user query • A way to organize information is via structured knowledge centered around entities • Large knowledge repositories and knowledge bases have become available 7
task • For example, to plan a travel - How to get there? - Where to stay? - What to do? • Task completion would provide support for the user when accomplishing complex search tasks Motivation 15
levels, 600 types) - Freebase Types (2 levels, 2K types) - Wikipedia Categories (34 levels, 600K types) - YAGO Taxonomy (19 levels, 500K types) • These vary a lot in terms of hierarchical structure and in how entity-type assignments are recorded Type-aware Entity Retrieval 27
Retrieval task is defined in a generative probabilistic framework P(q | e) • Both query and entity are considered in the term space as well as in the type space Type-aware Entity Retrieval 29
evaluation of dimensions in utilizing entity type information - Type taxonomy - Type representation - Retrieval model • We use a strong text-based baseline • We test with the DBpedia Entity collection v2 Type-aware Entity Retrieval 33
performs best • Hierarchical relationships from ancestor types improve retrieval effectiveness, but most specific types provide the best performance • Results regarding most effective type-aware retrieval model vary across configurations Type-aware Entity Retrieval 34
target type identification • Task: to find the main target types of a query, from a type taxonomy, such that these are the most specific category of entities that are relevant to the query. If no matching type can be found in the taxonomy then the query is assigned a special NIL-type 41
information for ad-hoc entity retrieval. We build a test collection for target entity type identification, and develop and evaluate a Learning-to-Rank approach for this problem. SUMMARY 46
a entity- oriented search query - For example, the intent of booking a hotel room • Refiner: a way to express an intent in an entity-oriented query - For example, for booking a hotel room: "booking", "book", "reservation", "rooms" 49
of type-level query patterns • Pick a Freebase type if it covers 100+ prominent entities • Get query suggestions for top 1000- entities per type • For each query, replace entity by type • Aggregate all frequencies for each (type, refiner) pair • Filter out all type-level refiners with frequency of 4- • Select 50 representative types by stratified sampling 63
of intent categories - Website, Property, Service, Other => Website => Property => Service vivienne westwood age vivienne westwood instagram vivienne westwood customer care 69
type-level refiners with intent category via crowdsourcing • We observe the proportions of refiners in each category Property: 28.6% Service: 54.06% Website: 5.34% Other: 12.08% 70
compound film location event food hotel disease restaurant travel destination 0 50 100 150 200 250 university house person newspaper airport basketball player album professional sports team game artwork 0 50 100 150 200 railway human language tv station political party amusement park exhibition venue chef programming language academic institution netflix genre 0 20 40 60 80 100 120 war currency blogger hobby football match sports championship star muscle olympic sport company 0 10 20 30 40 50 WroSicaO cycOone kingdoP PedicaO sSeciaOWy coPic book SubOisher oiO fieOd Wower beer counWry region eOecWion asWeroid beOief 0 10 20 30 40 50 3roSerWy WebsiWe Service 2Wher
sydney map => [city] map 2. Categories assigned to refiners vivienne westwood instagram => Website vivienne westwood age => Property vivienne westwood customer care => Service 3. Multiple refiners expressing an intent "booking", "book", "make a reservation", "rooms" 75 A knowledge base of entity- oriented search intents
all quadruples from 581 unseen types • 155K quadruples, 31K intent profiles - Excerpt of the KB, for intent ID <aviation.airline-65-customer_service> 81
of query suggestions that cover all the possible subtasks related to the task that the user is trying to achieve. Suggesting queries to support task-based search wedding cake wedding cake gallery wedding cake recipes wedding cake flavors 99
of query suggestions that cover all the possible subtasks related to the task that the user is trying to achieve. Suggesting queries to support task-based search wedding cake wedding cake gallery wedding cake recipes wedding cake flavors • This is the task understanding problem 100
relevance q0 Keyphrases API SUGGS. WEB SNIPPETS WEB DOCS. WH Suggesting queries to support task-based search • We propose an end-to-end generative probabilistic model 108
relevance • Query suggestion • We propose an end-to-end generative probabilistic model Query suggestions q0 Keyphrases API SUGGS. WEB SNIPPETS WEB DOCS. WH Suggesting queries to support task-based search 109
Tasks track datasets for the task understanding problem • We conduct a principled estimation of the components, and analyze the best performing estimators per component Suggesting queries to support task-based search 110
and refinement modes? - Can we do it without relying on log data / API? • We consider a two-step pipeline: - Candidate generation - Candidate ranking • And focus on the first component Generating suggestion candidates 113
as it depends on API suggestions • Log data is the most useful information source, but the other sources provide valuable suggestions too • Different method-source configurations contribute unique suggestions in both modes Generating suggestion candidates 115
query suggestions. We further study alternative methods and information sources for suggestion candidate generation, and build a test collection. SUMMARY 116
a wedding reception Recommended Tasks: Plan your wedding reception exit Announce the bridal party at a reception Throw a Hawaiian wedding reception Choose wedding reception activities 120
of task descriptions - Task description: a semi-structured document that explains the steps involved in how to complete a given task - Search mission: a set of queries that all share the same underlying task 122
recommendation Given a query, to return a ranked list of tasks that correspond to the task behind the query 2. Mission-based task recommendation Given a search mission, to return a ranked list of recommended tasks, corresponding to the queries in the mission 123
as our task repository 124 How to Make a Wedding Cake Co-authored by wikiHow Staff ✔ You can make a wedding cake for a customer if you bake for a living, or you might make a cake for loved one’s wedding to help them save money. If you love to bake, then you might even want to make your own wedding cake! Steps 1 Decide on the number and shape of the cake’s layers. Consider how many layers and what shape you want the cake to have. 2 Preheat the oven to the temperature indicated by your recipe. Many recipes call for the oven to be pre-heated to 350 °F (177 °C). 3 Prepare the cake batter according to your recipe’s instructions. Choose a recipe to create the cake batter for your cake. 4 Pour the batter into a greased, parchment-lined cake pan. Spray your cake pan with non-stick cooking spray. Explanation Main Act Detailed Act Title
query-based task recommendation, that combines a text-based ranking technique with continuous semantic representations • We experiment with different word embeddings and word function sets according to POS-tag 128
dariogarigliotti
yuta0306
s_uryu
aya_se
micheda
moda0
iwiwi
atsumoto
yumulab
takeshit_m
kei18
kurita
nttcom
chriscoyier
tammyeverts
destraynor
bryan
lara
frogandcode
addyosmani
denniskardys
notwaldorf
ufuk
afnizarnur
mojombo
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