Semantics on Demand: Can a Semantic Wiki Replace a Knowledge Base?
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Semantic Wiki Search
Semantic Wiki Search Peter Haase1, Daniel Herzig1,, Mark Musen2,andThanhTran1 1 Institute AIFB, Universit¨at Karlsruhe (TH), Germany {pha,dahe,dtr}@aifb.uni-karlsruhe.de 2 Stanford Center for Biomedical Informatics Research (BMIR) Stanford University, USA [email protected] Abstract. Semantic wikis extend wiki platforms with the ability to represent structured information in a machine-processable way. On top of the structured in- formation in the wiki, novel ways to search, browse, and present the wiki content become possible. However, while powerful query languages offer new opportuni- ties for semantic search, the syntax of formal query languages is not adequate for end users. In this work we present an approach to semantic search that combines the expressiveness and capabilities of structured queries with the simplicity of keyword interfaces and faceted search. Users articulate their information need in keywords, which are translated into structured, conjunctive queries. This transla- tion may result in multiple possible interpretations of the information need, which can then be selected and further refined by the user via facets. We have imple- mented this approach to semantic search as an extension to Semantic MediaWiki. The results of a user study in the SMW-based community portal semanticweb.org show the efficiency and effectiveness of the approach as well as its ease of use. 1 Introduction The availability of structured information on the Semantic Web enables new opportu- nities for information access. Search is no longer limited to matching keywords against documents, but instead complex information needs can be expressed in a structured way, with precise and structured answers as results [1,2,3]. -
Wikipedia Knowledge Graph with Deepdive
The Workshops of the Tenth International AAAI Conference on Web and Social Media Wiki: Technical Report WS-16-17 Wikipedia Knowledge Graph with DeepDive Thomas Palomares Youssef Ahres [email protected] [email protected] Juhana Kangaspunta Christopher Re´ [email protected] [email protected] Abstract This paper is organized as follows: first, we review the related work and give a general overview of DeepDive. Sec- Despite the tremendous amount of information on Wikipedia, ond, starting from the data preprocessing, we detail the gen- only a very small amount is structured. Most of the informa- eral methodology used. Then, we detail two applications tion is embedded in unstructured text and extracting it is a non trivial challenge. In this paper, we propose a full pipeline that follow this pipeline along with their specific challenges built on top of DeepDive to successfully extract meaningful and solutions. Finally, we report the results of these applica- relations from the Wikipedia text corpus. We evaluated the tions and discuss the next steps to continue populating Wiki- system by extracting company-founders and family relations data and improve the current system to extract more relations from the text. As a result, we extracted more than 140,000 with a high precision. distinct relations with an average precision above 90%. Background & Related Work Introduction Until recently, populating the large knowledge bases relied on direct contributions from human volunteers as well With the perpetual growth of web usage, the amount as integration of existing repositories such as Wikipedia of unstructured data grows exponentially. Extract- info boxes. These methods are limited by the available ing facts and assertions to store them in a struc- structured data and by human power. -
Expert Systems: an Introduction -46
GENERAL I ARTICLE Expert Systems: An Introduction K S R Anjaneyulu Expert systems encode human expertise in limited domains by representing it using if-then rules. This article explains the development and applications of expert systems. Introduction Imagine a piece of software that runs on your PC which provides the same sort of interaction and advice as a career counsellor helping you decide what education field to go into K S R Anjaneyulu is a and perhaps what course to pursue .. Or a piece of software Research Scientist in the Knowledge Based which asks you questions about your defective TV and provides Computer Systems Group a diagnosis about what is wrong with it. Such software, called at NeST. He is one of the expert systems, actually exist. Expert systems are a part of the authors of a book on rule larger area of Artificial Intelligence. based expert systems. One of the goals of Artificial Intelligence is to develop systems which exhibit 'intelligent' human-like behaviour. Initial attempts in the field (in the 1960s) like the General Problem Solver created by Allen Newell and Herbert Simon from Carnegie Mellon University were to create general purpose intelligent systems which could handle tasks in a variety of domains. However, researchers soon realized that developing such general purpose systems was too difficult and it was better to focus on systems for limited domains. Edward Feigenbaum from Stanford University then proposed the notion of expert systems. Expert systems are systems which encode human expertise in limited domains. One way of representing this human knowledge is using If-then rules. -
Racer - an Inference Engine for the Semantic Web
Racer - An Inference Engine for the Semantic Web Volker Haarslev Concordia University Department of Computer Science and Software Enineering http://www.cse.concordia.ca/~haarslev/ Collaboration with: Ralf Möller, Hamburg University of Science and Technology Basic Web Technology (1) Uniform Resource Identifier (URI) foundation of the Web identify items on the Web uniform resource locator (URL): special form of URI Extensible Markup Language (XML) send documents across the Web allows anyone to design own document formats (syntax) can include markup to enhance meaning of document’s content machine readable Volker Haarslev Department of Computer Science and Software Engineering Concordia University, Montreal 1 Basic Web Technology (2) Resource Description Framework (RDF) make machine-processable statements triple of URIs: subject, predicate, object intended for information from databases Ontology Web Language (OWL) based on RDF description logics (as part of automated reasoning) syntax is XML knowledge representation in the web What is Knowledge Representation? How would one argue that a person is an uncle? Jil We might describe family has_parent has_child relationships by a relation has_parent and its inverse has_child Joe Sue Now can can define an uncle a person (Joe) is an uncle if has_child and only if he is male he has a parent (Jil) and this ? parent has a second child this child (Sue) is itself a parent Sue is called a sibling of Joe and vice versa Volker Haarslev Department of Computer Science and Software Engineering Concordia University, -
Arxiv:1910.13561V1 [Cs.LG] 29 Oct 2019 E-Mail: [email protected] M
Noname manuscript No. (will be inserted by the editor) A Heuristically Modified FP-Tree for Ontology Learning with Applications in Education Safwan Shatnawi · Mohamed Medhat Gaber ∗ · Mihaela Cocea Received: date / Accepted: date Abstract We propose a heuristically modified FP-Tree for ontology learning from text. Unlike previous research, for concept extraction, we use a regular expression parser approach widely adopted in compiler construction, i.e., deterministic finite automata (DFA). Thus, the concepts are extracted from unstructured documents. For ontology learning, we use a frequent pattern mining approach and employ a rule mining heuristic function to enhance its quality. This process does not rely on predefined lexico-syntactic patterns, thus, it is applicable for different subjects. We employ the ontology in a question-answering system for students' content-related questions. For validation, we used textbook questions/answers and questions from online course forums. Subject experts rated the quality of the system's answers on a subset of questions and their ratings were used to identify the most appropriate automatic semantic text similarity metric to use as a validation metric for all answers. The Latent Semantic Analysis was identified as the closest to the experts' ratings. We compared the use of our ontology with the use of Text2Onto for the question-answering system and found that with our ontology 80% of the questions were answered, while with Text2Onto only 28.4% were answered, thanks to the finer grained hierarchy our approach is able to produce. Keywords Ontologies · Frequent pattern mining · Ontology learning · Question answering · MOOCs S. Shatnawi College of Applied Studies, University of Bahrain, Sakhair Campus, Zallaq, Bahrain E-mail: [email protected] M. -
Building Dialogue Structure from Discourse Tree of a Question
The Workshops of the Thirty-Second AAAI Conference on Artificial Intelligence Building Dialogue Structure from Discourse Tree of a Question Boris Galitsky Oracle Corp. Redwood Shores CA USA [email protected] Abstract ed, chat bot’s capability to maintain the cohesive flow, We propose a reasoning-based approach to a dialogue man- style and merits of conversation is an underexplored area. agement for a customer support chat bot. To build a dia- When a question is detailed and includes multiple sen- logue scenario, we analyze the discourse tree (DT) of an ini- tences, there are certain expectations concerning the style tial query of a customer support dialogue that is frequently of an answer. Although a topical agreement between ques- complex and multi-sentence. We then enforce what we call tions and answers have been extensively addressed, a cor- complementarity relation between DT of the initial query respondence in style and suitability for the given step of a and that of the answers, requests and responses. The chat bot finds answers, which are not only relevant by topic but dialogue between questions and answers has not been thor- also suitable for a given step of a conversation and match oughly explored. In this study we focus on assessment of the question by style, argumentation patterns, communica- cohesiveness of question/answer (Q/A) flow, which is im- tion means, experience level and other domain-independent portant for a chat bots supporting longer conversation. attributes. We evaluate a performance of proposed algo- When an answer is in a style disagreement with a question, rithm in car repair domain and observe a 5 to 10% im- a user can find this answer inappropriate even when a topi- provement for single and three-step dialogues respectively, in comparison with baseline approaches to dialogue man- cal relevance is high. -
D3.5 Semantic Wiki
D3.5 Semantic Wiki V 2.0 Project acronym: FLOOD-serv Public FLOOD Emergency Project full title: and Awareness SERvice Grant agreement no.: 693599 Responsible: SIVECO Contributors: SIVECO Document Reference: D3.5 Dissemination Level: PU Version: Final Date: 31/01/2018 This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 693599 D3.5 Semantic Wiki Table of contents Table of contents ............................................................................................................. 2 List of tables .................................................................................................................... 4 List of abbreviations ......................................................................................................... 5 Executive summary .......................................................................................................... 6 1 Introduction ............................................................................................................. 7 1.1 Document Purpose and Scope .................................................................................... 7 1.2 Target Audience........................................................................................................... 7 1.3 How Final Review Observations Were Addresses ....................................................... 7 2 Technical Specifications ........................................................................................... -
First-Orderized Researchcyc: Expressivity and Efficiency in A
First-Orderized ResearchCyc : Expressivity and Efficiency in a Common-Sense Ontology Deepak Ramachandran1 Pace Reagan2 and Keith Goolsbey2 1Computer Science Department, University of Illinois at Urbana-Champaign, Urbana, IL 61801 [email protected] 2Cycorp Inc., 3721 Executive Center Drive, Suite 100, Austin, TX 78731 {pace,goolsbey}@cyc.com Abstract The second sentence is non-firstorderizable if we consider Cyc is the largest existing common-sense knowledge base. Its “anyone else” to mean anyone who was not in the group of ontology makes heavy use of higher-order logic constructs Fianchetto’s men who went into the warehouse. such as a context system, first class predicates, etc. Many Another reason for the use of these higher-order con- of these higher-order constructs are believed to be key to structs is the ability to represent knowledge succinctly and Cyc’s ability to represent common-sense knowledge and rea- in a form that can be taken advantage of by specialized rea- son with it efficiently. In this paper, we present a translation soning methods. The validation of this claim is one of the of a large part (around 90%) of the Cyc ontology into First- Order Logic. We discuss our methodology, and the trade- contributions of this paper. offs between expressivity and efficiency in representation and In this paper, we present a tool for translating a significant reasoning. We also present the results of experiments using percentage (around 90%) of the ResearchCyc KB into First- VAMPIRE, SPASS, and the E Theorem Prover on the first- Order Logic (a process we call FOLification). -
Semantic Web & Relative Analysis of Inference Engines
International Journal of Engineering Research & Technology (IJERT) ISSN: 2278-0181 Vol. 2 Issue 10, October - 2013 Semantic Web & Relative Analysis of Inference Engines Ravi L Verma 1, Prof. Arjun Rajput 2 1Information Technology, Technocrats Institute of Technology 2Computer Engineering, Technocrats Institute of Technology, Excellence Bhopal, MP Abstract - Semantic web is an enhancement to the reasoners to make logical conclusion based on the current web that will provide an easier path to find, framework. Use of inference engine in semantic web combine, share & reuse the information. It brings an allows the applications to investigate why a particular idea of having data to be defined & linked in a way that solution has been reached, i.e. semantic applications can can be used for more efficient discovery, integration, provide proof of the derived conclusion. This paper is a automation & reuse across many applications.Today’s study work & survey, which demonstrates a comparison web is purely based on documents that are written in of different type of inference engines in context of HTML, used for coding a body of text with interactive semantic web. It will help to distinguish among different forms. Semantic web provides a common language for inference engines which may be helpful to judge the understanding how data related to the real world various proposed prototype systems with different views objects, allowing a person or machine to start with in on what an inference engine for the semantic web one database and then move through an unending set of should do. databases that are not connected by wires but by being about the same thing. -
Toward a Logic of Everyday Reasoning
Toward a Logic of Everyday Reasoning Pei Wang Abstract: Logic should return its focus to valid reasoning in real-world situations. Since classical logic only covers valid reasoning in a highly idealized situation, there is a demand for a new logic for everyday reasoning that is based on more realistic assumptions, while still keeps the general, formal, and normative nature of logic. NAL (Non-Axiomatic Logic) is built for this purpose, which is based on the assumption that the reasoner has insufficient knowledge and resources with respect to the reasoning tasks to be carried out. In this situation, the notion of validity has to be re-established, and the grammar rules and inference rules of the logic need to be designed accordingly. Consequently, NAL has features very different from classical logic and other non-classical logics, and it provides a coherent solution to many problems in logic, artificial intelligence, and cognitive science. Keyword: non-classical logic, uncertainty, openness, relevance, validity 1 Logic and Everyday Reasoning 1.1 The historical changes of logic In a broad sense, the study of logic is concerned with the principles and forms of valid reasoning, inference, and argument in various situations. The first dominating paradigm in logic is Aristotle’s Syllogistic [Aristotle, 1882], now usually referred to as traditional logic. This study was carried by philosophers and logicians including Descartes, Locke, Leibniz, Kant, Boole, Peirce, Mill, and many others [Bochenski,´ 1970, Haack, 1978, Kneale and Kneale, 1962]. In this tra- dition, the focus of the study is to identify and to specify the forms of valid reasoning Pei Wang Department of Computer and Information Sciences, Temple University, Philadelphia, USA e-mail: [email protected] 1 2 Pei Wang in general, that is, the rules of logic should be applicable to all domains and situa- tions where reasoning happens, as “laws of thought”. -
End-User Story Telling with a CIDOC CRM- Based Semantic Wiki
End-user storytelling with a CIDOC CRM-based semantic wiki Vincent Ribaud (reviewed by Patrick Le Bœuf) Département d'Informatique - U. B. O. Brest, France [email protected] Abstract: This paper presents the current state of an experiment intended to use the CIDOC CRM as a knowledge representation language. STEM freshers freely constitute groups of 2 to 4 members and choose a theme; groups have to model, structure, write and present a story within a web-hosted semantic wiki. The main part of the CIDOC CRM is used as an ontological core where students are hanging up classes and properties of the domain related to the story. The hypothesis is made that once the entry ticket has been paid, the CRM guides the end-user in a fairly natural manner for reading - and writing - the story. The intermediary assessment of the wikis allowed us to detect confusion between immaterial work and (physical) realisation of the work; and difficulty in having event-centred modelling. Final assessment results are satisfactory but may be improved. Some groups did not acquire modelling abilities - although this is a central issue in a semantic web course. Results also indicate that the scope of the course (semantic web) is somewhat too ambitious. This experience was performed in order to attract students to computer science studies but it did not produce the expected results. It did however succeed in arousing student interest, and it may contribute to the dissemination of ontologies and to making CIDOC CRM widespread. CIDOC 2010 ICOM General Conference Shanghai, China 2010-11-08 – 11-10 Introduction This paper presents the current state of an experiment intended to use the CIDOC CRM as a knowledge representation language inside a semantic wiki. -
Semantic Mediawiki in Operation: Experiences with Building a Semantic Portal
Semantic MediaWiki in Operation: Experiences with Building a Semantic Portal Daniel M. Herzig and Basil Ell Institute AIFB Karlsruhe Institute of Technology 76128 Karlsruhe, Germany fherzig, [email protected] http://www.aifb.kit.edu Abstract. Wikis allow users to collaboratively create and maintain con- tent. Semantic wikis, which provide the additional means to annotate the content semantically and thereby allow to structure it, experience an enormous increase in popularity, because structured data is more us- able and thus more valuable than unstructured data. As an illustration of leveraging the advantages of semantic wikis for semantic portals, we report on the experience with building the AIFB portal based on Seman- tic MediaWiki. We discuss the design, in particular how free, wiki-style semantic annotations and guided input along a predefined schema can be combined to create a flexible, extensible, and structured knowledge representation. How this structured data evolved over time and its flex- ibility regarding changes are subsequently discussed and illustrated by statistics based on actual operational data of the portal. Further, the features exploiting the structured data and the benefits they provide are presented. Since all benefits have its costs, we conducted a performance study of the Semantic MediaWiki and compare it to MediaWiki, the non- semantic base platform. Finally we show how existing caching techniques can be applied to increase the performance. 1 Introduction Web portals are entry points for information presentation and exchange over the Internet about a certain topic or organization, usually powered by a community. Leveraging semantic technologies for portals and exploiting semantic content has been proven useful in the past [1] and especially the aspect of providing seman- tic data got a lot of attention lately due to the Linked Open Data initiative.