Semantic and Distributed Entity Search in the Web of Data

Transcription

1 Semantic and Distributed Entity Search in the Web of Data Robert Neumayer Norwegian University of Science and Technology Trondheim, Norway March 6, /48

2 1. Entity Search and the Web of Data The Web of Data What are Entities? 2. Centralised Entity Search Entity Modelling Experiments 3. Federated Entity Search Introduction Experimental Results 4. P2P Entity Search Introduction and Approach Experiments 5. Conclusions and Future Work Future Work Outline 2/48

3 Overview Describe the main components of the last four years of my research Try to give a good motivation and show the whole picture Show real-world examples Pointers on future work Do it in an accessible way 3/48

4 What? Semantic and Distributed Entity Search in the Web of Data Definitions (in reverse order) Web of Data Entities Entity Search Centralised or distributed? 4/48

5 The Web of Data Blog post by Tim Heath... slight disagreement Terms: Linked Data Web of Data 5/48

6 The Web of Data Blog post by Tim Heath... slight disagreement Terms: Linked Data Web of Data... Linked Data is just an attempt to rebrand the Semantic Web... 5/48

7 The Web of Data Blog post by Tim Heath... slight disagreement Terms: Linked Data Web of Data... Personally I use the term Web of data largely interchangeably with the term Semantic Web... 5/48

8 The Web of Data Blog post by Tim Heath... slight disagreement Terms: Linked Data Web of Data... The precise term I use depends on the audience. With Semantic Web geeks I say Semantic Web, with others I tend to say Web of data... 5/48

9 ... How We Use the Terms Linked Data Technical foundation means of publishing/exchanging interconnected data Web of Data / Semantic Web Largely interchangeable an interconnected Web of Data available for search and research example wikipedia connecting to other resources 6/48

10 Linked Open Data 2007 BBC Later + TOTP Musicbrainz Magnatune Sem- Web- Central FOAF SIOC NEW! Ontoworld NEW! SW Conference Corpus ECS Southampton Freshmeat Open- Guides Jamendo US Census Data Gov- Track Project Gutenberg Geonames World Factbook Wikicompany Eurostat W3C WordNet DBpedia NEW! Open Cyc flickr wrappr NEW! Revyu lingvoj DBLP Berlin DBLP Hannover RDF Book Mashup 7/48

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12 Entities 1/4 Knowledge bases are growing, so what? Something s interesting when Google do it Google Knowledge graph (2012)

13 Entities 1/4 Knowledge bases are growing, so what? Something s interesting when Google do it Google Knowledge graph (2012)

14 Entities 1/4 Knowledge bases are growing, so what? Something s interesting when Google do it Google Knowledge graph (2012) 9/48

15 What is an entity? (Typed) object Entities 2/4 10/48

16 Once identified, the entity has Attributes and relations Entities 3/4 11/48

17 Entities 4/4 Free text Date Director Relations (Links) Outgoing Ingoing 12/48

18 The Entity Search Task ad-hoc entity retrieval 1 : answering arbitrary information needs related to particular aspects of objects [entities], expressed in unconstrained natural language and resolved using a collection of structured data Our main focus Realistic and frequent type of search 1 J. Pound, P. Mika, and H. Zaragoza. Ad-hoc object retrieval in the web of data. In: Proc. of the 19th Int. Conference on World Wide Web (WWW 10) /48

19 Top Google Searches People do search for entities Persons Products Events BBB12 is big brother Brazil /48

20 Overview (Centralised) entity search Federated entity search Peer-to-peer (P2P) networks 15/48

21 Overview (Centralised) entity search Federated entity search Peer-to-peer (P2P) networks 15/48

22 Overview (Centralised) entity search Federated entity search Peer-to-peer (P2P) networks 15/48

23 Overview of Publications 1/3 (Centralised) entity search Semantic Search Challenge 3 Hierarchical Entity Model 4 Strong Baselines 5 3 K. Balog, M. Ciglan, R. Neumayer, W. Wei, and K. Nørvåg. NTNU at SemSearch In: Proc. of the 4th Int. Semantic Search Workshop of the 20th Int. World Wide Web Conference WWW2011) R. Neumayer, K. Balog, and K. Nørvåg. On the Modeling of Entities for Ad-hoc Entity Search in the Web of Data. In: Proc. of the 34rd European Conference on Information Retrieval (ECIR 12) R. Neumayer, K. Balog, and K. Nørvåg. When Simple is (more than) Good Enough: Effective Semantic Search with (almost) no Semantics. In: Proc. of the 34rd European Conference on Information Retrieval (ECIR 12) /48

24 Overview of Publications 2/3 Federated entity search Collection ranking and selection 6 Ranking Distributed Knowledge Repositories 7 6 K. Balog, R. Neumayer, and K. Nørvåg. Collection Ranking and Selection for Federated Entity Search. In: Proc. of 18th Int. Symposium of String Processing and Information Retrieval (SPIRE 12). Lecture Notes in Computer Science R. Neumayer, K. Balog, and K. Nørvåg. Ranking Distributed Knowledge Repositories. In: Proc. of the Int. Conference on Theory and Practice of Digital Libraries Research and Advanced Technology for Digital Libraries (TPDL 12). Lecture Notes in Computer Science /48

25 Overview of Publications 3/3 Peer-to-peer (P2P) networks Aggregation of Document Frequencies 8 Hybrid Aggregation in P2P Networks 9 8 R. Neumayer, C. Doulkeridis, and K. Nørvåg. Aggregation of Document Frequencies in Unstructured P2P Networks. In: Proc. of 10th Int. Conference on Web Information Systems Engineering (WISE 09). Lecture Notes in Computer Science R. Neumayer, C. Doulkeridis, and K. Nørvåg. A Hybrid Approach for Estimating Document Frequencies in Unstructured P2P Networks. In: Information Systems 36.3 (2011). 18/48

26 1. Entity Search and the Web of Data The Web of Data What are Entities? 2. Centralised Entity Search Entity Modelling Experiments 3. Federated Entity Search Introduction Experimental Results 4. P2P Entity Search Introduction and Approach Experiments 5. Conclusions and Future Work Future Work 19/48

27 Centralised Entity Search Research questions How can traditional ad-hoc document retrieval techniques be applied in the context of the Web of Data? How can the structure of entities be exploited for the purpose of ad-hoc retrieval? How does field weighting affect search quality? 20/48

28 From Predicates to Fields: Structured Retrieval How to represent entity data in terms of structured fields? Text Serenity Serenity is a... Joss Whedon United States Films based on tv series Space Westerns Film Adam Baldwin Summer Glau Jewel Staite (a) Unstructured... Pred. type Value Name Serenity Attributes Serenity is a... OutRelations Joss Whedon United States Films based on tv series Space Westerns Film Adam Baldwin Summer Glau InRelations Best 2005 sci-fi film Favourite film (b) and Structured Entity Model 21/48

29 Fields and predicates Entity Modelling Approaches Somewhere in between one field and one field per predicate We consider: Unstructured entity model Collapse all predicates Structured entity model with predicate folding Collapse within predicate types Hierarchical entity model Use individual fields Predicate type weighting 22/48

30 Structured Entity Model Collapsing all fields per type Name, Attribute, InRelation, OutRelation Smoothing on type level Linear mixture of types (mixture of LMs) e p t... p t t... t 23/48

31 Hierarchical Entity Model Type folding viable alternative Preserve info about individual predicates Use individual fields Three model components Term generation Predicate generation Predicate type generation e p t... p t p t p t 24/48

32 2010/2011 Semantic Search Challenge Given a keyword query, targeting a particular entity, provide a ranked list of relevant entities (i.e., URIs) Queries Sampled from web search engine logs (142 in total) Data collection Billion Triple Challenge 2009 (BTC) dataset About 70 million entities From sources like dbpedia.org or livejournal.com Relevance judgments On a 3-point scale, collected using crowdsourcing 25/48

33 Experimental Results Ingoing relations have a marginal effect only Structured entity model improves compared to unstructured model Hierarchical model improves, but only for individual predicate types Overall our results are competitive with the ones achieved at evaluation initiatives Preprocessing, preprocessing Collection quality 26/48

34 When Simple is Good Enough Rather straigth forward approach Three components Extended preprocessing Process entity names Fielded representation Title and content fields Domain boosting Boost DBpedia Compare state-of-the-art fielded retrieval models 27/48

35 Results Outperform all results from Semantic Search Challenge Still not outperformed by others Entity titles answer entity queries very well Extent of improvements surprising 28/48

36 1. Entity Search and the Web of Data The Web of Data What are Entities? 2. Centralised Entity Search Entity Modelling Experiments 3. Federated Entity Search Introduction Experimental Results 4. P2P Entity Search Introduction and Approach Experiments 5. Conclusions and Future Work Future Work 29/48

37 Federated Search 1/2 Moving from centralised retrieval to a distributed setting Starting from a broker, query is routed to the right collection Main research question: Can federated entity search benefit from entity modelling? 30/48

38 Federated Search Central broker 1 Collection representation Summary A Summary B Summary C Collection A 2 Collection selection 3 Result merging Q 1 A C B 2 Q Collection B Q 3 Collection C 31/48

39 Collection Representation 1/2 Collection-centric model Treat each collection as one large document Low cost, less accurate results expected 32/48

40 Collection Representation 2/2 Entity-centric model Consider each collection in terms of its entities High cost, more accurate results expected 33/48

41 Collection Selection Predefined threshold Top-k collection selection Typically 5-20 AENN: All an Entity Needs is a Name Central repository of entity names AENN collection selection Trade-off between EC and CC approaches Precision-oriented Recall-oriented Balanced 34/48

42 Result merging Once we have multiple collections selected Central broker Summary A Summary B These collections rank their respective entities... and the resultant rankings have to be merged into one final list Summary C Q 1 A C B 2 3 Q Q Collection A Collection B Collection C 35/48

43 Experimental Setup Distributed environment Top 100 largest second-level domains from BTC Three sets with different handling of DBpedia Relevance Metrics Considered the #relevant entities from each collection Collection ranking and result merging: Standard IR metrics (MAP, MRR, ndcg) Collection selection: Analogues of precision and recall, plus the avg. #coll. selected 36/48

44 Experimental Results CC and EC methods are competitive Content-based methods stronger Small difference for the DBpedia-only collection AENN outperforms other title-only methods AENN has positive effects on collection selection 37/48

45 1. Entity Search and the Web of Data The Web of Data What are Entities? 2. Centralised Entity Search Entity Modelling Experiments 3. Federated Entity Search Introduction Experimental Results 4. P2P Entity Search Introduction and Approach Experiments 5. Conclusions and Future Work Future Work 38/48

46 P2P Search A query can originate from every peer and has to be routed via possibly many others Research questions: Is P2P search a viable alternative to broker-based (i.e., federated search) architectures for entity retrieval? How can the proposed frequency estimation technique be further improved? 39/48

47 Text documents, terms, and distribution Many problems are caused by distributed collections What is distributed and how? random is easy Local / global document frequencies Different numbers of documents per node Local importance and influence of collections Global information improves search results How frequent is a term on the global level? 40/48

48 We employ DESENT for P2P network creation Completely distributed and decentralised Hierarchical overlay generation Individual peers Zones formed by neighbouring peers Super zones based previous level DESENT 41/48

49 Local Term Selection Process Based on local peer s knowledge only Considers local terms and their frequencies Problems Number of documents per peer Document frequencies are unstable Local / global importance issues 42/48

50 Compare to central case Full info Central case without term info Lucene scoring Aggregated values score in between Portable to LM and entity use-case 43/48

51 1. Entity Search and the Web of Data The Web of Data What are Entities? 2. Centralised Entity Search Entity Modelling Experiments 3. Federated Entity Search Introduction Experimental Results 4. P2P Entity Search Introduction and Approach Experiments 5. Conclusions and Future Work Future Work 44/48

52 Summary of Contributions Analysis of retrieval models wrt. their applicability to entity search Hierarchical models Structured retrieval for entity search Formalisation of federated search task in a language model framework AENN method Benchmark data sets for federated entity search Entity search in P2P contexts 45/48

53 Query Target Type Identification Queries often target specific types (e.g. cars, actors,... ) Sub problem: DBPedia ontology target type identification What is a query s type? Ontology linking How to exploit this info? See CIKM 12 poster, partly INEX 12 submission 46/48

54 Query to Field/Predicate Mapping Which field/predicate best answers a query? Simple example: IMDB field:actor field:director field:trivia Example query: Clint Eastwood What is the best field to answer the query? What is the best field to answer the individual query terms? What results are we looking for (actor/director)? 47/48

55 Last Slide Three basic purposes of oral presentations (in the spirit of trusting Wikipedia 10 ) Inform Persuade Good will I tried to do all of these things! Thanks for help and support /48