Reorganizing Educational Institutional Domain using Faceted Ontological Principles

The purpose of this work is to find out how different library classification systems and linguistic ontologies arrange a particular domain of interest and what are the limitations for information retrieval. We use knowledge representation techniques and languages for construction of a domain specific ontology. This ontology would help not only in problem solving, but it would demonstrate the ease with which complex queries can be handled using principles of domain ontology, thereby facilitating better information retrieval.Comment: 26 pages, 12 figures, KNOWLEDGE ORGANIZATION Journal Pape

Knowledge Organization and Terminology: application to Cork

This PhD thesis aims to prove the relevance of texts within the conceptual strand of terminological work. Our methodology serves to demonstrate how linguists can infer knowledge information from texts and subsequently systematise it, either through semiformal or formal representations. We mainly focus on the terminological analysis of specialised corpora resorting to semi-automatic tools for text analysis to systematise lexical-semantic relationships observed in specialised discourse context and subsequent modelling of the underlying conceptual system. The ultimate goal of this methodology is to propose a typology that can help lexicographers to write definitions. Based on the double dimension of Terminology, we hypothesise that text and logic modelling do not go hand in hand since the latter does not directly relate to the former. We highlight that knowledge and language are crucial for knowledge systematisation, albeit keeping in mind that they pertain to different levels of analysis, for they are not isomorphic. To meet our goals, we resorted to specialised texts produced within the industry of cork. These texts provide us with a test bed made of knowledge-rich data which enable us to demonstrate our deductive mechanisms employing the Aristotelian formula: X=Y+DC through the linguistic and conceptual analysis of the semi-automatically extracted textual data. To explore the corpus, we resorted to text mining strategies where regular expressions play a central role. The final goal of this study is to create a terminological resource for the cork industry, where two types of resources interlink, namely the CorkCorpus and the OntoCork. TermCork is a project that stems from the organisation of knowledge in the specialised field of cork. For that purpose, a terminological knowledge database is being developed to feed an e-dictionary. This e-dictionary is designed as a multilingual and multimodal product, where several resources, namely linguistic and conceptual ones are paired. OntoCork is a micro domain-ontology where the concepts are enriched with natural language definitions and complemented with images, either annotated with metainformation or enriched with hyperlinks to additional information, such as a lexicographic resource. This type of e-dictionary embodies what we consider a useful terminological tool in the current digital information society: accounting for its main features, along with an electronic format that can be integrated into the Semantic Web due to its interoperability data format. This aspect emphasises its contribution to reduce ambiguity as much as possible and to increase effective communication between experts of the domain, future experts, and language professionals.Cette thèse vise à prouver la pertinence des textes dans le volet conceptuel du travail terminologique. Notre méthodologie sert à démontrer comment les linguistes peuvent déduire des informations de connaissance à partir de textes et les systématiser par la suite, soit à travers des représentations semi-formelles ou formelles. Nous nous concentrons principalement sur l'analyse terminologique de corpus spécialisé faisant appel à des outils semi-automatiques d'analyse de texte pour systématiser les relations lexico-sémantiques observées dans un contexte de discours spécialisé et la modélisation ultérieure du système conceptuel sous-jacent. L’objectif de cette méthodologie est de proposer une typologie qui peut aider les lexicographes à rédiger des définitions. Sur la base de la double dimension de la terminologie, nous émettons l'hypothèse que la modélisation textuelle et logique ne va pas de pair puisque cette dernière n'est pas directement liée à la première. Nous soulignons que la connaissance et le langage sont essentiels pour la systématisation des connaissances, tout en gardant à l'esprit qu'ils appartiennent à différents niveaux d'analyse, car ils ne sont pas isomorphes. Pour atteindre nos objectifs, nous avons eu recours à des textes spécialisés produits dans l'industrie du liège. Ces textes nous fournissent un banc d'essai constitué de données riches en connaissances qui nous permettent de démontrer nos mécanismes déductifs utilisant la formule aristotélicienne : X = Y + DC à travers l'analyse linguistique et conceptuelle des données textuelles extraites semi-automatiquement. Pour l'exploitation du corpus, nous avons recours à des stratégies de text mining où les expressions régulières jouent un rôle central. Le but de cette étude est de créer une ressource terminologique pour l'industrie du liège, où deux types de ressources sont liés, à savoir le CorkCorpus et l'OntoCork. TermCork est un projet qui découle de l'organisation des connaissances dans le domaine spécialisé du liège. À cette fin, une base de données de connaissances terminologiques est en cours de développement pour alimenter un dictionnaire électronique. Cet edictionnaire est conçu comme un produit multilingue et multimodal, où plusieurs ressources, à savoir linguistiques et conceptuelles, sont jumelées. OntoCork est une micro-ontologie de domaine où les concepts sont enrichis de définitions de langage naturel et complétés par des images, annotées avec des méta-informations ou enrichies d'hyperliens vers des informations supplémentaires. Ce type de dictionnaire électronique désigne ce que nous considérons comme un outil terminologique utile dans la société de l'information numérique actuelle : la prise en compte de ses principales caractéristiques, ainsi qu'un format électronique qui peut être intégré dans le Web sémantique en raison de son format de données d'interopérabilité. Cet aspect met l'accent sur sa contribution à réduire autant que possible l'ambiguïté et à accroître l'efficacité de la communication entre les experts du domaine, les futurs experts et les professionnels de la langue

Intelligence graphs for threat intelligence and security policy validation of cyber systems

While the recent advances in Data Science and Machine Learning attract lots of attention in Cyber Security because of their promise for effective security analytics, Vulnerability Analysis, Risk Assessment and Security Policy Validation remain slightly aside. This is mainly due to the relatively slow progress in the theoretical formulation and the technologi-cal foundation of the cyber security concepts such as logical vulnerability, threats and risks. In this article we are proposing a framework for logical analysis, threat intelligence and validation of security policies in cyber systems. It is based on multi-level model, consisting of ontology of situations and actions under security threats, security policies governing the security-related activities, and graph of the transactions. The framework is validated using a set of scenarios describing the most common security threats in digital banking and a proto-type of an event-driven engine for navigation through the intelligence graphs has been im-plemented. Although the framework was developed specifically for application in digital banking, the authors believe that it has much wider applicability to security policy analysis, threat intelligence and security by design of cyber systems for financial, commercial and business operations

Ontology Alignment Architecture for Semantic Sensor Web Integration

Abstract: Sensor networks are a concept that has become very popular in data acquisition and processing for multiple applications in different fields such as industrial, medicine, home automation, environmental detection, etc. Today, with the proliferation of small communication devices with sensors that collect environmental data, semantic Web technologies are becoming closely related with sensor networks. The linking of elements from Semantic Web technologies with sensor networks has been called Semantic Sensor Web and has among its main features the use of ontologies. One of the key challenges of using ontologies in sensor networks is to provide mechanisms to integrate and exchange knowledge from heterogeneous sources (that is, dealing with semantic heterogeneity). Ontology alignment is the process of bringing ontologies into mutual agreement by the automatic discovery of mappings between related concepts. This paper presents a system for ontology alignment in the Semantic Sensor Web which uses fuzzy logic techniques to combine similarity measures between entities of different ontologies. The proposed approach focuses on two key elements: the terminological similarity, which takes into account the linguistic and semantic information of the context of the entity's names, and the structural similarity, based on both the internal and relational structure of the concepts. This work has been validated using sensor network ontologies and the Ontology Alignment Evaluation Initiative (OAEI) tests. The results show that the proposed techniques outperform previous approaches in terms of precision and recall

Methodology for semantic enhancement of intelligence data

What follows is a contribution to the horizontal integration of warfighter intelligence data as defined in Chairman of the Joint Chiefs of Staff Instruction J2 CJCSI 3340.02AL: Horizontally integrating warfighter intelligence data improves the consumers’ production, analysis and dissemination capabilities. HI requires access (including discovery, search, retrieval, and display) to intelligence data among the warfighters and other producers and consumers via standardized services and architectures. These consumers include, but are not limited to, the combatant commands, Services, Defense agencies, and the Intelligence Community

A Study on the Use of Ontologies to Represent Collective Knowledge

The development of ontologies has become an area of considerable research interest over the past number of years. Domain ontologies are often developed to represent a shared understanding that in turn indicates cooperative effort by a user community. However, the structure and form that an ontology takes is predicated both on the approach of the developer and the cooperation of the user community. A shift has taken place in recent years from the use of highly specialised and expressive ontologies to simpler knowledge models, progressively developed by community contribution. It is within this context that this thesis investigates the use of ontologies as a means to representing collective knowledge. It investigates the impact of the community on the approach to and outcome of knowledge representation and compares the use of simple terminological ontologies with highly structured expressive ontologies in community-based narrative environments

Dwelling on ontology - semantic reasoning over topographic maps

The thesis builds upon the hypothesis that the spatial arrangement of topographic features, such as buildings, roads and other land cover parcels, indicates how land is used. The aim is to make this kind of high-level semantic information explicit within topographic data. There is an increasing need to share and use data for a wider range of purposes, and to make data more definitive, intelligent and accessible. Unfortunately, we still encounter a gap between low-level data representations and high-level concepts that typify human qualitative spatial reasoning. The thesis adopts an ontological approach to bridge this gap and to derive functional information by using standard reasoning mechanisms offered by logic-based knowledge representation formalisms. It formulates a framework for the processes involved in interpreting land use information from topographic maps. Land use is a high-level abstract concept, but it is also an observable fact intimately tied to geography. By decomposing this relationship, the thesis correlates a one-to-one mapping between high-level conceptualisations established from human knowledge and real world entities represented in the data. Based on a middle-out approach, it develops a conceptual model that incrementally links different levels of detail, and thereby derives coarser, more meaningful descriptions from more detailed ones. The thesis verifies its proposed ideas by implementing an ontology describing the land use ‘residential area’ in the ontology editor Protégé. By asserting knowledge about high-level concepts such as types of dwellings, urban blocks and residential districts as well as individuals that link directly to topographic features stored in the database, the reasoner successfully infers instances of the defined classes. Despite current technological limitations, ontologies are a promising way forward in the manner we handle and integrate geographic data, especially with respect to how humans conceptualise geographic space

Structural analysis and auditing of SNOMED hierarchies using abstraction networks

SNOMED is one of the leading healthcare terminologies being used worldwide. Due to its sheer volume and continuing expansion, it is inevitable that errors will make their way into SNOMED. Thus, quality assurance is an important part of its maintenance cycle. A structural approach is presented in this dissertation, aiming at developing automated techniques that can aid auditors in the discovery of terminology errors more effectively and efficiently. Large SNOMED hierarchies are partitioned, based primarily on their relationships patterns, into concept groups of more manageable sizes. Three related abstraction networks with respect to a SNOMED hierarchy, namely the area taxonomy, partial-area taxonomy, and disjoint partial-area taxonomy, are derived programmatically from the partitions. Altogether they afford high-level abstraction views of the underlying hierarchy, each with different granularity. The area taxonomy gives a global structural view of a SNOMED hierarchy, while the partial-area taxonomy focuses more on the semantic uniformity and hierarchical proximity of concepts. The disjoint partial-area taxonomy is devised as an enhancement of the partial-area taxonomy and is based on the partition of the entire collection of so-called overlapping concepts into singly-rooted groups. The taxonomies are exploited as the basis for a number of systematic auditing regimens, with a theme that complex concepts are more error-prone and require special attention in auditing activities. In general, group-based auditing is promoted to achieve a more efficient review within semantically uniform groups. Certain concept groups in the different taxonomies are deemed “complex” according to various criteria and thus deserve focused auditing. Examples of these include strict inheritance regions in the partial-area taxonomy and overlapping partial-areas in the disjoint partial-area taxonomy. Multiple hypotheses are formulated to characterize the error distributions and ratios with respect to different concept groups presented by the taxonomies, and thus further establish their efficacy as vehicles for auditing. The methodologies are demonstrated using SNOMED’s Specimen hierarchy as the test bed. Auditing results are reported and analyzed to assess the hypotheses. With the use of the double bootstrap and Fisher’s exact test (two-tailed), the aforementioned hypotheses are confirmed. Auditing on various complex concept groups based on the taxonomies is shown to yield a statistically significant higher proportion of errors

An ontology for formal representation of medication adherence-related knowledge : case study in breast cancer

Indiana University-Purdue University Indianapolis (IUPUI)Medication non-adherence is a major healthcare problem that negatively impacts the health and productivity of individuals and society as a whole. Reasons for medication non-adherence are multi-faced, with no clear-cut solution. Adherence to medication remains a difficult area to study, due to inconsistencies in representing medicationadherence behavior data that poses a challenge to humans and today’s computer technology related to interpreting and synthesizing such complex information. Developing a consistent conceptual framework to medication adherence is needed to facilitate domain understanding, sharing, and communicating, as well as enabling researchers to formally compare the findings of studies in systematic reviews. The goal of this research is to create a common language that bridges human and computer technology by developing a controlled structured vocabulary of medication adherence behavior—“Medication Adherence Behavior Ontology” (MAB-Ontology) using breast cancer as a case study to inform and evaluate the proposed ontology and demonstrating its application to real-world situation. The intention is for MAB-Ontology to be developed against the background of a philosophical analysis of terms, such as belief, and desire to be human, computer-understandable, and interoperable with other systems that support scientific research. The design process for MAB-Ontology carried out using the METHONTOLOGY method incorporated with the Basic Formal Ontology (BFO) principles of best practice. This approach introduces a novel knowledge acquisition step that guides capturing medication-adherence-related data from different knowledge sources, including adherence assessment, adherence determinants, adherence theories, adherence taxonomies, and tacit knowledge source types. These sources were analyzed using a systematic approach that involved some questions applied to all source types to guide data extraction and inform domain conceptualization. A set of intermediate representations involving tables and graphs was used to allow for domain evaluation before implementation. The resulting ontology included 629 classes, 529 individuals, 51 object property, and 2 data property. The intermediate representation was formalized into OWL using Protégé. The MAB-Ontology was evaluated through competency questions, use-case scenario, face validity and was found to satisfy the requirement specification. This study provides a unified method for developing a computerized-based adherence model that can be applied among various disease groups and different drug categories