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About Reasoning Technology
As I mentioned before, there are two types of inference methods: deduction, which is the process of deriving one proposition from a set of statements or propositions, and non-deduction methods, which are induction, projection, analogy, and abduction. Inference can be basically defined as a method of tracing the relationships among various facts.
As algorithms for finding them, the classical approaches are forward reasoning and backward reasoning. Machine learning approaches include relational learning, rule inference using decision trees, sequential pattern mining, and probabilistic generation methods.
Inference technology is a technology that combines such various methods and algorithms to obtain the inference results desired by the user, and this blog describes them as follows.
Implementation
Case-based reasoning is a technique for finding appropriate solutions to similar problems by referring to past problem-solving experience and case studies. This section provides an overview of this case-based reasoning technique, its challenges, and various implementations.
A knowledge graph is a graph structure that represents information as a set of related nodes (vertices) and edges (connections), and is a data structure used to connect information on different subjects or domains and visualize their relationships. This paper outlines various methods for automatic generation of this knowledge graph and describes specific implementations in python.
A knowledge graph is a graph structure that represents information as a set of related nodes (vertices) and edges (connections), and is a data structure used to connect information on different subjects or domains and visualize their relationships. This section describes various applications of the knowledge graph and concrete examples of its implementation in python.
- General Problem Solver and Application Examples, Implementation Examples in LISP and Python
The general problem solver specifically takes as input the description of the problem and constraints, and operates to execute algorithms to find an optimal or valid solution. These algorithms vary depending on the nature and constraints of the problem, and there are a variety of general problem-solving methods, including numerical optimization, constraint satisfaction, machine learning, and search algorithms. This section describes examples of implementations in LISP and Python for this GPS.
This paper describes an expert system, which is one of the evolutionary systems of chatbot. The expert system is a system that builds a flexible if/then rule system by combining data called rule knowledge. It was basically built based on Prolog and Lisp, which are one of the AI languages, but here I introduce CLIPS, which is built in Java, from downloading the tool to actually using it.
- User-Customized Learning Assistance with Natural Language Processing
User-customized learning aids utilizing natural language processing (NLP) are being offered in a variety of areas, including the education field and online learning platforms. This section describes the various algorithms used and their specific implementations.
- Expert Systems in Clojure Clara Rule and Rete4Frame
- Expert System with Clara Rule (1) Basic usage
- Expert System with Clara Rule (2) Advansed
The Satisfiability of Propositional Logic (SAT: Boolean Satisfiability) is the problem of determining whether or not there exists a variable assignment for which a given propositional logic expression is true. For example, if there is a problem “whether there exists an assignment of A, B, C, D, E, or F such that A and (B or C) and (D or E or F) are true,” this problem is converted into a propositional logic formula and whether the formula is satisfiable is determined.
Such problem setting plays an important role in many application fields, for example, circuit design, program analysis, problems in the field of artificial intelligence, and cryptography theory. From a theoretical aspect, it is known that the algorithm that can solve the SAT problem is an “NP-complete problem,” and current computers have not found an efficient solution for large-scale problems. Therefore, this is a field of technology where research is still being conducted to improve algorithm efficiency, such as increasing speed and developing heuristic search algorithms.
Reasoning Technology overview
- Reasoning Pattern
- Inference in terms of mathematical statistics and probabilistic methods
- Can you prove that theory?
Classical inference System
This paper describes an expert system, which is one of the evolutionary systems of chatbot. The expert system is a system that builds a flexible if/then rule system by combining data called rule knowledge. It was basically built based on Prolog and Lisp, which are one of the AI languages, but here I introduce CLIPS, which is built in Java, from downloading the tool to actually using it.
The aforementioned reasoning in prolog and core.logic is called backward reasoning. Backward inference is inference from a goal to a sub-goal. When the goal A=C is derived from the premise A=B, the sub-goal (B=C) is derived by saying, “To prove A=C, we should show that B=C since A-B.
This backward reasoning is a method frequently used in scientific research, for example, when a doctor diagnoses a disease, he or she considers all possible reasons that could cause the symptoms and chooses the one that fits among them, or when there is some equipment trouble, he or she gives all possible causes and finds the true cause among them, or when there is some hypothetical law, he or she finds the true cause among them, or when there is a problem in some equipment, he or she finds the true cause among them. When there is a problem with a piece of equipment, the process becomes a process of finding and organizing (theorizing) some hypothetical law (cause and effect).
In contrast to these backward-looking inferences, there is forward-looking inference. When deriving the goal A=C from the premise A=B, one can say, “A=B. In the example of failure analysis described earlier, this is a form of inference that connects the premises, such as “the power cable is not connected → the equipment does not work even if the switch is turned on.
- Expert Systems in Clojure Clara Rule and Rete4Frame
- Expert System with Clara Rule (1) Basic usage
- Expert System with Clara Rule (2) Advansed
Boolean satisfiability problem
The Satisfiability of Propositional Logic (SAT: Boolean Satisfiability) is the problem of determining whether or not there exists a variable assignment for which a given propositional logic expression is true. For example, if there is a problem “whether there exists an assignment of A, B, C, D, E, or F such that A and (B or C) and (D or E or F) are true,” this problem is converted into a propositional logic formula and whether the formula is satisfiable is determined.
Such problem setting plays an important role in many application fields, for example, circuit design, program analysis, problems in the field of artificial intelligence, and cryptography theory. From a theoretical aspect, it is known that the algorithm that can solve the SAT problem is an “NP-complete problem,” and current computers have not found an efficient solution for large-scale problems. Therefore, this is a field of technology where research is still being conducted to improve algorithm efficiency, such as increasing speed and developing heuristic search algorithms.
A subset S of vertices of an effective graph is said to be strongly connected if it satisfies the condition “for any two vertices u and v, u can be reached from v”. We say that S is a Strongly Connected Component (SCC) if no other vertex set can be added to the strongly connected set of vertices S to make it strongly connected. Any directed graph can be decomposed into a union of several strongly connected components that have no common part. This is called strongly connected component decomposition. By collapsing the strongly connected components into a single vertex, we get a DAG (directed graph without closed paths).
Given a logical formula, the problem of determining whether the entire formula can be made true by appropriately assigning boolean values to its logical variables is called the satisfiability problem (SAT). solving SAT is generally NP-complete, but it can be solved efficiently if there are restrictions on the form of the logical formula.
For a rooted tree, the nearest common ancestor of its vertices u and v is called the Lowest Common Ancestor (LCA) of u and v. There are various methods to find the LCA efficiently, and we will discuss two methods here.
- Introduction to Constraint Programming
- Current Trends and Techniques in SAT Solvers
- TheArt of Computer Programming Volume 4, Fascicle 6: Satisfiability
Uncertain Reasoning
This paper deals with the problem of creating a common knowledge system for domain ontologies, which can be shared and integrated in a collaborative framework. We propose a new Hierarchical Algorithm for Conceptual Fuzzy Set Representation Reference Ontology We apply a method of fuzzy logic reasoning based on instances as opposed to the original conceptual representation, which allows us to characterize and measure the degree relationships that exist between concepts in different ontologies. In this paper, we present an application of our approach in the multimedia domain.
Web data often manifest high levels of uncertainty. We focus on categorical Web data and we represent these uncertainty levels as first or second order uncertainty. By means of concrete examples, we show how to quantify and handle these uncertainties using the BetaBinomial and the Dirichlet-Multinomial models, as well as how take into account possibly unseen categories in our samples by using the Dirichlet Process.
Standard semantic technologies propose powerful means for knowledge representation as well as enhanced reasoning capabilities to modern applications. However, the question of dealing with uncertainty, which is ubiquitous and inherent to real world domain, is still considered as a major deficiency. We need to adapt those technologies to the context of uncertain representation of the world. Here, this issue is examined through the evidential theory, in order to model and reason about uncertainty in the assertional knowledge of the ontology. The evidential theory, also known as the DempsterShafer theory, is an extension of probabilities and proposes to assign masses on specific sets of hypotheses. Further on, thanks to the semantics (hierarchical structure, constraint axioms and properties defined in the ontology) associated to hypotheses, a consistent frame of this theory is automatically created to apply the classical combinations of information and decision process offered by this mathematical theory.
We present a design for a (fragment of) Breast Cancer ontology encoded in the probabilistic description logic P-SROIQ which supports determining the consistency of distinct statistical experimental results which may be described in diverse ways. The key contribution is a method for approximating sampling distributions such that the inconsistency of the approximation implies the statistical inconsistency of the continuous distributions.
We consider the fuzzy logic ALCI with semantics based on a finite residuated lattice. We show that the problems of satisfiability and subsumption of concepts in this logic are ExpTime-complete w.r.t. general TBoxes and PSpace-complete w.r.t. acyclic TBoxes. This matches the known complexity bounds for reasoning in crisp ALCI.
- Learning Terminological Na¨ıve Bayesian Classifiers Under Different Assumptions on Missing Knowledge
Knowledge available through Semantic Web standards can easily be missing, generally because of the adoption of the Open World Assumption (i.e. the truth value of an assertion is not necessarily known). However, the rich relational structure that characterizes ontologies can be exploited for handling such missing knowledge in an explicit way. We present a Statistical Relational Learning system designed for learning terminological na¨ıve Bayesian classifiers, which estimate the probability that a generic individual belongs to the target concept given its membership to a set of Description Logic concepts. During the learning process, we consistently handle the lack of knowledge that may be introduced by the adoption of the Open World Assumption, depending on the varying nature of the missing knowledge itself
We present DISPONTE, a semantics for probabilistic ontologies that is based on the distribution semantics for probabilistic logic programs. In DISPONTE each axiom of a probabilistic ontology is annotated with a probability. The probabilistic theory defines thus a distribution over normal theories (called worlds) obtained by including an axiom in a world with a probability given by the annotation. The probability of a query is computed from this distribution with marginalization. We also present the system BUNDLE for reasoning over probabilistic OWL DL ontologies according to the DISPONTE semantics. BUNDLE is based on Pellet and uses its capability of returning explanations for a query. The explanations are encoded in a Binary Decision Diagram from which the probability of the query is computed.
Predicting potential links between nodes in a network is a problem of great practical interest. Link prediction is mostly based on graph-based features and, recently, on approaches that consider the semantics of the domain. However, there is uncertainty in these predictions; by modeling it, one can improve prediction results. In this paper, we propose an algorithm for link prediction that uses a probabilistic ontology described through the probabilistic description logic crALC. We use an academic domain in order to evaluate this proposal.
In this paper we outline a shared knowledge representation based on RDF. It can be used in a distributed multi-tenant environment to store design knowledge. These RDF-graphs incorporate all necessary information to instantiate Bayesian network representations of certain problem solving cases, which are used to support the conceptual design tasks carried out by a salesperson during lead qualification.
The position paper provides a brief summary of log-linear description logics and their applications. We compile a list of five requirements that we believe a probabilistic description logic should have to be useful in practice. We demonstrate the ways in which log-linear description logics answer to these requirements.
This position paper proposes an interactive approach for developing information extractors based on the ontology definition process with knowledge about possible (in)correctness of annotations. We discuss the problem of managing and manipulating probabilistic dependencies.
This book contains the proceedings of the first three workshops on Uncertainty Reasoning for the Semantic Web (URSW) held at ISWC in 2005, 2006, and 2007. The papers presented here include revised and significantly expanded versions of papers presented at the workshops, as well as invited papers by leading experts in the field and related areas.
This book is the first comprehensive compilation of state-of-the-art research approaches to uncertainty reasoning in the context of the Semantic Web, capturing different models of uncertainty and approaches to deductive and inductive reasoning with uncertain formal knowledge.
This is the second volume on “Uncertainty Reasoning for the Semantic Web” and is the result of the Uncertainty Reasoning for the Semantic Web (URSW) workshops held at the International Semantic Web Conference (ISWC) in 2008, 2009, and 2010. It is a revised and significantly extended version of the paths presented at the workshops on Uncertainty Reasoning for the Semantic Web (URSW) at the International Semantic Web Conference (ISWC) in 2008, 2009, and 2010, and at the First International Workshop on Un- certainty in Description Logics (UniDL) in 2010. This is a revised and significantly expanded version of the paths presented at the 2010 First International Workshop on Un- certainty in Description Logics (UniDL).
The two volumes provide a comprehensive compilation of state-of-the-art research approaches to uncertainty reasoning in the context of the Semantic Web, capturing different models of uncertainty and approaches to inductive reasoning as well as inductive reasoning with uncertain formal knowledge.
- From the Uncertainty Reasoning for the Semantic Web 3 Proceedings
In this issue, we discuss Volume 3 of Uncertainty Reasoning for the Semantic Web. This time, we categorize the approaches to those uncertainties as follows. (1) Probabilistic and Dempster-Shafer models, (2) Fuzzy and possibility models, (3) Inductive reasoning and machine learning, and (4) Hybrid approaches.
Answer set Programming
Prolog, a logic programming language developed in the early 1970s, has attracted attention as a new artificial intelligence language that combines declarative statements based on predicate logic with theorem proving-based computational procedures, and was widely used in expert systems, natural language processing, and computational databases in the 1980s. Prolog is a Turing-complete language.
While Prolog is Turing-complete and has high computational power, it has also become clear that its base Horn clause logic program has limited applicability to real-world knowledge representation and problem solving due to its syntactic limitations and lack of reasoning capability.
To solve these problems, many attempts have been proposed since the late 1980s to extend the expressive power of logic programming and to enhance its reasoning capability. As a result, the concept of “answer set programming,” which combines the concepts of logic programming and constraint programming, was established in the late 1990s, and it is now one of the core languages of logic programming.
Inductive Logic Programming
The 18th International Conference on Inductive Logic Programming was held in Prague, September 10-12, 2008, and while the ILP community clearly continues to cherish its beloved framework of first-order logical representations, the research presented at ILP2008 showed that there is still room for both extensions to established ILP approaches, and the exploration of new logical induction frameworks such as Brave Induction, show that there is still room for both extensions of these approaches, and further into the areas of statistical relational learning, graph mining, the Semantic Web, bioinformatics, and cognitive science.
For almost two decades, the ILP conference series has been the premier forum for research on logic-based approaches to machine learning, and the 19th International Conference on Inductive Logic Programming, held July 2-4, 2009, in Leuven, continues this tradition. SRL-2009 – International Workshop on Statistical Relational Learning, MLG-2009 – 7th International Workshop on Mining and Learning with Graphs, making it a conference open to the rest of the community. Each of these three events has its own focus, emphasis, and traditions, but fundamentally they share the problem of learning about structured data in the form of graphs, relational descriptions, and logic as a subject of study. Thus, the events were held concurrently to promote greater interaction among the three communities.
In this issue, we discuss revised papers from the 20th International Conference on Inductive Logic Programming (ILP2010), held in Florence, Italy, June 27-30, 2010.
The ILP conference series began in 1991 and is a major international event on logic-based approaches to machine learning. In recent years, the scope of research has expanded significantly, with the integration of statistical learning and other probabilistic approaches being explored.
ILP2011 was held at Cumberland Lodge in the UK from July 31 to August 3, 2011, under the auspices of the Department of Computing at Imperial College London.
The 31 proceedings papers represent the diversity and vitality of current ILP research, including ILP theory, implementation, probabilistic ILP, biological applications, subgroup discovery, grammatical inference, relational kernels, Petri net learning, spatial learning, graph-based learning, and learning behavioral models.
Describes the 22nd International Conference on Inductive Logic Programming, ILP 2012, held in Dubrovnik on September 17-19, 2012 The ILP conference series began in 1991 and is the leading international forum on learning from structured data. Initially focused on induction in logic programming, it has expanded its scope in recent years and has attracted a great deal of attention and interest. It now focuses on all aspects of learning from structured data, including logic learning, multi-branch relational learning, data mining, statistical relational learning, graph and tree structure mining, and relational reinforcement learning.
The papers in ILP2012 provide a good representation of the breadth of current ILP research, including propositionalization, logical foundations, implementation, probabilistic ILP, applications to robotics and biology, grammatical inference, spatial learning, and graph-based learning.
ILP 2016 took place at the Warren House Conference Centre in London from September 4-6, 2016.Since its first edition in 1991, the annual ILP conference has been the premier international forum for learning from structured relational data It has been functioning. Initially focused on induction in logic programs, over the years it has greatly expanded its research horizons to include learning in logic, multi-relational data mining, statistical relational learning, graph and tree mining, learning in other (non-propositional) logic-based knowledge representation frameworks, exploring the intersection with statistical learning, other probabilistic He has made contributions on all aspects of the approach and others. Theoretical advances in these areas have also been accompanied by challenging applications of these techniques to important problems in areas such as bioinformatics, medicine, and text mining.
We describe the 27th International Conference on Inductive Logic Programming, ILP2017, held in Orléans, France, in September 2017. Contents include robot control, knowledge bases and medicine, statistical machine learning in image recognition, relational learning, logic-based event recognition systems, the problem of learning Boltzmann machine classifiers from relational data, parallel inductive logic programming, learning from interpretative transitions (LFIT), Lifted Relational Neural Networks (LRNN), and improvements to WOrd2Vec will be described.
Inductive logic programming (ILP) is a subfield of machine learning that relies on logic programming as a unified expression language for representing examples, background knowledge, and hypotheses. With its powerful expressive form based on first-order predicate logic, ILP provides an excellent vehicle for multi-relational learning and data mining.
The ILP conference series, initiated in 1991, will be the premier international forum for learning from structured or semi-structured relational data. Originally focused on the introduction of logic programs, over the years the scope of research has expanded significantly to include logic, multi-relational data mining, statistical relational learning, graph and tree mining, other learning (non – proposed) logic-based knowledge representation frameworks, statistical learning and other probabilistic Research into approaches has been reported.
In this issue, we describe the 29th International Conference on Inductive Logic Programming, held in Plovdiv, Bulgaria, September 3-5, 2019.
Inductive logic programming (ILP) is a subfield of machine learning that relies on logic programming as a unified representation language for expressing examples, background knowledge, and hypotheses. With its powerful expressive form based on first-order predicate logic, ILP provides an excellent means for multi-relational learning and data mining.
The ILP conference series, initiated in 1991, provides the premier international forum for learning from structured or semi-structured relational data. Originally focused on introducing logic programs, over the years the scope of research has expanded significantly to include logic, multi-relational data mining, statistical relational learning, graph and tree mining, other learning (non – proposed) logic-based knowledge representation frameworks, statistical learning and other probabilistic approaches and their intersections are being investigated.
In this issue, we discuss ILP2021, which was skipped a year due to the coronal pandemic. Inductive logic programming (ILP) is a branch of machine learning that focuses on learning logical representations from relational data. the ILP conference series was started in 1991 and is the leading international forum on learning from structured or semi-structured relational data, multi-relational learning and data mining. international forum on learning from structured or semi-structured relational data, multi-relational learning, and data mining. Initially focused on induction of logic programs, over the years the scope of research has broadened considerably to include all aspects of logic learning, statistical relational learning, graph and tree mining, learning other (non-propositional) logic-based knowledge representation frameworks, and exploring the intersection of statistical learning and other probabilistic approaches. The research will.
conference Paper
This issue contains tutorial papers from the summer school “Reasoning Web” (http://reasoningweb.org), held July 25-29, 2005. The purpose of the school will be to introduce the methods and issues of the Semantic Web, a major current attempt at Web research in which the World Wide Web Consortium W3C plays an important role.
The main idea of the Semantic Web is to enrich Web data with metadata that conveys the “meaning” of the data and allows Web-based systems to reason about the data (and metadata). Metadata used in Semantic Web applications is usually linked to concepts in the application domain that are shared by different applications. Such a conceptualization is called an ontology and specifies classes of objects and the relationships between them. Ontologies are defined by ontology languages that are based on logic and support formal reasoning. Just as the current Web is inherently heterogeneous in its data format and data semantics, the Semantic Web is inherently heterogeneous in its form of reasoning. In other words, a single form of reasoning has proven to be insufficient for the Semantic Web. For example, while ontological reasoning in general relies on monotonic negation, databases, web databases, and web-based information systems require non-monotonic reasoning. Constraint reasoning is needed to deal with time (because time intervals are dealt with). Topology-based reasoning, e.g., mobile computing applications, requires programming. On the other hand, (forward and backward) chaining is reasoning that deals with views, such as databases (because views, i.e., virtual data, can be derived from real data by operations such as merging and projection).
In this article, we describe the summer school “Reasoning Web 2006” (http://reasoningweb.org), organized by the Universidade Nova de Lisboa (New University of Lisbon), which was held in Lisbon from September 4 to 6, 2006. Reasoning is one of the central issues in the research and development of the Semantic Web. Indeed, the Semantic Web aims to enhance today’s Web with “metadata” carrying semantics and reasoning methods. The Semantic Web is a very active area of research and development involving both academia and industry.
The program of the Summer School “Reasoning Web 2006” will address the following issues. (1) Semantic Web query languages, (2) Semantic Web rules and ontologies, and (3) Bioinformatics and medical ontologies – industrial aspects.
Reasoning Web will be a summer school series focusing on theoretical foundations, state-of-the-art approaches, and practical solutions for reasoning in the Web of Semantics. This issue will be the tutorial notes from the Reasoning Web summer school 2007, held in Dresden, Germany, in September 2007.
The first part of the 2007 edition, “Fundamentals of Reasoning and Reasoning Languages,” surveys the concepts and methods of rule-based query languages. It also provides a comprehensive introduction to description logics and their use. The second part, “Rules and Policies,” deals with reactive rules and rule-based policy representation; the importance and promising solutions for rule exchange on the Web are discussed, along with an overview of current W3C efforts. A thorough discussion is provided. Part 3, “Applications of Semantic Web Reasoning,” presents practical uses of Semantic Web reasoning. The academic perspective is presented by contributions on reasoning in semantic wikis. The industrial perspective is presented by contributions on the importance of semantic technologies in enterprise search solutions, building an enterprise knowledge base with semantic wiki representation, and discovering and selecting semantic web services in B2B scenarios.
The Reasoning Web Summer School is a well-established event attended by academic and industrial professionals and doctoral students interested in fundamental and applied aspects of the Semantic Web. This issue contains the lecture transcripts of the 4th Summer School, held in Venice, Italy, in September 2008. The first three chapters cover (1) languages, formats, and standards employed to encode semantic information, (2) “soft” extensions useful in contexts such as multimedia and social network applications, and (3) controlled natural language techniques to bring ontology authoring closer to the end user and introductory content, while the remaining chapters cover key application areas are covered.
The Semantic Web is one of the major current endeavors in applied computer science. The goal of the Semantic Web is to enhance the existing Web with metadata and processing methods to provide advanced (so-called intelligent) capabilities to Web-based systems, especially context awareness and decision support.
The advanced capabilities required in Semantic Web application scenarios primarily require reasoning. Reasoning capabilities are provided by the Semantic Web languages currently under development. However, many of these languages have been developed from a function-centric (e.g., ontology reasoning, access validation) or application-centric (e.g., Web service search, composition) perspective. For Semantic Web systems and applications, a reasoning technology-centric perspective that complements the above activities is desirable.
This issue of Reasoning Web is a series of summer schools on theoretical foundations, modern approaches, and practical solutions for reasoning in the Web of Semantics. This book is the tutorial note of the 6th school held from August 30 to September 3, 2010.
This year’s focus is on the application of semantic technology to software engineering and suitable reasoning techniques. The application of semantic technology in software engineering is not so easy, and several challenges must be solved in order to apply reasoning to software modeling.
In this issue, we describe the 7th Reasoning Web Summer School 2011, held in Galway, Ireland, August 23-27, 2011 The Reasoning Web Summer School is an established event in the field of applications of reasoning techniques on the Web and attracts young researchers to this new field, targeting scientific discussions of existing researchers.
The 2011 Summer School featured 12 lectures, focusing on the application of reasoning to the “Web of Data”. The first four chapters covered the principles of Resource Description Framework (RDF) and Linked Data (Chapter 1), the description logic underlying the Web Ontology Language (OWL) (Chapter 2), the use of the query language SPARQL with OWL (Chapter 3), efficient and database infrastructure related to scalable RDF processing (Chapter 4), followed by an approach to scalable OWL reasoning on Linked Data in Chapter 5, rules and logic programming techniques related to Web reasoning in the following Chapter 6, and in Chapter 7, a combination of rule-based reasoning and OWL in particular. combination of rule-based reasoning and OWL is described in Chapter 7.
The Reasoning Web Summer School series has become a major educational event in the active field of reasoning on the Web, attracting both young and experienced researchers. The Reasoning Web Summer School series has become a major educational event in the active field of reasoning on the Web, attracting young and seasoned researchers alike.
The 2012 Summer School program was organized around the general motif of “Advanced Query Response on the Web. It also focused on application areas related to the Semantic Web where query response plays an important role and where, by its nature, query response poses new challenges and problems.
In this issue, we describe the 9th Reasoning Web Summer School 2013, held in Mannheim, Germany, from July 30 to August 2, 2013.
The 2013 Summer School covered various aspects of web reasoning, from extensible and lightweight formats such as RDF to more expressive logic languages based on description logic, as well as basic reasoning used in answer set programming and ontology-based data access techniques, and emerging topics such as geospatial information handling and inference-driven information extraction and integration are also covered.
In this issue, we describe the 10th Reasoning Web Sum- mer School (RW 2014), held in Athens, Greece, from September 8-13, 2014.
The theme of the conference 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 fundamental technologies (e.g., elastic cloud computing infrastructure) that enable data management and knowledge discovery technologies that handle terabytes and petabytes of data. Reflecting this industrial reality, the report introduces recent advances in aspects of big data such as the Semantic Web and Linked Data, as well as the fundamentals of inference techniques for tackling big data applications.
This article describes the tutorial papers prepared for the 11th Reasoning Web Summer School (RW 2015) held in Berlin, Germany, from July 31 to August 4, 2015.The 2015 edition of the School was hosted by the Free University of Berlin, Germany, Computer Science The theme for 2015 is “Web Logic Rules” (findings on the Semantic Web, Linked Data, ontologies, rules, and logic).
In this issue, we describe the 12th Reasoning Web Summer School (RW2016) held in Aberdeen, UK, from September 5 to 9, 2016. The content covered knowledge graphs, linked data, semantics, fuzzy RDF, and logical foundations for building and querying OWL knowledge bases.
In this issue, we describe the 13th Reasoning Web, held in London, UK, in July 2017. The theme of this year’s conference was “Semantic Interoperability on the Web” and encompassed themes such as data integration, open data management, reasoning on linked data, mapping databases and ontologies, query answering on ontologies, hybrid reasoning with rules and ontologies, and dynamic ontology-based systems. This issue also focuses on these topics, as well as basic techniques of reasoning used in answer set programming and ontologies.
In this issue, we describe the 14th Reasoning Web, held in Esch-sur-Alzette, Luxembourg, in September 2018. Specifically, we will discuss normative reasoning, a quick survey on efficient search combining text corpora and knowledge bases, large-scale probabilistic knowledge bases, the application of Conditional Random Fields (CRAFs) to knowledge base generation tasks, the use of DBpedia and large cross-domain knowledge graphs such as Wikidata, automatic construction of large 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.
In this issue, we describe the 15th Reasoning Web, held in Bolzano, Italy, in September 2019. The topic will be Explainable AI, with a detailed description and analysis of the main reasoning and explanation methods for ontologies using description logic: tableau procedures and axiom pinpointing algorithms, semantic query responses to knowledge bases, data provenancing, entity-centric knowledge base applications, formal concept analysis, an approach to explaining data by lattice theory, learning interpretable models from data, logical problems such as proposition satisfiability, discrete problems such as constraint satisfaction, and learning full-scale mathematical optimization tasks, distributed computing systems, and explainable AI planning will be described.
This issue of Reasoning Web is dedicated to the 16th Reasoning Web, which will be held virtually in June 2020 due to Corona’s influence. The main theme will be “Declarative Artificial Intelligence”. Specifically, I will give an overview of high-level research directions and open problems related to lightweight description logic (DL) ontology explainable AI (XAI), stream inference, solution set programming (ASP), limit datalogs (a recent declarative query language for data analysis), and knowledge graphs. An overview will be given.
In this issue, we describe the 17th Reasoning Web held in Leuven, Belgium in 20219. Specifically, I will discuss fundamentals on querying graph-structured data, reasoning with ontology languages based on description logics and non-monotonic rule languages, combining symbolic reasoning and deep learning, the Semantic Web and knowledge graphs and machine learning, building information modeling (BIM), the Geospatially Linked Open Data, Ontology Evaluation Techniques, Planning Agents, Cloud-based Electronic Health Record (EHR) Systems, COVID Pandemic Management, Belief Revision and its application to Description Logic and Ontology Repair, Temporal Equilibrium Logic (TEL) and its solution Set Programming (ASP), an introduction and review of Shapes Constraint Language (SHACL), a W3C recommended language for RDF data validation, and score-based Explanations will be presented.
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