Semantic Information Access Atilla ELÇİ Dept of Computer

Semantic Information Access Atilla ELÇİ Dept. of Computer Engineering Eastern Mediterranean University 14/05/'07 upd 22/04/08 Cmp. E 588 Spring 2008 EMU 1

Semantic Info Access Shortcomings of common-place Web and search technology p Applications of Sem. Tech to knowledge access: p n n n p Semantic search and browse tools Natural language generation Device independence. Davies et al. Ch. 8. 14/05/'07 upd 22/04/08 Cmp. E 588 Spring 2008 EMU 2

Shortcomings of Current Web and Search Technology p Query construction: n n n p Lack of semantics: n n p Missing the context to disambiguate the user’s query. Presentation of results: n p Inability to handle synonymy & polisemy Missed semantic links Lack of context: n p Syntactic units such as keywords/terms are used. Polysemy: multiple meanings Query ambiguity: # of keywords used per query (circa 2000): 2. 2! Often too many results Managing heterogeneity: n Providing a coherent view of diverse sources and types of information: very difficult and unsatisfactory at the best. Lots of data but lacking information! High recall but low precision! 14/05/'07 upd 22/04/08 Cmp. E 588 Spring 2008 EMU 3

Advantages of Semantic Web Technology p Resolving shortcomings of the current Web and search engines by: n n Exploiting machine-processable metadata Using ontological concepts to define queries Using semantic relations in defining queries Providing information not simply data Future search engines must adapt to “information-centric” approach rather than document-centric one in order to seek: n n Relevant sections not simply documents Digest of info from several docs/sections 14/05/'07 upd 22/04/08 Cmp. E 588 Spring 2008 EMU 4

Semantic Search and Browse Tools p Searching the XML: n n n p Searching semantic data: RDF: n p Quiz. XML: XML-aware search; produces index map of keywords vs tags XSearch semantic search engine: provides semantically-relevant document fragments in response to a query (tags & keywords). XRANK XML search engine: query term is matched against document content & document markup. Extends Google Page Rank Algorithm. Quiz. RDF: free-text search & RDF annotation search. Provides searching browsing. Exploiting domain-specific knowledge: n Rocha et al. ’s Search Arch: p p n n Spead activation: keyword-based document search, and Using domain-specific semantic model Guha et al. ’s ABS (activity-based search): people, places, events, news items. Combines searching conventşional search engine and enhancing findings through semantic knowledge base (RDF annotation) Popov et al. ’s KIM (knowledge and information management) infrastructure, in order to enhance search: p p Exploits ontological knowledge base Provides automated semantic annotation method 14/05/'07 upd 22/04/08 Cmp. E 588 Spring 2008 EMU 5

Semantic Search and Browse Tools p Searching for Semantic Web resources: n n p p p Wikipedia entry: Semantic Search “attempts to augment and improve traditional Research Searches by leveraging XML and RDF data from semantic networks to disambiguate semantic search queries and web text in order to increase relevancy of results. ” Study Google’s search algorithm if you can find it. Take a look at the Alexa Web Search Platform search engine. Take a look at Web. Crawler. com paradigm: metasearch engine of search engines. At last, a semantic search engine: See Hakia. com: n n p p Swoogle Semantic Web Search Engine: to find ontologies and related instance data on the Web. Other ontology search engines? Mostly for RDF (instance data). A Turkish initiative but in the USA. Check Partners, one of them is KVK. Just raised another US$5 M totalling so far US$18 M. Another one: Power. Set. Another recent one: Ask. Me. Now. 14/05/'07 upd 22/04/08 Cmp. E 588 Spring 2008 EMU 6

Semantic Search & Browse Tools p (cont’d) Semantic Browsing: n n n Magpie: plug-in that adds an ontology-based semantic layer onto the web pages as they are browsed. CS AKTive. Space: web application for UK CS research domain. Haystack: a browser for semweb info agregating and visualizing RDF data from multiple arbitrary locations. W 3 C’s Annotea Project & Amaya Yuce’s Site Insight. 14/05/'07 upd 22/04/08 Cmp. E 588 Spring 2008 EMU 7

Natural Language Generation (NLG) from Ontologies Def. : taking structured data in a knowledge base as input and producing natural language text, tailored to the presentational context and the target reader. p Taxonomy / ontology verbalizers: n n n p Template-based or text-generator-based Takes advantage of tax/ont hierarchy, user history, and available semantic annotation in KB. Exs. : Wilcock’s general purpose verbalizer, Ontogeneration Project Summarizers: n n n Ontosum using RDF triples Miakt: domain- & ontology-specific Approach: p p Verbalize based on discourse schema: active-action, passiveaction, attribute, part-whole Semantic agregation 14/05/'07 upd 22/04/08 Cmp. E 588 Spring 2008 EMU 8

Device Independence at Presentation Layer p Skim through Sect. 8. 4 14/05/'07 upd 22/04/08 Cmp. E 588 Spring 2008 EMU 9

Advanced Semantic Querying p Leveraging the Expressivity of Grounded Conjunctive Query Languages n n By Alissa Kaplunova, Ralf Möller and Michael Wessel PDF (325. 7 KB) 14/05/'07 upd 01/06/08 Cmp. E 588 Spring 2008 EMU 10

Conferences p ESWC-08 Workshop on Semantic Search : n n Workshop pf ESWC 2008, 1 -5 June 2008, Tenerife, Spain. In recent years we have witnessed tremendous interest and substantial economic exploitation of search technologies. On the other hand semantic repositories and reasoning engines have advanced to a state where querying and processing of this knowledge can scale to realistic IR scenarios. As such, semantic technologies are now in a state to provide significant contributions to IR problems. This workshop intends to investigate the potential and the challenges of Semantic Search systems. Main topics of interest of the workshop cluster around the areas: p p Tasks and Interaction Paradigms for Semantic Search, Query Construction and Resource Modelling for Semantic Search, Algorithms and Infrastructures for Semantic Search, and Evaluation of Semantic Search. 14/05/'07 upd 22/04/08 Cmp. E 588 Spring 2008 EMU 11

Commercial Conferences p Data Modeling Seminars and Workshops by Wilshire Conferences: n Designing and Building Ontologies: n DAMA Symposium + Wilshire Meta-Data Conference 2007 Semantic Technology Conference n An ontology is a formal description of the meaning of the information stored in a system. It resembles a conceptual model, but goes much beyond a conceptual model in that the formal definitions allow the system to infer class membership based on properties. Additionally, inference engines, running on ontologies, allow users to extract and integrate information stored in distributed systems. This workshop, which will contain a number of live demos and student exercises, will cover practical issues in employing ontologies. A 4 -DAY Seminar with Dave Mc. Comb and Simon Robe, $1795. 14/05/'07 upd 22/04/08 Cmp. E 588 Spring 2008 EMU 12

References John Davies, Rudi Studer, Paul Warren (Editors): Semantic Web Technologies: Trends and Research in Ontology-based Systems, John Wiley & Sons (July 11, 2006). ISBN: 0470025964. Ch. 8. : pp. 139 -169. p W 3 C Semantic Web Tools Wiki page: p n Check Jena, Sem. Web, Protégé, Swoop, etc. 14/05/'07 upd 22/04/08 Cmp. E 588 Spring 2008 EMU 13