An Application Ontology for Reproducibility of Machine Learning Solutions

With Artificial Intelligence and Machine Learning (ML) on the rise, organisations of different scales and nature are looking to utilise ML systems to support their day-to-day operations. Many enterprises find it difficult to adapt existing ML solutions to their organisations without huge investments in solution understanding, customisation, infrastructure enablement and workforce training. Some organisations utilise external service providers to provision their standard analytics services, and this often leads to solutions that either do not fit well with their organisation goals or may lead to the loss of expert knowledge behind the establishment of the AI system. This paper aims to address some of these challenges by proposing an ontology for ensuring the reproducibility of ML models in research as well as their integration within application environments. Our work will ensure that the knowledge about a developed ML system or process is accumulated and recorded within an organisation and can be used in the future, either by new employees or other teams within the organisation. This approach can also be utilised by researchers and developers of ML systems to record and publish metadata of their studies, ensuring that future researchers can reuse their work with minimal effort

ToCo: An ontology for representing hybrid telecommunication networks

The TOUCAN project proposed an ontology for telecommunication networks with hybrid technologies – the TOUCAN Ontology (ToCo), available at http://purl.org/toco/, as well as a knowledge design pattern Device-Interface-Link (DIL) pattern. The core classes and relationships forming the ontology are discussed in detail. The ToCo ontology can describe the physical infrastructure, quality of channel, services and users in heterogeneous telecommunication networks which span multiple technology domains. The DIL pattern is observed and summarised when modelling networks with various technology domains. Examples and use cases of ToCo are presented for demonstration

Enhancing Access to Legal Data through Ontology-based Representation:A Case Study with Brazilian Judicial Appeals

In Brazil, legal requirements for public information access, as mandated by Law no 12.527/2011, have amplified the role of the open data portals in disseminating data of collective and general interest. Despite legal provisions, there are persistent difficulties in presenting data in first-class semantic formats, which ultimately creates obstacles for digital citizens to fully exercise their newfound rights to information access. These obstacles can be addressed by building semantic data warehouses to enhance the use of open data through computational ontologies. In this paper, we demonstrate the use of a well-founded legal ontology for representing data from legal decisions extracted from a Brazilian judicial organ website. We focused our approach on a specific type of appeal in the Brazilian legal system, the Request for Standardization (RS) of interpretation of federal law, which seeks to standardize the understanding of the Appeals Panels of Federal Special Courts. Employing web scraping techniques, we built a complete ETL (Extract, Transform, Load) process to triplify data on RS appeals and their rulings. We used a gUFO-based OWL renderization of a previously developed OntoUML ontology (called OntoRS) to transform the extracted data into a suitable RDF format and populate a Virtuoso triple store. Thus, the OntoRS ontology allowed us to perform SPARQL queries to obtain new insights, metrics and small RDF graphs.</p

AspectMMKG: A Multi-modal Knowledge Graph with Aspect-aware Entities

Multi-modal knowledge graphs (MMKGs) combine different modal data (e.g., text and image) for a comprehensive understanding of entities. Despite the recent progress of large-scale MMKGs, existing MMKGs neglect the multi-aspect nature of entities, limiting the ability to comprehend entities from various perspectives. In this paper, we construct AspectMMKG, the first MMKG with aspect-related images by matching images to different entity aspects. Specifically, we collect aspect-related images from a knowledge base, and further extract aspect-related sentences from the knowledge base as queries to retrieve a large number of aspect-related images via an online image search engine. Finally, AspectMMKG contains 2,380 entities, 18,139 entity aspects, and 645,383 aspect-related images. We demonstrate the usability of AspectMMKG in entity aspect linking (EAL) downstream task and show that previous EAL models achieve a new state-of-the-art performance with the help of AspectMMKG. To facilitate the research on aspect-related MMKG, we further propose an aspect-related image retrieval (AIR) model, that aims to correct and expand aspect-related images in AspectMMKG. We train an AIR model to learn the relationship between entity image and entity aspect-related images by incorporating entity image, aspect, and aspect image information. Experimental results indicate that the AIR model could retrieve suitable images for a given entity w.r.t different aspects.Comment: Accepted by CIKM 202

Building Semantic Knowledge Graphs from (Semi-)Structured Data: A Review

Knowledge graphs have, for the past decade, been a hot topic both in public and private domains, typically used for large-scale integration and analysis of data using graph-based data models. One of the central concepts in this area is the Semantic Web, with the vision of providing a well-defined meaning to information and services on the Web through a set of standards. Particularly, linked data and ontologies have been quite essential for data sharing, discovery, integration, and reuse. In this paper, we provide a systematic literature review on knowledge graph creation from structured and semi-structured data sources using Semantic Web technologies. The review takes into account four prominent publication venues, namely, Extended Semantic Web Conference, International Semantic Web Conference, Journal of Web Semantics, and Semantic Web Journal. The review highlights the tools, methods, types of data sources, ontologies, and publication methods, together with the challenges, limitations, and lessons learned in the knowledge graph creation processes.publishedVersio

Advancing Disambiguation of Actors Against Multiple Linked Open Data Sources

Disambiguation is an important step in the semantic data transformation process. In this scope, the process sought to eliminate the ambiguity of which person a record is describing. \emph{Constellation of Correspondence} or CoCo is a data integration project focused on historical epistolary data. In its data transformation flow, actor records from source data are linked to actor entities in an external linked open data source to enrich the actors' information with metadata found in external databases. This work presents an advanced disambiguation system for CoCo data transformation flow. The system has managed to deliver a reliable and flexible linking system that provides advantages,hi such as the incorporation of an additional external database, novel linking rule definition and implementation, and a more transparent linking result provenance presentation and management. This work also evaluates linking process performance in various linking cases by employing the help of a human expert judge to evaluate whether the proposed valid link made by the linking systems are indeed accurate or not. The system and the proposed rule configuration delivers a satisfactory performance on the easier, more common case but still struggles to deliver good precision on rarer edge cases. There are insightful observations made regarding the data that was observed during the development and evaluation of the system. Firstly is the importance of naming similarity in determining a link between two actors and the imperfection of name similarity in the majority of the valid linking case. This observation justifies the need for dissimilarity tolerance in naming comparison despite the importance of naming similarity. This imperfect state of the systems inspires the several future works that this work proposes. The proposed future works are the further fine-tuning of the linking rule and selection rule and the advancing the evaluation by increasing the completeness of the evaluation and the research of a more automated evaluation process

Promocijas darbs

Elektroniskā versija nesatur pielikumusDarbā izstrādātas oriģinālas metodes, kas ļauj vizuālus uz paplašinātām UML veida grafu diagrammām balstītus rīkus izmantot praktisku ontoloģiju un semantisko datu vaicājumu veidošanai un attēlošanai. OWL ontoloģiju vizuālas modelēšanas jomā izveidoti līdzekļi konkrētam lietojumam specifiskas notācijas uzdošanai un izmantošanai, tādi ka: mehānisms lietotāja definētu notāciju uzdošanai, ontoloģiju vizualizācijas parametru ietvars, ontoloģiju eksporta modulis un uz gramatikām balstīta priekšāteikšanas metode. Darbā piedāvāts risinājums vizuālai bagātīgu datu vaicājumu veidošanai pār RDF datubāzēm, un to translēšanai uz tekstuālu SPARQL valodu, kurā pierakstītie vaicājumi var tikt tieši izpildīti pār RDF datu bāzēm. Atslēgvārdi: OWL, OWLGrEd, teksta priekšāteicējs, domēnspecifiska ontoloģiju attēlošana, SPARQL, vizuāli vaicājumi, ViziQuerThe doctoral thesis develops original methods that allow visual tools that are based on extended UML-style graph diagrams to be used for creating and visualising practical ontologies and semantic data queries. In the field of visual modeling of OWL ontologies, tools have been developed for creating modeling notations specific to particular applications, such as a mechanism for creating user-defined notations, a framework for ontology visualisation parameters, an ontology export module and a grammar-based auto-completion method. The doctoral thesis presents a solution for the visual formulation of rich data queries over RDF databases, and their translation into the standard textual SPARQL query language. Keywords: OWL, OWLGrEd, text auto-completion, Domain-Specific Ontology Representation, SPARQL, Visual Queries, ViziQuer

Anytime bottom-up rule learning for large-scale knowledge graph completion

Knowledge graph completion is the task of predicting correct facts that can be expressed by the vocabulary of a given knowledge graph, which are not explicitly stated in that graph. Broadly, there are two main approaches for solving the knowledge graph completion problem. Sub-symbolic approaches embed the nodes and/or edges of a given graph into a low-dimensional vector space and use a scoring function to determine the plausibility of a given fact. Symbolic approaches learn a model that remains within the primary representation of the given knowledge graph. Rule-based approaches are well-known examples. One such approach is AnyBURL. It works by sampling random paths, which are generalized into Horn rules. Previously published results show that the prediction quality of AnyBURL is close to current state of the art with the additional benefit of offering an explanation for a predicted fact. In this paper, we propose several improvements and extensions of AnyBURL. In particular, we focus on AnyBURL’s capability to be successfully applied to large and very large datasets. Overall, we propose four separate extensions: (i) We add to each rule a set of pairwise inequality constraints which enforces that different variables cannot be grounded by the same entities, which results into more appropriate confidence estimations. (ii) We introduce reinforcement learning to guide path sampling in order to use available computational resources more efficiently. (iii) We propose an efficient sampling strategy to approximate the confidence of a rule instead of computing its exact value. (iv) We develop a new multithreaded AnyBURL, which incorporates all previously mentioned modifications. In an experimental study, we show that our approach outperforms both symbolic and sub-symbolic approaches in large-scale knowledge graph completion. It has a higher prediction quality and requires significantly less time and computational resources