A Solid Foundation of A Solid Foundation of Semantic Computing Semantic Computing toward Web Intelligence toward Web Intelligence Mitsuru Ishizuka Mitsuru Ishizuka School of Information Science and Technology School of Information Science and Technology
New Tech. Committee on Semantic New Tech. Committee on Semantic Computing in IEEE Computer Soc. Computing in IEEE Computer Soc. 2
Semantic Technology Conf. Semantic Technology Conf. June 2010, San Francisco June 2010, San Francisco 3
Semantic Computing Semantic Computing � Toward Semantic-level Content Utilization by computers, beyond its surface-level processing. In many domains: Applications: natural language texts, semantic annotation to contents, image and video, semantic computing of textual documents, audio and speech, semantic software engineering, semi-structured data, semantic search engine, behavior of software semantic multimedia services, and network, context-aware devices and services, data and web mining, semantic GIS system, etc. semantic interfaces, semantic trusted computers, etc. 4
Semantic Computing at present Semantic Computing at present � Increasing interests in many domains. � Most technologies are partial and ad hoc at present. � We need a solid foundation of semantic computing. --------------------------------- � Natural language plays a major role to express and convey the semantic meaning. It should thus becomes the first focus and the core of the semantic computing � We need a common and universal language that computers and human can understand, to represent concept meaning at a certain level. 5
CDL (Concept Description Language) CDL (Concept Description Language) as a solid core of semantic computing as a solid core of semantic computing The aims of CDL are 1) to realize machine understandability of Web text contents, and 2) to overcome language barrier on the Web. 6
Major Differences from Semantic Web Major Differences from Semantic Web Semantic Web Semantic Computing Semantic Web Semantic Computing based on CDL based on CDL Target of representation: � � Target of representation: Meta-data extracted from Semantic concepts expressed in Web contents. texts. Domain-dependent � � Universal vocabulary (+ ontologies (which cause the additional specific vocabulary difficulty of wide inter- in a domain if necessary), and boundary usage) pre-defined relation set. RDF / OWL (description � � CDL.nl (richer than RDF) logic is hard for ordinary people to understand) Main body: Tim Berners-Lee says that: Institute of Semantic Computing (ISeC) Institute of Semantic Computing (ISeC) “Data Web” or “Linked Data” is more in Japan in Japan adequate rather than “ the Semantic Web”. Int’l Standardization Activity: (2007) W3C Common Web Language(CWL)- -XG XG W3C Common Web Language(CWL) 7
Incubator Group Activity at W3C Incubator Group Activity at W3C from Oct. 2006 to May 2008 2008 from Oct. 2006 to May 8
9 nd Incubator Group at W3C Incubator Group at W3C from June 2008 from June 2008 2 nd 2
From Machine Translation From Machine Translation English Japanese Chinese Transfer method Pivot Pivot UNL UNL (Universal CDL (Concept CDL (Universal (Concept Pivot Networking Language) Description Language) Language Networking Language) Description Language) Language method Standardization in W3C CWL (Common Web CWL (Common Web Language) Language) 10
CDL Representation CDL Representation Text example: � “John reported to Alice that he bought a computer yesterday.” CDL graph notation: � Event#A01 tmp = ‘past’ agt Event#B01 tmp = ‘past’ John# agt report#a01 obj buy#b01 gol obj computer#b02 ral = = ‘ ‘def’ ’ tim Alice# yesterday#b03 Green: node Blue: hyper-node 11
CDL Representation CDL Representation Text example: � “John reported to Alice that he bought a computer yesterday.” CDL text notation: � {#A01 Event tmp=‘past’; {#B01 Event tmp=‘past’; <#b01:buy;> <#b02:computer ral=‘def’;> <#b03:yesterday;> [#b01 agt #John] [#b01 obj #b02] [#b01 tim #b03] } <#John:John;> <#Alice:Alice;> <#a01:report;> [#a01 agt #John] Orange: entity [#a01 gol #Alice] Blue: relation [#a01 obj #B01] } 12
CDL (UNL) Relations – – 44 labels 44 labels CDL (UNL) Relations Restrictive Semantic Roles Logical Intra-Event Inter-Entity Restrictive [Agent Relations] [Instrument Relations] [Logical Relations] cnt (content, namely) agt (agent) ins (instrument) and (conjunction) fmt (range, from-to) cag (co-agent) met (method, means) orr (disjunction, alternative) fmr (origin) aoj (thing w/ attribute) [State Relations] [Concept Relations] mod (modification) cao (co-thing w/ attribute) src (source, initial state) equ (equivalent) nam (name) ptn (partner) gol (goal, final state) icl (included) per (proportion, rate) [Object Relations] via (interm. place or state) iof (an instance of) pof (part of) obj (affected thing) [Time Relations] Intra- and Inter-Event pos (possessor) cob (affected co-thing) tim (time) [Cause Relations] qua (quantity) opl (affected place) tmf (initial time) con (condition) tto (destination) ben (beneficiary) tmt (final time) pur (purpose, objective) [Place Relations] dur (duration) rsn (reason) plc (place) [Manner Relations] [Sequence Relations] plf (initial place) man (manner) coo (co-occurence) plt (final place) bas (basis for a standard) seq (sequence) scn (scene) Discourse 13
Semantic Role Labels in PropBank Semantic Role Labels in PropBank The focus is on Predicate-Argument Structure. Arg0 (prototypical agent) � Arg1 (prototypical patient) � These are defined wrt Arg2 (indirect object/benefactive/instrument/attribute/end state) � each word sense. Arg3 (start point/benefactive/instrument/attribute) � Ex) buy :: Arg4 (end point) � Arg0 : buyer Arg5 ( ) � Arg1 : thing bought TMP (time) � Arg2 : seller (bought-from) LOC (location) � Arg3 : price paid DIR (direction) � Arg4 : benefactive (bought-for) MNR (manner) � PRP (purpose) � CAU (cause) � This set is not sufficient for representing every MOD (modal verb) � concept expressed in natural language texts. NEG (negative marker) � It cannot be used for every language due to its ADV (general-purpose modifier) � language (English) dependency. DIS (discourse particle and clause) � PRD (secondary predication) � 14
Rich Attributes in UNL and CDL Rich Attributes in UNL and CDL � Express subjectivity evaluation of the writer/speaker for the sentence. � Ex.) tense, aspect, mood, etc. Writer’s feeling and judgements � Time with respect to writer � @ability @get-benefit @give-benefit @past @present @future @conclusion @consequence @sufficient @grant Writer’s view on aspect of event � @grant-not @although @discontented @expectation @wish @begin @complete @continue @custom @insistence @intention @want @will @need @end @experience @progress @repeat @state @obligation @obligation-not @should Writer’s view of reference � @unavoidable @certain @inevitable @may @generic @def @indef @not @ordinal @possible @probable @rare @regret @unreal Writer’s view of emphasis, focus � @admire @blame @contempt @regret and topic @surprised @troublesome Describing logical characters and � @emphasis @entry @qfocus @theme @title @topic properties of concepts Writer’s attitudes @transitive @symmetric @identifiable � @disjoint @affirmative @confirmation @exclamation @imperative @interrogative @invitation Modifying attribute on aspect � @politeness @respect @vocative @just @soon @yet @not Writer’s view of reference � Attribute for convention � @generic @def @indef @not @ordinal @passive @pl @angle_bracket @brace @double_parenthesis @double_quote @parenthesis @single_quote @square_bracket 15
The defining method of one unique The defining method of one unique sense of a word in UW UW sense of a word in （ Patent of UN Univ. ） （ Patent of UN Univ. ） � Defining category swallow(icl>bird) the bird “One swallow does not make a summer” swallow(icl>action) the action of swallowing “at one swallow” swallow(icl>quantity) the quantity “take a swallow of water” � Defining possible case relations spring(agt>thing,obj>wood) bending or dividing something spring(agt>thing,obj>mine)) blasting something spring(agt>thing,obj>person, escaping (from) prison src>prison)) spring(agt>thing,gol>place) jumping up “to spring up” spring(agt>thing,gol>thing) jumping on “to spring on” spring(obj>liquid) gushing out “to spring out” 16
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