Knowledge Graph semantic enhancement of input data for improving AI

Intelligent systems designed using machine learning algorithms require a large number of labeled data. Background knowledge provides complementary, real world factual information that can augment the limited labeled data to train a machine learning algorithm. The term Knowledge Graph (KG) is in vogue as for many practical applications, it is convenient and useful to organize this background knowledge in the form of a graph. Recent academic research and implemented industrial intelligent systems have shown promising performance for machine learning algorithms that combine training data with a knowledge graph. In this article, we discuss the use of relevant KGs to enhance input data for two applications that use machine learning -- recommendation and community detection. The KG improves both accuracy and explainability

LODE: Linking Digital Humanities Content to the Web of Data

Numerous digital humanities projects maintain their data collections in the form of text, images, and metadata. While data may be stored in many formats, from plain text to XML to relational databases, the use of the resource description framework (RDF) as a standardized representation has gained considerable traction during the last five years. Almost every digital humanities meeting has at least one session concerned with the topic of digital humanities, RDF, and linked data. While most existing work in linked data has focused on improving algorithms for entity matching, the aim of the LinkedHumanities project is to build digital humanities tools that work "out of the box," enabling their use by humanities scholars, computer scientists, librarians, and information scientists alike. With this paper, we report on the Linked Open Data Enhancer (LODE) framework developed as part of the LinkedHumanities project. With LODE we support non-technical users to enrich a local RDF repository with high-quality data from the Linked Open Data cloud. LODE links and enhances the local RDF repository without compromising the quality of the data. In particular, LODE supports the user in the enhancement and linking process by providing intuitive user-interfaces and by suggesting high-quality linking candidates using tailored matching algorithms. We hope that the LODE framework will be useful to digital humanities scholars complementing other digital humanities tools

Text Summarization Techniques: A Brief Survey

In recent years, there has been a explosion in the amount of text data from a variety of sources. This volume of text is an invaluable source of information and knowledge which needs to be effectively summarized to be useful. In this review, the main approaches to automatic text summarization are described. We review the different processes for summarization and describe the effectiveness and shortcomings of the different methods.Comment: Some of references format have update

Semantic Photo Manipulation with a Generative Image Prior

Despite the recent success of GANs in synthesizing images conditioned on inputs such as a user sketch, text, or semantic labels, manipulating the high-level attributes of an existing natural photograph with GANs is challenging for two reasons. First, it is hard for GANs to precisely reproduce an input image. Second, after manipulation, the newly synthesized pixels often do not fit the original image. In this paper, we address these issues by adapting the image prior learned by GANs to image statistics of an individual image. Our method can accurately reconstruct the input image and synthesize new content, consistent with the appearance of the input image. We demonstrate our interactive system on several semantic image editing tasks, including synthesizing new objects consistent with background, removing unwanted objects, and changing the appearance of an object. Quantitative and qualitative comparisons against several existing methods demonstrate the effectiveness of our method.Comment: SIGGRAPH 201

Knowledge Graphs and Large Language Models for Intelligent Applications in the Tourism Domain

In the current era of big data, the World Wide Web is transitioning from being merely a repository of content to a complex web of data. Two pivotal technologies underpinning this shift are Knowledge Graphs (KGs) and Data Lakes. Concurrently, Artificial Intelligence has emerged as a potent means to leverage data, creating knowledge and pioneering new tools across various sectors. Among these advancements, Large Language Models (LLM) stand out as transformative technologies in many domains. This thesis delves into an integrative exploration, juxtaposing the structured world of KGs and the raw data reservoirs of Data Lakes, together with a focus on harnessing LLM to derive meaningful insights in the domain of tourism. Starting with an exposition on the importance of KGs in the present digital milieu, the thesis delineates the creation and management of KGs that utilize entities and their relations to represent intricate data patterns within the tourism sector. In this context, we introduce a semi-automatic methodology for generating a Tourism Knowledge Graph (TKG) and a novel Tourism Analytics Ontology (TAO). Through integrating information from enterprise data lakes with public knowledge graphs, the thesis illustrates the creation of a comprehensive semantic layer built upon the raw data, demonstrating versatility and scalability. Subsequently, we present an in-depth investigation into transformer-based language models, emphasizing their potential and limitations. Addressing the exigency for domain-specific knowledge enrichment, we conduct a methodical study on knowledge enhancement strategies for transformers based language models. The culmination of this thesis is the presentation of an innovative method that fuses large language models with domain-specific knowledge graphs, targeting the optimisation of hospitality offers. This approach integrates domain KGs with feature engineering, enriching data representation in LLMs. Our scientific contributions span multiple dimensions: from devising methodologies for KG construction, especially in tourism, to the design and implementation of a novel ontology; from the analysis and comparison of techniques for enriching LLMs with specialized knowledge, to deploying such methods in a novel framework that effectively combines LLMs and KGs within the context of the tourism domain. In our research, we explore the potential benefits and challenges arising from the integration of knowledge engineering and artificial intelligence, with a specific emphasis on the tourism sector. We believe our findings offer a promising avenue and serve as a foundational platform for subsequent studies and practical implementations for the academic community and the tourism industry alike