Semantic Web: – Interoperability,Usability,Applicability

Symbolic Systems in Artificial Intelligence which are based on formal logic and deductive reasoning are fundamentally different from Artificial Intelligence systems based on artificial neural networks, such as deep learning approaches. The difference is not only in their inner workings and general approach, but also with respect to capabilities. Neural-symbolic Integration, as a field of study, aims to bridge between the two paradigms. In this paper, we will discuss neural-symbolic integration in its relation to the Semantic Web field, with a focus on promises and possible benefits for both, and report on some current research on the topic.

This article gives an overview of recent efforts focusing on integrating heterogeneous data using Knowledge Graphs. I introduce a pipeline consisting of five steps to integrate semi-structured or unstructured content. I discuss some of the key applications of this pipeline through three use-cases, and present the lessons learnt along the way while designing and building data integration systems.

For a domain with a strong focus on unambiguous identifiers and meaning, the Semantic Web research field itself has a surprisingly ill-defined sense of identity. Started at the end of the 1990s at the intersection of databases, logic, and Web, and influenced along the way by all major tech hypes such as Big Data and machine learning, our research community needs to look in the mirror to understand who we really are. The key question amid all possible directions is pinpointing the important challenges we are uniquely positioned to tackle. In this article, we highlight the community’s unconscious bias toward addressing the Paretonian 80% of problems through research – handwavingly assuming that trivial engineering can solve the remaining 20%. In reality, that overlooked 20% could actually require 80% of the total effort and involve significantly more research than we are inclined to think, because our theoretical experimentation environments are vastly different from the open Web. As it turns out, these formerly neglected “trivialities” might very well harbor those research opportunities that only our community can seize, thereby giving us a clear hint of how we can orient ourselves to maximize our impact on the future. If we are hesitant to step up, more pragmatic minds will gladly reinvent technology for the real world, only covering a fraction of the opportunities we dream of.

A vision of a truly multilingual Semantic Web has found strong support with the Linguistic Linked Open Data community. Standards, such as OntoLex-Lemon, highlight the importance of explicit linguistic modeling in relation to ontologies and knowledge graphs. Nevertheless, there is room for improvement in terms of automation, usability, and interoperability. Neural Language Models have achieved several breakthroughs and successes considerably beyond Natural Language Processing (NLP) tasks and recently also in terms of multimodal representations. Several paths naturally open up to port these successes to the Semantic Web, from automatically translating linguistic information associated with structured knowledge resources to multimodal question-answering with machine translation. Language is also an important vehicle for culture, an aspect that deserves considerably more attention. Building on existing approaches, this article envisions joint forces between Neural Language Models and Semantic Web technologies for multilingual, transcultural, and multimodal information access and presents open challenges and opportunities in this direction.

The current hype of Artificial Intelligence (AI) mostly refers to the success of machine learning and its sub-domain of deep learning. However, AI is also about other areas, such as Knowledge Representation and Reasoning, or Distributed AI, i.e., areas that need to be combined to reach the level of intelligence initially envisioned in the 1950s. Explainable AI (XAI) now refers to the core backup for industry to apply AI in products at scale, particularly for industries operating with critical systems. This paper reviews XAI not only from a Machine Learning perspective, but also from the other AI research areas, such as AI Planning or Constraint Satisfaction and Search. We expose the XAI challenges of AI fields, their existing approaches, limitations and opportunities for Knowledge Graphs and their underlying technologies.

Knowledge graphs (KGs) contain rich resources that represent human knowledge in the world. There are mainly two kinds of reasoning techniques in knowledge graphs, symbolic reasoning and statistical reasoning. However, both of them have their merits and limitations. Therefore, it is desirable to combine them to provide hybrid reasoning in a knowledge graph. In this paper, we present the first work on the survey of methods for hybrid reasoning in knowledge graphs. We categorize existing methods based on applications of reasoning techniques, and introduce the key ideas of them. Finally, we re-examine the remaining research problems to be solved and provide an outlook to future directions for hybrid reasoning in knowledge graphs.

The current state of the semantic web is focused on data. This is a worthwhile progress in web content processing and interoperability. However, this does only marginally contribute to knowledge improvement and evolution. Understanding the world, and interpreting data, requires knowledge. Not knowledge cast in stone for ever, but knowledge that can seamlessly evolve; not knowledge from one single authority, but diverse knowledge sources which stimulate confrontation and robustness; not consistent knowledge at web scale, but local theories that can be combined. We discuss two different ways in which semantic web technologies can greatly contribute to the advancement of knowledge: semantic eScience and cultural knowledge evolution.

Computational Creativity (or artificial creativity) is a multidisciplinary field, researching how to construct computer programs that model, simulate, exhibit or enhance creative behaviour. This vision paper explores a potential of the Semantic Web and its technologies for creative AI. Possible uses of the Semantic Web and semantic technologies are discussed, regarding three types of creativity: i) exploratory creativity, ii) combinational creativity, and iii) transformational creativity and relevant research questions. For exploratory creativity, how can we explore the limits of what is possible, while remaining bound by a set of existing domain axioms, templates, and rules, expressed with semantic technologies? To achieve a combinational creativity, how can we combine or blend existing concepts, frames, ontology design patterns, and other constructs, and benefit from cross-fertilization? Ultimately, can we use ontologies and knowledge graphs, which describe an existing domain with its constraints and, applying a meta-rule for transformational creativity, start dropping constraints and adding new constraints to produce novel artifacts? Together with these new challenges, the paper also provides pointers to emerging and growing application domains of Semantic Web related to computational creativity: from recipe generation to scientific discovery and creative design.

Irrespective of data size and complexity, query and exploration tools for accessing data resources remain a central linkage for human–data interaction. A fundamental barrier in making query interfaces easier to use, ultimately as easy as online shopping, is the lack of faceted, interactive capabilities. We propose to repurpose existing ontologies by transforming them into nested facet systems (NFS) to support human–data interaction. Two basic issues need to be addressed for this to happen: one is that the structure and quality of ontologies need to be examined and elevated for the purpose of NFS; the second is that mappings from data-source specific metadata to a corresponding NFS need to be developed to support this new generation of NFS-enabled web-interfaces. The purpose of this paper is to introduce the concept of NFS and outline opportunities involved in using ontologies as NFS for querying and exploring data, especially in the biomedical domain.

Ontologies have been used on the Web to enable semantic interoperability between parties that publish information independently of each other. They have also played an important role in the emergence of Linked Data. However, many ontologies on the Web do not see much use beyond their initial deployment and purpose in one dataset and therefore should rather be called what they are – (local) schemas, which per se do not provide any interoperable semantics. Only few ontologies are truly used as a shared conceptualization between different parties, mostly in controlled environments such as the BioPortal. In this paper, we discuss open challenges relating to true re-use of ontologies on the Web and raise the question: “are we better off with just one ontology on the Web?”

In this deliberately provocative position paper, we claim that more than ten years into Linked Data there are still (too?) many unresolved challenges towards arriving at a truly machine-readable and decentralized Web of data. We take a deeper look at key challenges in usage and adoption of Linked Data from the ever-present “LOD cloud” diagram.1 Herein, we try to highlight and exemplify both key technical and non-technical challenges to the success of LOD, and we outline potential solution strategies. We hope that this paper will serve as a discussion basis for a fresh start towards more actionable, truly decentralized Linked Data, and as a call to the community to join forces.

Modern life is increasingly made more comfortable, efficient, and sustainable by the smart systems that surround us: smart buildings monitor and adjust temperature levels to achieve occupant comfort while optimizing energy consumption; smart energy grids reconfigure dynamically to make the best use of ad-hoc energy produced by a host of distributed energy producers; smart factories can be reconfigured on the shop-floor to efficiently produce a diverse range of products. These complex systems can only be realized by tightly integrating components in the physical space (sensors, actuators) with advanced software algorithms in the cyber-space, thus creating so-called Cyber-Physical Systems (CPS). Semantic Web technologies (SWT) have seen a natural uptake in several areas based on CPS, given that CPS are data and knowledge intensive while providing advanced functionalities typical of semantics-based intelligent systems. Yet, so far, this uptake has primarily happened within the boundaries of application domains resulting in somewhat disconnected research communities. In this paper, we take a cross-domain perspective by synthesizing our experiences of using SWTs during the engineering and operation of CPSs in smart manufacturing, smart buildings, and smart grids. We discuss use cases that are amenable to the use of SWTs, benefits and challenges of using these technologies in the CPS lifecycle as well as emerging future trends. While non-exhaustive, our paper aims at opening up a dialog between these fields and at putting the foundation for a research area on semantics in CPS.

In the last decade, ontologies have become widely adopted in a variety of fields ranging from biomedicine, to finance, engineering, law, and cultural heritage. The ontology engineering field has been strengthened by the adoption of several standards pertaining to ontologies, by the development or extension of ontology building tools, and by a wider recognition of the importance of standardized vocabularies and formalized semantics. Research into ontology engineering has also produced methods and tools that are used more and more in production settings. Despite all these advancements, ontology engineering is still a difficult process, and many challenges still remain to be solved. This paper gives an overview of how the ontology engineering field has evolved in the last decade and discusses some of the unsolved issues and opportunities for future research.

We discuss currently open issues in the discovery and representation of knowledge patterns in computational processing of meaning, in order to improve interoperability and cognitive validity of web-based semantics. We present the current state of knowledge patterns (KP) in Knowledge Representation, the Semantic Web and Cognitive Sciences, focusing on an intensional abstraction of heterogeneous predicates as a formal foundation for KP.

Ontology Based Data Access (OBDA) refers to a range of techniques, algorithms and systems that can be used to deal with the heterogeneity of data that is common inside many organisations as well as in inter-organisational settings and more openly on the Web. In OBDA, ontologies are used to provide a global view over multiple local datasets; and mappings are commonly used to describe the relationships between such global and local schemas. Since its inception, this area has evolved in several directions. Initially, the focus was on the translation of original sources into a global schema, and its materialisation, including non-OBDA approaches such as the use of Extract Transform Load (ETL) workflows in data warehouses and, more recently, in data lakes. Then OBDA-based query translation techniques, relying on mappings, were proposed, with the aim of removing the need for materialisation, something especially useful for very dynamic data sources. We think that we are now witnessing the emergence of a new generation of OBDA approaches. It is driven by the fact that a new set of declarative mapping languages, most of which stem from the W3C Recommendation R2RML for Relational Databases (RDB), are being created. In this vision paper, we enumerate the reasons why new mapping languages are being introduced. We discuss why it may be relevant to work on translations among them, so as to benefit from the engines associated to each of them whenever one language and/or engine is more suitable than another. We discuss the emerging concept of “mapping translation”, the basis for this new generation of OBDA, together with some of its desirable properties: information preservation and query result preservation. We show several scenarios where mapping translation can be or is being already applied, even though this term has not necessarily been used in existing literature.

When you meet a delivery robot in a narrow street it stops to let you pass. It was built to give you precedence. What happens if you run into a robot that was not trained by or for humans? The existence in our environment of robots which do not abide by human behavioral rules and social systems might sound odd, but is a case we may encounter in the future. In this paper, self-taught robots are artificial embodied agents that, thanks for instance to AI learning techniques, manage to survive in the environment without embracing behavioral or judgment rules given and used by humans. The paper argues that our ontological systems are not suitable to understand and cope with artificial agents. The arguments are speculative rather than empirical, and the goal is to drive attention to new ontological challenges.

More than two decades have passed since the establishment of the initial cornerstones of the Semantic Web. Since its inception, opinions have remained divided regarding the past, present and potential future impact of the Semantic Web. In this paper – and in light of the results of over two decades of development on both the Semantic Web and related technologies – we reflect on the current status of the Semantic Web, the impact it has had thus far, and future challenges. We first review some of the external criticism of this vision that has been put forward by various authors; we draw together the individual critiques, arguing both for and against each point based on the current state of adoption. We then present the results of a questionnaire that we have posed to the Semantic Web mailing list in order to understand respondents’ perspective(s) regarding the degree to which the original Semantic Web vision has been realised, the impact it can potentially have on the Web (and other settings), its success stories thus far, as well as the degree to which they agree with the aforementioned critiques of the Semantic Web in terms of both its current state and future feasibility. We conclude by reflecting on future challenges and opportunities in the area.

This paper discusses a shift of focus in research on Cultural Heritage semantic portals, based on Linked Data, and envisions and proposes new directions of research. Three generations of portals are identified: Ten years ago the research focus in semantic portal development was on data harmonization, aggregation, search, and browsing (“first generation systems”). At the moment, the rise of Digital Humanities research has started to shift the focus to providing the user with integrated tools for solving research problems in interactive ways (“second generation systems”). This paper envisions and argues that the next step ahead to “third generation systems” is based on Artificial Intelligence: future portals not only provide tools for the human to solve problems but are used for finding research problems in the first place, for addressing them, and even for solving them automatically under the constraints set by the human researcher. Such systems should preferably be able to explain their reasoning, which is an important aspect in the source critical humanities research tradition. The second and third generation systems set new challenges for both computer scientists and humanities researchers.

Machine Learning methods have been introduced in the Semantic Web for solving problems such as link and type prediction, ontology enrichment and completion (both at terminological and assertional level). Whilst initially mainly focussing on symbol-based solutions, recently numeric-based approaches have received major attention, motivated by the need to scale on the very large Web of Data. In this paper, the most representative proposals, belonging to the aforementioned categories are surveyed, jointly with the analysis of their main peculiarities and drawbacks. Afterwards the main envisioned research directions for further developing Machine Learning solutions for the Semantic Web are presented.