492 research outputs found
LaSER: Language-specific event recommendation
While societal events often impact people worldwide, a significant fraction of events has a local focus that primarily affects specific language communities. Examples include national elections, the development of the Coronavirus pandemic in different countries, and local film festivals such as the CĂ©sar Awards in France and the Moscow International Film Festival in Russia. However, existing entity recommendation approaches do not sufficiently address the language context of recommendation. This article introduces the novel task of language-specific event recommendation, which aims to recommend events relevant to the user query in the language-specific context. This task can support essential information retrieval activities, including web navigation and exploratory search, considering the language context of user information needs. We propose LaSER, a novel approach toward language-specific event recommendation. LaSER blends the language-specific latent representations (embeddings) of entities and events and spatio-temporal event features in a learning to rank model. This model is trained on publicly available Wikipedia Clickstream data. The results of our user study demonstrate that LaSER outperforms state-of-the-art recommendation baselines by up to 33 percentage points in MAP@5 concerning the language-specific relevance of recommended events
Content Recommendation Through Linked Data
Nowadays, people can easily obtain a huge amount of information from the Web, but often they have no criteria to discern it. This issue is known as information overload. Recommender systems are software tools to suggest interesting items to users and can help them to deal with a vast amount of information. Linked Data is a set of best practices to publish data on the Web, and it is the basis of the Web of Data, an interconnected global dataspace.
This thesis discusses how to discover information useful for the user from the vast amount of structured data, and notably Linked Data available on the Web. The work addresses this issue by considering three research questions: how to exploit existing relationships between resources published on the Web to provide recommendations to users; how to represent the user and his context to generate better recommendations for the current situation; and how to effectively visualize the recommended resources and their relationships.
To address the first question, the thesis proposes a new algorithm based on Linked Data which exploits existing relationships between resources to recommend related resources. The algorithm was integrated into a framework to deploy and evaluate Linked Data based recommendation algorithms. In fact, a related problem is how to compare them and how to evaluate their performance when applied to a given dataset. The user evaluation showed that our algorithm improves the rate of new recommendations, while maintaining a satisfying prediction accuracy. To represent the user and their context, this thesis presents the Recommender System Context ontology, which is exploited in a new context-aware approach that can be used with existing recommendation algorithms. The evaluation showed that this method can significantly improve the prediction accuracy. As regards the problem of effectively visualizing the recommended resources and their relationships, this thesis proposes a visualization framework for DBpedia (the Linked Data version of Wikipedia) and mobile devices, which is designed to be extended to other datasets.
In summary, this thesis shows how it is possible to exploit structured data available on the Web to recommend useful resources to users. Linked Data were successfully exploited in recommender systems. Various proposed approaches were implemented and applied to use cases of Telecom Italia
A framework for active software engineering ontology
The passive structure of ontologies results in the ineffectiveness to access and manage the knowledge captured in them. This research has developed a framework for active Software Engineering Ontology based on a multi-agent system. It assists software development teams to effectively access, manage and share software engineering knowledge as well as project information to enable effective and efficient communication and coordination among teams. The framework has been evaluated through the prototype system as proof-of-concept experiments
Pattern-based design applied to cultural heritage knowledge graphs
Ontology Design Patterns (ODPs) have become an established and recognised
practice for guaranteeing good quality ontology engineering. There are several
ODP repositories where ODPs are shared as well as ontology design methodologies
recommending their reuse. Performing rigorous testing is recommended as well
for supporting ontology maintenance and validating the resulting resource
against its motivating requirements. Nevertheless, it is less than
straightforward to find guidelines on how to apply such methodologies for
developing domain-specific knowledge graphs. ArCo is the knowledge graph of
Italian Cultural Heritage and has been developed by using eXtreme Design (XD),
an ODP- and test-driven methodology. During its development, XD has been
adapted to the need of the CH domain e.g. gathering requirements from an open,
diverse community of consumers, a new ODP has been defined and many have been
specialised to address specific CH requirements. This paper presents ArCo and
describes how to apply XD to the development and validation of a CH knowledge
graph, also detailing the (intellectual) process implemented for matching the
encountered modelling problems to ODPs. Relevant contributions also include a
novel web tool for supporting unit-testing of knowledge graphs, a rigorous
evaluation of ArCo, and a discussion of methodological lessons learned during
ArCo development
Ontology matching: state of the art and future challenges
shvaiko2013aInternational audienceAfter years of research on ontology matching, it is reasonable to consider several questions: is the field of ontology matching still making progress? Is this progress significant enough to pursue some further research? If so, what are the particularly promising directions? To answer these questions, we review the state of the art of ontology matching and analyze the results of recent ontology matching evaluations. These results show a measurable improvement in the field, the speed of which is albeit slowing down. We conjecture that significant improvements can be obtained only by addressing important challenges for ontology matching. We present such challenges with insights on how to approach them, thereby aiming to direct research into the most promising tracks and to facilitate the progress of the field
Semantic Systems. The Power of AI and Knowledge Graphs
This open access book constitutes the refereed proceedings of the 15th International Conference on Semantic Systems, SEMANTiCS 2019, held in Karlsruhe, Germany, in September 2019. The 20 full papers and 8 short papers presented in this volume were carefully reviewed and selected from 88 submissions. They cover topics such as: web semantics and linked (open) data; machine learning and deep learning techniques; semantic information management and knowledge integration; terminology, thesaurus and ontology management; data mining and knowledge discovery; semantics in blockchain and distributed ledger technologies
Recommended from our members
Open Research Knowledge Graph
As we mark the fifth anniversary of the alpha release of the Open Research
Knowledge Graph (ORKG), it is both timely and exhilarating to celebrate the significant
strides made in this pioneering project. We designed this book as a tribute
to the evolution and achievements of the ORKG and as a practical guide encapsulating
its essence in a form that resonates with both the general reader and the
specialist.
The ORKG has opened a new era in the way scholarly knowledge is curated, managed,
and disseminated. By transforming vast arrays of unstructured narrative text
into structured, machine-processable knowledge, the ORKG has emerged as an
essential service with sophisticated functionalities. Over the past five years, our
team has developed the ORKG into a vibrant platform that enhances the accessibility
and visibility of scientific research. This book serves as a non-technical guide
and a comprehensive reference for new and existing users that outlines the
ORKG’s approach, technologies, and its role in revolutionizing scholarly communication.
By elucidating how the ORKG facilitates the collection, enhancement, and
sharing of knowledge, we invite readers to appreciate the value and potential of
this groundbreaking digital tool presented in a tangible form.
Looking ahead, we are thrilled to announce the upcoming unveiling of promising
new features and tools at the fifth-year celebration of the ORKG’s alpha release.
These innovations are set to redefine the boundaries of machine assistance enabled
by research knowledge graphs. Among these enhancements, you can expect
more intuitive interfaces that simplify the user experience, and enhanced machine learning
models that improve the automation and accuracy of data curation.
We also included a glossary tailored to clarifying key terms and concepts associated
with the ORKG to ensure that all readers, regardless of their technical background,
can fully engage with and understand the content presented. This book
transcends the boundaries of a typical technical report. We crafted this as an inspiration
for future applications, a testament to the ongoing evolution in scholarly
communication that invites further collaboration and innovation. Let this book serve
as both your guide and invitation to explore the ORKG as it continues to grow and
shape the landscape of scientific inquiry and communication
Ingest and Storage of 3D Objects in a Digital Preservation System
The DURAARK project is developing methods and tools for the Long-Term Preservation (LTP) of architectural knowledge, including approaches to: enrich Building Information Models with “as built” information from scans, semantically enrich building models with additional data sets, preserve 3D models for future reuse.
This deliverable defines the necessary steps for ingest and storage of 3D objects into anexisting OAIS compliant digital preservation system. It discusses how the gaps, which were previously identified and presented in deliverable D6.6.1, have been addressed in
the DURAARK project so far. Developed methods and tools will be run against the DURAARK test set. Lastly, the existing drafts of the metadata schemas buildm for descriptive information and e57m and ifcm as technical metadata schemas for E57 and
IFC respectively, will be extended significantly and presented in a digital preservation context
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