In recent years, continuous integration and deployment (CI/CD) has become increasingly popular in both the opensource community and industry. Evaluating CI/CD performance is a critical aspect of software development, as it not only helps minimize execution costs but also ensures faster feedback for developers. Despite its importance, there is limited fine-grained knowledge about the performance of CI/CD processes, while this knowledge is essential for identifying bottlenecks and optimization opportunities. Moreover, the availability of large-scale, publicly accessible datasets of CI/CD logs remains scarce. The few datasets that do exist are often outdated and lack comprehensive coverage. To address this gap, we introduce GHALogs, a new dataset comprising 116k CI/CD workflows executed using GitHub Actions (GHA) across 25k public code projects spanning 20 different programming languages. This dataset includes 513k workflow runs encompassing 2.3 million individual steps. For each workflow run, we provide detailed metadata along with complete run logs. To the best of our knowledge, this is the largest dataset of CI/CD runs that includes full log data. The inclusion of these logs enables more in-depth analysis of CI/CD pipelines, offering insights that cannot be gleaned solely from code repositories. We postulate that this dataset will facilitate future CI/CD pipeline behavior research through log-based analysis. Potential applications include performance evaluation (e.g., measuring task execution times) and root cause analysis (e.g., identifying reasons for pipeline failures).

Misinformation has a pervasive thread running through society, causing confusion, mistrust, and uncertainty. The detection, tracking, and countering of misinformation is a very active research area with an intense need for data about circulating claims and their attributes, fact-checks, and verification outcomes. Although various relevant datasets exist, they tend to be of limited scope in terms of time coverage, topics, country, language, and quantity. In this paper, we introduce CimpleKG as an open and continuously updated semantic resource. CimpleKG links daily updated data from 77 fact-checking organisations with over 217k documents from static misinformation datasets. The knowledge graph is also augmented with relevant textual features and entities extracted from the textual data integrated into the graph. At the time of writing, the knowledge graph contains more than 15m triples, including 263k+ distinct entities and 1m textual features with over 203k fact-checked claims, spanning 26 languages and 36 countries. CimpleKG is publicly available and has been used in various research studies and web applications. Resource Type: Knowledge Graph. License: CC BY-NC-SA 4.0. SPARQL Endpoint: https://purl.org/net/cimplekg/sparql . KG Releases: https://purl.org/net/cimplekg/knowledge-graph . KG Explorer: https://purl.org/net/cimplekg/explorer .

Traditional topic modeling approaches generally rely on document-term co-occurrence statistics to find latent topics in a collection of documents. However, relying only on such statistics can yield incoherent or hard to interpret results for the end-users in many applications where the interest lies in interpreting the resulting topics (e.g. labeling documents, comparing corpora, guiding content exploration, etc.). In this work, we propose to leverage external common sense knowledge, i.e. information from the real world beyond word co-occurrence, to find topics that are more coherent and more easily interpretable by humans. We introduce the Common Sense Topic Model (CSTM), a novel and efficient approach that augments clustering with knowledge extracted from the ConceptNet knowledge graph. We evaluate this approach on several datasets alongside commonly used models using both automatic and human evaluation, and we show how it shows superior affinity to human judgement. The code for the experiments as well as the training data and human evaluation are available at https://github.com/D2KLab/CSTM.

Pre-trained word embeddings constitute an essential building block for many NLP systems and applications, notably when labeled data is scarce. However, since they compress word meanings into a fixed-dimensional representation, their use usually lack interpretability beyond a measure of similarity and linear analogies that do not always reflect real-world word relatedness, which can be important for many NLP applications. In this paper, we propose a model which extracts topics from text documents based on the common-sense knowledge available in ConceptNet [Speer et al., 2017] - a semantic concept graph that explicitly encodes real-world relations between words - and without any human supervision. When combining both ConceptNet’s knowledge graph and graph embeddings, our approach outperforms other baselines in the zero-shot setting, while generating a human-understandable explanation for its predictions through the knowledge graph. We study the importance of some modeling choices and criteria for designing the model, and we demonstrate that it can be used to label data for a supervised classifier to achieve an even better performance without relying on any humanly-annotated training data. We publish the code of our approach at https://github.com/D2KLab/ZeSTE and we provide a user friendly demo at https://zeste.tools.eurecom.fr/.

SKG4J targeted contributions at the interface between Artificial Intelligence, Data Management and its implications for journalistic practice. The first version of the workshop accepted three submissions with topics emphasising the complementary requirements for delivering realistic journalistic knowledge extraction/management platforms.

More than 2 millions of new books are published every year and choosing a good book among the huge amount of available options can be a challenging endeavor. Recommender systems help in choosing books by providing personalized suggestions based on the user reading history. However, most book recommender systems are based on collaborative filtering, involving a long onboarding process that requires to rate many books before providing good recommendations. Tinderbook provides book recommendations, given a single book that the user likes, through a card-based playful user interface that does not require an account creation. Tinderbook is strongly rooted in semantic technologies, using the DBpedia knowledge graph to enrich book descriptions and extending a hybrid state-of-the-art knowledge graph embeddings algorithm to derive an item relatedness measure for cold start recommendations. Tinderbook is publicly available ( http://www.tinderbook.it ) and has already generated interest in the public, involving passionate readers, students, librarians, and researchers. The online evaluation shows that Tinderbook achieves almost 50% of precision of the recommendations.

No abstract available.

Three major French cultural institutions—the French National Library (BnF), Radio France and the Philharmonie de Paris—have come together in order to develop shared methods to describe semantically their catalogs of music works and events. This process comprises the construction of knowledge graphs representing the data contained in these catalogs following a novel agreed upon ontology that extends CIDOC-CRM and FRBRoo, the linking of these graphs and their open publication on the web. A number of specialized tools that allow for the reproduction of this process are developed, as well as web applications for easy access and navigation through the data. The paper presents one of the main outcomes of this project—the DOREMUS knowledge graph, consisting of three linked datasets describing classical music works and their associated events (e.g., performances in concerts). This resource fills an important gap between library content description and music metadata. We present the DOREMUS pipeline for lifting and linking the data, the tools developed for these purposes, as well as a search application allowing to explore the data.

Knowledge Graphs have proven to be extremely valuable to recommender systems, as they enable hybrid graph-based recommendation models encompassing both collaborative and content information. Leveraging this wealth of heterogeneous information for top-N item recommendation is a challenging task, as it requires the ability of effectively encoding a diversity of semantic relations and connectivity patterns. In this work, we propose entity2rec, a novel approach to learning user-item relatedness from knowledge graphs for top-N item recommendation. We start from a knowledge graph modeling user-item and item-item relations and we learn property-specific vector representations of users and items applying neural language models on the network. These representations are used to create property-specific user-item relatedness features, which are in turn fed into learning to rank algorithms to learn a global relatedness model that optimizes top-N item recommendations. We evaluate the proposed approach in terms of ranking quality on the MovieLens 1M dataset, outperforming a number of state-of-the-art recommender systems, and we assess the importance of property-specific relatedness scores on the overall ranking quality.

Ensuring data quality in Linked Open Data is a complex process as it consists of structured information supported by models, ontologies and vocabularies and contains queryable endpoints and links. In this paper, the authors first propose an objective assessment framework for Linked Data quality. The authors build upon previous efforts that have identified potential quality issues but focus only on objective quality indicators that can measured regardless on the underlying use case. Secondly, the authors present an extensible quality measurement tool that helps on one hand data owners to rate the quality of their datasets, and on the other hand data consumers to choose their data sources from a ranked set. The authors evaluate this tool by measuring the quality of the LOD cloud. The results demonstrate that the general state of the datasets needs attention as they mostly have low completeness, provenance, licensing and comprehensibility quality scores.

In this paper, we present the 3cixty Knowledge Base, which collects and harmonizes descriptions of events, places, transportation facilities and user-generated data such as reviews of the city and Expo site of Milan. This knowledge base is used by a set of web and mobile applications to guide Expo Milano 2015 visitors in the city and in the exhibit, allowing them to find places, satellite events and transportation facilities around Milan. As of July 24th, 2015 the knowledge base contains 18665 unique events, 225821 unique places, 94789 reviews, and 9343 transportation facilities, collected from several static, near- and real time local and global data providers, including Expo Milano 2015 official services and numerous social media platforms. The ontologies used as a backbone for structuring the knowledge base follow a rigorous development method where the design principle has generally been to re-use existing ontologies when they exist. We think that the lessons learned from this development will be useful for similar endeavors in other cities or large events around the world with a similar ecosystem of data provisioning services.

The Web enables to have access to silo-ed information describing news articles, often offering a multitude of viewpoints that, once combined, can provide a broader picture of the story being reported on the news. In this paper, we propose an approach that automatically extracts representative features of a news item, namely named entities, from textual content attached to a video item (subtitles) and from a set of documents from the Web collected using entity expansion techniques. Approaches relying on entity expansion generally try to collect and process the important facts behinds a particular news item, but they are often too dependent on frequency-based functions and information retrieval techniques thus neglecting the multi-dimensional relationships that are established among the entities. We propose a concentric-based approach that enables to represent the context of a news item, by harmonizing into a single model the representative entities, which can be extracted using information retrieval and natural language processing techniques (Core), and other entities that get prominent according to different dimensions such as informativeness, semantic connectivity, or popularity (Crust). We compare our approach with a baseline by analyzing the compactness of the generated summary on an existing gold standard available on the Web. Results of the experiments show that our approach converges faster to the ideal compact news snapshot with an improvement of 36.9% over the baseline.

We live in a constantly evolving world where news stories and relevant facts are happening every moment. For each of those stories, numerous news articles, posts, and social media reactions are created, offering a multitude of viewpoints about what is happening around us. Many applications have tried to deal with this complexity from very different angles, targeting particular needs, reconstructing certain parts of the story, and exploiting certain visualization paradigms. In this paper, we identify those challenges and study how an adequate news story representation can effectively support the different phases of the news consumption process. We propose an innovative model called News Semantic Snapshot (NSS) that is designed to capture the entire context of a news item. This model can feed very different applications assisting the users before, during, and after the news story consumption. It formalizes a duality in the news annotations that distinguishes between representative entities and relevant entities, and considers different relevancy dimensions that are incorporated into the model in the form of concentric layers. Finally, we analyze the impact of this NSS on existing prototypes and how it can support future ones.

We present GERBIL, an evaluation framework for semantic entity annotation. The rationale behind our framework is to provide developers, end users and researchers with easy-to-use interfaces that allow for the agile, fine-grained and uniform evaluation of annotation tools on multiple datasets. By these means, we aim to ensure that both tool developers and end users can derive meaningful insights pertaining to the extension, integration and use of annotation applications. In particular, GERBIL provides comparable results to tool developers so as to allow them to easily discover the strengths and weaknesses of their implementations with respect to the state of the art. With the permanent experiment URIs provided by our framework, we ensure the reproducibility and archiving of evaluation results. Moreover, the framework generates data in machine-processable format, allowing for the efficient querying and post-processing of evaluation results. Finally, the tool diagnostics provided by GERBIL allows deriving insights pertaining to the areas in which tools should be further refined, thus allowing developers to create an informed agenda for extensions and end users to detect the right tools for their purposes. GERBIL aims to become a focal point for the state of the art, driving the research agenda of the community by presenting comparable objective evaluation results.

An ever increasing number of social services offer thousands of diverse events per day. Users tend to be overwhelmed by the massive amount of information available, especially with limited browsing options perceived in many event web services. To alleviate this information overload, a recommender system becomes a vital component for assisting users selecting relevant events. However, such system faces a number of challenges owed to the the inherent complex nature of an event. In this paper, we propose a novel hybrid approach built on top of Semantic Web. On the one hand, we use a content-based system enriched with Linked Data to overcome the data sparsity, a problem induced by the transiency of events. On the other hand, we incorporate a collaborative filtering to involve the social aspect, an influential feature in decision making. This hybrid system is enhanced by the integration of a user diversity model designed to detect user propensity towards specific topics. We show how the hybridization of CB+CF systems and the integration of interest diversity features are important to improve predictions. Experimental results demonstrate the effectiveness of our approach using precision and recall measures.

Spoken term detection (STD) is a key technology for spoken information retrieval. As compared to the conventional speech transcription and keyword spotting, STD is an open-vocabulary task and has to address out-of-vocabulary (OOV) terms. Approaches based on subword units, for example phones, are widely used to solve the OOV issue; however, performance on OOV terms is still substantially inferior to that of in-vocabulary (INV) terms. The performance degradation on OOV terms can be attributed to a multitude of factors. One particular factor we address in this article is the unreliable confidence estimation caused by weak acoustic and language modeling due to the absence of OOV terms in the training corpora. We propose a direct posterior confidence derived from a discriminative model, such as multilayer perceptron (MLP). The new confidence considers a wide-range acoustic context which is usually important for speech recognition and retrieval; moreover, it localizes on detected speech segments and therefore avoids the impact of long-span word context which is usually unreliable for OOV term detection. In this article, we first develop an extensive discussion about the modeling weakness problem associated with OOV terms, and then propose our approach to address this problem based on direct poster confidence. Our experiments carried out on spontaneous and conversational multiparty meeting speech, demonstrate that the proposed technique provides a significant improvement in STD performance as compared to conventional lattice-based confidence, in particular for OOV terms. Furthermore, the new confidence estimation approach is fused with other advanced techniques for OOV treatment, such as stochastic pronunciation modeling and discriminative confidence normalization. This leads to an integrated solution for OOV term detection that results in a large performance improvement.

We present a method combining semantic inferencing and visual analysis for finding automatically media (photos and videos) illustrating events. We report on experiments validating our heuristic for mining media sharing platforms and large event directories in order to mutually enrich the descriptions of the content they host. Our overall goal is to design a web-based environment that allows users to explore and select events, to inspect associated media, and to discover meaningful, surprising or entertaining connections between events, media and people participating in events. We present a large dataset composed of semantic descriptions of events, photos and videos interlinked with the larger Linked Open Data cloud and we show the benefits of using semantic web technologies for integrating multimedia metadata.

In this paper, we describe two examples of implementations of the Media Fragments URI specification which is currently being developed by the W3C Media Fragments Working Group. The group's mission is to create standard addressing schemes for media fragments on the Web using Uniform Resource Identifiers (URIs). We describe two scenarios to illustrate the implementations. More specifically, we show how User Agents (UA) will either be able to resolve media fragment URIs without help from the server, or will make use of a media fragments-aware server. Finally, we present some ongoing discussions and issues regarding the implementation of the Media Fragments specification.

Enabling an intelligent access to multimedia data requires a powerful description language. In this paper we demonstrate why the MPEG-7 standard fails to fulfill this task. We introduce then our proposition: an audio-visual specific description language modular reduced but designed to be extensible. This language is centered on the notions of descriptor and structure with a well-defined semantics. A descriptor can be a low-level feature automatically extracted from the signal or a higher semantic concept that will be used to annotate the video documents. The descriptors can be combined into structures according to defined models that provide description patterns.