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![Mo%va%on Saracevic[1997]: “ The success or failure of any interac%ve system and technology is con%ngent on the extent to which user issues, the human factors, are addressed right from the beginning to the very end…..” Peter Mika[2009]: “Considering the informa%on needs of end users is cri%cal to the success of Seman%c Search” !](https://image.slidesharecdn.com/identifyinginformationneedsbymodellingcollectivequerypatterns-111023091103-phpapp01/75/Identifying-Information-Needs-by-Modelling-Collective-Query-Patterns-4-2048.jpg)
![Related Work Analysis for the Web of Documents • Studying the search behavior of Web users [Silverstein et al. (1999), Jansen and Spink (2005), Jansen et al. (2005) and Spink et al. (2002)]. • Improving the search experience of Web users: -‐ Query Recommenda(ons [Baeza-‐Yates et al. (2004) and Wen et al. (2001)] -‐ Query Expansion [Cui et al. (2002a)]](https://image.slidesharecdn.com/identifyinginformationneedsbymodellingcollectivequerypatterns-111023091103-phpapp01/75/Identifying-Information-Needs-by-Modelling-Collective-Query-Patterns-9-2048.jpg)
![Related Work (Cont’d) Analysis for the Web of Data • Moller et al. [10] iden%fied pa>erns of Linked Data usage with respect to different types of agents. • Arias et al. [1] analyzed the structure of the SPARQL queries to iden(fy most frequent language elements. • Kirchberg et al. [8] introduced a new no%on of ‘relevance of a LD resource’ as the ‘rela%onship between traffic and the resource and whether it changes over %me windows’](https://image.slidesharecdn.com/identifyinginformationneedsbymodellingcollectivequerypatterns-111023091103-phpapp01/75/Identifying-Information-Needs-by-Modelling-Collective-Query-Patterns-10-2048.jpg)
Identifying Information Needs by Modelling Collective Query Patterns
- 1. Iden%fying Informa%on Needs by Modelling Collec%ve Query Pa:erns K.Elbedweihy, S. Mazumdar, A.E. Cano, S.N. Wrigley, F.Ciravegna OAK Research Group, Department of Computer Science, University of Sheffield
- 2. Informa%on Needs Informa(on needs “the set of concepts and proper%es users refer to while using SPARQL queries.”
- 3. Informa%on Needs (Cont’d) ! ! PREFIX dbo: <http://dbpedia.org/ontology/> SELECT ?manufacturer WHERE {! <http://dbpedia.org/resource/Acura_ZDX> ! query !dbo:manufacturer ?manufacturer. !}! ! • User’s informa%on needs: type concept: “h:p://dbpedia.org.../Automobile” property: “dbo:manufacturer”
- 4. Mo%va%on Saracevic[1997]: “ The success or failure of any interac%ve system and technology is con%ngent on the extent to which user issues, the human factors, are addressed right from the beginning to the very end…..” Peter Mika[2009]: “Considering the informa%on needs of end users is cri%cal to the success of Seman%c Search” !
- 5. Mo%va%on understand how to use logs of queries iden%fy informa%on needs consume such analysis be:er understanding and insight into the data usage !
- 6. Outline • Introduc%on • Related Work • Approach -‐ Formalising Query Logs -‐ Analysing Query Logs -‐ Consuming Query Log Analyses • Dataset & Findings
- 7. Introduc%on Moseley Audio Scrobbler LOV Linked User Feedback Slideshare 2RDF tags2con delicious Bricklink Sussex 295 Dataset 31 billion RDF triples Folk (DBTune) Reading St. GTAA Magna- Lists Andrews Klapp- tune stuhl- Resource NTU DB club Lists Resource Tropes Lotico Semantic yovisto John Music Man- Lists “September 2011” Music Tweet chester Hellenic Peel Brainz NDL (DBTune) (Data Brainz Reading subjects FBD (zitgist) Lists Open EUTC Incubator) Linked Hellenic Library Open t4gm Produc- Crunch- PD Surge RDF info tions Discogs base Library Radio Ontos Source Code Crime ohloh Plymouth (Talis) (Data News LEM Ecosystem Reading RAMEAU Reports business Incubator) Crime data.gov. Portal Linked Data Lists SH UK Music Jamendo (En- uk Brainz (DBtune) LinkedL Ox AKTing) FanHubz gnoss ntnusc (DBTune) SSW CCN Points Thesau- Last.FM Poké- Thesaur Popula- artists pédia Didactal us rus W LIBRIS tion (En- (DBTune) Last.FM ia theses. LCSH Rådata reegle research patents MARC AKTing) (rdfize) my fr nå! data.gov. data.go Codes Ren. NHS uk v.uk Good- Experi- Classical List Energy (En- win flickr ment (DB Pokedex Family Norwe- Genera- AKTing) Mortality BBC wrappr Sudoc PSH Tune) gian (En- tors Program MeSH AKTing) semantic mes BBC IdRef GND CO2 educatio OpenEI web.org SW Energy Sudoc ndlna Emission n.data.g Music Dog VIAF EEA (En- Chronic- Linked (En- ov.uk Portu- Food UB AKTing) ling Event MDB AKTing) guese Mann- Europeana BBC America Media DBpedia Calames heim Ord- Recht- Wildlife Deutsche Open Revyu DDC Openly spraak. Finder Bio- lobid Election nance legislation Local nl RDF graphie Resources NSZL Swedish Data Survey Tele- data Ulm EU New Book Project data.gov.uk graphis bnf.fr Catalog Open Insti- York URI Open Mashup Cultural tutions Times Greek P20 UK Post- Burner Calais Heritage codes DBpedia ECS Wiki statistics lobid GovWILD data.gov. Taxon iServe South- Organi- LOIUS BNB Brazilian uk Concept ECS ampton sations Geo World OS BibBase STW GESIS Poli- ESD South- ECS Names Fact- (RKB ticians stan- reference ampton data.gov.uk book Freebase Explorer) Budapest dards data.gov. NASA EPrints uk intervals Project OAI Lichfield transport (Data DBpedia data Guten- Pisa Spen- data.gov. Incu- dcs RESEX Scholaro- ISTAT ding bator) Fishes berg DBLP DBLP uk Geo meter Immi- Scotland of Texas (FU (L3S) Pupils & Uberblic DBLP gration Species Berlin) IRIT Exams Euro- dbpedia data- (RKB London TCM ACM stat lite open- Explorer) NVD Gazette (FUB) Gene IBM Traffic Geo ac-uk Scotland TWC LOGD Eurostat Daily DIT Linked UN/ Data UMBEL Med ERA Data LOCODE DEPLOY Gov.ie CORDIS YAGO New- lingvoj Disea- (RKB some SIDER RAE2001 castle LOCAH CORDIS Explorer) Linked Eurécom Eurostat Drug CiteSeer Roma (FUB) Sensor Data GovTrack (Ontology (Kno.e.sis) Open Bank Pfam Course- Central) riese Enipedia Cyc Lexvo LinkedCT ware Linked PDB UniProt VIVO EURES EDGAR dotAC US SEC Indiana ePrints IEEE (Ontology totl.net (rdfabout) Central) WordNet RISKS (VUA) Taxono UniProt US Census EUNIS Twarql HGNC Semantic Cornetto (Bio2RDF) (rdfabout) my VIVO FTS XBRL PRO- ProDom STITCH Cornell LAAS SITE KISTI NSF Scotland Geo- GeoWord LODE graphy Net WordNet WordNet JISC (W3C) (RKB Climbing Linked Affy- KEGG SMC Explorer) SISVU Pub VIVO UF Piedmont GeoData metrix Drug ECCO- Finnish Journals PubMed Gene SGD Chem Munici- Accomo- El AGROV Ontology TCP Media dations Alpine bible palities Viajero OC Ski ontology Tourism KEGG Ocean Austria Enzyme PBAC Geographic Metoffice GEMET ChEMBL Italian Drilling OMIM KEGG Weather Open public Codices AEMET Linked MGI Pathway schools Forecasts Data Open InterPro GeneID Publications EARTh Thesau- KEGG Turismo rus Colors Reaction de Zaragoza Product Smart KEGG User-generated content Weather DB Link Medi Glycan Janus Stations Product Care KEGG AMP UniParc UniRef UniSTS Government Types Italian Homolo Com- Yahoo! Airports Museums pound Ontology Google Gene Geo Art Planet National wrapper Chem2 Cross-domain Radio- Bio2RDF activity UniPath JP Sears Open Linked OGOLOD way Life sciences Corpo- Amster- Reactome dam medu- Open rates Numbers Museum cator As of September 2011
- 8. Introduc%on Semantic Query Logs
- 9. Related Work Analysis for the Web of Documents • Studying the search behavior of Web users [Silverstein et al. (1999), Jansen and Spink (2005), Jansen et al. (2005) and Spink et al. (2002)]. • Improving the search experience of Web users: -‐ Query Recommenda(ons [Baeza-‐Yates et al. (2004) and Wen et al. (2001)] -‐ Query Expansion [Cui et al. (2002a)]
- 10. Related Work (Cont’d) Analysis for the Web of Data • Moller et al. [10] iden%fied pa>erns of Linked Data usage with respect to different types of agents. • Arias et al. [1] analyzed the structure of the SPARQL queries to iden(fy most frequent language elements. • Kirchberg et al. [8] introduced a new no%on of ‘relevance of a LD resource’ as the ‘rela%onship between traffic and the resource and whether it changes over %me windows’
- 11. Related Work (Cont’d) How our work is different: Our focus is on iden%fying informa%on needs by modelling query pa5erns of Linked Data users. approach to formalize seman%c query log analysis set of methods for extrac%ng pa:erns in the query logs visualiza%on of informa%on needs
- 12.
- 13. Formalizing Query Logs • Proposed ontology ‘Qlog’ used to represent the main concepts and rela%ons extracted from a query log entry. • A log entry follows the Combined Log Format (CLF):
- 14. Qlog Ontology Log Entry Concepts Query Logs Analysis Concepts
- 15.
- 16. Consuming Query Logs Analysis • How to consume the query logs analysis? -‐ Automa%c query sugges%ons -‐ Recommender systems -‐ Search tools (disambigua%on and ranking results) -‐ Visualiza%ons (to gain understanding of dataset usage) 1. Concept Graph 2. Predicate sequence tree
- 17. Consuming Query Logs Analysis: Visualiza%ons Concept Graph Predicate sequence tree
- 18. Steps for Consuming Query Logs Analysis Identify Gather Build Render Instance Class Vis Vis Types Size Tables Query Logs A1 A2 A3 A4 Knowledge Base Identify Build Build Render KB Predicate Transition Vis Vis Sequence Matrix Tables B1 B2 B3 B4
- 19.
- 20. Dataset • The data used in this study is made available by the USEWOD2011 data challenge. • The logs contained around 5 million queries issued to DBpedia over a %me period of almost 4 months. Number of analyzed queries 4951803 Number of unique triple pa:erns 2641098 Number of unique subjects 1168945 Number of unique predicates 2003 Number of unique objects 196221 Number of unique vocabularies 323
- 21. Analyzing Dbpedia usage pa:erns
- 22. Analyzing Dbpedia usage pa:erns (Cont’d)
- 23. Ques%ons Ques%ons? !