Data-Driven Shape Analysis and Processing

Data-driven methods play an increasingly important role in discovering geometric, structural, and semantic relationships between 3D shapes in collections, and applying this analysis to support intelligent modeling, editing, and visualization of geometric data. In contrast to traditional approaches, a key feature of data-driven approaches is that they aggregate information from a collection of shapes to improve the analysis and processing of individual shapes. In addition, they are able to learn models that reason about properties and relationships of shapes without relying on hard-coded rules or explicitly programmed instructions. We provide an overview of the main concepts and components of these techniques, and discuss their application to shape classification, segmentation, matching, reconstruction, modeling and exploration, as well as scene analysis and synthesis, through reviewing the literature and relating the existing works with both qualitative and numerical comparisons. We conclude our report with ideas that can inspire future research in data-driven shape analysis and processing.Comment: 10 pages, 19 figure

Linked Data Quality Assessment and its Application to Societal Progress Measurement

In recent years, the Linked Data (LD) paradigm has emerged as a simple mechanism for employing the Web as a medium for data and knowledge integration where both documents and data are linked. Moreover, the semantics and structure of the underlying data are kept intact, making this the Semantic Web. LD essentially entails a set of best practices for publishing and connecting structure data on the Web, which allows publish- ing and exchanging information in an interoperable and reusable fashion. Many different communities on the Internet such as geographic, media, life sciences and government have already adopted these LD principles. This is confirmed by the dramatically growing Linked Data Web, where currently more than 50 billion facts are represented. With the emergence of Web of Linked Data, there are several use cases, which are possible due to the rich and disparate data integrated into one global information space. Linked Data, in these cases, not only assists in building mashups by interlinking heterogeneous and dispersed data from multiple sources but also empowers the uncovering of meaningful and impactful relationships. These discoveries have paved the way for scientists to explore the existing data and uncover meaningful outcomes that they might not have been aware of previously. In all these use cases utilizing LD, one crippling problem is the underlying data quality. Incomplete, inconsistent or inaccurate data affects the end results gravely, thus making them unreliable. Data quality is commonly conceived as fitness for use, be it for a certain application or use case. There are cases when datasets that contain quality problems, are useful for certain applications, thus depending on the use case at hand. Thus, LD consumption has to deal with the problem of getting the data into a state in which it can be exploited for real use cases. The insufficient data quality can be caused either by the LD publication process or is intrinsic to the data source itself. A key challenge is to assess the quality of datasets published on the Web and make this quality information explicit. Assessing data quality is particularly a challenge in LD as the underlying data stems from a set of multiple, autonomous and evolving data sources. Moreover, the dynamic nature of LD makes assessing the quality crucial to measure the accuracy of representing the real-world data. On the document Web, data quality can only be indirectly or vaguely defined, but there is a requirement for more concrete and measurable data quality metrics for LD. Such data quality metrics include correctness of facts wrt. the real-world, adequacy of semantic representation, quality of interlinks, interoperability, timeliness or consistency with regard to implicit information. Even though data quality is an important concept in LD, there are few methodologies proposed to assess the quality of these datasets. Thus, in this thesis, we first unify 18 data quality dimensions and provide a total of 69 metrics for assessment of LD. The first methodology includes the employment of LD experts for the assessment. This assessment is performed with the help of the TripleCheckMate tool, which was developed specifically to assist LD experts for assessing the quality of a dataset, in this case DBpedia. The second methodology is a semi-automatic process, in which the first phase involves the detection of common quality problems by the automatic creation of an extended schema for DBpedia. The second phase involves the manual verification of the generated schema axioms. Thereafter, we employ the wisdom of the crowds i.e. workers for online crowdsourcing platforms such as Amazon Mechanical Turk (MTurk) to assess the quality of DBpedia. We then compare the two approaches (previous assessment by LD experts and assessment by MTurk workers in this study) in order to measure the feasibility of each type of the user-driven data quality assessment methodology. Additionally, we evaluate another semi-automated methodology for LD quality assessment, which also involves human judgement. In this semi-automated methodology, selected metrics are formally defined and implemented as part of a tool, namely R2RLint. The user is not only provided the results of the assessment but also specific entities that cause the errors, which help users understand the quality issues and thus can fix them. Finally, we take into account a domain-specific use case that consumes LD and leverages on data quality. In particular, we identify four LD sources, assess their quality using the R2RLint tool and then utilize them in building the Health Economic Research (HER) Observatory. We show the advantages of this semi-automated assessment over the other types of quality assessment methodologies discussed earlier. The Observatory aims at evaluating the impact of research development on the economic and healthcare performance of each country per year. We illustrate the usefulness of LD in this use case and the importance of quality assessment for any data analysis

Flexible RDF data extraction from Wiktionary - Leveraging the power of community build linguistic wikis

We present a declarative approach implemented in a comprehensive opensource framework (based on DBpedia) to extract lexical-semantic resources (an ontology about language use) from Wiktionary. The data currently includes language, part of speech, senses, definitions, synonyms, taxonomies (hyponyms, hyperonyms, synonyms, antonyms) and translations for each lexical word. Main focus is on flexibility to the loose schema and configurability towards differing language-editions ofWiktionary. This is achieved by a declarative mediator/wrapper approach. The goal is, to allow the addition of languages just by configuration without the need of programming, thus enabling the swift and resource-conserving adaptation of wrappers by domain experts. The extracted data is as fine granular as the source data in Wiktionary and additionally follows the lemon model. It enables use cases like disambiguation or machine translation. By offering a linked data service, we hope to extend DBpedia’s central role in the LOD infrastructure to the world of Open Linguistics.

Knowledge Graphs Meet Multi-Modal Learning: A Comprehensive Survey

Knowledge Graphs (KGs) play a pivotal role in advancing various AI applications, with the semantic web community's exploration into multi-modal dimensions unlocking new avenues for innovation. In this survey, we carefully review over 300 articles, focusing on KG-aware research in two principal aspects: KG-driven Multi-Modal (KG4MM) learning, where KGs support multi-modal tasks, and Multi-Modal Knowledge Graph (MM4KG), which extends KG studies into the MMKG realm. We begin by defining KGs and MMKGs, then explore their construction progress. Our review includes two primary task categories: KG-aware multi-modal learning tasks, such as Image Classification and Visual Question Answering, and intrinsic MMKG tasks like Multi-modal Knowledge Graph Completion and Entity Alignment, highlighting specific research trajectories. For most of these tasks, we provide definitions, evaluation benchmarks, and additionally outline essential insights for conducting relevant research. Finally, we discuss current challenges and identify emerging trends, such as progress in Large Language Modeling and Multi-modal Pre-training strategies. This survey aims to serve as a comprehensive reference for researchers already involved in or considering delving into KG and multi-modal learning research, offering insights into the evolving landscape of MMKG research and supporting future work.Comment: Ongoing work; 41 pages (Main Text), 55 pages (Total), 11 Tables, 13 Figures, 619 citations; Paper list is available at https://github.com/zjukg/KG-MM-Surve

APREGOAR: Development of a geospatial database applied to local news in Lisbon

Project Work presented as the partial requirement for obtaining a Master's degree in Geographic Information Systems and ScienceHá informações valiosas em formato de texto não estruturado sobre a localização, calendarização e a essências dos eventos disponíveis no conteúdo de notícias digitais. Vários trabalhos em curso já tentam extrair detalhes de eventos de fontes de notícias digitais, mas muitas vezes não com a nuance necssária para representar com precisão onde as coisas realmente acontecem. Alternativamente, os jornalistas poderiam associar manualmente atributos a eventos descritos nos seus artigos enquanto publicam, melhorando a exatidão e a confiança nestes atributos espaciais e temporais. Estes atributos poderiam então estar imediatamente disponíveis para avaliar a cobertura temática, temporal e espacial do conteúdo de uma agência, bem como melhorar a experiência do utilizador na exploração do conteúdo, fornecendo dimensões adicionais que podem ser filtradas. Embora a tecnologia de atribuição de dimensões geoespaciais e temporais para o emprego de aplicaçãoes voltadas para o consumidor não seja novidade, tem ainda de ser aplicada à escala das notícias. Além disso, a maioria dos sistemas existentes suporta apenas uma definição pontual da localização dos artigos, que pode não representar bem o(s) local(is) real(ais) dos eventos descritos. Este trabalho define uma aplicação web de código aberto e uma base de dados espacial subjacente que suporta i) a associação de múltiplos polígonos a representar o local onde cada evento ocorre, os prazos associados aos eventos, em linha com os atributos temáticos tradicionais associados aos artigos de notícias; ii) a contextualização de cada artigo através da adição de mapas de eventos em linha para esclarecer aos leitores onde os eventos do artigo ocorrem; e iii) a exploração dos corpora adicionados através de filtros temáticos, espaciais e temporais que exibem os resultados em mapas de cobertura interactivos e listas de artigos e eventos. O projeto foi aplicado na área da grande Lisboa de Portugal. Para além da funcionalidade acima referida, este projeto constroi gazetteers progressivos que podem ser reutilizados como associações de lugares, ou para uma meta-análise mais aprofundada do lugar, tal como é percebido coloquialmente. Demonstra a facilidade com que estas dimensões adicionais podem ser incorporadas com grade confiança na precisão da definição, geridas, e alavancadas para melhorar a gestão de conteúdo das agências noticiosas, a compreensão dos leitores, a exploração dos investigadores, ou extraídas para combinação com outros conjuntos dos dados para fornecer conhecimentos adicionais.There is valuable information in unstructured text format about the location, timing, and nature of events available in digital news content. Several ongoing efforts already attempt to extract event details from digital news sources, but often not with the nuance needed to accurately represent the where things actually happen. Alternatively, journalists could manually associate attributes to events described in their articles while publishing, improving accuracy and confidence in these spatial and temporal attributes. These attributes could then be immediately available for evaluating thematic, temporal, and spatial coverage of an agency’s content, as well as improve the user experience of content exploration by providing additional dimensions that can be filtered. Though the technology of assigning geospatial and temporal dimensions for the employ of consumer-facing applications is not novel, it has yet to be applied at scale to the news. Additionally, most existing systems only support a single point definition of article locations, which may not well represent the actual place(s) of events described within. This work defines an open source web application and underlying spatial database that supports i) the association of multiple polygons representing where each event occurs, time frames associated with the events, inline with the traditional thematic attributes associated with news articles; ii) the contextualization of each article via the addition of inline event maps to clarify to readers where the events of the article occur; and iii) the exploration of the added corpora via thematic, spatial, and temporal filters that display results in interactive coverage maps and lists of articles and events. The project was applied to the greater Lisbon area of Portugal. In addition to the above functionality, this project builds progressive gazetteers that can be reused as place associations, or for further meta analysis of place as it is colloquially understood. It demonstrates the ease of which these additional dimensions may be incorporated with a high confidence in definition accuracy, managed, and leveraged to improve news agency content management, reader understanding, researcher exploration, or extracted for combination with other datasets to provide additional insights

Web knowledge bases

Knowledge is key to natural language understanding. References to specific people, places and things in text are crucial to resolving ambiguity and extracting meaning. Knowledge Bases (KBs) codify this information for automated systems — enabling applications such as entity-based search and question answering. This thesis explores the idea that sites on the web may act as a KB, even if that is not their primary intent. Dedicated kbs like Wikipedia are a rich source of entity information, but are built and maintained at an ongoing cost in human effort. As a result, they are generally limited in terms of the breadth and depth of knowledge they index about entities. Web knowledge bases offer a distributed solution to the problem of aggregating entity knowledge. Social networks aggregate content about people, news sites describe events with tags for organizations and locations, and a diverse assortment of web directories aggregate statistics and summaries for long-tail entities notable within niche movie, musical and sporting domains. We aim to develop the potential of these resources for both web-centric entity Information Extraction (IE) and structured KB population. We first investigate the problem of Named Entity Linking (NEL), where systems must resolve ambiguous mentions of entities in text to their corresponding node in a structured KB. We demonstrate that entity disambiguation models derived from inbound web links to Wikipedia are able to complement and in some cases completely replace the role of resources typically derived from the KB. Building on this work, we observe that any page on the web which reliably disambiguates inbound web links may act as an aggregation point for entity knowledge. To uncover these resources, we formalize the task of Web Knowledge Base Discovery (KBD) and develop a system to automatically infer the existence of KB-like endpoints on the web. While extending our framework to multiple KBs increases the breadth of available entity knowledge, we must still consolidate references to the same entity across different web KBs. We investigate this task of Cross-KB Coreference Resolution (KB-Coref) and develop models for efficiently clustering coreferent endpoints across web-scale document collections. Finally, assessing the gap between unstructured web knowledge resources and those of a typical KB, we develop a neural machine translation approach which transforms entity knowledge between unstructured textual mentions and traditional KB structures. The web has great potential as a source of entity knowledge. In this thesis we aim to first discover, distill and finally transform this knowledge into forms which will ultimately be useful in downstream language understanding tasks

Enhancing Emergency Decision-making with Knowledge Graphs and Large Language Models

Emergency management urgently requires comprehensive knowledge while having a high possibility to go beyond individuals' cognitive scope. Therefore, artificial intelligence(AI) supported decision-making under that circumstance is of vital importance. Recent emerging large language models (LLM) provide a new direction for enhancing targeted machine intelligence. However, the utilization of LLM directly would inevitably introduce unreliable output for its inherent issue of hallucination and poor reasoning skills. In this work, we develop a system called Enhancing Emergency decision-making with Knowledge Graph and LLM (E-KELL), which provides evidence-based decision-making in various emergency stages. The study constructs a structured emergency knowledge graph and guides LLMs to reason over it via a prompt chain. In real-world evaluations, E-KELL receives scores of 9.06, 9.09, 9.03, and 9.09 in comprehensibility, accuracy, conciseness, and instructiveness from a group of emergency commanders and firefighters, demonstrating a significant improvement across various situations compared to baseline models. This work introduces a novel approach to providing reliable emergency decision support.Comment: 26 pages, 6 figure

AI for IT Operations (AIOps) on Cloud Platforms: Reviews, Opportunities and Challenges

Artificial Intelligence for IT operations (AIOps) aims to combine the power of AI with the big data generated by IT Operations processes, particularly in cloud infrastructures, to provide actionable insights with the primary goal of maximizing availability. There are a wide variety of problems to address, and multiple use-cases, where AI capabilities can be leveraged to enhance operational efficiency. Here we provide a review of the AIOps vision, trends challenges and opportunities, specifically focusing on the underlying AI techniques. We discuss in depth the key types of data emitted by IT Operations activities, the scale and challenges in analyzing them, and where they can be helpful. We categorize the key AIOps tasks as - incident detection, failure prediction, root cause analysis and automated actions. We discuss the problem formulation for each task, and then present a taxonomy of techniques to solve these problems. We also identify relatively under explored topics, especially those that could significantly benefit from advances in AI literature. We also provide insights into the trends in this field, and what are the key investment opportunities