This work presents experiences and lessons learned in the development of ontologies using a combination of OWL for domain modeling and SHACL for the validation of RDF knowledge graphs that follow the OWL model. In our work, through a real-world use case in the domain of railway transport, we propose a joint OWL+SHACL development approach, including the use of SKOS for the representation of reference data, that is coupled to the definition of the ontology. In this approach, classes and properties are represented in both languages, and constraints may be represented in one or both languages according to the need for ontology reasoning and knowledge graph correctness requirements.

Reification in knowledge graphs has been present since the inception of RDF to allow capturing additional information in triples, usually metadata. The need of adopting or changing a metadata representation in a pre-existing graph to enhance the knowledge capture and access can lead to inducing complex structural changes in the graph, according the target representation’s schema. In these situations, it is necessary to decide whether to construct the knowledge graph again from its original sources, or to re-construct it using the current version of the graph. In this paper we conduct an empirical study to analyze which re-construction approach is more suitable for switching the representation approach from the created graph ensuring that the additional represented knowledge is preserved. We study four well-known metadata representations, using mapping languages to construct the graph, and SPARQL CONSTRUCT queries to re-construct it. With this work we aim to provide insights about the impact of re-construction on metadata representations interoperability and the implications of different approaches.

In probabilistic topic models, a topic is characterised by a set of words, with a probability associated to each of them. Even though it is not necessary to understand the meaning of topics to perform common downstream tasks where topic models are used, such as topic inference or document similarity, there have been attempts to uncover the semantics of topics by providing labels to them, consisting in a couple of concepts. In this paper we propose a methodology, Conversational Probabilistic Topic Labelling (CPTL), to study whether conversational models can be used to generate labels that describe probabilistic topics given their most representative keywords. We evaluate and compare the performance of a selection of conversational models for the topic label generation task with the performance of a task-specific language model trained to generate topic labels.

Abstract Knowledge graphs have been widely adopted across organizations and research domains, fueling applications that span interactive browsing to large-scale analysis and data science. One design decision in knowledge graph deployment is choosing a representation that optimally supports the application’s consumers. Currently, however, there is no consensus on which representations best support each consumer scenario. In this work, we analyze the fitness of popular knowledge graph representations for three consumer scenarios: knowledge exploration, systematic querying, and graph completion. We compare the accessibility for knowledge exploration through a user study with dedicated browsing interfaces and query endpoints. We assess systematic querying with SPARQL in terms of time and query complexity on both synthetic and real-world datasets. We measure the impact of various representations on the popular graph completion task by training graph embedding models per representation. We experiment with four representations: Standard Reification, N-Ary Relationships, Wikidata qualifiers, and RDF-star. We find that Qualifiers and RDF-star are better suited to support use cases of knowledge exploration and systematic querying, while Standard Reification models perform most consistently for embedding model inference tasks but may become cumbersome for users. With this study, we aim to provide novel insights into the relevance of the representation choice and its impact on common knowledge graph consumption scenarios.

Support for PhD students and their advisors in decision-making before and along their PhD journeys requires providing them with a deep understanding and knowledge of the life-cycle of a PhD. This means giving them access to a thorough understanding of causal relations between events, decisions, and the possible outcome. This knowledge can be attained primarily from insider stories, study reports, communications threads with advisors and colleagues, interviews, and scholarly databases. However, it is unclear how to give this knowledge a reasonable structure (due to the heterogeneity of concepts and data sources) so that we can use it for decision-making during the PhD journey. In this paper, we explore how to analyze and model PhD stories to uncover and extract causal relationships found within each story to get insights into the co-occurrences and causalities. We analyze these stories with thematic analysis to understand their main points and we use concept maps to create semi-formal graphs of connected events and objects where the relationships are being emphasized from the perspective of cause and effect. Our results at this point are a collection of PhD stories in the form of concept maps, thematic codes, a proposed approach for goal-directed PhD story modeling which we describe in this paper.

The adoption of Knowledge Graphs (KGs) by public and private organizations to integrate and publish data has increased in recent years. Ontologies play a crucial role in providing the structure for KGs, but are usually disregarded when designing Application Programming Interfaces (APIs) to enable browsing KGs in a developer-friendly manner. In this paper we provide a systematic review of the state of the art on existing approaches to ease access to ontology-based KG data by application developers. We propose two comparison frameworks to understand specifications, technologies and tools responsible for providing APIs for KGs. Our results reveal several limitations on existing API-based specifications, technologies and tools for KG consumption, which outline exciting research challenges including automatic API generation, API resource path prediction, ontology-based API versioning, and API validation and testing.

A large number of datasets are being made available on the Web using a variety of formats and according to diverse data models. Ontology Based Data Integration (OBDI) has been traditionally proposed as a mechanism to facilitate access to such heterogeneous datasets, providing a unified view over their data by means of ontologies. Recently, the term “Virtual Knowledge Graph Access” has begun to be used to refer to the mechanisms that provide query-based access to knowledge graphs virtually generated from heterogeneous data sources. Several OBDI engines exist in the state of the art, with overlapping capabilities but also clear differences among them (in terms of the data formats that they can deal with, mapping languages that they support, query expressivity that they allow, etc.). These engines have been evaluated with different testbeds and benchmarks. However, their heterogeneity has made it difficult to come up with a common comprehensive benchmark that allows for comparisons among them to facilitate their selection by practitioners, and more importantly, for their continuous improvement by the teams that maintain them. In this paper we present GTFS-Madrid-Bench, a benchmark to evaluate OBDI engines that can be used for the provision of access mechanisms to virtual knowledge graphs. Our proposal introduces several scenarios that aim at measuring the query capabilities, performance and scalability of all these engines, considering their heterogeneity. The data sources used in our benchmark are derived from the GTFS data files of the subway network of Madrid. They have been transformed into several formats (CSV, JSON, SQL and XML) and scaled up. The query set aims at addressing a representative number of SPARQL 1.1 features while covering usual queries that data consumers may be interested in.

With the ongoing growth in number of digital articles in a wider set of languages and the expanding use of different languages, we need annotation methods that enable browsing multi-lingual corpora. Multilingual probabilistic topic models have recently emerged as a group of semi-supervised machine learning models that can be used to perform thematic explorations on collections of texts in multiple languages. However, these approaches require theme-aligned training data to create a language-independent space. This constraint limits the amount of scenarios that this technique can offer solutions to train and makes it difficult to scale up to situations where a huge collection of multi-lingual documents are required during the training phase. This paper presents an unsupervised document similarity algorithm that does not require parallel or comparable corpora, or any other type of translation resource. The algorithm annotates topics automatically created from documents in a single language with cross-lingual labels and describes documents by hierarchies of multi-lingual concepts from independently-trained models. Experiments performed on the English, Spanish and French editions of JCR-Acquis corpora reveal promising results on classifying and sorting documents by similar content.

We present librAIry, a novel architecture to store, process and analyze large collections of textual resources, integrating existing algorithms and tools into a common, distributed, high-performance workflow. Available text mining techniques can be incorporated as independent plug&play modules working in a collaborative manner into the framework. In the absence of a pre-defined flow, librAIry leverages on the aggregation of operations executed by different components in response to an emergent chain of events. Extensive use of Linked Data (LD) and Representational State Transfer (REST) principles are made to provide individually addressable resources from textual documents. We have described the architecture design and its implementation and tested its effectiveness in real-world scenarios such as collections of research papers, patents or ICT aids, with the objective of providing solutions for decision makers and experts in those domains. Major advantages of the framework and lessons-learned from these experiments are reported.

There are many scenarios where we may want to find pairs of textually similar documents in a large corpus (e.g. a researcher doing literature review, or an R&D project manager analyzing project proposals). To programmatically discover those connections can help experts to achieve those goals, but brute-force pairwise comparisons are not computationally adequate when the size of the document corpus is too large. Some algorithms in the literature divide the search space into regions containing potentially similar documents, which are later processed separately from the rest in order to reduce the number of pairs compared. However, this kind of unsupervised methods still incur in high temporal costs. In this paper, we present an approach that relies on the results of a topic modeling algorithm over the documents in a collection, as a means to identify smaller subsets of documents where the similarity function can then be computed. This approach has proved to obtain promising results when identifying similar documents in the domain of scientific publications. We have compared our approach against state of the art clustering techniques and with different configurations for the topic modeling algorithm. Results suggest that our approach outperforms (> 0.5) the other analyzed techniques in terms of efficiency.

This paper presents a generic approach to integrate environmental sensor data efficiently, allowing the detection of relevant situations and events in near real-time through continuous querying. Data variety is addressed with the use of the Semantic Sensor Network ontology for observation data modelling, and semantic annotations for environmental phenomena. Data velocity is handled by distributing sensor data messaging and serving observations as RDF graphs on query demand. The stream processing engine presented in the paper, morph-streams++, provides adapters for different data formats and distributed processing of streams in a cluster. An evaluation of different configurations for parallelization and semantic annotation parameters proves that the described approach reduces the average latency of message processing in some cases.

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.

Scientific workflows are a popular mechanism for specifying and automating data-driven in silico experiments. A significant aspect of their value lies in their potential to be reused. Once shared, workflows become useful building blocks that can be combined or modified for developing new experiments. However, previous studies have shown that storing workflow specifications alone is not sufficient to ensure that they can be successfully reused, without being able to understand what the workflows aim to achieve or to re-enact them. To gain an understanding of the workflow, and how it may be used and repurposed for their needs, scientists require access to additional resources such as annotations describing the workflow, datasets used and produced by the workflow, and provenance traces recording workflow executions. In this article, we present a novel approach to the preservation of scientific workflows through the application of research objects—aggregations of data and metadata that enrich the workflow specifications. Our approach is realised as a suite of ontologies that support the creation of workflow-centric research objects. Their design was guided by requirements elicited from previous empirical analyses of workflow decay and repair. The ontologies developed make use of and extend existing well known ontologies, namely the Object Reuse and Exchange (ORE) vocabulary, the Annotation Ontology (AO) and the W3C PROV ontology (PROVO). We illustrate the application of the ontologies for building Workflow Research Objects with a case-study that investigates Huntington’s disease, performed in collaboration with a team from the Leiden University Medial Centre (HG-LUMC). Finally we present a number of tools developed for creating and managing workflow-centric research objects.

R2RML is used to specify transformations of data available in relational databases into materialised or virtual RDF datasets. SPARQL queries evaluated against virtual datasets are translated into SQL queries according to the R2RML mappings, so that they can be evaluated over the underlying relational database engines. In this paper we describe an extension of a well-known algorithm for SPARQL to SQL translation, originally formalised for RDBMS-backed triple stores, that takes into account R2RML mappings. We present the result of our implementation using queries from a synthetic benchmark and from three real use cases, and show that SPARQL queries can be in general evaluated as fast as the SQL queries that would have been generated by SQL experts if no R2RML mappings had been used.

RDF and SPARQL are established standards for data interchange and querying on the Web. While they have been shown to be useful and applicable in many scenarios, they are not sufficiently adequate for dealing with streams of data and their intrinsic continuous nature. In the last years data and query languages have been proposed to extend both RDF and SPARQL for streams and continuous processing, under the name of RDF Stream Processing – RSP. These efforts resulted in several models and implementations that, at a first look, appear to propose alternative syntaxes but equivalent semantics. However, when asked to continuously answer the same queries on the same data streams, they provide different answers at disparate moments due to the heterogeneity of their operational semantics. These discrepancies render the process of understanding and comparing continuous query results complex and misleading. In this work, the authors propose RSP-QL, a comprehensive model that formally defines the semantics of an RSP system. RSP-QL makes explicit the hidden assumptions of currently available RSP systems, allows defining a formal notion of correctness for RSP query results and, thus, explains why available implementations provide different answers at disparate moments.

Provenance plays a major role when understanding and reusing the methods applied in a scientific experiment, as it provides a record of inputs, the processes carried out and the use and generation of intermediate and final results. In the specific case of in-silico scientific experiments, a large variety of scientific workflow systems (e.g., Wings, Taverna, Galaxy, Vistrails) have been created to support scientists. All of these systems produce some sort of provenance about the executions of the workflows that encode scientific experiments. However, provenance is normally recorded at a very low level of detail, which complicates the understanding of what happened during execution. In this paper we propose an approach to automatically obtain abstractions from low-level provenance data by finding common workflow fragments on workflow execution provenance and relating them to templates. We have tested our approach with a dataset of workflows published by the Wings workflow system. Our results show that by using these kinds of abstractions we can highlight the most common abstract methods used in the executions of a repository, relating different runs and workflow templates with each other.

In the entertainment domain users tweet about their expectations and opinions regarding upcoming, current and past experiences, while companies advertise and promote the shows. This characterization, important for customers and companies, goes beyond traditional sentiment analysis where the polarity of the sentiments expressed in opinions is usually identified as positive, negative or neutral. The authors investigate different tweet representation models, including bags of words and probabilistic topic models, to shed light on the semantics of the messages. Their experiments show that topic-based models generated with Latent Dirichlet Allocation (LDA) yield, most of the times, better categorizations when compared to TF-IDF based features, particularly when these models are enriched with natural language features and specific Twitter slang.

Enabling Subject Matter Experts (SMEs) to formulate knowledge without the intervention of Knowledge Engineers (KEs) requires providing SMEs with methods and tools that abstract the underlying knowledge representation and allow them to focus on modeling activities. Bridging the gap between SME-authored models and their representation is challenging, especially in the case of complex knowledge types like processes, where aspects like frame management, data, and control flow need to be addressed. In this paper, we describe how SME-authored process models can be provided with an operational semantics and grounded in a knowledge representation language like F-logic to support process-related reasoning. The main results of this work include a formalism for process representation and a mechanism for automatically translating process diagrams into executable code following such formalism. From all the process models authored by SMEs during evaluation 82 percent were well formed, all of which executed correctly. Additionally, the two optimizations applied to the code generation mechanism produced a performance improvement at reasoning time of 25 and 30 percent with respect to the base case, respectively.

Sensor networks are increasingly being deployed in the environment for many different purposes. The observations that they produce are made available with heterogeneous schemas, vocabularies and data formats, making it difficult to share and reuse this data, for other purposes than those for which they were originally set up. The authors propose an ontology-based approach for providing data access and query capabilities to streaming data sources, allowing users to express their needs at a conceptual level, independent of implementation and language-specific details. In this article, the authors describe the theoretical foundations and technologies that enable exposing semantically enriched sensor metadata, and querying sensor observations through SPARQL extensions, using query rewriting and data translation techniques according to mapping languages, and managing both pull and push delivery modes.

Semantic Sensor Web infrastructures use ontology-based models to represent the data that they manage; however, up to now, these ontological models do not allow representing all the characteristics of distributed, heterogeneous, and web-accessible sensor data. This paper describes a core ontological model for Semantic Sensor Web infrastructures that covers these characteristics and that has been built with a focus on reusability. This ontological model is composed of different modules that deal, on the one hand, with infrastructure data and, on the other hand, with data from a specific domain, that is, the coastal flood emergency planning domain. The paper also presents a set of guidelines, followed during the ontological model development, to satisfy a common set of requirements related to modelling domain-specific features of interest and properties. In addition, the paper includes the results obtained after an exhaustive evaluation of the developed ontologies along different aspects (i.e., vocabulary, syntax, structure, semantics, representation, and context).

Folksonomies emerge as the result of the free tagging activity of a large number of users over a variety of resources. They can be considered as valuable sources from which it is possible to obtain emerging vocabularies that can be leveraged in knowledge extraction tasks. However, when it comes to understanding the meaning of tags in folksonomies, several problems mainly related to the appearance of synonymous and ambiguous tags arise, specifically in the context of multilinguality. The authors aim to turn folksonomies into knowledge structures where tag meanings are identified, and relations between them are asserted. For such purpose, they use DBpedia as a general knowledge base from which they leverage its multilingual capabilities.

In this paper the authors present an approach for the semantic annotation of RESTful services in the geospatial domain. Their approach automates some stages of the annotation process, by using a combination of resources and services: a cross-domain knowledge base like DBpedia, two domain ontologies like GeoNames and the WGS84 vocabulary, and suggestion and synonym services. The authors’ approach has been successfully evaluated with a set of geospatial RESTful services obtained from ProgrammableWeb.com, where geospatial services account for a third of the total amount of services available in this registry.

The W3C Semantic Sensor Network Incubator group (the SSN-XG) produced an OWL 2 ontology to describe sensors and observations — the SSN ontology, available at http://purl.oclc.org/NET/ssnx/ssn. The SSN ontology can describe sensors in terms of capabilities, measurement processes, observations and deployments. This article describes the SSN ontology. It further gives an example and describes the use of the ontology in recent research projects.

In this paper we present the process that has been followed for the development of an application that makes use of several heterogeneous Spanish public datasets that are related to three themes of INSPIRE Directive, specifically Administrative Units, Hydrography, and Statistical Units. Our application aims at analysing existing relations between the Spanish coastal area and different statistical variables such as population, unemployment, dwelling, industry, and building trade. Besides providing methodological guidelines for the generation, publishing and exploitation of Linked Data from such datasets, we provide an important innovation with respect to other similar processes followed in other initiatives by dealing with the geometrical information of features.

Current web application development requires highly qualified staff, dealing with an extensive number of architectures and technologies. When these applications incorporate semantic data, the list of skill requirements becomes even larger, leading to a high adoption barrier for the development of semantically enabled Web applications. This paper describes VPOET, a tool focused mainly on two types of users: web designers and web application developers. By using this tool, web designers do not need specific skills in semantic web technologies to create web templates to handle semantic data. Web application developers incorporate those templates into their web applications, by means of a simple mechanism based in HTTP messages. End-users can use these templates through a Google Gadget. As web designers play a key role in the system, an experimental evaluation has been conducted, showing that VPOET provides good usability features for a representative group of web designers in a wide range of competencies in client-side technologies, ranging from amateur HTML developers to professional web designers.

Debugging inconsistent OWL ontologies is a time-consuming task. Debugging services included in existing ontology engineering tools are still far from providing adequate support to ontology developers and domain experts for this task, due to their lack of efficiency or precision when explaining the main causes for inconsistencies. We present a catalogue of common antipatterns found in inconsistent ontologies that can be used in combination with these tools to make this task more effective.

A Semantic Web portal is a Web application that offers information and services related to a specific domain, and that has been developed with Semantic Web technology. For the time being, the main difference with respect to a traditional Web portal is based on technological aspects: traditional Web portals are based on standard Web technology (HTML, XML, servlets, JSPs, etc.); semantic portals are based on that technology plus the use of Semantic Web languages like RDF, RDF Schema and OWL. This paper describes a unifying architecture for both types of portals, based on the MVC paradigm, which is implemented in the ODESeW framework. ODESeW has been used successfully in the development of a set of portals for the management of European R&D projects and for the management of research groups.

We describe the architecture of the ODESeW 2.0 Semantic Web application development platform, which has been used to generate the internal and external Web sites of several R&D projects.

In this paper we present a model for building ontology translation systems between ontology languages and/or ontology tools, where translation decisions are defined at four different layers: lexical, syntax, semantic, and pragmatic. This layered approach provides a major contribution to the current state of the art in ontology translation, since it makes ontology translation systems easier to build and understand and, consequently, to maintain and reuse. As part of this model, we propose a method that guides in the process of developing ontology translation systems according to this approach. The method identifies four main activities: feasibility study, analysis of source, and target formats, design, and implementation of the translation system, with their decomposition in tasks, and recommends the techniques to be used inside each of them.

This paper presents WebODE as a workbench for ontological engineering that not only allows the collaborative edition of ontologies at the knowledge level, but also provides a scalable architecture for the development of other ontology development tools and ontology-based applications. First, we will describe the knowledge model of WebODE, which has been mainly extracted and improved from the reference model of METHONTOLOGY's intermediate representations. Later, we will present its architecture, together with the main functionalities of the WebODE ontology editor, such as its import/export service, translation services, ontology browser, inference engine and axiom generator, and some services that have been integrated in the workbench: WebPicker, OntoMerge and the OntoCatalogue.