Recent Trends in Semantic Search Technologies
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The document discusses semantic search and provides examples of innovative semantic search applications. It describes Peter Mika as a senior research scientist at Yahoo who leads semantic search research. Thanh Tran is introduced as the CEO of Semsolute, a semantic search technologies company. The agenda outlines why semantic search is important given the rise of semantic data on the web. It then defines semantic search and discusses different types of semantic models that can be used. Examples of semantic search applications presented include entity search, factual search, relational search, semantic auto-completion, results aggregation, and conversational search. Technological components like query interpretation, ranking, aggregation, and presentation are also outlined.
Recent Trends in Semantic Search Technologies
- 1. Peter Mika| Yahoo! Research, Spain pmika@yahoo-inc.com Thanh Tran | Semsolute, Germany Tran@semsolute.com Semantic Search on the Rise
- 2. About the speakers Peter Mika Senior Research Scientist Head of Semantic Search group at Yahoo! Labs Expertise: Semantic Search, Web Object Retrieval, Natural Language Processing Tran Duc Thanh CEO of Semsolute, Semantic Search Technologies Company Served as Assistant Professor for Karlsruhe Institute of Technology and Stanford University Expertise: Semantic Search, Semantic / Linked Data Management
- 3. Agenda Why Semantic Search What is Semantic Search Innovative Semantic Search Applications Behind the Scene Questions
- 5. Why Semantic Search? I. “We are at the beginning of search.“ (Marissa Mayer) Solved large classes of queries, e.g. navigational Remaining queries are hard, not solvable by brute force, require deep understanding of the world and human cognition, e.g. Ambiguous searches: paris hilton Imprecise or overly precise searches Searches for descriptions: 34 year old computer scientist living in barcelona Background knowledge and metadata can help to address poorly solved queries Many of these queries would not be asked by users, who learned over time what search technology can and can not do.
- 6. Why Semantic Search? II. The Semantic Web is now a reality Large amounts of data published in RDF Linked Data Metadata in HTML Facebook‟s Open Graph Protocol Schema.org Casual users Don‟t know SPARQL Unaware of the schema of the data Searching data instead or in addition to searching documents Enable innovative search applications / tasks
- 7. What is Semantic Search?
- 8. Semantic Search: Using Semantic Models for Search Semantic search is a retrieval paradigm that Exploits the semantics of the data or explicit background knowledge to understand user intent and the meaning of content Incorporates the intent of the query and the meaning of content into the search process (semantic models)
- 9. Semantic Search: Different Kinds / Different Uses of Semantic Models Wide range of semantic search systems Employ different semantic models, possibly at different steps of the search process and in order to support different tasks Query formulation Query processing / understanding Ranking Result presentation Result / query refinement
- 10. Semantic models Semantics is concerned with the meaning of the resources made available for search Various representations of meaning Word-level models: models of relationships among words Taxonomies, thesauri, dictionaries of entity names Inference along linguistic relations, e.g. broader/narrower terms Concept-level models: models of relationships among objects Ontologies capture entities in the world and their relationships Inference along domain-specific relations
- 11. Graph-based Conceptual Models Core of W3C standards for knowledge representation and data exchange: RDF, OWL Large amount of data / knowledge on the Web available as graphs Linked Data: hundreds of interconnected datasets capturing domain-independent and domain-specific knowledge Metadata in HTML RDFa, microdata, Facebook‟s OGP Private graphs Google‟s Knowledge Graph Facebook Graph Yahoo‟s Knowledge Base (talk yesterday) Microsoft's Satori
- 12. Linked Data
- 13. Where can you find Linked Data? Downloads Dbpedia data dumps SPARQL access LOD cache by OpenLink: 51 billion triples Keyword search Sindice by SindiceTech
- 14. Google Knowledge Graph Start with Freebase‟s database, which had 12 million entities As of June 2012, Knowledge Graph has 500 million entities and over 3.5 billion relationships between those entities Prioritize properties based on what users were most
- 15. Facebook‟s Open Graph Protocol The „Like‟ button provides publishers with a way to promote their content on Facebook and build communities Shows up in profiles and news feed Site owners can later reach users who have liked an object Facebook Graph API allows 3rd party developers to access the data Open Graph Protocol is an RDFa-based format that allows to describe the object that the user „Likes‟
- 16. Facebook‟s Open Graph Protocol RDF vocabulary to be used in conjunction with RDFa Simplify the work of developers by restricting the freedom in RDFa Activities, Businesses, Groups, Organizations, People, Places, Products and Entertainment Only HTML <head> accepted http://opengraphprotocol.org/ <html xmlns:og="http://opengraphprotocol.org/schema/"> <head> <title>The Rock (1996)</title> <meta property="og:title" content="The Rock" /> <meta property="og:type" content="movie" /> <meta property="og:url" content="http://www.imdb.com/title/tt0117500/" /> <meta property="og:image" content="http://ia.media- imdb.com/images/rock.jpg" /> … </head> ...
- 17. Semantic Web markup: schema.org Agreement on a shared set of schemas for common types of web content Use a single format to communicate the same information to all three search engines Bing, Google, and Yahoo! (June, 2011), Yandex (Nov, 2011) Microdata and RDFa support Schemas for most common web content Business listings, images/video, recipes, reviews, products, jobs… Community public-vocabs@w3.org
- 18. Schema.org
- 19. Current state of metadata on the Web Analysis of the Bing/Yahoo! Search Crawl US crawl, January, 2012 31% of webpages, 5% of domains contain some metadata P. Mika, T. Potter. Metadata Statistics for a Large Web Corpus, LDOW 2012 WebDataCommons.org Data extracted from a public crawl (commoncrawl.org) February, 2012 results show 11% of URLs with metadata compared to 5% in 2009/2010 data 7.3 billion triples available for download H.Mühleisen, C.Bizer.Web Data Commons - Extracting Structured Data from Two Large Web Corpora, LDOW 2012 Large increase in RDFa and microdata adoption compared to microformats
- 20. Where can you find HTML metadata? Web Data Commons Glimmer: glimmer.research.yahoo.com Online index of the schema.org data in Web Data Commons
- 21. Innovative Semantic Search Applications
- 22. Innovative Semantic Search Applications Entity search: entity/entities as results Factual search: direct answers, facts (about entities) Relational search: complex relationships between entities Semantic auto-completion: suggesting queries based on the intent of the provided inputs Results aggregation / analysis / prediction: apply computational models Semantic log analysis: understanding user behavior in terms of objects Semantic profiling: recommendations based on particular interests Semantic context: contextual model of users / interests Support for complex tasks, e.g. booking a vacation using a combination of services Conversational search
- 24. Entity Search: Entity Summary
- 25. Entity Search: Entity-based Navigation / Exploration
- 26. Factual Search
- 28. Semantic auto-completion: Facebook Graph Search
- 29. Semantic Auto-completion: Semsolute‟s semantic search engine Vorlesung Knowledge Discovery - Institut AIFB Syntactic Completions Keywords Semantic Completions 2 9
- 31. Contextual (pervasive, ambient) search Yahoo! Connected TV: Widget engine embedded into the TV Yahoo! IntoNow: recognize audio and show related content
- 32. Interactive Voice Search Siri Question-Answering Variety of backend sources including Wolfram Alpha and various Yahoo! services Task completion E.g. schedule an event
- 33. Conversational Search Google‟s Interactive Voice Search
- 34. Conversational Search Parlance EU project Complex dialogs around a set of objects Restaurant Area Price range Type of cuisine Complete system Automated Speech Recognition (ASR) Spoken Language Understanding (SLU) Interaction Management Knowledge Base Natural Language Generation (NLG) Text-to-Speech (TTS) Video Commercial alternatives from Nuance
- 35. Behind the Scene
- 36. Main Technological Building Blocks Query Interpretation Spelling Correction Query Segmentation Entity Recognition Query Intent Interpretation for Semantic Auto-Completion Ranking Entity Ranking Relationship Ranking Aggregation Result Fusion Rank / Score Aggregation Result Presentation Summary Generation Visualization
- 37. Semsolute‟s Building Blocks - Keyword / Key Phrase Interpretation Entity “address company san francisco” Semantic entity index Inverted index for entities / triples Return entities / entities‟ relationships as results to keys Semantic entity ranking Structured language model: one language model for every attribute Returns entities‟ LMs that most likely generate the keywords, i.e. the entity descriptions that best match
- 38. Relationship s / Structure Entity “address company san francisco” Semsolute‟s Building Blocks – Semantic Graph Construction Offline component: query- independent schema graph Reuse schema Pseudo-schema construction: all possible connections between classes of entities, e.g. friendships between users Online component: query- specific keyword matching elements Connect keyword matching elements / entities to the classes they belong to
- 39. Relationship s / Structure Entity “address company san francisco” Semsolute‟s Building Blocks – Graph Exploration Top-k graph exploration Shortest-path based algorithm that finds top-k graphs connecting keyword matching elements Top-k graph ranking Language model based Aggregated model that combines the LMs of entities matching the keywords
- 40. Semsolute‟s Building Blocks – Query Generation & Processing TripleRelationship s / Structure Entity Address of companies located in San Francisco? “address company san francisco” Graph to query mapping Translation rules that map top ranked graphs to structured queries (SQL, SPARQL) Translation rules that map structured queries to natural language questions Graph matching Triple index: cover index supporting different triple patterns Various join implementations
- 41. Yahoo! Spark: Entity Recommendation in Search Different use cases in Web Search Some users are short on time Need direct answers Query expansion, question-answering, information boxes, rich results… Other users want to explore Long term interests such as sports, celebrities, movies and music Long running tasks such as travel planning Spark is a search assistance tool for exploration Recommend related entities given the user‟s current query Based on explicit relations in a Knowledge Base
- 43. Spark example I.
- 45. High-Level Architecture View Entity graph Data preprocessing Feature extraction Model learning Feature sources Editorial judgements Datapack Ranking model Ranking and disambiguation Entity data Features
- 46. Spark challenges Interpretation and disambiguation Obama and Toyota are places in Japan, but maybe the user is not looking for them The popularity of “obama” is not a sign of the popularity of a Japanese town Ranking “Release me” from Engelbert Humperdinck should rank higher than “Lesbian Seagull” which only appeared on the soundtrack of a Beavis and Butthead episode Editorial relevance vs. what people click Large-scale data processing and ML Knowledge Base built from Wikipedia, Yahoo! data, Web extraction Feature extraction from query logs, Flickr and Twitter data Entity graph Data preprocessing Feature extraction Model learning Feature sources Editorial judgements Datapack Ranking model Ranking and disambiguation Entity data Features
- 47. Contact Peter Mika pmika@yahoo-inc.com @pmika Tran Duc Thanh thanh.tran@semsolute.com
- 48. Resources
- 49. Resources Detailed information Peter Mika. Entity Search on the Web, Keynote at Web of Linked Entities WS Peter Mika, Thanh Tran. Semantic search tutorial SemTech2012 Books Ricardo Baeza-Yates and Berthier Ribeiro-Neto. Modern Information Retrieval. ACM Press. 2011 Survey papers Thanh Tran, Peter Mika. Survey of Semantic Search Approaches. Under submission, 2012. Conferences and workshops ISWC, ESWC, WWW, SIGIR, CIKM, SemTech Semantic Search workshop series Exploiting Semantic Annotations in Information Retrieval (ESAIR) Entity-oriented Search (EOS) workshop Web of Linked Entities (WoLE) workshop
Editor's Notes
- #4: Mobile: Google interactive voice search (conversation), Siri (Peter)Facebook’s Graph Search (Thanh)Knowledge Graph (infoboxes)... entity search (“tom cruise actor”) to list/category queries (“tom cruise spouses”) to question-answering (“tom cruise height”) (Thanh)Spark (Yahoo!): related entity recommendation (Peter)Thanh’s search engine: auto-complete based on the schema/data, entity search to relational search using Yago data (Thanh)Glimmer: RDF search engine (Peter)
- #9: Semantic search can be seen as a retrieval paradigm Centered on the use of semanticsIncorporates the semantics entailed by the query and (or) the resources into the matching process, it essentially performs semantic search.
- #18: Facebook invited, but continues to pursue OGP
- #30: We implemented the search paradigms and integrated them as separate search modules into a demonstrator system of the Information Workbench7 that has been developed as a showcase for interaction with the Web of data. In particular, keyword search is implemented according to the design and technologies employed by standard Semantic Web search engines. Like Sindice and FalconS, we use an invertedindex to store and retrieve RDF resources based on terms. Also using the inverted index, faceted search is implemented based on the techniques discussed in [25]. Result completion is based on recent work discussed for the TASTIER system [8]. For computing join graphs, we use the top-k procedure elaborated in [9]. This technique is also used for computing top-k interpretations, i.e. to support query completion. We choose to display the top-6 queries and the top-25 results respectively.