機械学習技術 人工知能技術 自然言語処理技術 セマンティックウェブ技術 オントロジー技術 デジタルトランスフォーメーション技術 AI学会論文 知識情報処理技術 AI学会論文を集めて 推論技術
In the previous article, we discussed Reasoning Web 2013. In this issue, we describe the 10th Reasoning Web Sum- mer School (RW 2014), held in Athens, Greece, from September 8-13, 2014.
The Reasoning Web series is an annual summer school initiated in 2005 by the European Network of Excellence REWERSE. since 2005, the school has become the leading educational event in the field of reasoning techniques on the web, driving both young and seasoned researchers. The 2014 edition of the School is organized by the Faculty of Informatics and Telecommunications of the National University of Kapodistria, Athens (Prof. Manolis Koubarakis) and the Institute of Communication and Computer Systems of the National Technical University of Athens (Prof. Giorgos Stamou and Stoilos). As in previous years, this year’s Summer School was co-located with the 8th International Conference on Web Reasoning and Rule Systems (RR 2014).
The Semantic Web and Linked Data research areas have been comprehensively covered in recent Reasoning Web Summer Schools, as many of the advanced features required in Semantic Web and Linked Data use cases require reasoning. 2014. The theme of the school will be “Reasoning on the Web in the Age of Big Data.”
The invention of new technologies such as sensors, social networking platforms, and smartphones has enabled organizations to tap into vast amounts of previously unavailable data and combine it with their own internal proprietary data. At the same time, significant progress has been made in the underlying technologies (e.g., elastic cloud computing infrastructure) that enable data management and knowledge discovery technologies to handle terabytes and petabytes of data. the 2014 Reasoning Web School lecture program will reflect this industrial reality and will focus on Recent advances in aspects of Big Data such as the Semantic Web and Linked Data, as well as the fundamentals of inference techniques to address Big Data applications.
Lecture slides for all tutorials can be found on the Summer School website (http://rw2014.di.uoa.gr/).
Details are given below.
Linked Open Data
With Linked Data, a very pragmatic approach towards achieving the vision of the Semantic Web has gained some traction in the last years. The term Linked Data refers to a set of best practices for publishing and interlinking structured data on the Web. While many standards, methods and technologies developed within by the Semantic Web community are applicable for Linked Data, there are also a number of specific characteristics of Linked Data, which have to be considered. In this article we introduce the main concepts of Linked Data. We present an overview of the Linked Data life-cycle and discuss individual approaches as well as the state-of-the-art with regard to extraction, authoring, link- ing, enrichment as well as quality of Linked Data. We conclude the chapter with a discussion of issues, limitations and further research and development challenges of Linked Data. This article is an updated version of a similar lecture given at Reasoning Web Summer School 2013.
While the amount of knowledge available as linked data grows, so does the need for providing end users with access to this knowledge. Especially question answering systems are receiving much interest, as they provide intuitive access to data via natural language and shield end users from technical aspects related to data modelling, vocabularies and query languages. This tutorial gives an introduction to the rapidly developing field of question answering over linked data. It gives an overview of the main challenges involved in the interpretation of a user’s information need expressed in natural language with respect to the data that is queried. The paper summarizes the main existing approaches and systems including available tools and resources, bench- marks and evaluation campaigns. Finally, it lists the open topics that will keep question answering over linked data an exciting area of research in the years to come.
RDF and Graph Databases
RDF has become recently a very popular data model used in a variety of applications and use cases in both academia and industry. Query processing and evaluation is a central component in data management in general and is, thus, unsurprisingly one of the most active areas of research in the field of RDF data management. In this chap- ter we provide an overview of query processing techniques for the RDF data model using different system architectures. We survey techniques for both centralized and distributed RDF stores, including peer-to-peer, federated and cloud-based systems.
The use of graphs in analytic environments is getting more and more widespread, with applications in many different environments like social network analysis, fraud detection, industrial management, knowledge analysis, etc. Graph databases are one important solution to consider in the management of large datasets. The course will be oriented to tackle four important aspects of graph management. First, to give a characterization of graphs and the most common operations applied on them. Second, to review the technologies for graph management and focus on the particular case of Sparksee. Third, to analyze in depth some important applications and how graphs are used to solve them. Fourth, to understand the use of benchmarking to make the requirements of the user compatible with the growth of the technologies for graph management.
Description Logic Based Ontologies
This chapter gives an overview of the description logics underlying the OWL2 Web Ontology Language and its three tractable profiles, OWL 2 RL, OWL 2 EL and OWL 2 QL. We consider the syntax and semantics of these description logics as well as main reasoning tasks and their computational complexity. We also discuss the semantical foundations for first-order and datalog rewritings of conjunctive queries over knowledge bases given in the OWL 2 profiles, and outline the architecture of the ontology-based data access system Ontop.
The need for an ontological layer on top of data, associated with advanced reasoning mechanisms able to exploit ontological knowledge, has been acknowledged in the database, knowledge representation and Semantic Web communities. We focus here on the ontology-based data querying problem, which consists in querying data while taking ontological knowledge into account. To tackle this problem, we consider a logical framework based on existential rules, also called Datalog±.
In this course, we introduce fundamental notions on ontology-based query answering with existential rules. We present basic reasoning techniques, explain the relationships with other formalisms such as lightweight description logics, and review decidability results as well as associated algorithms. We end with ongoing research and some challenging issues.
Though processing time-dependent data has been investigated for a long time, the research on temporal and especially stream reasoning over linked open data and ontologies is reaching its high point these days. In this tutorial, we give an overview of state-of-the art query languages and engines for temporal and stream reasoning. On a more detailed level, we discuss the new language STARQL (Reasoning-based Query Language for Streaming and Temporal ontology Access). STARQL is designed as an expressive and flexible stream query framework that offers the possibility to embed different (temporal) description logics as filter query languages over ontologies, and hence it can be used within the OBDA paradigm (Ontology Based Data Access described in “Ontology Based Data Access (ODBA), generative AI and GNN” in the classical sense) and within the ABDEO paradigm (Accessing Big Data over Expressive Ontologies).
Applications
Probabilistic Databases (PDBs) lie at the expressive inter- section of databases, first-order logic, and probability theory. PDBs em- ploy logical deduction rules to process Select-Project-Join (SPJ) queries, which form the basis for a variety of declarative query languages such as Datalog, Relational Algebra, and SQL. They employ logical consistency constraints to resolve data inconsistencies, and they represent query an- swers via logical lineage formulas (aka.“data provenance”) to trace the dependencies between these answers and the input tuples that led to their derivation. While the literature on PDBs dates back to more than 25 years of research, only fairly recently the key role of lineage for establishing a closed and complete representation model of relational operations over this kind of probabilistic data was discovered. Although PDBs benefit from their efficient and scalable database infrastructures for data storage and indexing, they couple the data computation with probabilistic inference, the latter of which remains a #P-hard problem also in the context of PDBs.
In this chapter, we provide a review on the key concepts of PDBs with a particular focus on our own recent research results related to this field. We highlight a number of ongoing research challenges related to PDBs, and we keep referring to an information extraction (IE) scenario as a running application to manage uncertain and temporal facts obtained from IE techniques directly inside a PDB setting.
The Semantic Web is finally leaving the lab. In this article, we examine some practical, industry-oriented Semantic Web systems and discuss the costs and benefits on this disruptive technology. We focus on applications for cities and citizens and present a set of key challenges and solutions made possible using semantics at scale. When applicable, we re- port on the differentiating factors for Semantic Technologies, showcasing their unique capabilities, as well as the cost of this paradigm shift.
In the next article, we will discuss Reasoning Web 2015.
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