Resolving Common Analytical Tasks in Text Databases

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1 Resolving Common Analytical Tasks in Text Databases The work is funded by the Federal Ministry of Economic Affairs and Energy (BMWi) under grant agreement 01MD15010B. Database Systems and Text-based Information Systems DATEXIS Sebastian Arnold, Alexander Löser, Torsten Kilias ACM Eighteenth International Workshop On Data Warehousing and OLAP

2 What is a Text Database? Text Databases¹ enable the user to query structured data out of text sources. See as a Data Warehouse of documents Extract: document retrieval Transform: linguistic text processing pipeline Load: aggregate into relational DB ² Our recent work on INDREX³ integrates ETL and linguistic queries into a RDBMS. ¹Agichtein, Gravano (2003): Querying Text Databases for Efficient Information Extraction ²Marchionini (2006): Exploratory Search: From Finding to Understanding ³Kilias, Löser, Andritsos (2015): INDREX: In-Database Relation Extraction 2

3 Search Objective¹: Analytical Tasks in Text Databases Select a supplier for VOIP technology. Sequence of Analytical Tasks¹ Goal: diversely structured results Query Method: Short keyword queries² Click streams, sessions¹ Query Translation: (Query,Task Objective) SQL schema adapted from 1 ¹Guo, Agichtein, (2010): Ready to Buy or Just Browsing?: Detecting Web Searcher Goals from Interaction Data ²Kato, Yamamoto, Ohshima, Tanaka (2014): Investigating Users Query Formulations for Cognitive Search Intents 3

4 Search Objective¹: Analytical Tasks in Text Databases Select a supplier for VOIP technology. Sequence of Analytical Tasks¹ Goal: diversely structured results Query Method: Short keyword queries² Click streams, sessions¹ Query Translation: (Query,Task Objective) SQL We observe interaction and want to predict the underlying tasks. 4

5 GoOLAP¹ is a Exploratory Search Interface on a large Web Text Database 37 million distinct facts 2 million Web documents 6 million named entities OpenCalais types Focused crawling² with iterative ETL process 500 users per day Our Prototype: GoOLAP.info Observed Sample 102,360 keyword queries and click behaviour ( ) 86,4% analytical tasks (explore, resolve, relate, list, compare, answer) ¹Löser, Arnold, Fiehn (2012): The GoOLAP Fact Retrieval Framework ²Boden, Löser, Nagel, Pieper (2011) FactCrawl: a fact retrieval framework for Full-Text indices 5

6 Overview of Six Common Analytical Tasks Task Interpretation Example Scope EXPLORE tell me all about X cisco systems undirected¹ information gathering³ RESOLVE tell me all about the X related to Y cisco systems CEO interpretation² association RELATE tell me all about the relation of X1 and X2 cisco and siemens integration² relateness LIST show me a list of X VOIP suppliers directed open¹ COMPARE compare X1 and X2 tcp udp comparison comparison² ANSWER answer question X foundation of siemens directed closed¹ fact finding³ ¹Rose, Levinson (2004): Understanding User Goals in Web Search ²Marchionini (2006): Exploratory Search: From Finding to Understanding ³Kellar, Watters, Shepherd (2006): A Goal-based Classification of Web Information Tasks 6

7 Overview of Six Common Analytical Tasks Task Interpretation Example Result Structure EXPLORE tell me all about X cisco systems overview of a universal relation RESOLVE tell me all about the X related to Y cisco systems CEO overview of the associated X RELATE tell me all about the relation of X1 and X2 cisco and siemens overview of the discrimination LIST show me a list of X VOIP suppliers structured list COMPARE compare X1 and X2 tcp udp comparison tabular comparison ANSWER answer question X foundation of siemens single fact or value Tasks are also assiciated with the intended structure of result presentation. 7

8 Resolving Tasks from Keywords Back to problem: We observe a query and want to predict the underlying task. q = T = RESOLVE CEO of Cisco Systems SQL 8

9 Resolving Tasks from Keywords To resolve the task from query method, we establish a three-step pipeline: 1. Annotate the keyword query 2. Extract features from annotations 3. Train a multi-class classifier 9

10 Query Analysis (1): Interactive Annotation We want to recognize named entities and concepts in the query Problem: segmentation of multi-word entities Problem: word sense disambiguation for short queries Problem: long tail distribution of concepts Our approach: interactive annotation as you type q = cisco systems CEO POS NNP NNS NNP q* = Company Position 10

11 Query Analysis (2): Fallback Annotation What if the user is lazy? We need a fallback strategy. Tokenize and POS-tag single words Build a set of n-gram segment candidates S1...Sn Lookup segments in named entity index, assign scores(s) (number of facts) Merge with user annotations and assign scoreq(q) (fact distribution) Decide for the highest scored interpretation q = cisco systems CEO POS NNP NNS NNP S1= Comp Product Position S2= Company q* = Company Position 11

12 Query Analysis (3): Feature Extraction We transform the annotated query q* into simplified tag sequences: q = cisco systems CEO POS NNP NNP q* = Company Position SEQpos = NNP,NNP SEQtype = Company,Position We match 185 patterns on the sequences to generate a feature vector x, e.g. #NNP(SEQpos) #Company(SEQtype)? Product Product ~2(SEQtype) (proximity patterns) 12

13 Query Analysis (4): Classification We predict the most likely task from query formulation From each query, we observe feature vector x of size m We estimate a task intention T from K=7 classes (six types and OTHER) Naive Bayes Classification: We train two models: C-SUP uses supervised, expert-labeled training data (small set) C-SEMI is built using machine-labeled training data (large set) 13

14 Result Visualization We present the most likely task predictions to the user and select the top one RESOLVE CEO of Cisco Systems 0,978 LIST Systems of CEO 0,031 EXPLORE Company Cisco Systems 0,012 We measure user interaction and re-train the classifier with feedback Events are logged, e.g. click on different interpretation (explicit) refine query interact with the result Implicit feedback is received using heuristics for event patterns in session 14

15 Evaluation Setup Data Set Q-EXP (expert-labeled) Q-ML (machine-labeled) total queries ,360 after filtering ,430 EXPLORE 58.07% 10.69% RESOLVE 12.37% 39.64% RELATE 6.08% 14.03% LIST 4.82% 6.99% COMPARE 0.63% 0.21% ANSWER 4.40% 4.05% non-analytical 13.63% 24.39% Data set taken from GoOLAP query log (non-zero results, no bounces) We measure Precision, Recall, AUC and F1 using 10-fold cross validation 15

16 Evaluation Results (1): Classifier Implementation By transforming queries into SEQpos and SEQtype sequences, we achieve 96.8% accuracy and 90.1% F1 score using simple Naive Bayes classification. Setup: three classification implementations (Baseline, Language Models, expert and machine-labeled Naive Bayes), three feature sets (POS, SEQpos, SEQtype) 16

17 Evaluation Results (2): Per-Class Comparison By extrapolating our supervised classifier C-SUP on 85,430 queries, we get a semi-supervised classifier C-SEMI that performs better for rare task classes. 17

18 Common Analytical Query Patterns The most typical patterns are simple combinations of entity mentions. Our system is able to discriminate them best by means of their semantic type. 18

19 Which Features Correlate with Class Labels? By using a combination of POS tags 19

20 We resolve six common analytical search tasks from keyword queries: EXPLORE, RESOLVE, RELATE, LIST, COMPARE, ANSWER. taxonomy inferred from related work and examination of large query log interactive approach to query segmentation trained classifier with 85,430 keyword queries 96.8% accuracy, 90.1% F1 (10-fold cross validation) Live demo: Conclusion and Future Work Future work: focus on entity linking Improve recall for in-query entity recognition: sequence learning of rare entities, long-tail concepts and relations use embeddings to adapt to specialized domains (e.g. fashion) Improve precision for entity disambiguation: use context information and latent relational dependence (relations <> text) long term: predict higher-level search objective from sessions 20

21 Questions? THANK YOU! Database Systems and Text-based Information Systems DATEXIS Sebastian Arnold, Alexander Löser, Torsten Kilias 21

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