Search

Graphs are, by nature, ‘unifying abstractions’ that can leverage interconnectedness to represent, explore, predict, and explain real- and digital-world phenomena. Although real users and consumers of graph instances and graph workloads understand these abstractions, future problems will require new abstractions and systems. What needs to happen in the next decade for big graph processing to continue to succeed?

Graphs are, by nature, ‘unifying abstractions’ that can leverage interconnectedness to represent, explore, predict, and explain real- and digital-world phenomena. Although real users and consumers of graph instances and graph workloads understand these abstractions, future problems will require new abstractions and systems. What needs to happen in the next decade for big graph processing to continue to succeed?

This paper describes STILTool, an open-source tool for the automatic evaluation of the quality of semantic annotations computed by semantic table interpretation approaches. STILTool provides a graphical interface allowing users to analyse the correctness of the annotations of tabular data. The tool also provides a set of statistics in order to identify the most common error patterns.

This paper describes MantisTable, an open source Semantic Table Interpretation tool, which automatically annotates tables using a Knowledge Graph. MantisTable provides a graphical interface allowing users to analyse the results of the semantic table interpretation process and validate the final annotations. The tool also provides a guided mode for viewing and editing annotations by users. Thanks to MantisTable features, it is possible to create semantic annotations and favour the publication and exchange of tabular data.

Identifying the research topics that best describe the scope of a scientific publication is a crucial task for editors, in particular because the quality of these annotations determine how effectively users are able to discover the right content in online libraries. For this reason, Springer Nature, the world’s largest academic book publisher, has traditionally entrusted this task to their most expert editors. These editors manually analyse all new books, possibly including hundreds of chapters, and produce a list of the most relevant topics. Hence, this process has traditionally been very expensive, time-consuming, and confined to a few senior editors. For these reasons, back in 2016 we developed Smart Topic Miner (STM), an ontology-driven application that assists the Springer Nature editorial team in annotating the volumes of all books covering conference proceedings in Computer Science. Since then STM has been regularly used by editors in Germany, China, Brazil, India, and Japan, for a total of about 800 volumes per year. Over the past three years the initial prototype has iteratively evolved in response to feedback from the users and evolving requirements. In this paper we present the most recent version of the tool and describe the evolution of the system over the years, the key lessons learnt, and the impact on the Springer Nature workflow. In particular, our solution has drastically reduced the time needed to annotate proceedings and significantly improved their discoverability, resulting in 9.3 million additional downloads. We also present a user study involving 9 editors, which yielded excellent results in term of usability, and report an evaluation of the new topic classifier used by STM, which outperforms previous versions in recall and F-measure.

In this paper, we present a preliminary approach that uses a set of NLP and Deep Learning methods for extracting entities and relationships from research publications and then integrates them in a Knowledge Graph. More specifically, we (i) tackle the challenge of knowledge extraction by employing several state-of-the-art Natural Language Processing and Text Mining tools, (ii) describe an approach for integrating entities and relationships generated by these tools, and (iii) analyse an automatically generated Knowledge Graph including 10, 425 entities and 25, 655 relationships in the field of Semantic Web.

This paper describes MantisTable, an open source Semantic Table Interpretation tool, which automatically annotates tables using a Knowledge Graph. MantisTable provides a graphical interface allowing users to analyse the results of the semantic table interpretation process and validate the final annotations. The tool also provides a guided mode for viewing and editing annotations by users. Thanks to MantisTable features, it is possible to create semantic annotations and favour the publication and exchange of tabular data.

Lean kernels (LKs) are an effective optimization for deriving the causes of unsatisfiability of a propositional formula. Interestingly, no analogous notion exists for explaining consequences of description logic (DL) ontologies. We introduce LKs for DLs using a general notion of consequence-based methods, and provide an algorithm for computing them which incurs in only a linear time overhead. As an example, we instantiate our framework to the DL ALC. We prove formally and empirically that LKs provide a tighter approximation of the set of relevant axioms for a consequence than syntactic locality-based modules.

The current Web of Data contains a large amount of interlinked data. However, there is still a limited understanding about the quality of the links connecting entities of different and distributed data sets. Our goal is to provide a collection of indicators that help assess existing interlinking. In this paper, we present a framework for the intrinsic evaluation of RDF links, based on core principles of Web data integration and foundations of Information Retrieval. We measure the extent to which links facilitate the discovery of an extended description of entities, and the discovery of other entities in other data sets. We also measure the use of different vocabularies. We analysed links extracted from a set of data sets from the Linked Data Crawl 2014 using these measures.

Lean kernels (LKs) are an effective optimization for deriving the causes of unsatisfiability of a propositional formula. Interestingly, no analogous notion exists for explaining consequences of description logic (DL) ontologies. We introduce LKs for DLs using a general notion of consequence-based methods, and provide an algorithm for computing them which incurs in only a linear time overhead. As an example, we instantiate our framework to the DL ALC. We prove formally and empirically that LKs provide a tighter approximation of the set of relevant axioms for a consequence than syntactic locality-based modules.

Large numbers of websites contain (human-readable) infor- mation about scheduled events, of which some may display media cap- tured at these events. This information is, however, often incomplete and always locked into the sites. This prevents users from creating overviews of media associated with an event from multiple websites. We carried out exploratory user studies with potential end-users to guide the design of a web-based environment for supporting event-based services. Based on our results, our goal is to provide support for exploring and selecting events and associated media, and for discovering meaningful, surprising or entertaining connections between events, media and participants by consuming linked data. We assembled a large collection of event and as- sociated media descriptions, which we interlinked with the Linked Open Data cloud. The dataset is obtained from three large public event directories (last.fm, eventful, upcoming) represented with the LODE ontology and from large media directories (ickr, youtube) represented with the Media Ontology.We present the results from the user studies, the conversion, interlinking and publication of the data following the best practices of the Semantic Web community, and our initial application design.

Large numbers of websites contain (human-readable) infor- mation about scheduled events, of which some may display media cap- tured at these events. This information is, however, often incomplete and always locked into the sites. This prevents users from creating overviews of media associated with an event from multiple websites. We carried out exploratory user studies with potential end-users to guide the design of a web-based environment for supporting event-based services. Based on our results, our goal is to provide support for exploring and selecting events and associated media, and for discovering meaningful, surprising or entertaining connections between events, media and participants by consuming linked data. We assembled a large collection of event and as- sociated media descriptions, which we interlinked with the Linked Open Data cloud. The dataset is obtained from three large public event directories (last.fm, eventful, upcoming) represented with the LODE ontology and from large media directories (ickr, youtube) represented with the Media Ontology.We present the results from the user studies, the conversion, interlinking and publication of the data following the best practices of the Semantic Web community, and our initial application design.

Inspired by insights from theories of human reasoning and language, we propose additions to the Manchester OWL Syntax to improve comprehensibility. These additions cover: functional and inverse functional properties, negated conjunction, the definition of exceptions, and existential and universal restrictions. By means of an empirical study, we demonstrate the effectiveness of a number of these additions, in particular: the use of solely to clarify the uniqueness of the object in a functional property; the replacement of and with intersection in conjunction, which was particularly beneficial in negated conjunction; the use of except as a substitute for and not; and the replacement of some with including and only with noneOrOnly, which helped in certain situations to clarify the nature of these restrictions.

Inspired by insights from theories of human reasoning and language, we propose additions to the Manchester OWL Syntax to improve comprehensibility. These additions cover: functional and inverse functional properties, negated conjunction, the definition of exceptions, and existential and universal restrictions. By means of an empirical study, we demonstrate the effectiveness of a number of these additions, in particular: the use of solely to clarify the uniqueness of the object in a functional property; the replacement of and with intersection in conjunction, which was particularly beneficial in negated conjunction; the use of except as a substitute for and not; and the replacement of some with including and only with noneOrOnly, which helped in certain situations to clarify the nature of these restrictions.