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![30
Facet
Ratios
=>
Keyword
Clustering
Method
to
my
Madness:
•
Tokenize
text
with
auto
phrasing,
stop
words
and
synonyms
-‐
store
tokens
in
a
multi-‐valued
field
with
DocValues
-‐
(yes
you
can
facet
on
a
text
field
but
it
tends
to
hit
a
wall
-‐
2M
word
limit
on
facet
values)
•
Using
the
/terms
handler,
get
each
term
in
the
text
field.
•
Submit
two
queries
-‐
one
with
text_field:[term]
(positive
Q)
-‐
one
with
-‐text_field:[term]
(negative
Q)
•
Calculate
the
following
ratio:
•
Take
the
xlog(x)
of
this
ratio
(for
better
discrimination)
-‐for
each
term,
take
the
best
related
terms
above
some
threshold
Facet
counts
(posizve
Q)
————————————
Total
counts
(posizve
Q)
———————————————
Facet
counts
(negazve
Q)
————————————-‐
Total
counts
(negazve
Q)](https://image.slidesharecdn.com/tedsullivanrev-2017-171004181047/75/A-Multifaceted-Look-At-Faceting-Ted-Sullivan-Lucidworks-30-2048.jpg)
Uploaded byLucidworks
A Multifaceted Look At Faceting - Ted Sullivan, Lucidworks
This document discusses using facets in Solr to facilitate relevant search. It provides an overview of facet history and how facets represent metadata that provides context about search results. Facets can be used for visualization, analytics, and understanding language semantics from text. The document argues that facets are dynamic context discovery tools that can be leveraged to find similar items and enhance search in various ways such as query autofiltering, typeahead suggestions, and text analytics.
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A Multifaceted Look At Faceting - Ted Sullivan, Lucidworks
- 1. A Multifaceted LookAt Faceting - Using Facets “Under the Hood" to Facilitate Relevant Search Ted Sullivan Senior Solutions Architect, Lucidworks Professional Services
- 2. Agenda • Facet history- why solr faceting rocks • Text - unstructured? I hardly think so! • Using facets as context mining engines
- 3. 3 01 Facets are Metadata Facet:“A particular aspect or feature of something.” Metadata: ”Data about data" - attributes, aspects, descriptors, features, properties, traits Facets == Metadata Metadata Semantics: what, where, when, why name, size, shape, color, material, texture manufacturer, number of outlets, voltage, is pre-assembled … address, phone number, birth date, user rating hand size, likes country music, believes in climate change … Metadata Dependencies: Some metadata fields depend on “what" the “thing” is e.g. People have different attributes than Toaster Ovens
- 4. 4 01 A Little History: Facet Technology Terminology Verity K2 Parametric Search Fast ESP Navigators MS Fast Refiners Endeca Dimensions Solr Facets Google (& Autonomy too) Facets? Facets? We don’t need no stinkin’ facets!
- 5. 5 Traditional Uses of Facets Faceted Navigation - aka “Refinement” / “Drill down” Allows initial query to be ambiguous without requiring the user to “rethink” what to search for. Neatly handles the old “No Results Found” trial-and-error bug-a-boo. Down sides: Facets should not be used as a ‘band-aid’ for poorly tuned relevance!!! •The “need” for faceted navigation forces us to favor recall over precision. (Maybe this is why Google avoids them!) •… because you have to drill-in to something. If users have to use facets to drill in to what they really want - Why search in the first place - why not just browse? Facet 'noise' - false positives due to poor precision / high recall causes weird outliers (ML techniques like Signal Aggregation to improve relevance do not help here)
- 6. 6 Visualization Facets show ahigh-level or global 'context' of what the result set is “about" Dashboards - Search Driven BI: Eye Candy: Pie charts, bar charts, histograms, etc. - make use of the basic statistical nature of facets (i.e. counts - but now lots of things mean, median, std, skewness, etc). Data Analytics: Solr now enhances facet statistics to include many more useful mathematical calculations. Use basic analytics such as mean, standard deviation and the like to do more complex analytics similar to what is done with Databases in OLAP “cubes”. Time-series: Range Facets on a Date-Time (Trie)Field Advantage - analytics are search driven so that the “cube” can change with the query Facet Analytics -‐ Maturing Rapidly
- 7. 7 Solr Facets are Dynamic not Static In other search engines like Verity, Fast or Endeca: Facet values are computed at index time - thereby making them Static at query time. Lucene did not have faceting originally -Solr added faceting “on the fly” - i.e. at query time (before Lucene added it) -Solr faceting is thus Dynamic! The main hurdle to doing it this way is to make it FAST (mission accomplished - thanks Yonik!) Although this would seem to be what we engineers call a "bolt on" - in hindsight this was a very fortuitous evolutionary path!! Once you do this, there are serious advantages over index-time faceting!! One main advantage is flexibility -In Solr - you can facet on just about anything - even things that weren’t thought about when the collection was designed (function queries - extensible with ValueSource impls!) -Good, good, good!!!
- 8. 8 Very Brief Survey of Solr Faceting Methods Metadata fields -‐ prefer non-‐tokenized field types (you can facet on tokenized fields too -‐ but why would you want to?) enum method => filter cached filter queries fc method => uses the FieldCache ( it now uses DocValues) facet query facet prefix facet range function queries and Value Sources pivot facets excluding and tagging JSON Facets Facet performance tuning (gotchas) -‐ I could talk about this at length but … nah! Read the Wikis!
- 9. 9 Language Semantics Nouns, Verbs, Adjectives, Adverbs, Prepositions, etc. What type of thing is Is? Car What it’s name? Lamborghini Aventador Where is it Made? Italy How much horsepower does it have? A hellova lot How fast does it go? Very How much does it cost? If you have to ask … My Search Philosophy: Humans use language to search because that is how we reason about things. Search engines need to do a better job of understanding language to better help us to find the things we are looking for. Search index schemas describe a machine oriented / data-‐centric view of things -‐ want to translate that to and from language-‐centric views -‐ from a search engine perspective -‐ descriptive text is “unstructured” data -‐ but not to us!
- 10. 10 Metadata and Text Transforms Metadata: Data-‐centric view of things <=> Text: Language-‐centric view of things Metadata terms are embedded in language Compose descriptive text about a thing from its attributes or properties -‐> Create linguistic expressions from metadata Deduce attributes or properties of a thing from descriptive text -‐> Compute metadata by linguistic analyses of text Search problem -‐ match terms in query with things in index -‐> Knowledge of word meanings is power!!! -‐> Facet metadata constitutes knowledge that can be leveraged!!
- 11. FACETS ARE CONTEXTDISCOVERY TOOLS Lemma 1: Similar things occur in similar contexts Lemma 2: Facets are context exploration tools Assertion: Facets can be used to find similar things
- 12. 12 Exploiting Facet Metadata Facets provide a sort of global metadata CONTEXT for a search result set In addition to faceting, how can we exploit metadata to enhance search? ❖ Turning facet metadata inside-‐out: Query Autofiltering ❖ Using Facets to build contextual typeahead suggester: •Pivot facets to construct phrases from structured data. •Extract related information using facets at index time to enable security trimming and dynamic boosting ❖ Using Facets for text analytics to generate better facets •Facet ratios of positive and negative queries on key terms -‐> detects “key term clusters” •Document clustering using key term cluster vectors -‐> detects key term categories
- 13. 13 Example: Detecting User Intent in eCommerce Separating the ‘What’ from the ‘What about’ ( i.e. a Thing vs. a Really BIG Thing) microwave safe dishes ‘microwave safe’ -‐ adjective phrase compact microwave oven ‘microwave oven’ -‐ noun phrase microwave ‘microwave’ -‐ noun -‐ contraction for ‘microwave oven’ coffee filter ‘coffee filter’ -‐ noun-‐noun phrase -‐ a filter coffee ‘coffee’ -‐ noun -‐ a beverage coff coffee table ‘coffee table’ -‐ noun-‐noun phrase -‐ table coffee colored sheets ‘coffee colored’ -‐ adjective phrase coffee ice cream coffee flavored ice cream milk chocolate a type of chocolate chocolate milk flavored milk
- 14. 14 Query Autofiltering Uses field values to generate a “reverse lookup map” that maps values to fields that contain them. -‐ Inverts the “uninverted map" -‐ ah … another type of inverted map -‐ values -‐> fields -‐ Uses the Lucene SynonymMap Finite State Machine (FST) implementation Uses this map to parse the query to find terms in the query related to specific metadata fields. Example: ‘red sofa’ maps red => color sofa => product_type Selects the longest contiguous phrase in its lexicon to match against parts of the query If have ‘coffee’ and ‘coffee filter’ in the lexicon (i.e. the Solr collection) the query ‘coffee filter’ will only match ‘coffee filter’ Can construct either a Solr filter query (fq) or boost query (bq) using this information.
- 15. 15 Query Autofiltering -‐ Knowledge Mining Doing this is a way of exploiting the field/value relationships in the collection metadata. So what it effectively does is extract the knowledge that is built-‐in to your collection due to the facet metadata that it contains and applies that knowledge to parsing of the query: •It knows that ‘red’ is a color because ‘red’ is a value in the ’color’ field. •It ‘Short circuits’ the search-‐then-‐drill-‐in paradigm -‐> just search! •But as the telemarketers say: “Wait! there’s more! …” The knowledge about what terms mean and the properties of the term field (single valued vs. multi-‐valued) provide other opportunities that can be exploited!
- 16. 16 Query Autofiltering -‐ Language Logic Can provide a semblance of “natural language processing” by breaking a query into semantic parts and applying those appropriately Natural Language Boolean vs Mathematical Boolean Language usage of boolean terms like ‘AND’ and ‘OR’ is contextual!! “show me green or blue shirts” is equivalent to “show me green and blue shirts” The user means ‘both’ in each case so ‘and’ and ‘or’ are synonyms in this usage context! but in “show me fast and inexpensive cars” -‐ ‘and’ means AND! Depends on field cardinality! If color is single-‐valued and ‘attributes’ is multi-‐valued. Users understand this intuitively -‐ Search Engines don’t but Query Autofilter can get this right!
- 17. 17 Query Autofiltering -‐ Extensions -‐ Query Patterns Once you know what individual query terms and phrases mean, you can exploit this by creating templates for popular query patterns Query Pattern: Terms + Facet fields that will be captured by Query AutoFilter Query Template: Query template with placeholders for field values filled in if user query matches the pattern. Example: Music Ontology User Query: Who’s in The Who Query Pattern: (who's in,was in,were in,member of,members of)|${hasPerformer_ss} Query Template: memberOfGroup_ss:${hasPerformer_ss} User Query: Songs Beatles Covered Query Pattern: (song,songs)|${hasPerformer_ss}|covered Query Template: hasPerformer_ss:${hasPerformer_ss} AND version_s:Cover
- 18. 18 Canned Demo -‐ Who’s In The Who
- 19. 19 Typeahead -‐ Priming the Pump with Pivot Facet Patterns Construct semantically meaningful phrases from multiple metadata fields ✦Inverse of Query AutoFiltering -‐ creates suggestions that we know how to process!! ✦Uses Solr Pivot Facets to translate field patterns to suggested query phrases Examples: ${hasPerformer_ss} ${Recording_Type_s}s => Beatles Songs, Led Zeppelin Songs, Billy Joel Songs, Frank Zappa Songs etc. ${genres_ss} ${Musician_Type_ss}s => Classical Pianists, Hard Rock Guitarists, Jazz Drummers ${Recording_Type_s}s ${hasPerformer_ss} Covered (with fq version_s:Cover) => Songs Jimi Hendrix Covered
- 20. 20 Building a Suggester with Dynamic Context Assertion: Facets can be used to find similar things. Example: John Lennon and Paul McCartney share many attributes, activities, group memberships, in common -‐> They are closely related entities. Search Agendas: Users tend to have some high level goal when searching (e.g. Find out information about The Beatles) Agenda’s can change in a session, but it is likely that queries issued within a short period of time will have a similar goal. Conclusion: Facet meta-‐information from facets can be used to associate similar things or concepts within a search session.
- 21. 21 Building a Suggester with Dynamic Context Suggester Builder Design (Fusion Connector) Uses Facet Queries against a Content Collection to create additional metadata for the Suggester or Typeahead Collection. This contextual metadata can then be used for: • Security Trimming of Typeahead suggestions • Dynamic boosting of similar suggestions within a user session
- 22. 22 Building a Suggester with Dynamic Context Bring back other fields in addition to displayed suggestion text (i.e., the ones that were calculated using faceting) If a query is used to search, temporarily store its associated metadata in a circular cache on the browser. When submitting the next typeahead query, add the cached information from the queue as boost queries. Type ‘j’ -‐ get back Jai Johnny Johanson Bands Jai Johnny Johanson Groups J.J. Johnson Jai Johnny Johanson Juke Joint Jezebel Juke Joint Jimmy Just searched for ‘Paul McCartney’ then type ‘j’ John Lennon John Lennon Songs John Lennon Songs Covered James P Johnson Songs (?) John Lennon Originals Hey Jude
- 23. 23 Building a Suggester with Dynamic Context Paul McCartney’s “Meta-‐informational Context”: genres_ss: Rock, Rock & Roll, Soft Rock, Pop Rock hasPerformer_ss: Beatles, Paul McCartney, José Feliciano, Jimi Hendrix, Joe Cocker, Aretha Franklin, Bon Jovi, Elvis Presley ( … and many more) composer_ss: Paul McCartney, John Lennon, Ringo Starr, George Harrison, George Jackson, Michael Jackson, Sonny Bono memberOfGroup_ss: Beatles, Wings Dynamic Boost Query: genres_ss:”Rock”^50 genres_ss:”Rock & Roll”^50 genres_ss:”Soft Rock”^50 genres_ss:”Pop Rock”^50 hasPerformer_ss:”Beatles”^50 hasPerformer_ss:”Paul McCartney”^50 hasPerformer_ss:”José Feliciano”^50 hasPerformer_ss:”Jimi Hendrix”^50 composer_ss:”Paul McCartney”^50 composer_ss:”John Lennon”^50 composer_ss:”Ringo Starr”^50 composer_ss:”George Harrison”^50 memberOfGroup_ss:”Beatles”^50 memberOfGroup_ss:”Wings”^50
- 24. 24 Text Mining Analyses Problem: Metadata needs to be improved for useful application of QAF (i.e. Real World) Case 1: Extracting product type and product attributes metadata from short product descriptions in eCommerce data -‐ dealing with precision and recall Case 2: Large text documents. Want to extract keywords and assign categories to documents. Interesting properties of facets when directed towards unstructured text: Facet ratios of positive and negative queries yield “keyword clusters” Document clustering of keyword cluster vectors give crisp categories
- 25. 25 Auto phrasing vs. Auto filtering Auto Phrasing -‐Multi-‐term phrases that refer to a single entity. -‐Used as a workaround to Solr “Multi-‐term synonym problem” -‐That is now fixed (as of 6.4.1 -‐ thanks Steve Rowe!) -‐Is Auto phrasing solution now obsolete? -‐Answer: NOT!!!, that was exploiting a side effect of what it does! -‐ Uses knowledge from a phrase list to determine what is an auto phrase -‐Works on tokenized text fields (implemented as a Lucene TokenFilter) Query Auto Filtering -‐ Utilizes information from non-‐tokenized text fields -‐ inherently solves multi-‐term problem Strategy for “unstructured text”: Use auto phrasing to extract phrase metadata ( keywords ) from unstructured text This metadata can then be consumed by Query Autofilter at search time.
- 26. 26 Simple Keyword Analysis “Unstructured”Text Lucene Analyzer with Auto Phrasing Extensions Spark Job Metadata we would like to have but don’t have -‐ requires lots of manual curation == $$$ Have short descriptive text fields that can be mined to glean useful metadata such as product type, material, size. Special Sauce Ingredients: ➡Semantically pure lexicons (things, brands, attributes, dimensions, logos, materials) of key terms ➡Auto phrasing-‐based Lucene Analysis to extract key terms and “stop phrases” (e.g. Mr Coffee) ➡Expansions and Relations based on noun phrases in lexicon. Contextually aware management of precision and recall. ➡Tricks to deal with “leather case for iPhone”, “DSLR camera with 50-‐mm lens”
- 27. 27 Expansions and Relations Motivation: eCommerce Use Case: Search for ‘iPhone’ -‐ get iPhone cases and iPhone chargers mixed in. -‐ Want to have both BUT want iPhones at the TOP of the result set. => TF/IDF doesn’t always deliver on this (can’t control relevance -‐ you get what you get) i.e. -‐ want recall for up sell opportunities -‐ so relax precision a bit. Relevance (what I want is on top) is still very important Search for ‘iPhone case’ Now I want precision -‐ just show me iPhone cases please ‘cause I already got a stinkin’ iPhone!! Why else would I be looking for accessories for it ???
- 28. 28 Expansions and Relations Noun phrases have structure: end table side table dining room table picnic table coffee table folding table => Are ALL types of tables table cloth table setting table lamp table chair => Are table related things. Expansions -‐ IS-‐A relationships Phrases that end in ‘table’ are specific types of tables classify ‘end table’ as ‘table’ too => search for ‘table’ returns all types of tables => search for ‘end table’ just returns end tables Relations -‐ IS-‐LIKE Relationships Phrases that start with ‘table’ are table related things Add table related things to fq for ‘table’ as OR list Boost search term ‘table’ more than table related things -‐ get both but tables are first Table related things don’t have relations -‐ search is more specific -‐ just get that thing!
- 29. 29 Unstructured Text -‐ Oh My! The problem of unstructured text is that it is … well unstructured …. or is it? (Linguists don’t think so!) We search but don’t typically facet on unstructured text fields (i.e. tokenized fields). Even though in Solr we can facet on anything -‐ Get all of the tokenized terms and their counts as facet values -‐> very high cardinality -‐ Absolutely useless for UI drill in -‐ so this is basically a no-‐no at query time => But that is not all that facets are good for so … wait a minute (light-‐bulb moment)! <= What if we DID facet on the tokens and used their stats to do some text analysis? => It turns out we can use facets to detect keywords in documents. <= Keywords -‐ Terms that occur in relatively few documents (but not too few). -‐ Tend to be important words in some subjects but not others -‐ i.e. their usage is highly contextual to a subject! Keywords for the same subject area tend to occur together because they share the same context! Facets are a great context mining tool!! Sounds like a FIT!
- 30. 30 Facet Ratios => Keyword Clustering Method to my Madness: • Tokenize text with auto phrasing, stop words and synonyms -‐ store tokens in a multi-‐valued field with DocValues -‐ (yes you can facet on a text field but it tends to hit a wall -‐ 2M word limit on facet values) • Using the /terms handler, get each term in the text field. • Submit two queries -‐ one with text_field:[term] (positive Q) -‐ one with -‐text_field:[term] (negative Q) • Calculate the following ratio: • Take the xlog(x) of this ratio (for better discrimination) -‐for each term, take the best related terms above some threshold Facet counts (posizve Q) ———————————— Total counts (posizve Q) ——————————————— Facet counts (negazve Q) ————————————-‐ Total counts (negazve Q)
- 31. 31 Facet Ratios => Keyword Clusters Authentication 1002.7227722772277 firewall 561.5247524752475 authorization 401.08910891089107 passwords 374.34983498349834 plugging 160.43564356435644 transport 88.81258840169731 tied 80.21782178217822 weblogic,bootstrap,computationally, finely,usernames 56.152475247524755 ssl 40.10891089108911 login,augments,dialog,encapsulated, fallback,privileged 34.87731381833836 kerberos 32.087128712871284 permission 28.64922206506365 password 26.739273927392738 acls,realm,conversely 25.523852385238524 streaming 23.874351720886374 enabling 23.174037403740375 remote 22.91937765205092 protocols 20.054455445544555 globally,bind,indirectly,redirects 18.51180502665651 ldap 16.043564356435642 proxy 14.585058505850585 memberships 14.465508845966564 permissions 13.369636963696369 protect 12.534034653465346 grained 11.918076379066479 linux 11.523002023959302 advice 11.45968882602546 authenticate 11.23049504950495 hash 11.184215536938309 message 10.106182271770484 plugins 10.027227722772277 header,controlled,crafting 9.43739079790332 username 8.913091309130913 logins
- 32. 32 Facet Ratios => Keyword Clusters Phase II -‐ check related terms for correlation (count agreements in their related terms) 8984.39603960396 authorization 4411.980198019802 passwords 4010.891089108911 firewall 802.1782178217823 usernames 601.6336633663367 password 508.046204620462 realm 505.3722772277228 ssl 481.3069306930693 login 425.7715156130997 ldap 418.52776582006027 kerberos 320.8712871287129 bootstrap,finely 256.69702970297027 permission 216.98263268949847 permissions 213.9141914191419 acls 185.392299229923 remote 167.1204620462046 enabling 153.14311431143113 streaming 132.12347117064647 username 120.32673267326733 bind 106.95709570957095 conversely 53.478547854785475 logins 46.09200809583721 advice 39.269812336882225 requests 32.52073856034252 zookeeper 31.13477101134771 plugin 28.422165293452423 admin 26.739273927392738 bother 25.465975168945466 controls 24.68240670220868 native 22.716551301147813 require
- 33. 33 Keyword Vector Document Clustering Use the Keyword Vectors to compute distances between documents rather than raw TF/IDF => Higher Signal To Noise Tokenizer Compute Keyword Vector K-Means Clustering Cluster: 98 stump_the_chump: 15159.853372701356 stump: 12931.059994928455 prize: 12378.463050783357 sight: 2943.012345679012 tough: 2872.8905092427412 question: 2827.6045026881716 judge: 2353.9344100731737 submit: 2250.350305525309 session: 2147.8922671532514 panel: 1888.958487954128 hostetter: 1722.9000585471174 grant: 1600.741568627451 chump: 1558.9513516128222 lucene_revolution: 1353.774672198919 spot: 1211.5869933577087 award: 1048.082490095137 mock: 1005.0931680939833 conference: 903.0025141117053 muir: 878.7673037468955 seat: 870.9154155915239 hot: 799.5070748299321 Get a list of documents for each cluster - label the clusters ==> Document Category
- 34. 34 Keyword Vector Document Clustering Cluster: 85 young_generation: 58393.71450722004 throughput_collector: 51879.272543859726 tenured_space: 45769.06697989158 young_generation_collector: 36786.321736596736 tenure: 33389.738840692735 stop_the_world: 31288.96145142277 concurrent_low_pause_collector: 29612.759802867382 useadaptivesizepolicy: 26927.686004351213 useparnewgc: 26819.583333333332 useparalleloldgc: 25450.34188034188 jvm: 25354.346667094775 young_space: 22546.65166222556 useparallelgc: 22168.126984126982 collector: 21226.31425547997 survivor_space: 20836.967617437283 heap: 18883.16487771459 garbage_collection: 18247.692641501046 garbage_collector: 17929.789619546355 command_line_options: 10111.764705882353 sweep: 9803.141574757969
- 35.