relevant. Hence, we feel the need to tailor Semantic Web technologies to achieve that semantic representation at least for NFTS. This article reports on an ontology that leverages the Ontology for Agents, Systems, and Integration of Services ("OASIS") towards the semantic representation of smart contracts responsible for managing ERC721-compliant NFTs and running on the Ethereum blockchain. Called Ether-OASIS, the proposed ontology adopts OASIS and tailors its behaviouristic approach to the Ethereum blockchain by conceiving smart contracts as agents running on the blockchain and, consequently, smart contract interactions as agent commitments. Smart contracts are represented in terms of their actions, purposes and tokens that they manage, thus realising a blockchain that is more usable both by users and automated applications. The ontology is evaluated using standard ontological metrics and applied on a case study concerning the minting and transferring of NFTs that digitise batches of wheat. [ABSTRACT FROM AUTHOR]

When reusing existing ontologies for publishing a dataset in RDF (or developing a new ontology), preference may be given to those providing extensive subcategorization for important classes (denoted as focus classes). The subcategories may consist not only of named classes but also of compound class expressions. We define the notion of focused categorization power of a given ontology, with respect to a focus class and a concept expression language, as the (estimated) weighted count of the categories that can be built from the ontology's signature, conform to the language, and are subsumed by the focus class. For the sake of tractable initial experiments we then formulate a restricted concept expression language based on existential restrictions, and heuristically map it to syntactic patterns over ontology axioms (so-called FCE patterns). The characteristics of the chosen concept expression language and associated FCE patterns are investigated using three different empirical sources derived from ontology collections: first, the concept expression pattern frequency in class definitions; second, the occurrence of FCE patterns in the Tbox of ontologies; and last, for class expressions generated from the Tbox of ontologies (through the FCE patterns); their 'meaningfulness' was assessed by different groups of users, yielding a 'quality ordering' of the concept expression patterns. The complementary analyses are then compared and summarized. To allow for further experimentation, a web-based prototype was also implemented, which covers the whole process of ontology reuse from keyword-based ontology search through the FCP computation to the selection of ontologies and their enrichment with new concepts built from compound expressions. [ABSTRACT FROM AUTHOR]

Semantic representation is a key enabler for several application domains, and the multi-agent systems realm makes no exception. Among the methods for semantically representing agents, one has been essentially achieved by taking a behaviouristic vision, through which one can describe how they operate and engage with their peers. The approach essentially aims at defining the operational capabilities of agents through the mental states related with the achievement of tasks. The OASIS ontology — An Ontology for Agent, Systems, and Integration of Services, presented in 2019 — pursues the behaviouristic approach to deliver a semantic representation system and a communication protocol for agents and their commitments. This paper reports on the main modelling choices concerning the representation of agents in OASIS 2, the latest major upgrade of OASIS, and the achievement reached by the ontology since it was first introduced, in particular in the context of ontologies for blockchains. [ABSTRACT FROM AUTHOR]

Semantic representation is a key enabler for several application domains, and the multi-agent systems realm makes no exception. Among the methods for semantically representing agents, one has been essentially achieved by taking a behaviouristic vision, through which one can describe how they operate and engage with their peers. The approach essentially aims at defining the operational capabilities of agents through the mental states related with the achievement of tasks. The OASIS ontology — An Ontology for Agent, Systems, and Integration of Services, presented in 2019 — pursues the behaviouristic approach to deliver a semantic representation system and a communication protocol for agents and their commitments. This paper reports on the main modelling choices concerning the representation of agents in OASIS 2, the latest major upgrade of OASIS, and the achievement reached by the ontology since it was first introduced, in particular in the context of ontologies for blockchains. [ABSTRACT FROM AUTHOR]

The process of maintenance of an underlying semantic model that supports data management and addresses the interoperability challenges in the domain of telemedicine and integrated care is not a trivial task when performed manually. We present a methodology that leverages the provided serializations of the Health Level Seven International (HL7) Fast Health Interoperability Resources (FHIR) specification to generate a fully functional OWL ontology along with the semantic provisions for maintaining functionality upon future changes of the standard. The developed software makes a complete conversion of the HL7 FHIR Resources along with their properties and their semantics and restrictions. It covers all FHIR data types (primitive and complex) along with all defined resource types. It can operate to build an ontology from scratch or to update an existing ontology, providing the semantics that are needed, to preserve information described using previous versions of the standard. All the results based on the latest version of HL7 FHIR as a Web Ontology Language (OWL-DL) ontology are publicly available for reuse and extension. [ABSTRACT FROM AUTHOR]

The global pandemic over the past two years has reset societal agendas by identifying both strengths and weaknesses across all sectors. Focusing in particular on global health delivery, the ability of health care facilities to scale requirements and to meet service demands has detected the need for some national services and organisations to modernise their organisational processes and infrastructures. Core to requirements for modernisation is infrastructure to share information, specifically structural standardised approaches for both operational procedures and terminology services. Problems of data sharing (aka interoperability) is a main obstacle when patients are moving across healthcare facilities or travelling across border countries in cases where emergency treatment is needed. Experts in healthcare service delivery suggest that the best possible way to manage individual care is at home, using remote patient monitoring which ultimately reduces cost burden both for the citizen and service provider. Core to this practice will be advancing digitalisation of health care underpinned with safe integration and access to relevant and timely information. To tackle the data interoperability issue and provide a quality driven continuous flow of information from different health care information systems semantic terminology needs to be provided intact. In this paper we propose and present ContSonto a formal ontology for continuity of care based on ISO 13940:2015 ContSy and W3C Semantic Web Standards Language OWL (Web Ontology Language). ContSonto has several benefits including semantic interoperability, data harmonization and data linking. It can be use as a base model for data integration for different healthcare information models to generate knowledge graph to support shared care and decision making. [ABSTRACT FROM AUTHOR]

VocBench is an open source web platform for the collaborative development of datasets complying with Semantic Web standards. Since its public release – five years ago – as an open source platform, VocBench has attracted a growing user community consisting of public organizations, companies and independent users looking for open source solutions for maintaining their thesauri, code lists and authority resources. The focus on collaboration, the differentiation of user roles and the workflow management for content validation and publication have been the strengths of the platform, especially for those organizations requiring a distributed, yet centrally controlled, publication environment. In 2017, a new, completely reengineered, version of the system has been released, broadening the scope of the platform: funded by the ISA2 programme of the European Commission, VocBench 3 offers a general-purpose collaborative environment for development of any kind of RDF dataset (with dedicated facilities for ontologies, thesauri and lexicons), improving the editing capabilities of its predecessor, while still maintaining the peculiar aspects that determined its success. In this article, we review the requirements and the new objectives set for version 3, and then introduce the new characteristics that were implemented for this new incarnation of the platform [ABSTRACT FROM AUTHOR]

The Description Logic SROIQ (D) , as the logical core of the W3C standard Web Ontology Language (OWL 2), is a widely used formalism for ontologies in the life sciences. Bio-health applications including health-care and life science domains commonly have a need to represent temporal information such as medication frequency or stage-based development. Different classes of temporal phenomena may generate different sorts of requirements on SROIQ (D) or extensions of SROIQ (D). In this paper, we deliver the first precise investigation into identifying exactly what kinds of temporal requirements are most important for bio-health ontologies. We conduct an empirical investigation of the OBO Foundry using a bespoke methodological approach by searching each of its ontologies for specific temporal features and go on to calculate the importance of these features using a sophisticated set of measures. By doing so, we derive a formal set of Temporal Requirements that act as a set of guidelines which a language or logical extension to OWL 2 would need to satisfy in order to meet the temporal requirements of bio-health ontologies. [ABSTRACT FROM AUTHOR]

This paper focusses on the question of knowledge-based modeling of discharge summaries for semantic requesting in the context of hospital reporting process. An ontology was designed and implemented to capture patient data and required expert knowledge. The modeling process was carried out manually using Protégé after an initial reverse engineering of the official format of discharge summaries. An OWL 2 model was built integrating discharge summaries ontology and ICD 10 hierarchy. The framework is operationalized using the OpenLink Virtuoso database system (RDF store), enabling SPARQL queries and basic reasoning features. The evaluation was performed by implementing a use case based on patient comorbidities. [ABSTRACT FROM AUTHOR]

Although the Semantic Web Rule Language (SWRL) is not a W3C standard, it is widely used for semantic web-based projects as well as for innovative rule-based applications. Thereby, it is used to infer new knowledge from a given fact base. Today, SWRL rules are developed and managed by technical experts in text-based editors using software applications such as the Stanford Protégé toolkit. In this paper we present a visual approach which allows users to analyse and modify SWRL rules. By building upon a visual modeling language, the approach includes validation mechanisms and layouting algorithms for visually representing new as well as existing rules. The approach further provides import and export interfaces to common SWRL exchange formats. In this way, its compatibility with widely-used reasoners and semantic web platforms is guaranteed. For ensuring its feasibility, the approach has been prototypically realized using the SeMFIS platform and evaluated using the sample rules as provided in the SWRL specification. [ABSTRACT FROM AUTHOR]

In recent years, there has been a growing interest in the application of foundational ontologies, i.e., formal ontological theories in the philosophical sense, to provide a theoretically sound foundation for improving the theory and practice of conceptual modeling and knowledge representation. This paper addresses one particular foundational theory of events termed UFO-B, which has been successfully employed as a reference model for addressing problems from complex media management, enterprise architecture, software engineering, and modeling of events in petroleum exploration. Despite its success, there is still no formalization of UFO-B in a decidable knowledge representation language that could support reasoning about complex events and event relations. We address this gap by proposing a number of alternative translations from UFO-B's original axiomatization (in first-order logic and in the Alloy formal language) to the description logic SROIQ , which is the formal underpinning of OWL 2 DL. Additionally, to support practical applications, we translated these SROIQ theories to OWL 2 DL TBoxes, which were validated by showing that all the intended models of UFO-B (the logical models of the UFO-B specification in Alloy) that we generated are consistent with these UFO-B TBoxes. In a sense, the specification in Alloy implements the specification in first-order logic, while the OWL 2 TBoxes implement the SROIQ specifications. Incidentally, the methodology that we designed for the translation from UFO-B's original axiomatization in FOL and Alloy to SROIQ came to be a key contribution of this work by providing us evidence of the inadequacy of DLs for the specification of comprehensive foundational ontologies. [ABSTRACT FROM AUTHOR]

This paper describes a method by which the Web Ontology Language (OWL) can be used to specify a highly structured health record, following internationally recognised standards such as ISO 13606 and HL7 CDA. The structured record is coded using schemes such as SNOMED, ICD or LOINC, with the coding applied statically, on the basis of the predefined structure, or dynamically, on the basis of data values entered in the health record. The highly structured, coded record can then be linked with external knowledge sources which are themselves coded using the Resource Description Framework. These methods have been used to implement dynamic decision support in the open source cityEHR health records system. The effectiveness of the decision support depends on the scope and quality of the clinical coding and the sophistication of the algorithm used to match the structured record with knowledge sources. [ABSTRACT FROM AUTHOR]

A hot point of phthisiology in Russia during the last decade is the rising morbidity of tuberculosis associated with the drug resistant Micobacteria tuberculosis. The causes of MBT resistance are often related with human factor. Reducing the errors rate in complex Clinical Decision-making in phthisiology requires adequate software support and treatment monitoring. The methodology for structuring of the diagnosis characteristics at the moments of Clinical Decisions in phthisiology is represented. The Quality models and real Clinical Situations have the digital codes. The ontological modeling of the real Clinical Situations is used for analysis, construction the inference rules and improvement of Clinical Decisions and treatment results. [ABSTRACT FROM AUTHOR]

We propose a method to extract and integrate fuzzy information granules from a populated OWL ontology. The purpose of this approach is to represent imprecise knowledge within an OWL ontology, as motivated by the fact that the Semantic Web is full of imprecise and uncertain information coming from perceptual data, incomplete data, data with errors, etc. In particular, we focus on Fuzzy Set Theory as a means for representing and processing information granules corresponding to imprecise concepts usually expressed by linguistic terms. The method applies to numerical data properties. The values of a property are first clustered to form a collection of fuzzy sets. Then, for each fuzzy set, the relative σ-count is computed and compared with a number of predefined fuzzy quantifiers, which are therefore used to define new assertions that are added to the original ontology. In this way, the extended ontology provides both a punctual view and a granular view of individuals w.r.t. the selected property. We use a real-world ontology concerning hotels and populated with data of the Italian city of Pisa, to illustrate the method and to test its implementation. We show that it is possible to extract granular properties that can be described in natural language and smoothly integrated in the original ontology by means of annotated assertions. [ABSTRACT FROM AUTHOR]

We present Ontop, an open-source Ontology-Based Data Access (OBDA) system that allows for querying relational data sources through a conceptual representation of the domain of interest, provided in terms of an ontology, to which the data sources are mapped. Key features of Ontop are its solid theoretical foundations, a virtual approach to OBDA, which avoids materializing triples and is implemented through the query rewriting technique, extensive optimizations exploiting all elements of the OBDA architecture, its compliance to all relevant W3C recommendations (including SPARQL queries, R2RML mappings, and OWL2QL and RDFS ontologies), and its support for all major relational databases. [ABSTRACT FROM AUTHOR]

We investigate the application of distributional semantics models for facilitating unsupervised extraction of biomedical terms from unannotated corpora. Term extraction is used as the first step of an ontology learning process that aims to (semi-)automatic annotation of biomedical concepts and relations from more than 300K PubMed titles and abstracts. We experimented with both traditional distributional semantics methods such as Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA) as well as the neural language models CBOW and Skip-gram from Deep Learning. The evaluation conducted concentrates on sepsis, a major life-threatening condition, and shows that Deep Learning models outperform LSA and LDA with much higher precision. [ABSTRACT FROM AUTHOR]

Norms and policies are a fundamental component of nowadays open distributed systems operating on the Internet, and a crucial application is the field of Access Control. In such context, policies have been widely used to express data consumer permissions and prohibitions to get access to certain data, whereas obligations have been modelled and applied more infrequently. In this paper, we present a unified semantic model for specifying data consumer and data provider obligations in the context of an exchange of logically formalized information between agents, inside a Multiagent System. We explain how to specify those obligations, in a formal way; we present how to enforce the data provider obligations on the data released by the agent before returning it to the data consumer; we show how to monitor the fulfilment or violation of the specified data consumer obligations; all of that in the context of a complete reasoning-based obligation framework. [ABSTRACT FROM AUTHOR]

The increasing number of terms used in textbooks for information management (IM) in hospitals makes it difficult for medical informatics students to grasp IM concepts and their interrelations. Formal ontologies which comprehend and represent the essential content of textbooks can facilitate the learning process in IM education. The manual construction of such ontologies is time-consuming and thus very expensive [3]. Moreover, most domain experts lack skills in using a formal language like OWL[2] and usually have no experience with standard editing tools like Protégé 2 [4,5]. This paper presents an ontology modeling approach based on Excel2OWL, a self-developed tool which efficiently supports domain experts in collaboratively constructing ontologies from textbooks. This approach was applied to classic IM textbooks, resulting in an ontology called SNIK. Our method facilitates the collaboration between domain experts and ontologists in the development process. Furthermore, the proposed approach enables ontologists to detect modeling errors and also to evaluate and improve the quality of the resulting ontology rapidly. This approach allows us to visualize the modeled textbooks and to analyze their semantics automatically. Hence, it can be used for e-learning purposes, particularly in the field of IM in hospitals. [ABSTRACT FROM AUTHOR]

The re-engineering of vocabularies into ontologies can save considerable time in the development of ontologies. Current methods that guide the re-engineering of thesauri into ontologies often convert vocabularies merely syntactically and ignore problems arising from interpreting vocabularies as ontologies, i.e. as sets of statements of facts. Current re-engineering methods also do not make use of the semantic capabilities of formal languages in order to detect logical mistakes and improve vocabularies. In this paper, we introduce a content-focused method for building domain-specific ontologies based on a thesaurus, a popular type of vocabulary. Application of the method results in an ontology that not only adheres to the semantics of the description logic OWL, but also contains a semantically rich description of the modeled entities, enables non-trivial, automated reasoning, and can be integrated with other ontologies following the same development principles. We explain the motivation and sub-activities for each of the steps in our method and illustrate their application through a case study in the domain of agricultural fertilizers based on the ACROVOC Thesaurus. Our method shows, first and foremost, that a considerable manual effort is required to derive a semantically rich ontology from a thesaurus, particularly in connection with the alignment to a top-level ontology as well as for the identification and formal specification of membership conditions. Applying our method will likely change the structure of a thesaurus considerably. Our method is particularly useful where a highly reliable is-a hierarchy or consistent definitions are crucial. [ABSTRACT FROM AUTHOR]

Effective knowledge management of highly sensitive information requires a meticulous design of access-control policies. These determine which organizational actors have access to what knowledge and under which circumstances. An effective design of such access-control policies requires not only a command of an apt representation formalism, but an in-depth understanding of the dynamic privacy needs of the organization. Acquiring these competencies is the central goal of any education or knowledge management process. We present a controlled experiment designed to examine the differences in novices' competencies in using two ontology formalisms - Frames and OWL - while constructing access-control ontologybased policies. The two differ in the level of structuration, and the abstract thinking they require. The findings offer partial support for Bloom's predictions. The results show that students performed relatively well in both formalisms with respect to the tasks of comprehending and implementing access-control policies. However, when it came to synthesizing new accesscontrol policies, the students found the Frames formalism significantly simpler than the OWL where they failed miserably. [ABSTRACT FROM AUTHOR]

The formal representation of clinical knowledge is still an open research topic. Classical representation languages for clinical guidelines are used to produce diagnostic and treatment plans. However, they have important limitations, e.g. when looking for ways to re-use, combine, and reason over existing clinical knowledge. These limitations are especially problematic in the context of multimorbidity; patients that suffer from multiple diseases. To overcome these limitations, this paper proposes a model for clinical guidelines (TMR4I) that allows the re-use and combination of knowledge from multiple guidelines. Semantic Web technology is applied to implement the model, allowing us to automatically infer interactions between recommendations, such as recommending the same drug more than once. It relies on an existing Linked Data set, DrugBank, for identifying drugdrug interactions. We evaluate the model by applying it to two realistic case studies on multimorbidity that combine guidelines for two (Duodenal Ulcer and Transient Ischemic Attack) and three diseases (Osteoarthritis, Hypertension and Diabetes) and compare the results with existing methods. [ABSTRACT FROM AUTHOR]

The Visual Notation for OWL Ontologies (VOWL) is a well-specified visual language for the user-oriented representation of ontologies. It defines graphical depictions for most elements of the Web Ontology Language (OWL) that are combined to a force-directed graph layout visualizing the ontology. In contrast to related work, VOWL aims for an intuitive and comprehensive representation that is also understandable to users less familiar with ontologies. This article presents VOWL in detail and describes its implementation in two different tools: ProtégéVOWL and WebVOWL. The first is a plugin for the ontology editor Protégé, the second a standalone web application. Both tools demonstrate the applicability of VOWL by means of various ontologies. In addition, the results of three user studies that evaluate the comprehensibility and usability of VOWL are summarized. They are complemented by findings from an interview with experienced ontology users and from testing the visual scope and completeness of VOWL with a benchmark ontology. The evaluations helped to improve VOWL and confirm that it produces comparatively intuitive and comprehensible ontology visualizations. [ABSTRACT FROM AUTHOR]

Traditional approaches for querying the Web of Data often involve centralised warehouses that replicate remote data. Conversely, Linked Data principles allow for answering queries live over the Web by dereferencing URIs to traverse remote data sources at runtime. A number of authors have looked at answering SPARQL queries in such a manner; these link-traversal based query execution (LTBQE) approaches for Linked Data offer up-to-date results and decentralised (i.e., client-side) execution, but must operate over incomplete dereferenceable knowledge available in remote documents, thus affecting response times and "recall" for query answers. In this paper, we study the recall and effectiveness of LTBQE, in practice, for the Web of Data. Furthermore, to integrate data from diverse sources, we propose lightweight reasoning extensions to help find additional answers. From the state-of-the-art which (1) considers only dereferenceable information and (2) follows rdfs:seeAlso links, we propose extensions to consider (3) owl:sameAs links and reasoning, and (4) lightweight RDFS reasoning. We then estimate the recall of link-traversal query techniques in practice: we analyse a large crawl of the Web of Data (the BTC'11 dataset), looking at the ratio of raw data contained in dereferenceable documents vs. the corpus as a whole and determining how much more raw data our extensions make available for query answering. We then stress-test LTBQE (and our extensions) in real-world settings using the FedBench and DBpedia SPARQL Benchmark frameworks, and propose a novel benchmark called QWalk based on random walks through diverse data. We show that link-traversal query approaches often work well in uncontrolled environments for simple queries, but need to retrieve an unfeasible number of sources for more complex queries. We also show that our reasoning extensions increase recall at the cost of slower execution, often increasing the rate at which results return; conversely, we show that reasoning aggravates performance issues for complex queries. [ABSTRACT FROM AUTHOR]

Representing uncertain information is crucial for modeling real world domains. In this paper we present a technique for the integration of probabilistic information in Description Logics (DLs) that is based on the distribution semantics for probabilistic logic programs. In the resulting approach, that we called DISPONTE, the axioms of a probabilistic knowledge base (KB) can be annotated with a real number between 0 and 1. A probabilistic knowledge base then defines a probability distribution over regular KBs called worlds and the probability of a given query can be obtained from the joint distribution of the worlds and the query by marginalization. We present the algorithm BUNDLE for computing the probability of queries from DISPONTE KBs. The algorithm exploits an underlying DL reasoner, such as Pellet, that is able to return explanations for queries. The explanations are encoded in a Binary Decision Diagram from which the probability of the query is computed. The experimentation of BUNDLE shows that it can handle probabilistic KBs of realistic size. [ABSTRACT FROM AUTHOR]

Web-based data collection of non-reactive data is becoming increasingly important for many social science fields. Being able to introduce and automatically enforce policies that regulate the collection and the use of those data is crucial for taking into account the privacy and confidentiality wishes of data providers. Those policies are currently expressed in natural language or in a language lacking a formal semantics, therefore, in order to comply with them researchers need to apply them manually. It is therefore clear that realizing new technologies for automatically supporting the enforcement and monitoring of various type of policies in Web-based data collection is an important open issue. In this paper, we propose an application independent formal model of policies that can be automatically enforced and whose specification allows to take into account the meaning of the managed data. Given that for enforcing the policies on the collected data, their semantics is crucial, in this paper we propose an OWL 2 Social Network Ontology to capture the nature of social network data collected from the Web. We also propose techniques for semantic enrichment of the data, which are then exploited in the specification and enforcement of the formalized policies. [ABSTRACT FROM AUTHOR]

This paper describes the publication and linking of (parts of) PAROLE SIMPLE CLIPS (PSC), a large scale Italian lexicon, to the Semantic Web and the Linked Data cloud using the lemon model. The main challenge of the conversion is discussed, namely the reconciliation between the PSC semantic structure which contains richly encoded semantic information, following the qualia structure of the Generative Lexicon theory and the lemon view of lexical sense as a reified pairing of a lexical item and a concept in an ontology. The result is two datasets: one consists of a list of lemon lexical entries with their lexical properties, relations and senses; the other consists of a list of OWL individuals representing the referents for the lexical senses. These OWL individuals are linked to each other by a set of semantic relations and mapped onto the SIMPLE OWL ontology of higher level semantic types. [ABSTRACT FROM AUTHOR]

The PAROLE/SIMPLE 'lemon' Ontology and Lexicon are the OWL/RDF version of the PAROLE/SIMPLE lexicons (defined during the PAROLE (LE2-4017) and SIMPLE (LE4-8346) IV FP EU projects) once mapped onto lemon model and LexInfo ontology. Original PAROLE/SIMPLE lexicons contain morphological, syntactic and semantic information, organized according to a common model and to common linguistic specifications for 12 European languages. The data set we describe includes the PAROLE/SIMPLE model mapped to lemon and LexInfo ontology and the Spanish & Catalan lexicons. All data are published in the Data Hub and are distributed under CC Attribution 3.0 Unported license. The Spanish lexicon contains 199466 triples and 7572 lexical entries fully annotated with syntactic and semantic information. The Catalan lexicon contains 343714 triples and 20545 lexical entries annotated with syntactic information half of which are also annotated with semantic information. In this paper we describe the resulting data, the mapping process and the benefits obtained. We demonstrate that the Linked Open Data principles prove essential for datasets such as original PAROLE/SIMPLE lexicons where harmonization and interoperability were crucial. The resulting data is lighter and better suited for exploitation. In addition, it facilitates further extensions and linking to external resources such as WordNet, lemonUby, DBpedia etc. [ABSTRACT FROM AUTHOR]

To increase the interoperability and accessibility of data in sensor-rich systems, there has been a recent proliferation of the use of Semantic Web technologies in sensor-rich systems. Quite a range of such applications have emerged, such as hazard monitoring and rescue, context-aware computing, environmental monitoring, field studies, internet of things, and so on. These systems often assume a centralized paradigm for data processing, which does not always hold in reality especially when the systems are deployed in a hostile environment. At runtime, the infrastructure of systems deployed in such an environment is also prone to interference or damage, causing part of the infrastructure to have limited network connection or even to be detached from the rest. A solution to such a problem would be to push the intelligence, such as semantic reasoning, down to the device layer. A key enabler for such a solution is to run semantic reasoning on resource-constrained devices. This paper shows how reasoner composition (i.e. to automatically adjust a reasoning approach to preserve only a 'well-suited' amount of reasoning for a given ontology) can achieve resource-efficient semantic reasoning. Two novel reasoner composition algorithms are introduced and implemented. Evaluation indicates that the reasoner composition algorithms greatly reduce the resources required for OWL reasoning, potentially facilitating greater semantic reasoning on sensor devices. [ABSTRACT FROM AUTHOR]

Clinical decision support systems (CDSSs) often base their knowledge and advice on human expertise. Knowledge representation needs to be in a format that can be easily understood by human users as well as supporting ongoing knowledge engineering, including evolution and consistency of knowledge. This paper reports on the development of an ontology specification for managing knowledge engineering in a CDSS for assessing and managing risks associated with mentalhealth problems. The Galatean Risk and Safety Tool, GRiST, represents mentalhealth expertise in the form of a psychological model of classification. The hierarchical structure was directly represented in the machine using an XML document. Functionality of the model and knowledge management were controlled using attributes in the XML nodes, with an accompanying paper manual for specifying how end-user tools should behave when interfacing with the XML. This paper explains the advantages of using the web-ontology language, OWL, as the specification, details some of the issues and problems encountered in translating the psychological model to OWL, and shows how OWL benefits knowledge engineering. The conclusions are that OWL can have an important role in managing complex knowledge domains for systems based on human expertise without impeding the end-users' understanding of the knowledge base. The generic classification model underpinning GRiST makes it applicable to many decision domains and the accompanying OWL specification facilitates its implementation. [ABSTRACT FROM AUTHOR]

Standardized terminologies are introduced in healthcare with the intention of improving information quality, which is important for enhancing the quality of healthcare itself. The International Classification for Nursing Practice (ICNP®) is a unified language system that presents an ontology for nursing terminology; it is meant for documentation of nursing diagnoses, nursing interventions and patient outcomes. This paper presents an information model and an application for teaching nursing students how to use ICNP to assist in the planning of nursing care. The model is an integration of ICNP and our catalog ontology, patient journal ontology, and ontology defining task sets. The application for learning nursing terminology offers descriptions of patient situations and then prompts the student to supply nursing statements for diagnoses, goals and interventions. The nursing statements may be selected from catalogues containing premade solutions based on ICNP, or they may be constructed directly by selecting terms from ICNP. [ABSTRACT FROM AUTHOR]

The RDF collections and containers is one of the most used features by RDF technicians and practitioners. Although some work has been published in past, there is not a standard and accepted way for defining collections within OWL DL frameworks. Here, we attempt to address this issue with the introduction of the Collections Ontology (CO) version 2.0. CO is an OWL 2 DL ontology developed for creating sets, bags and lists of resources, and for inferring collection properties even in the presence of incomplete information. [ABSTRACT FROM AUTHOR]

Most literature searching in biomedicine is now conducted via PubMed, Google Scholar or other web-based bibliographic search mechanisms. Yet until now a public, open, interoperable and complete web-adapted information schema for bibliographic citations, bibliographic references and scientific discourse has not been available. Such a schema, expressed in the form of a description logic compatible with current web semantics approaches, would provide the ability to treat bibliographic references and citations, and rhetorical discourse in scientific publications, as semantic metadata on the web, with all the benefits that implies for organization, search and mash-up of web-based scientific information.In this paper we present CiTO + SWAN, a set of fully harmonized ontology modules resulting from the harmonization of CiTO (the Citation Typing Ontology) with SWAN (Semantic Web Applications in Neuromedicine), which we have developed by jointly adapting and evolving version 1.6 of CiTO, the Citation Typing Ontology, and version 1.2 of the SWAN Scientific Discourse Ontology (v1.2). The CiTO + SWAN model is specified in OWL 2 DL, is fully modular, and inherently supports agent-based searching and mash-ups.Through the harmonization activity presented here, and previous work that harmonized SWAN with the SIOC (Semantically-Interlinked Online Communities) Ontology for describing blogs, wikis and discussion groups, we have construct the basis of a powerful new web framework for scientific communications. [ABSTRACT FROM AUTHOR]

Healthcare information is distributed through multiple heterogeneous and autonomous systems. Access to, and sharing of, distributed information sources are a challenging task. To contribute to meeting this challenge, this paper presents a formal, complete and semi-automatic transformation service from Relational Databases to Web Ontology Language. The proposed service makes use of an algorithm that allows to transform several data models of different domains by deploying mainly inheritance rules. The paper emphasizes the relevance of integrating the proposed approach into an ontology-based interoperability service to achieve semantic interoperability. [ABSTRACT FROM AUTHOR]

OGOLOD is a Linked Open Data dataset derived from different biomedical resources by an automated pipeline, using a tailored ontology as a scaffold. The key contribution of OGOLOD is that it links, in new RDF triples, genetic human diseases and orthologous genes, paving the way for a more efficient translational biomedical research exploiting the Linked Open Data cloud. [ABSTRACT FROM AUTHOR]

The dynamic domains change over time, in which the modified knowledge in the Ontology that represents the domain has to be considered. In such situation, several versions of the same Ontology can be found with unknown differences between them. Several methods for Ontology's alignment exist, however very few are interested in the comparison between versions. This study implements a method of comparing Ontology's versions. It extracts differences between OWL Ontology's versions. In order to calculate the distance between two Concepts, the method is based on the combination of similarity measures proposed by Levenshtein and OWL's scheme of characteristics. The algorithm used within the method integrates different heuristics matchers for comparing Ontology versions. At the end of the study, a description of Software tool is given. The result of executing this approach on three versions of Cancer disease Ontology is summarized in a change data- base that includes all the existing differences. For the evaluation, two criteria: Recall and Precision are used to validate the model. [ABSTRACT FROM AUTHOR]

The Web Ontology Language OWL is currently the most prominent formalism for representing ontologies in Semantic Web applications. OWL is based on description logics, and automated reasoners are used to infer knowledge implicitly present in OWL ontologies. However, because typical description logics obey the classical principle of explosion, reasoning over inconsistent ontologies is impossible in OWL. This is so despite the fact that inconsistencies are bound to occur in many realistic cases, e.g., when multiple ontologies are merged or when ontologies are created by machine learning or data mining tools.In this paper, we present four-valued paraconsistent description logics which can reason over inconsistencies. We focus on logics corresponding to OWL DL and its profiles. We present the logic $\mathcal {SROIQ}4$, showing that it is both sound relative to classical $\mathcal {SROIQ}$ and that its embedding into $\mathcal {SROIQ}$ is consequence preserving. We also examine paraconsistent varieties of $\mathcal{EL}^{++}$, DL-Lite, and Horn-DLs. The general framework described here has the distinct advantage of allowing classical reasoners to draw sound but nontrivial conclusions from even inconsistent knowledge bases. Truth-value gaps and gluts can also be selectively eliminated from models (by inserting additional axioms into knowledge bases). If gaps but not gluts are eliminated, additional classical conclusions can be drawn without affecting paraconsistency. [ABSTRACT FROM AUTHOR]

The widespread introduction of satellite imagery with several spatial, spectral and temporal resolutions is a real opportunity to analyze and characterize the urban environment. These last few years, the Object-Based Image Analysis (OBIA) approach has been largely developed and applied for urban applications. However, a major issue in this approach is domain knowledge formalization and exploitation. In this paper, after a detailed presentation of related works in the domain of semantic interpretation using ontologies, a taxonomy of urban objects and a framework to describe urban objects and their spatial organization is presented. This taxonomy is then formalized in a domain ontology using the semantic web ontology language and rules (OWL and SWRL). [ABSTRACT FROM AUTHOR]

This paper describes the Ontology of units of Measure and related concepts (OM), an OWL ontology of the domain of quantities and units of measure. OM supports making quantitative research data more explicit, so that the data can be integrated, verified and reproduced. The various options for modeling the domain are discussed. For example, physical quantities can be modeled either as classes, instances or properties. The design choices made are based on use cases from our own projects and general experience in the field. The use cases have been implemented as tools and web services. OM is compared with QUDT, another active effort for an OWL model in this domain. We note possibilities for integration of these efforts. We also discuss the role OWL plays in our approach. [ABSTRACT FROM AUTHOR]

Multiple myeloma (MM) is a malignant disorder characterized by the monoclonal proliferation of B cell derived plasma cells in the bone marrow. The diagnosis depends on the identification of abnormal monoclonal marrow plasma cells, monoclonal protein in the serum or urine, evidence of end-organ damage, and a clinical picture consistent with MM. The distinction between MM stages- monoclonal gammopathy of undetermined significance or indolent myeloma-is critical in guiding therapy. This paper describes how to produce ontology-driven semiological rules base (SRB) and a consultation form to aid in the diagnosis of plasma cells diseases. We have extracted the MM sub-ontology from the NCI Thesaurus. Using Protégé 3.4.2 and owl1, criteria in the literature for the diagnosis and staging of MM have been added to the ontology. All quantitative parameters have been transformed to a qualitative format. A formal description of MM variants and stages has been given. The obtained ontology has been checked by a reasoner and instantiated to obtain a SRB. The form created has been tested and evaluated utilizing 63 clinical medical reports. The likelihood for a disease being the correct diagnosis is determined by computing a ratio. The resulting tool is relevant for MM diagnosis and staging. [ABSTRACT FROM AUTHOR]

This paper presents use cases for modular development of ontologies using the OWL imports mechanism. Many of the methods are inspired by work in modular development in software engineering. The approach is aimed at developers of large ontologies covering multiple subdomains that make use of OWL reasoners for inference. Such ontologies are common in biomedical sciences, but nothing in the paper is specific to biomedicine. There are four groups of use cases: (i) organisation and factoring of ontologies; (ii) maintaining stable interfaces and bindings between ontologies and between ontologies and software; (iii) localization of ontologies to the requirements of specific sites and (iv) extension of ontologies and encapsulation of modifications. OWL's axiom-oriented import mechanism has many similarities with import mechanisms in object-oriented software but also important differences - in particular, the effects of OWL imports are global, and the order in which modules are imported is irrelevant. The advantages and disadvantages of OWL's axiom-oriented approach are discussed, and suggestions are made for extensions to allow axioms to be filtered out as well as added - a mechanism that we term 'adaptation' to distinguish it from the standard import mechanism. Finally we discuss possible alternatives and practical experience with the approaches presented. [ABSTRACT FROM AUTHOR]

This paper presents use cases for modular development of ontologies using the OWL imports mechanism. Many of the methods are inspired by work in modular development in software engineering. The approach is aimed at developers of large ontologies covering multiple subdomains that make use of OWL reasoners for inference. Such ontologies are common in biomedical sciences, but nothing in the paper is specific to biomedicine. There are four groups of use cases: (i) organisation and factoring of ontologies; (ii) maintaining stable interfaces and bindings between ontologies and between ontologies and software; (iii) localization of ontologies to the requirements of specific sites and (iv) extension of ontologies and encapsulation of modifications. OWL's axiom-oriented import mechanism has many similarities with import mechanisms in object-oriented software but also important differences - in particular, the effects of OWL imports are global, and the order in which modules are imported is irrelevant. The advantages and disadvantages of OWL's axiom-oriented approach are discussed, and suggestions are made for extensions to allow axioms to be filtered out as well as added - a mechanism that we term 'adaptation' to distinguish it from the standard import mechanism. Finally we discuss possible alternatives and practical experience with the approaches presented. [ABSTRACT FROM AUTHOR]

The Unified Enterprise Modelling Language (UEML) aims to support precise semantic definition of a wide variety of enterprise- and IS-modelling languages. In the longer run, it is also intended as a hub for integrated use of enterprise and information system (IS) models expressed in different languages. To achieve this, UEML provides a common ontology that interrelates the semantics of many existing modelling languages. This paper presents the motivation and background for the UEML work. It then presents the structure and contents of UEML's evolving common ontology, the Unified Enterprise Modelling Ontology (UEMO), which has been established through analyses of 130 modelling constructs from a selection of 10 enterprise- and IS-modelling languages. It goes on to discuss the current state of UEMO and its further evolution. Finally, conclusions and paths for further work are offered. [ABSTRACT FROM AUTHOR]

Knowledge representation using ontology has been proposed to resolve the issues of semantic heterogeneity in data interoperability and data sharing among heterogeneous systems because ontology is a powerful expressive language to represent the required knowledge about data of a specific domain and has been applied in many fields, such as Semantic Web, e-commerce and information interoperability, etc. However, building ontology for information source manually is not only hard and error-prone, but also very personal if there is no common guideline. We had proposed a framework for data interoperability using semantic Web service to resolve the semantic heterogeneity in healthcare environment. We found that learning ontology from existing information resources is a good solution to explicitly express the semantics of an information source, but some semantics may be missing during the ontology learning process. Since relational database is widely used for storing source data, in this paper a new approach of learning OWL ontology from a relational database and information embedded in an application is proposed. In order to acquire the complete conceptual information, a group of learning rules are used to obtain OWL ontology, including classes, properties, property characteristics, cardinalities and instances. A semi-automatic application called OntoApp that implemented all proposed learning rules has been developed and tested; the results show promising in knowledge learning from information source. [ABSTRACT FROM AUTHOR]