DOCSLIB.ORG

Knowledge on the Web: Towards Robust and Scalable Harvesting of Entity-Relationship Facts

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Knowledge on the Web: Towards Robust and Scalable Harvesting of Entity-Relationship Facts Knowledge on the Web: Towards Robust and Scalable Harvesting of Entity-Relationship Facts Gerhard Weikum Max Planck Institute for Informatics http://www.mpi-inf.mpg.de/~weikum/ Acknowledgements 2/38 Vision: Turn Web into Knowledge Base comprehensive DB knowledge fact of human knowledge assets extraction • everything that (Semantic (Statistical Web) Web) Wikipedia knows • machine-readable communities • capturing entities, (Social Web) classes, relationships Source: DB & IR methods for knowledge discovery. Communications of the ACM 52(4), 2009 3/38 Knowledge as Enabling Technology • entity recognition & disambiguation • understanding natural language & speech • knowledge services & reasoning for semantic apps • semantic search: precise answers to advanced queries (by scientists, students, journalists, analysts, etc.) German chancellor when Angela Merkel was born? Japanese computer science institutes? Politicians who are also scientists? Enzymes that inhibit HIV? Influenza drugs for pregnant women? ... 4/38 Knowledge Search on the Web (1) Query: sushi ingredients? Results: Nori seaweed Ginger Tuna Sashimi ... Unagi http://www.google.com/squared/5/38 Knowledge Search on the Web (1) Query:Query: JapaneseJapanese computerscomputeroOputer science science ? institutes ? http://www.google.com/squared/6/38 Knowledge Search on the Web (2) Query: politicians who are also scientists ? ?x isa politician . ?x isa scientist Results: Benjamin Franklin Zbigniew Brzezinski Alan Greenspan Angela Merkel … http://www.mpi-inf.mpg.de/yago-naga/7/38 Knowledge Search on the Web (2) Query: politicians who are married to scientists ? ?x isa politician . ?x isMarriedTo ?y . ?y isa scientist Results (3): [ Adrienne Clarkson, Stephen Clarkson ], [ Raúl Castro, Vilma Espín ], [ Jeannemarie Devolites Davis, Thomas M. Davis ] http://www.mpi-inf.mpg.de/yago-naga/8/38 Knowledge Search on the Web (3) http://www-tsujii.is.s.u-tokyo.ac.jp/medie/ 9/38 Take-Home Message If music was invented Information is not Knowledge. 20 years ago Knowledge is not Wisdom. [when the Web was created], Wisdom is not Truth we'd all be playing Truth is not Beauty. one-string instruments. Beauty is not Music. Music is the best. (Udi Manber (Frank Zappa VP Engineering jazz&rock musician Google) 1940 – 1993) → extract facts from Web sources → organize them in an automatically built knowledge base → answer questions in terms of entities and relations 10/38 Related Work Yago-Naga Text2Onto Kylin Powerset ReadTheWeb KOG Hakia Avatar Cyc ontologies fact extraction UIMA entity search statist. ranking kosmix (Semantic (Statistical KnowItAll Web) Web) TextRunner WolframAlpha SWSE StatSnowball sig.ma online communities EntityCube DBpedia question answering Cimple TrueKnowledge (Social Web) DBlife Freebase GoogleSquared Answers START 11/38 Outline 3 What and Why Building a Large Knowledge Base Consistent Growth of the Knowledge Base Adding Multimodal Knowledge Challenges: Scope, Scale, Robustness ... 12/38 Information Extraction (IE): Text to Relations bornOn (Max Planck, 23 April 1858) [0.99] bornIn (Max Planck, Kiel) [0.9] type (Max Planck, physicist) [0.9] Max Karl Ernst Ludwig Planck was born in Kiel, Germany, on April 23, 1858, the son of advisor (Max Planck, Kirchhoff) [0.6] Julius Wilhelm and Emma (née Patzig) Planck. advisor (Max Planck, Helmholtz) [0.6] Planck studied at the Universities of Munich and Berlin,AlmaMater (Max Planck, TU Munich) [0.5] where his teachers included Kirchhoff and Helmholtz,plays (Max Planck, piano) [0.7] and received his doctorate of philosophy at Munichspouse in 1879. (Max Planck, Marie Merck) [0.9] He was Privatdozent in Munich from 1880 to 1885, spousethen (Max Planck, Marga Hösslin) [0.8] Associate Professor of Theoretical Physics at Kiel until 1889, in which year he succeeded Kirchhoff as Professor at Berlin University, where he remained until his retirementPerson in 1926. BirthDate BirthPlace ... Afterwards he became President of the Kaiser Wilhelm Society Max Planck 4/23, 1858 Kiel for the Promotion of Science, a post he held until 1937. IE buildsAlbert data Einstein space (with 3/14, uncertain1879 Ulm data) He was also a gifted pianist and is said to have atMahatma one time Gandhi 10/2, 1869 Porbandar considered music as a career. • confidence < 1 (sometimes << 1) Planck was twice married. Upon his appointment,• knowledgePerson in 1885, base from Award many sources to Associate Professor in his native town Kiel he married a friend of his childhood, Marie• high Merck, computationalMax who Planckdied cost Nobel Prize in Physics in 1909. He remarried her cousin Marga von Hösslin.Marie Curie Nobel Prize in Physics Three of his children died young, leaving him withMarie two sons. Curie Nobel Prize in Chemistry IE: combine NLP, pattern matching, statistical learning 13/38 IE for Knowledge Harvesting • YAGO knowledge base from Wikipedia infoboxes & categories and integration with WordNet taxonomy • NAGA search on RDF graph with entity-relationship LM for ranking {{Infobox scientist | name = Max Planck | birth_date = {{birth date|1858|4|23|mf=y}} | birth_place = [[Kiel]], [[Holstein]] | death_date = {{death date and age|mf=yes|1947|10|4}} | death_place = [[Göttingen]], [[West Germany]] | nationality = [[Germany|German]] | field = [[Physics]] | alma_mater = [[Ludwig-Maximilians-Universität München]] | work_institutions = [[University of Kiel]]<br /> [[Humboldt University of Berlin|University of Berlin]]<br /> [University of Göttingen]]<br /> [[Kaiser-Wilhelm-Gesellschaft]]<br /> | doctoral_advisor = [[Alexander von Brill]] | doctoral_students = [[Gustav Ludwig Hertz]]<br /> … | known_for = [[Planck constant]]<br /> [[Planck postulate]]<br /> 14/38 YAGO Knowledge Base (F. Suchanek et al.: WWW‘07) Entity Entities Facts 40 Mio. RDF triples subclass KnowItAll 30 000 subclass subclass( entity1-relation-entity2, SUMOOrganization 20 000Person 60 000 subject-predicate-objectLocation ) WordNet 120 000 80 000 subclass subclass subclass Cyc 300 000 5 Mio. subclass subclass Accuracy Scientist Politician Country TextRunnersubclass n/a 8 Mio. ≈ 95% YAGO 2 Mio.subclass 19 Mio.instanceOf instanceOf State DBpedia Biologist 2 Mio. 103 Mio. instanceOf Freebase ???Physicist 156 Mio. City instanceOf Wolfram α ??? > 1 Trio. Germany instanceOf instanceOf locatedIn Oct 23, 1944 Erwin_Planck diedOn locatedIn Kiel Schleswig- FatherOf bornIn Holstein Nobel Prize hasWon instanceOf citizenOf diedOn Oct 4, 1947 Max_Planck Max_Planck Angela Merkel Society YAGO Apr 23, 1858 bornOn means means means means (0.9) means(0.1) “Max “Max Karl Ernst “Angela “Angela Planck”IWP Ludwig Planck” Merkel” Dorothea Merkel” 15/38 Leveraging YAGO for Entity Extraction Existing knowledge base boosts entity detection & disambiguation (similarity of string-in-context to target entity-in-context) 16/38 Outline 3 What and Why 3 Building a Large Knowledge Base Consistent Growth of the Knowledge Base Adding Multimodal Knowledge Challenges: Scope, Scale, Robustness ... 17/38 Growing the Knowledge Base Word + Wikipedia Net Web sources YAGO Core YAGO Extractors YAGO GathererGatherer YAGO Core YAGO Hypotheses YAGO Checker ScrutinizerGatherer YAGO Core YAGO G r o w i n g knows ≈ all entities focus on facts 18/38 Pattern-Based Harvesting (Dipre, Snowball, Text2Onto, Leila, StatSnowball, etc.) Facts & Fact Candidates Patterns (Hillary, Bill) X and her husband Y (Carla, Nicolas) X and Y on their honeymoon (Angelina, Brad) (Victoria, David) X and Y and their children (Hillary, Bill) X has been dating with Y (Carla, Nicolas) X loves Y (Angelina, Brad) (Yoko, John) … • good for recall (Kate, Pete) • noisy, drifting (Carla, Benjamin) • not robust enough (Larry, Google) 19/38 SOFIE: Self-Organizing Framework for IE (F. Suchanek et al.: WWW‘09) Integrate methods: • textual/linguistic pattern-based IE with statistics seeds → patterns → facts → patterns → ... (Hillary, Bill) → X and her husband Y → (Carla, Nicolas), (Carla, Mick) → • declarative rule-based IE with constraints functional dependencies: marriedTo is a function inclusion dependencies: presidentOf ⊆ citizenOf Address problems: • pattern selection („and her husband“, „has been dating“, ...) • reasoning on mutual consistency of facts • entity disambiguation („Merkel“ → AngelaMerkel, MaxMerkel, ...; „MPI“ → MaxPlanckInstitute, MessagePassingInterface) Unified solution by Weighted Max-Sat solver (high accuracy and much faster than MCMC for prob. graphical models) 20/38 SOFIE Example occurs (X and her husband Y, Hillary, Bill) [100] Spouse (HillaryClinton, occurs (X Y and their children, Hillary, Bill) [40] BillClinton) occurs (X and her husband Y, Victoria, David) [60] Spouse (CarlaBruni, occurs (X dating with Y, Rebecca, David) [20] NicolasSarkozy) occurs (X dating with Y, Victoria, Tom) [10] Spouse (Victoria, David) expresses (and her husband, Spouse) Spouse (Rebecca, David) expresses (and their children, Spouse) Spouse (Victoria, Tom) expresses (dating with, Spouse) ∀Spousex,y,z,w: (Victoria, R(x,y) ∧David)R(x,z) ⇒⇒¬y=zSpouse (alt.: ¬ R(x,y)(Rebecca, ∨¬ R(x,z)) David) Spouse∀ x,y,z,w: (Victoria, R(x,y) ∧ David)R(w,y) ⇒⇒¬x=wSpouse(alt.: ¬ R(x,y)(Victoria, ∨¬ R(x,z))Tom) …... occurs∀ x,y: R(x,y) (husband, ⇒ R(y,x) Victoria, David) ∧ expresses (husband, Spouse) … ⇒ Spouse (Victoria, David) occurs∀ p,x,y: (dating, occurs (p,Rebecca, x, y) ∧ expressesDavid) ∧ expresses (p, R) ⇒ (dating,R (x, y) Spouse) ⇒ Spouse (Rebecca, David) … occurs∀ p,x,y: (husband, occurs (p, Victoria, x, y) ∧ R David)(x, y) ⇒ ∧ expressesSpouse (Victoria, (p, R) David) ⇒ expresses (husband, Spouse)
Load more

Recommended publications
  • Franck-Hertz Experiment
    IIA2 Modul Atomic/Nuclear Physics Franck-Hertz Experiment This experiment by JAMES FRANCK and GUSTAV LUDWIG HERTZ from 1914 (Nobel Prize 1926) is one of the most impressive comparisons in the search for quantum theory: it shows a very simple arrangement in the existence of discrete stationary energy states of the electrons in the atoms. ÜÔ ÖÑÒØ Á Á¾ ¹ ÜÔ ÖÑÒØ This experiment by JAMES FRANCK and GUSTAV LUDWIG HERTZ from 1914 (Nobel Prize 1926) is one of the most impressive comparisons in the search for quantum theory: it shows a very simple arrangement in the existence of discrete stationary energy states of the electrons in the atoms. c AP, Department of Physics, University of Basel, September 2016 1.1 Preliminary Questions • Explain the FRANCK-HERTZ experiment in our own words. • What is the meaning of the unit eV and how is it defined? • Which experiment can verify the 1. excitation energy as well? • Why is an anode used in the tube? Why is the current not measured directly at the grid? 1.2 Theory 1.2.1 Light emission and absorption in the atom There has always been the question of the microscopic nature of matter, which is a key object of physical research. An important experimental approach in the "world of atoms "is the study of light absorption and emission of light from matter, that the accidental investigation of the spectral distribution of light absorbed or emitted by a particular substance. The strange phenomenon was observed (first from FRAUNHOFER with the spectrum of sunlight), and was unexplained until the beginning of this century when it finally appeared: • If light is a continuous spectrum (for example, incandescent light) through a gas of a particular type of atom, and subsequently , the spectrum is observed, it is found that the light is very special, atom dependent wavelengths have been absorbed by the gas and therefore, the spectrum is absent.
    [Show full text]
  • A Survey of Top-Level Ontologies to Inform the Ontological Choices for a Foundation Data Model
    A survey of Top-Level Ontologies To inform the ontological choices for a Foundation Data Model Version 1 Contents 1 Introduction and Purpose 3 F.13 FrameNet 92 2 Approach and contents 4 F.14 GFO – General Formal Ontology 94 2.1 Collect candidate top-level ontologies 4 F.15 gist 95 2.2 Develop assessment framework 4 F.16 HQDM – High Quality Data Models 97 2.3 Assessment of candidate top-level ontologies F.17 IDEAS – International Defence Enterprise against the framework 5 Architecture Specification 99 2.4 Terminological note 5 F.18 IEC 62541 100 3 Assessment framework – development basis 6 F.19 IEC 63088 100 3.1 General ontological requirements 6 F.20 ISO 12006-3 101 3.2 Overarching ontological architecture F.21 ISO 15926-2 102 framework 8 F.22 KKO: KBpedia Knowledge Ontology 103 4 Ontological commitment overview 11 F.23 KR Ontology – Knowledge Representation 4.1 General choices 11 Ontology 105 4.2 Formal structure – horizontal and vertical 14 F.24 MarineTLO: A Top-Level 4.3 Universal commitments 33 Ontology for the Marine Domain 106 5 Assessment Framework Results 37 F. 25 MIMOSA CCOM – (Common Conceptual 5.1 General choices 37 Object Model) 108 5.2 Formal structure: vertical aspects 38 F.26 OWL – Web Ontology Language 110 5.3 Formal structure: horizontal aspects 42 F.27 ProtOn – PROTo ONtology 111 5.4 Universal commitments 44 F.28 Schema.org 112 6 Summary 46 F.29 SENSUS 113 Appendix A F.30 SKOS 113 Pathway requirements for a Foundation Data F.31 SUMO 115 Model 48 F.32 TMRM/TMDM – Topic Map Reference/Data Appendix B Models 116 ISO IEC 21838-1:2019
    [Show full text]
  • Artificial Intelligence
    BROAD AI now and later Michael Witbrock, PhD University of Auckland Broad AI Lab @witbrock Aristotle (384–322 BCE) Organon ROOTS OF AI ROOTS OF AI Santiago Ramón y Cajal (1852 -1934) Cerebral Cortex WHAT’S AI • OLD definition: AI is everything we don’t yet know how program • Now some things that people can’t do: • unique capabilities (e.g. Style transfer) • superhuman performance (some areas of speech, vision, games, some QA, etc) • Current AI Systems can be divided by their kind of capability: • Skilled (Image recognition, Game Playing (Chess, Atari, Go, DoTA), Driving) • Attentive (Trading: Aidyia; Senior Care: CareMedia, Driving) • Knowledgeable, (Google Now, Siri, Watson, Cortana) • High IQ (Cyc, Soar, Wolfram Alpha) GOFAI • Thought is symbol manipulation • Large numbers of precisely defined symbols (terms) • Based on mathematical logic (implies (and (isa ?INST1 LegalAgreement) (agreeingAgents ?INST1 ?INST2)) (isa ?INST2 LegalAgent)) • Problems solved by searching for transformations of symbolic representations that lead to a solution Slow Development Thinking Quickly Thinking Slowly (System I) (System II) Human Superpower c.f. other Done well by animals and people animals Massively parallel algorithms Serial and slow Done poorly until now by computers Done poorly by most people Not impressive to ordinary people Impressive (prizes, high pay) "Sir, an animal’s reasoning is like a dog's walking on his hind legs. It is not done well; but you are surprised to find it done at all.“ - apologies to Samuel Johnson Achieved on computers by high- Fundamental design principle of power, low density, slow computers simulation of vastly different Computer superpower c.f. neural hardware human Recurrent Deep Learning & Deep Reasoning MACHINE LEARNING • Meaning is implicit in the data • Thought is the transformation of learned representations http://karpathy.github.io/2015/05/21/rnn- effectiveness/ .
    [Show full text]
  • The Franck-Hertz Experiment: 100 Years Ago and Now
    The Franck-Hertz experiment: 100 years ago and now A tribute to two great German scientists Zoltán Donkó1, Péter Magyar2, Ihor Korolov1 1 Institute for Solid State Physics and Optics, Wigner Research Centre for Physics, Budapest, Hungary 2 Physics Faculty, Roland Eötvös University, Budapest, Hungary Franck-Hertz experiment anno (~1914) The Nobel Prize in Physics 1925 was awarded jointly to James Franck and Gustav Ludwig Hertz "for their discovery of the laws governing the impact of an electron upon an atom" Anode current Primary experimental result 4.9 V nobelprize.org Elv: ! # " Accelerating voltage ! " Verh. Dtsch. Phys. Ges. 16: 457–467 (1914). Franck-Hertz experiment anno (~1914) The Nobel Prize in Physics 1925 was awarded jointly to James Franck and Gustav Ludwig Hertz "for their discovery of the laws governing the impact of an electron upon an atom" nobelprize.org Verh. Dtsch. Phys. Ges. 16: 457–467 (1914). Franck-Hertz experiment anno (~1914) “The electrons in Hg vapor experience only elastic collisions up to a critical velocity” “We show a method using which the critical velocity (i.e. the accelerating voltage) can be determined to an accuracy of 0.1 V; its value is 4.9 V.” “We show that the energy of the ray with 4.9 V corresponds to the energy quantum of the resonance transition of Hg (λ = 253.6 nm)” ((( “Part of the energy goes into excitation and part goes into ionization” ))) Important experimental evidence for the quantized nature of the atomic energy levels. The Franck-Hertz experiment: 100 years ago and now Franck-Hertz experiment: published in 1914, Nobel prize in 1925 Why is it interesting today as well? “Simple” explanation (“The electrons ....”) → description based on kinetic theory (Robson, Sigeneger, ...) Modern experiments Various gases (Hg, He, Ne, Ar) Modern experiment + kinetic description (develop an experiment that can be modeled accurately ...) → P.
    [Show full text]
  • Knowledge Graphs on the Web – an Overview Arxiv:2003.00719V3 [Cs
    January 2020 Knowledge Graphs on the Web – an Overview Nicolas HEIST, Sven HERTLING, Daniel RINGLER, and Heiko PAULHEIM Data and Web Science Group, University of Mannheim, Germany Abstract. Knowledge Graphs are an emerging form of knowledge representation. While Google coined the term Knowledge Graph first and promoted it as a means to improve their search results, they are used in many applications today. In a knowl- edge graph, entities in the real world and/or a business domain (e.g., people, places, or events) are represented as nodes, which are connected by edges representing the relations between those entities. While companies such as Google, Microsoft, and Facebook have their own, non-public knowledge graphs, there is also a larger body of publicly available knowledge graphs, such as DBpedia or Wikidata. In this chap- ter, we provide an overview and comparison of those publicly available knowledge graphs, and give insights into their contents, size, coverage, and overlap. Keywords. Knowledge Graph, Linked Data, Semantic Web, Profiling 1. Introduction Knowledge Graphs are increasingly used as means to represent knowledge. Due to their versatile means of representation, they can be used to integrate different heterogeneous data sources, both within as well as across organizations. [8,9] Besides such domain-specific knowledge graphs which are typically developed for specific domains and/or use cases, there are also public, cross-domain knowledge graphs encoding common knowledge, such as DBpedia, Wikidata, or YAGO. [33] Such knowl- edge graphs may be used, e.g., for automatically enriching data with background knowl- arXiv:2003.00719v3 [cs.AI] 12 Mar 2020 edge to be used in knowledge-intensive downstream applications.
    [Show full text]
  • Using Linked Data for Semi-Automatic Guesstimation
    Using Linked Data for Semi-Automatic Guesstimation Jonathan A. Abourbih and Alan Bundy and Fiona McNeill∗ jabourbih@acm.org, bundy@staffmail.ed.ac.uk, f.j.mcneill@ed.ac.uk University of Edinburgh, School of Informatics 10 Crichton Street, Edinburgh, EH8 9AB, United Kingdom Abstract and Semantic Web systems. Next, we outline the process of GORT is a system that combines Linked Data from across guesstimation. Then, we describe the organisation and im- several Semantic Web data sources to solve guesstimation plementation of GORT. Finally, we close with an evaluation problems, with user assistance. The system uses customised of the system’s performance and adaptability, and compare inference rules over the relationships in the OpenCyc ontol- it to several other related systems. We also conclude with a ogy, combined with data from DBPedia, to reason and per- brief section on future work. form its calculations. The system is extensible with new Linked Data, as it becomes available, and is capable of an- Literature Survey swering a small range of guesstimation questions. Combining facts to answer a user query is a mature field. The DEDUCOM system (Slagle 1965) was one of the ear- Introduction liest systems to perform deductive query answering. DE- The true power of the Semantic Web will come from com- DUCOM applies procedural knowledge to a set of facts in bining information from heterogeneous data sources to form a knowledge base to answer user queries, and a user can new knowledge. A system that is capable of deducing an an- also supplement the knowledge base with further facts.
    [Show full text]
  • Why Has AI Failed? and How Can It Succeed?
    Why Has AI Failed? And How Can It Succeed? John F. Sowa VivoMind Research, LLC 10 May 2015 Extended version of slides for MICAI'14 ProblemsProblems andand ChallengesChallenges Early hopes for artificial intelligence have not been realized. Language understanding is more difficult than anyone thought. A three-year-old child is better able to learn, understand, and generate language than any current computer system. Tasks that are easy for many animals are impossible for the latest and greatest robots. Questions: ● Have we been using the right theories, tools, and techniques? ● Why haven’t these tools worked as well as we had hoped? ● What other methods might be more promising? ● What can research in neuroscience and psycholinguistics tell us? ● Can it suggest better ways of designing intelligent systems? 2 Early Days of Artificial Intelligence 1960: Hao Wang’s theorem prover took 7 minutes to prove all 378 FOL theorems of Principia Mathematica on an IBM 704 – much faster than two brilliant logicians, Whitehead and Russell. 1960: Emile Delavenay, in a book on machine translation: “While a great deal remains to be done, it can be stated without hesitation that the essential has already been accomplished.” 1965: Irving John Good, in speculations on the future of AI: “It is more probable than not that, within the twentieth century, an ultraintelligent machine will be built and that it will be the last invention that man need make.” 1968: Marvin Minsky, technical adviser for the movie 2001: “The HAL 9000 is a conservative estimate of the level of artificial intelligence in 2001.” 3 The Ultimate Understanding Engine Sentences uttered by a child named Laura before the age of 3.
    [Show full text]
  • Bohr Model of Hydrogen
    Chapter 3 Bohr model of hydrogen Figure 3.1: Democritus The atomic theory of matter has a long history, in some ways all the way back to the ancient Greeks (Democritus - ca. 400 BCE - suggested that all things are composed of indivisible \atoms"). From what we can observe, atoms have certain properties and behaviors, which can be summarized as follows: Atoms are small, with diameters on the order of 0:1 nm. Atoms are stable, they do not spontaneously break apart into smaller pieces or collapse. Atoms contain negatively charged electrons, but are electrically neutral. Atoms emit and absorb electromagnetic radiation. Any successful model of atoms must be capable of describing these observed properties. 1 (a) Isaac Newton (b) Joseph von Fraunhofer (c) Gustav Robert Kirch- hoff 3.1 Atomic spectra Even though the spectral nature of light is present in a rainbow, it was not until 1666 that Isaac Newton showed that white light from the sun is com- posed of a continuum of colors (frequencies). Newton introduced the term \spectrum" to describe this phenomenon. His method to measure the spec- trum of light consisted of a small aperture to define a point source of light, a lens to collimate this into a beam of light, a glass spectrum to disperse the colors and a screen on which to observe the resulting spectrum. This is indeed quite close to a modern spectrometer! Newton's analysis was the beginning of the science of spectroscopy (the study of the frequency distri- bution of light from different sources). The first observation of the discrete nature of emission and absorption from atomic systems was made by Joseph Fraunhofer in 1814.
    [Show full text]
  • Sterns Lebensdaten Und Chronologie Seines Wirkens
    Sterns Lebensdaten und Chronologie seines Wirkens Diese Chronologie von Otto Sterns Wirken basiert auf folgenden Quellen: 1. Otto Sterns selbst verfassten Lebensläufen, 2. Sterns Briefen und Sterns Publikationen, 3. Sterns Reisepässen 4. Sterns Züricher Interview 1961 5. Dokumenten der Hochschularchive (17.2.1888 bis 17.8.1969) 1888 Geb. 17.2.1888 als Otto Stern in Sohrau/Oberschlesien In allen Lebensläufen und Dokumenten findet man immer nur den VornamenOt- to. Im polizeilichen Führungszeugnis ausgestellt am 12.7.1912 vom königlichen Polizeipräsidium Abt. IV in Breslau wird bei Stern ebenfalls nur der Vorname Otto erwähnt. Nur im Emeritierungsdokument des Carnegie Institutes of Tech- nology wird ein zweiter Vorname Otto M. Stern erwähnt. Vater: Mühlenbesitzer Oskar Stern (*1850–1919) und Mutter Eugenie Stern geb. Rosenthal (*1863–1907) Nach Angabe von Diana Templeton-Killan, der Enkeltochter von Berta Kamm und somit Großnichte von Otto Stern (E-Mail vom 3.12.2015 an Horst Schmidt- Böcking) war Ottos Großvater Abraham Stern. Abraham hatte 5 Kinder mit seiner ersten Frau Nanni Freund. Nanni starb kurz nach der Geburt des fünften Kindes. Bald danach heiratete Abraham Berta Ben- der, mit der er 6 weitere Kinder hatte. Ottos Vater Oskar war das dritte Kind von Berta. Abraham und Nannis erstes Kind war Heinrich Stern (1833–1908). Heinrich hatte 4 Kinder. Das erste Kind war Richard Stern (1865–1911), der Toni Asch © Springer-Verlag GmbH Deutschland 2018 325 H. Schmidt-Böcking, A. Templeton, W. Trageser (Hrsg.), Otto Sterns gesammelte Briefe – Band 1, https://doi.org/10.1007/978-3-662-55735-8 326 Sterns Lebensdaten und Chronologie seines Wirkens heiratete.
    [Show full text]
  • Logic-Based Technologies for Intelligent Systems: State of the Art and Perspectives
    information Article Logic-Based Technologies for Intelligent Systems: State of the Art and Perspectives Roberta Calegari 1,* , Giovanni Ciatto 2 , Enrico Denti 3 and Andrea Omicini 2 1 Alma AI—Alma Mater Research Institute for Human-Centered Artificial Intelligence, Alma Mater Studiorum–Università di Bologna, 40121 Bologna, Italy 2 Dipartimento di Informatica–Scienza e Ingegneria (DISI), Alma Mater Studiorum–Università di Bologna, 47522 Cesena, Italy; giovanni.ciatto@unibo.it (G.C.); andrea.omicini@unibo.it (A.O.) 3 Dipartimento di Informatica–Scienza e Ingegneria (DISI), Alma Mater Studiorum–Università di Bologna, 40136 Bologna, Italy; enrico.denti@unibo.it * Correspondence: roberta.calegari@unibo.it Received: 25 February 2020; Accepted: 18 March 2020; Published: 22 March 2020 Abstract: Together with the disruptive development of modern sub-symbolic approaches to artificial intelligence (AI), symbolic approaches to classical AI are re-gaining momentum, as more and more researchers exploit their potential to make AI more comprehensible, explainable, and therefore trustworthy. Since logic-based approaches lay at the core of symbolic AI, summarizing their state of the art is of paramount importance now more than ever, in order to identify trends, benefits, key features, gaps, and limitations of the techniques proposed so far, as well as to identify promising research perspectives. Along this line, this paper provides an overview of logic-based approaches and technologies by sketching their evolution and pointing out their main application areas. Future perspectives for exploitation of logic-based technologies are discussed as well, in order to identify those research fields that deserve more attention, considering the areas that already exploit logic-based approaches as well as those that are more likely to adopt logic-based approaches in the future.
    [Show full text]
  • Talking to Computers in Natural Language
    feature Talking to Computers in Natural Language Natural language understanding is as old as computing itself, but recent advances in machine learning and the rising demand of natural-language interfaces make it a promising time to once again tackle the long-standing challenge. By Percy Liang DOI: 10.1145/2659831 s you read this sentence, the words on the page are somehow absorbed into your brain and transformed into concepts, which then enter into a rich network of previously-acquired concepts. This process of language understanding has so far been the sole privilege of humans. But the universality of computation, Aas formalized by Alan Turing in the early 1930s—which states that any computation could be done on a Turing machine—offers a tantalizing possibility that a computer could understand language as well. Later, Turing went on in his seminal 1950 article, “Computing Machinery and Intelligence,” to propose the now-famous Turing test—a bold and speculative method to evaluate cial intelligence at the time. Daniel Bo- Figure 1). SHRDLU could both answer whether a computer actually under- brow built a system for his Ph.D. thesis questions and execute actions, for ex- stands language (or more broadly, is at MIT to solve algebra word problems ample: “Find a block that is taller than “intelligent”). While this test has led to found in high-school algebra books, the one you are holding and put it into the development of amusing chatbots for example: “If the number of custom- the box.” In this case, SHRDLU would that attempt to fool human judges by ers Tom gets is twice the square of 20% first have to understand that the blue engaging in light-hearted banter, the of the number of advertisements he runs, block is the referent and then perform grand challenge of developing serious and the number of advertisements is 45, the action by moving the small green programs that can truly understand then what is the numbers of customers block out of the way and then lifting the language in useful ways remains wide Tom gets?” [1].
    [Show full text]
  • Wikitology Wikipedia As an Ontology
    Creating and Exploiting a Web of Semantic Data Tim Finin University of Maryland, Baltimore County joint work with Zareen Syed (UMBC) and colleagues at the Johns Hopkins University Human Language Technology Center of Excellence ICAART 2010, 24 January 2010 http://ebiquity.umbc.edu/resource/html/id/288/ Overview • Introduction (and conclusion) • A Web of linked data • Wikitology • Applications • Conclusion introduction linked data wikitology applications conclusion Conclusions • The Web has made people smarter and more capable, providing easy access to the world's knowledge and services • Software agents need better access to a Web of data and knowledge to enhance their intelligence • Some key technologies are ready to exploit: Semantic Web, linked data, RDF search engines, DBpedia, Wikitology, information extraction, etc. introduction linked data wikitology applications conclusion The Age of Big Data • Massive amounts of data is available today on the Web, both for people and agents • This is what‟s driving Google, Bing, Yahoo • Human language advances also driven by avail- ability of unstructured data, text and speech • Large amounts of structured & semi-structured data is also coming online, including RDF • We can exploit this data to enhance our intelligent agents and services introduction linked data wikitology applications conclusion Twenty years ago… Tim Berners-Lee‟s 1989 WWW proposal described a web of relationships among named objects unifying many info. management tasks. Capsule history • Guha‟s MCF (~94) • XML+MCF=>RDF
    [Show full text]