Machine Learning Technology Artificial Intelligence Technology Natural Language Processing Technology Semantic Web Technology Ontology Technology Reasoning Technology Knowledge Information Technology Collecting AI Conference Papers Digital Transformation Technology
In the previous article, we described Reasoning Web 2017. In this issue, we describe the 14th Reasoning Web, held in Esch-sur-Alzette, Luxembourg, in September 2018.
Specifically, I will present a quick survey on normative reasoning, efficient retrieval combining text corpora and knowledge bases, large-scale probabilistic knowledge bases, how to apply Conditional Random Fields (CRAFs) to the task of generating knowledge bases, and the application of CRAFs to the task of generating knowledge bases, such as DBpedia and We will discuss large-scale cross-domain knowledge graphs such as Wikidata, automatic construction of large-scale knowledge graphs (KGs) and learning rules from knowledge graphs, processing large RDF graphs, developing stream processing applications in a web environment, and reasoning about very large knowledge bases.
The details are described below.
We discuss some essential issues for the formal representation of norms to implement normative reasoning, and we show how to capture those requirements in a computationally oriented formalism, Defeasible Deontic Logic, and we provide the description of this logic, and we illustrate its use to model and reasoning with norms with the help of legal examples.
This is a quick survey about efficient search on a text corpus combined with a knowledge base. We provide a high-level description of two systems for searching such data efficiently. The first and older system, Broccoli, provides a very convenient UI that can be used without expert knowledge of the underlying data. The price is a limited query language. The second and newer system, QLever, provides an efficient query engine for SPARQL+Text, an extension of SPARQL to text search. As an outlook, we discuss the question of how to provide a system with the power of QLever and the convenience of Broccoli. Both Broccoli and QLever are also useful when only searching a knowledge base
Large-scale probabilistic knowledge bases are becoming increasingly important in academia and industry alike. They are constantly extended with new data, powered by modern information extraction tools that associate probabilities with knowledge base facts. This tutorial is dedicated to give an understanding of various query answering and reasoning tasks that can be used to exploit the full potential of probabilistic knowledge bases. In the first part of the tutorial, we focus on (tuple-independent) probabilistic databases as the simplest probabilistic data model. In the second part of the tutorial, we move on to richer representations where the probabilistic database is extended with ontological knowledge. For each part, we review some known data complexity results as well as discuss some recent results
In this tutorial we discuss how Conditional Random Fields can be applied to knowledge base population tasks. We are in particular interested in the cold-start setting which assumes as given an ontology that models classes and properties relevant for the domain of interest, and an empty knowledge base that needs to be populated from unstructured text. More specifically, cold-start knowledge base population consists in predicting semantic structures from an input document that instantiate classes and properties as defined in the ontology. Considering knowledge base population as structure prediction, we frame the task as a statistical inference problem which aims at predicting the most likely assignment to a set of ontologically grounded output variables given an input document. In order to model the conditional distribution of these output variables given the input variables derived from the text, we follow the approach adopted in Conditional Random Fields. We decompose the cold-start knowledge base population task into the specific problems of entity recognition, entity linking and slot-filling, and show how they can be modeled using Conditional Random Fields.
Large-scale cross-domain knowledge graphs, such as DBpedia or Wikidata, are some of the most popular and widely used datasets of the Semantic Web. In this paper, we introduce some of the most popular knowledge graphs on the Semantic Web. We discuss how machine learning is used to improve those knowledge graphs, and how they can be exploited as background knowledge in popular machine learning tasks, such as recommender systems.
Advances in information extraction have enabled the automatic construction of large knowledge graphs (KGs) like DBpedia, Freebase, YAGO and Wikidata. Learning rules from KGs is a crucial task for KG completion, cleaning and curation. This tutorial presents state-ofthe-art rule induction methods, recent advances, research opportunities as well as open challenges along this avenue. We put a particular emphasis on the problems of learning exception-enriched rules from highly biased and incomplete data. Finally, we discuss possible extensions of classical rule induction techniques to account for unstructured resources (e.g., text) along with the structured ones.
In the last years, huge RDF graphs with trillions of triples were created. To be able to process this huge amount of data, scalable RDF stores are used, in which graph data is distributed over compute and storage nodes for scaling efforts of query processing and memory needs. The main challenges to be investigated for the development of such RDF stores in the cloud are: (i) strategies for data placement over compute and storage nodes, (ii) strategies for distributed query processing, and (iii) strategies for handling failure of compute and storage nodes. In this manuscript, we give an overview of how these challenges are addressed by scalable RDF stores in the cloud.
The goal of the tutorial is to outline how to develop and deploy a stream processing application in a Web environment in a repro ducible way.
To this extent, we intend to
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- survey existing research outcomes from the Stream Reasoning /RDF Stream Processing that arise in querying and reasoning on a variety of highly dynamic data,
- introduce stream reasoning techniques as powerful tools to use when addressing a data-centric problem characterized both by variety and velocity (such as those typically found on the modern Web),
- present a relevant Web-centric use-case that requires to address simultaneously data velocity and variety, and
- guide the participants through the development of a Web stream processing application
This tutorial gives an overview of current methods for performing reasoning on very large knowledge bases. The first part of the lectures is dedicated to an introduction of the problem and of related technologies. Then, the tutorial continues discussing the state-of-the-art for reasoning on very large inputs with particular emphasis on the strengths and weaknesses of current approaches. Finally, the tutorial concludes with an outline of some of the most important research directions in this field.
In the next article, we will discuss Reasoning Web 2019.
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