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Knowledge Management Institute 707.009 Foundations of Knowledge Management g g Broad Knowledge Bases Markus Strohmaier Univ. Ass. / Assistant Professor Knowledge Management Institute Graz University of Technology, Austria e-mail:

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Knowledge Management Institute

707.009 Foundations of Knowledge Management g g „Broad Knowledge Bases“

Markus Strohmaier

  • Univ. Ass. / Assistant Professor

Knowledge Management Institute Graz University of Technology, Austria e-mail: markus.strohmaier@tugraz.at web: http://www.kmi.tugraz.at/staff/markus

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Rückblick

Homonyme: Mehrdeutige Benennungen (z B Homonyme: Mehrdeutige Benennungen (z.B. Bank) Homophone: Gleichlautende Benennungen (z.B. Mohr, Moor)

Objekt

„Reale Welt“

Homographen: Gleiche Schreibweisen (z.B. Wach(-)s(-)tube) Synonyme: Mehrere Bezeichnungen stehen für denselben Begriff (Auto PKW)

Sem iotisches Dreieck

denselben Begriff (Auto, PKW) Antonyme: Gegensätze (z.B. hart - weich) Hyper/Hyponyme: Abstraktere / Spezifischere Begriffe (z.B. Fahrzeug / PKW)

W ort Ausdruck Sym bol Begriff Konzept

Dreieck

Formale Begriffssysteme zielen oft darauf ab wenig Raum für Interpretation zu lassen!

– Homonymzusätze (Qualifikatoren) – (z.B. „Ring <Schmuckstück>, Ring <Mathematik>)

Sprache Wissen

( „ g , g ) – Korrekte Zuordnung von Begriffen und Benennungen oft erst aus dem Kontext heraus interpretierbar!

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Rückblick

Thesauraus Hie a chie Ontologie Konzepte Eigenschaften Beziehungen I ndex Schlagworte Taxonom ie Hierarchie Gehört zu Kl ifik ti Hierarchie Äquivalenz Assoziation Beziehungen Regeln Schlagworte Liste Katalog Lexikon Klassifikation

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Overview

  • Knowledge Organization (last lecture)
  • Broad Knowledge Bases

– Ontologies – WordNet WordNet – ConceptNet – And more Systems Perspective

  • Knowledge Acquisition (next lecture)

Based in part on slides prepared by D. Reisinger

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Reading the Web Reading the Web NELL: Never Ending Language Learning

htt // t l d / t / http://rtw.ml.cmu.edu/rtw/ http://techcrunch.com/2010/10/09/nell-computer- l i t t / https://www.nytimes.com/2010/10/05/science/05 t ht l? 1

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language-carnegie-tctv/ compute.html?_r=1

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Konzeptueller Graph und Semantisches Konzeptueller Graph und Semantisches Netz

Eine geordnete Zusam m enstellung von Begriffen und Eine geordnete Zusam m enstellung von Begriffen und Bezeichnungen, deren Zusam m enhang über beliebige Beziehungen m iteinander definiert w ird.

Graphische Begriffsnetze mit definierter Semantik Sowohl Begriffe als auch Beziehungen sind typisiert und es existiert eine Grammatik für deren Verwendung e e G a at ü de e e e du g Zur Überführung von Information in anwendbares Wissen sind „verwandt-mit“-Relationen nicht mehr ausreichend -> Sprung vom Thesaurus zum semantischen Netz Eingeführt von Linguisten, um die Bedeutung von Wörtern entsprechend ihrer Verwendung darzustellen

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Ontologie – Eine Definition

"A t l i f l li it ifi ti f h d "An ontology is a form al, explicit specification of a shared conceptualization of a dom ain of I nterest. ... For AI system s, w hat „exists“ is that w hich can be represented„ ( Gruber)

Eine Ontologie ist eine formale Beschreibung von Konzepten und Beziehungen, eine abstrakte, Konzepten und Beziehungen, eine abstrakte, vereinfachte Sicht auf die Welt

Explicit: festgeschrieben, definiert F l f li i A fb d h hi l b Formal: formalisierter Aufbau, daher maschinenlesbar Shared: Übereinkunft einer Community Domain of Interest: Wissensgebiet Domain of Interest: Wissensgebiet Conceptualisation: Begrifflichkeiten schaffen

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Bestandteile von Ontologien

Klassen ( Concepts) Relationen zw ischen Klassen Eigenschaften von Klassen

Ontologie

I nstanzen Regeln Einschränkungen

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Begrifflichkeiten

Ontology Engineering: Entwicklung Verwendung und

  • Ontology Engineering: Entwicklung, Verwendung und

Instandhaltung von Ontologien

  • Meta-Ontologie: eine Ontologie, die einer anderen Ontologie

d li t b t hi t B h ib O t l i zugrunde liegt = abstrahierte Beschreibung von Ontologien und so die Verknüpfung des Wissens verschiedener Domänen Off W lt A h O t l i llt t ti ll

  • Offene-Welt-Annahme: Ontologien sollten potentiell von

anderen Ontologien verwendbar bzw. einbindbar sein

  • Ontology Mapping: aufeinander Abbilden von Ontologien
  • Ontology Merge: Konsolidierung, Zusammenführen von

Ontologien

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Nutzen

To share common understanding of the structure of

  • „To share common understanding of the structure of

information among people and software agents

  • To enable reuse of domain knowledge
  • To make domain assumptions explicit
  • To separate domain knowledge from the operational

knowledge knowledge

  • To analyze domain knowledge“ (Noy, McGuinness)

Interoperabilität in heterogenen Landschaften erreichen Informations- und Interaktionsqualität steigern Zeitersparnis Kostensenkung Zeitersparnis, Kostensenkung

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Einsatzbereiche

Eine unterstützende Technologie des W issensm anagem ents

Wissens-Engineering und -Repräsentation Informationsretrieval extraktion und visualisierung

Eine unterstützende Technologie des W issensm anagem ents

Informationsretrieval, -extraktion und -visualisierung Informationsmodellierung und -integration Künstliche Intelligenz, Entscheidungsunterstützung g , g g Integration von Anwendungssystemen (EAI), Offene Systeme u.v.m

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Anwendung: Semantic Web – Warum?

Probleme bei herkömmlichen Info-Retrieval am Web Probleme bei herkömmlichen Info Retrieval am Web

– Hoher Recall, geringe Precision (Google!) – Suchresultate hängen stark vom in der Anfrage verwendeten Vokabular ab in der Anfrage verwendeten Vokabular ab – Resultate sind einzelne Web-Seiten – Suchergebnisse sind anderen Softwarewerkzeugen nicht zugänglich Softwarewerkzeugen nicht zugänglich

Verbesserungen in der Suchtechnologie können Probleme nicht lösen! Die Bedeutung des Web-Inhaltes ist den Suchmaschinen nicht zugänglich!

Tim Berners Lee explaining some ideas related to the Semantic Web on Video:

http://www.technologyreview.com/video/semantic

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Lösungsansätze

Möglicher Lösungsansatz Möglicher Lösungsansatz

– Die Repräsentation bleibt wie sie ist, aber – wir entwickeln verbesserte Methoden (Künstliche Intelligenz und Computerlinguistik) die es erlauben die das Programme die Bedeutung Computerlinguistik), die es erlauben, die das Programme die Bedeutung der Inhalte verstehen

Der Ansatz des Semantic Web

– Die Inhalte des Webs werden in einer Form repräsentiert die es Software- Die Inhalte des Webs werden in einer Form repräsentiert, die es Software Werkzeugen leichter erlaubt, die Inhalte zu verarbeiten – Andere “intelligente” Techniken nutzen dies aus, um neuartige Anwendungen zu ermöglichen

Gesam theit von über das I nternet zugänglichen Ressourcen, die eine sem antische Struktur besitzen und durch Meta-Daten beschrieben sind.

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Semantic Web - Definition

The Semantic Web is an extension of the „The Semantic Web is an extension of the current w eb in which information is given a w ell-defined m eaning, better enabling computers and people to work in cooperation ” cooperation.” (Berners-Lee, Hendler, Lassila) “The Semantic Web is a vision: the idea of having data on the web defined and linked in a way that it can be used by machines in a way that it can be used by machines not just for display purposes, but for autom ation, integration and reuse of data across various applications.” (W3C)

  • > Angleichung der formalen an

die natürliche Sprache

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RDF / RDF Schema

Objekt Subjekt Prädikat

Bedingung: Definition beliebiger Klassen, Properties, deren

Zielsetzung: „say anything about anything“

g g g , p , Wiederverwendung RDF = „Ressource Description Framework“ RDF-Modell ist ein formal fundiertes grafisches Modell (gerichteter Graph) RDF-Modell ist ein formal fundiertes grafisches Modell (gerichteter Graph) Drei Elemente: Subjekt (Knoten), Prädikat (Kante), Objekt (Knoten): „Tripel“

Subjekt: Ressource über die eine Aussage getroffen wird – Subjekt: Ressource, über die eine Aussage getroffen wird – Prädikat: Art der Beziehung zwischen Subjekt und Objekt – Objekt: „Wert“ der Beziehung

Vokabulare können von anderen RDF-Graphen referenziert werden (URIs)

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Eine vereinfachte „Napoleon-Ontologie“ p g

A f i W b it

Name G b Dt

rdf: property

„Thing“

rdf: subclass

Auf einer Website: „Napoleon ist 1.50 gross und leistete einen Beitrag

Person Größe Geb.Dt.

zur alten Geschichte.“ Klassenebene

rdf: type

Wissensch.

leistet Beitrag

Klassenebene

„Napoleon“

Name h / / / h / / / rdf: type

Instanzenebene

„150“

Größe http: / / x/ Napoleon hat Adres. http: / / x/ Alte Geschichte leistet Beitrag

Instanzenebene

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Ontologieeditor Ontologieeditor Beispiel: Protégé

h / / f d d / http: / / protege.stanford.edu/

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Freebase

MOVIE DEMO: http://mqlx.com/~david/parallax/

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Two current research efforts focusing on the Two current research efforts focusing on the construction of broad knowledge bases

WordNet ConceptNet

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Wordnet Wordnet

http://wordnet.princeton.edu/

W dN t i j t ( t t d i 1985) WordNet is a project (started in 1985) at the Cognitive Science Laboratory at the Princeton University. Nouns, verbs, adjectives and adverbs are grouped into sets of cognitive synonyms (synsets) each expressing a synonyms (synsets), each expressing a distinct concept. Synsets are interlinked by means of conceptual-semantic and lexical relations The resulting network lexical relations. The resulting network

  • f meaningfully related words and

concepts can be navigated with the browser One purpose of the dataset is

  • browser. One purpose of the dataset is

to support Natural Language Processing. RDF-Modelle unter:

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RDF-Modelle unter: http://www.semanticweb.org/library/

http://wordnet.princeton.edu/man2.1/wnstats.7WN#toc2

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Wordnet Glossary [Excerpt] Wordnet Glossary [Excerpt] [http://wordnet.princeton.edu/gloss]

sense

– A meaning of a word in WordNet. Each sense of a word is in a different synset.

Example:

  • strike work stoppage -- (a group's refusal to work in protest against low pay or

strike, work stoppage (a group s refusal to work in protest against low pay or bad work conditions; "the strike lasted more than a month before it was settled")

  • strike -- ((baseball) a pitch that the batter swings at and misses, or that the batter

hits into foul territory, or that the batter does not swing at but the umpire judges to be in the area over home plate and between the batter's knees and shoulders; "this

synset

A synonym set; a set of words that are interchangeable in some context (Sharing

be in the area over home plate and between the batter s knees and shoulders; this pitcher throws more strikes than balls")

– A synonym set; a set of words that are interchangeable in some context (Sharing the same word sense). Example: car, auto, automobile, autocar

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Wordnet Glossary [Excerpt] Wordnet Glossary [Excerpt] [http://wordnet.princeton.edu/gloss]

hypernym hypernym

– The generic term used to designate a whole class of specific instances. Y is a hypernym of X if X is a (kind of) Y.

hyponym

Illustration: vehicle

hyponym

– The specific term used to designate a member of a class. X is a hyponym of Y if X is a (kind of) Y.

holonym

Illustration: automotive vehicle

holonym

– The name of the whole of which the meronym names a part. Y is a holonym of X if X is a part of Y.

meronym

Illustration: Car

meronym

– The name of a constituent part of, the substance of, or a member of something. X is a meronym of Y if X is a part of Y.

sister

Illustration: Engine

– Matching strings that are both the immediate hyponyms of the same superordinate (or hypernym). Illustration: automotive vehicle, motor vehicle

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motor vehicle

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Wordnet Glossary [Excerpt] Wordnet Glossary [Excerpt] [http://wordnet.princeton.edu/gloss]

b f base form

– The base form of a word or collocation is the form to which inflections are added. Illustration: Base form of playing, played, plays, play

part of speech

– WordNet defines "part of speech" as either noun, verb, adjective, or adverb. Same as syntactic category.

collocation

– A collocation in WordNet is a string of two or more words, connected by spaces Illustration: {buy\VERB fast\ADJECTIVE skis\NOUN} g , y p

  • r hyphens.

Examples are: man-eating shark, blue-collar, depend on, line of products.

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W d t Wordnet

http://wordnet.princeton.edu/doc

DEMO WordNet Browser/Babylon y

Each sense matching the search selected displayed as follows: displayed as follows: Sense n [{synset_offset}] [<lex_filename>] word1[#sense_number][, word2...]

synset_offset is the byte offset of the synset in the data pos file corresponding to the syntactic category data.pos file corresponding to the syntactic category, lex_filename is the name of the lexicographer file that the synset comes from, word1 is the first word in the synset (note that this is not necessarily the search word) and sense number is the WordNet sense word) and sense_number is the WordNet sense number assigned to the preceding word. synset_offset , lex_filename , and sense_number are generated if the appropriate Options are specified.

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ConceptNet ConceptNet

http://web.media.mit.edu/~hugo/conceptnet/ http://conceptnet.media.mit.edu/ Th C tN t k l d b i ti The ConceptNet knowledgebase is a semantic network consisting of concepts and relations between concepts. Commonsense knowledge in ConceptNet encompasses the spatial, physical, social, l d h l i l f temporal, and psychological aspects of everyday life.

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Wordnet vs ConceptNet Wordnet vs. ConceptNet

Liu, H. & Singh, P. (2004) ConceptNet: A Practical Commonsense Reasoning Toolkit. BT Technology Journal,. Volume 22, Kluwer Academic Publishers.

I C t t In Conceptnet,

  • 1. nodes can be compound elements representing higher-order

compound concepts p p

Conceptnet, does not distinguish between word senses

2 Extends some of WordNet‘s relationships (synonym is-a

  • 2. Extends some of WordNet s relationships (synonym, is-a,

part-of) to more than twenty semantic relations

including, for example, CapableOf, EffectOf, SubeventOf, PropertyOff, MotivationOf, etc MotivationOf, etc

  • 3. Knowledge is more informal, defeasible and practically
  • riented
  • riented

Contains knowledge that is defeasible (often true, but not always – e.g. EffectOf(“fall of bicycle’, ‘get hurt’)

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ConceptNet ConceptNet

http://web.media.mit.edu/~hugo/conceptnet/ http://conceptnet.media.mit.edu/

O ll Obj ti Overall Objective: Represent commonsense knowledge, which is knowledge that every Represent commonsense knowledge, which is knowledge that every person is assumed to possess. Commonsense knowledge is typically

  • mmitted from social communications

ConceptNet was designed to make practical context-based inferences

  • ver real-world texts.

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ConceptNet ConceptNet

Liu, H. & Singh, P. (2004) ConceptNet: A Practical Commonsense Reasoning Toolkit. BT Technology Journal,. Volume 22, Kluwer Academic Publishers.

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ConceptNet ConceptNet

Liu, H. & Singh, P. (2004) ConceptNet: A Practical Commonsense Reasoning Toolkit. BT Technology Journal,. Volume 22, Kluwer Academic Publishers.

I i 2 C tN t t i d In version 2, ConceptNet contained, 1.6 million assertions interrelating 300 000 nodes 300.000 nodes. f t th b f ti f counts the number of times a fact is uttered in the OMCS corpus. i counts how many times an y assertion was inferred during the relaxation phase

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ConceptNet ConceptNet

http://web.media.mit.edu/~hugo/conceptnet/ http://conceptnet.media.mit.edu/

C tN t t t t l i t k l ld d t ConceptNet supports textual-reasoning tasks over real-world documents including for example

  • topic-jisting (e.g. a news article containing the concepts, “gun,”

“convenience store,” “demand money” and “make getaway” might suggest the topics “robbery” and “crime”) suggest the topics robbery and crime ),

  • affect-sensing (e.g. this email is sad and angry),
  • analogy-making (e.g. “scissors,” “razor,” “nail clipper,” and “sword” are

gy g ( g pp perhaps like a “knife” because they are all “sharp,” and can be used to “cut something”),

  • text summarization
  • text summarization
  • and others

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ConceptNet‘s Relations

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ConceptNet Features ConceptNet Features

Liu, H. & Singh, P. (2004) ConceptNet: A Practical Commonsense Reasoning Toolkit. BT Technology Journal,. Volume 22, Kluwer Academic Publishers.

C t t l i hb h d Contextual neighbourhoods

  • Provided by the API method Get Context()

– Performs spreading activation radiating outward from a source node – Considering the number and strengths of all paths which connect the two nodes

T i G i Topic Generation

  • Utilizing Get Context() as well, Example: Query Expansion

g () p y p

– Entering ‚restaurant‘ would return related queries such as ‚order food‘, ‚waiter‘ and ‚menu‘

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ConceptNet Features ConceptNet Features

Liu, H. & Singh, P. (2004) ConceptNet: A Practical Commonsense Reasoning Toolkit. BT Technology Journal,. Volume 22, Kluwer Academic Publishers.

A l ki Analogy making

  • Analogy

– Two ConceptNet nodes are analogous if their sets of back-edges (incoming edges)

  • verlap
  • verlap
  • ConceptNet‘s GetAnalogousConcepts() supports Analogy making

Projection

  • Projection is graph traversal from an origin node, following a single

transitive relation type (Modus ponens: If A->B and B->C then A->C) Affect Sensing

  • Uses ConceptNet‘s method GuessMood()
  • Uses ConceptNet s method GuessMood()
  • Leveraging edges between concepts and specified affect categories

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ConceptNet

DEMO

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ConceptNet – Application Example

S i t t f Wiki di [1] Summarize text from Wikipedia[1]:

“A car accident or car crash is an incident in which an automobile collides with anything that causes damage to the automobile, including other automobiles, telephone poles buildings or trees or in which the driver loses control of the vehicle telephone poles, buildings or trees, or in which the driver loses control of the vehicle and damages it in some other way, such as driving into a ditch or rolling over. Sometimes a car accident may also refer to an automobile striking a human or

  • animal. Car crashes — also called road traffic accidents (RTAs), traffic collisions,

auto accidents, road accidents, personal injury collisions, motor vehicle accidents (MVAs), — kill an estimated 1.2 million people worldwide each year, and injure about forty times this number.” [1] http://en.wikipedia.org/wiki/Car_accident

Text summarization provided by ConceptNet

“Car accident or car crash was incident in which. Automobile collided with anything that cause damaged to automobile include other automobile telephone pole building that cause damaged to automobile include other automobile telephone pole building

  • r tree. Driver lost control of vehicle and damages. Drove into ditch. Rolled . Car

accident referred to automobile. Car crash called road traffic accident. Killed estimate 1. Injured about forty timed number.”

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j y

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F N t A B i f O i FrameNet – A Brief Overview

http://framenet.icsi.berkeley.edu/

Th B k l F N t j t i ti li l i l The Berkeley FrameNet project is creating an on-line lexical resource for English, based on frame semantics and supported by corpus evidence. The aim is to document the range of semantic and syntactic combinatory possibilities (valences) of each word in each of its combinatory possibilities (valences) of each word in each of its senses, through computer-assisted annotation of example sentences and automatic tabulation and display of the annotation results. The major product of this work, the FrameNet lexical database, currently contains more than 10,000 lexical units (defined below), more currently contains more than 10,000 lexical units (defined below), more than 6,100 of which are fully annotated, in more than 825 semantic frames, exemplified in more than 135,000 annotated sentences.

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F N t A B i f O i FrameNet – A Brief Overview

http://framenet.icsi.berkeley.edu/

Semantic frames are schematic representations of situation types Semantic frames are schematic representations of situation types (eating, spying, removing, classifying, etc.) together with lists of the kinds of participants, props, and other conceptual roles that are seen as components of such situations seen as components of such situations. Example: Cause_change_of_position_on_a_scale

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FrameNet

Source: Excerpt of Framenet, http://framenet.icsi.berkeley.edu/, accessed on July 2nd, 2007

Verb “Increase” is related to Frame:

Who causes Who causes increase? Increase of what numbers? numbers? What causes increase?

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What is increased?

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F N t A B i f O i FrameNet – A Brief Overview

http://framenet.icsi.berkeley.edu/

Lexical Units invoke Frames. Lexical Units invoke Frames. Example: LUs for Cause_change_of_position_on_a_scale

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C A B i f O i Cyc – A Brief Overview

www.cyc.com

  • Began as a research project in 1984
  • Initiated and conducted by Cycorp Inc.
  • Project founder and CEO Doug Lenat:

j g

– Watch his Google video of the year 2006!

– Computers Versus Common Sense http://video.google.com/videoplay?docid=-

7704388615049492068&q=engedu

  • Initially „hand-crafted“ knowledge base -> now based on several strategies

Initially „hand crafted knowledge base now based on several strategies

  • "Once you have a truly massive amount of information integrated as knowledge,

then the human-software system will be superhuman, in the same sense that ki d ith iti i h d t ki d b f iti " D mankind with writing is superhuman compared to mankind before writing." ~ Doug Lenat, June 21, 2001

  • Open Source Version available
  • „OpenCyc is the open source version of the Cyc technology, the world's largest and

most complete general knowledge base and commonsense reasoning engine.” (http://www cyc com/cyc/opencyc/overview)

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(http://www.cyc.com/cyc/opencyc/overview)

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Cyc

www.cyc.com

Cyc‘s Objective

  • Cycorp's goal is to break the "software brittleness bottleneck" once and

for all by constructing a foundation of basic "common sense" knowledge for all by constructing a foundation of basic common sense knowledge - a semantic substratum of terms, rules, and relations - that will enable a variety of knowledge-intensive products and services. What is Cyc?

  • The Cyc knowledge base (KB) is a formalized representation of a vast
  • The Cyc knowledge base (KB) is a formalized representation of a vast

quantity of fundamental human knowledge: facts, rules of thumb, and heuristics for reasoning about the objects and events of everyday life. The di f t ti i th f l l C L d ib d b l medium of representation is the formal language CycL, described below. The KB consists of terms--which constitute the vocabulary of CycL--and assertions which relate those terms.

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Wh t d C k ? What does Cyc know?

http://www.cyc.com/cyc/technology/whatiscyc_dir/whatdoescycknow

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Next week…

we will talk about how to construct such knowledge bases incl bases incl. Games with a purpose and

  • ther participative forms of
  • ther participative forms of

knowledge acquisition

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http://www.peekaboom.org/

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Any further questions? y q See you next week! See you next week!

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