question answering over linked data (reasoning web summer school)

Philipp Cimiano, Christina Unger and André Freitas

10th Reasoning Web Summer School

Understand how Question Answering (QA) can address Linked Data consumption challenges.

Provide you a quick overview of the state-of-the-art.

Provide you the fundamental pointers to develop your own QA system.

Motivation & Context

Challenges for QA over Linked Data

The Anatomy of a QA System

QA over Linked Data (Case Studies)

Evaluation of QA over Linked Data

Do-it-yourself (DIY): Core Resources

Trends

Take-away Message

Motivation & Context

Humans are built-in with natural language communication capabilities.

Very natural way for humans to communicate information needs.

The archetypal AI system.

A research field on its own.

Empirical bias: Focus on the development and evaluation of approaches and systems to answer questions over a knowledge base.

Multidisciplinary: ◦ Natural Language Processing

◦ Information Retrieval

◦ Knowledge Representation

◦ Databases

◦ Linguistics

◦ Artificial Intelligence

◦ Software Engineering

◦ ...

From the QA expert perspective ◦ QA depends on mastering different semantic computing

techniques.

QA System

Keyword Search: ◦ User still carries the major efforts in interpreting the data.

◦ Satisfying information needs may depend on multiple search operations.

◦ Answer-driven information access.

◦ Input: Keyword search

Typically specification of simpler information needs.

◦ Output: documents, structured data.

QA: ◦ Delegates more ‘interpretation effort’ to the machines.

◦ Query-driven information access.

◦ Input: natural language query

Specification of complex information needs.

◦ Output: direct answer.

Structured Queries: ◦ A priori user effort in understanding the schemas behind

databases.

◦ Effort in mastering the syntax of a query language.

◦ Satisfying information needs may depend on multiple querying operations.

◦ Input: Structured query

◦ Output: data records, aggregations, etc

QA: ◦ Delegates more ‘semantic interpretation effort’ to the

machine.

◦ Input: natural language query

◦ Output: direct natural language answer

Keyword search: ◦ Simple information needs.

◦ Vocabulary redundancy (large document collections, Web).

Structured queries: ◦ Demand for absolute precision/recall guarantees.

◦ Small & centralized schemas.

◦ More data volume/smaller schema size.

QA: ◦ Heterogeneous and schema-less data.

◦ Specification of complex information needs.

◦ More automated semantic interpretation.

QA is usually associated with the delegation of more of the ‘interpretation effort’ to the machines.

QA, keyword search and structured queries are complementary data access perspectives.

QA making its way to the industry.

Challenges for QA over Linked Data

Example: What is the currency of the Czech Republic? SELECT DISTINCT ?uri WHERE { res:Czech_Republic dbo:currency ?uri . } Main challenges:

Mapping natural language expressions to vocabulary elements (accounting for lexical and structural differences).

Handling meaning variations (e.g. ambiguous or vague expressions, anaphoric expressions).

URIs are language independent identifiers.

Their only actual connection to natural language is by the labels that are attached to them.

dbo:spouse rdfs:label “spouse”@en , “echtgenoot”@nl .

Labels, however, do not capture lexical variation:

wife of

husband of

married to

Which Greek cities have more than 1 million inhabitants?

SELECT DISTINCT ?uri

WHERE {

?uri rdf:type dbo:City .

?uri dbo:country res:Greece .

?uri dbo:populationTotal ?p .

FILTER (?p > 1000000)

Often the conceptual granularity of language does not coincide with that of the data schema.

When did Germany join the EU?

SELECT DISTINCT ?date

WHERE {

res:Germany dbp:accessioneudate ?date .

Who are the grandchildren of Bruce Lee?

WHERE {

res:Bruce_Lee dbo:child ?c .

?c dbo:child ?uri .

In addition, there are expressions with a fixed, dataset-independent meaning.

Who produced the most films?

WHERE {

?x rdf:type dbo:Film .

?x dbo:producer ?uri .

ORDER BY DESC(COUNT(?x))

OFFSET 0 LIMIT 1

Different datasets usually follow different schemas, thus provide different ways of answering an information need.

Example:

The meaning of expressions like the verbs to be, to have, and prepositions of, with, etc. strongly depends on the linguistic context.

Which museum has the most paintings?

?museum dbo:exhibits ?painting .

Which country has the most caves?

?cave dbo:location ?country .

The number of non-English actors on the web is growing substantially. ◦ Accessing data.

◦ Creating and publishing data.

Semantic Web: In principle very well suited for multilinguality, as URIs are

language-independent.

But adding multilingual labels is not common practice (less than a quarter of the RDF literals have language tags, and most of those tags are in English).

Requirement: Completeness and accuracy

(Wrong answers are worse than no answers)

In the context of the Semantic Web:

QA systems need to deal with heterogeneous and imperfect data. ◦ Datasets are often incomplete.

◦ Different datasets sometimes contain duplicate information, often using different vocabularies even when talking about the same things.

◦ Datasets can also contain conflicting information and inconsistencies.

Data is distributed among a large collection of interconnected datasets.

Example: What are side effects of drugs used for the treatment of Tuberculosis?

SELECT DISTINCT ?x

WHERE {

disease:1154 diseasome:possibleDrug ?d1.

?d1 a drugbank:drugs .

?d1 owl:sameAs ?d2.

?d2 sider:sideEffect ?x.

Requirement: Real-time answers, i.e. low processing time.

In the context of the Semantic Web:

Datasets are huge. ◦ There are a lot of distributed datasets that might be

relevant for answering the question.

◦ Reported performance of current QA systems amounts to ~20-30 seconds per question (on one dataset).

Bridge the gap between natural languages and data.

Deal with incomplete, noisy and heterogeneous datasets.

Scale to a large number of huge datasets.

Use distributed and interlinked datasets.

Integrate structured and unstructured data.

Low maintainability costs (easily adaptable to new datasets and domains).

The Anatomy of a QA System

Categorization of question, answer and data types.

Important for: ◦ What information in the question can be used? ◦ Scoping the QA system. ◦ Understanding the challenges before attacking the

problem.

Based on: ◦ Chin-Yew Lin: Question Answering. ◦ Farah Benamara: Question Answering Systems: State of

the Art and Future Directions.

Natural Language Interfaces (NLI) ◦ Input: Natural language queries

◦ Output:

QA: Direct answers.

NLI: Database records, text snippets, documents, data visualizations.

What is in the question?

The part of the question that says what is being asked: ◦ Wh-words:

who, what, which, when, where, why, and how

◦ Wh-words + nouns, adjectives or adverbs: “which party …”, “which actress …”, “how long …”, “how tall

…”.

Question focus is the property or entity that is being sought by the question ◦ “In which city was Barack Obama born?”

◦ “What is the population of Galway?”

Question topic: What the question is generally about ◦ “What is the height of Mount Everest?”

(geography, mountains)

◦ “Which organ is affected by the Meniere’s disease?”

(medicine)

Useful for distinguishing different processing strategies ◦ FACTOID:

PREDICATIVE QUESTIONS: “Who was the first man in space?”

“What is the highest mountain in Korea?”

“How far is Earth from Mars?”

“When did the Jurassic Period end?”

“Where is the Taj Mahal?”

LIST: “Give me all cities in Germany.”

SUPERLATIVE: “What is the highest mountain?”

YES-NO: “Was Margaret Thatcher a chemist?”

Useful for distinguishing different processing strategies ◦ OPINION:

“What do most Americans think of gun control?”

◦ CAUSE & EFFECT: “What is the most frequent cause for lung cancer?”

◦ PROCESS: “How do I make a cheese cake?”

◦ EXPLANATION & JUSTIFICATION: “Why did the revenue of IBM drop?”

◦ ASSOCIATION QUESTION: “What is the connection between Barack Obama and

Indonesia?”

◦ EVALUATIVE OR COMPARATIVE QUESTIONS: “What is the difference between impressionism and

expressionism?”

Usually: ◦ Rules + Part-of-Speech Tags + Regular Expressions

... goes a long way!

What is in the data ?

Structure level: ◦ Structured data.

◦ Semi-structured data.

◦ Unstructured data.

Data source distribution: ◦ Single dataset (centralized).

◦ Enumerated list of multiple, distributed datasets.

◦ Web-scale.

Domain Scope: ◦ Open domain ◦ Domain specific

Data Type: ◦ Structured Data ◦ Text ◦ Image ◦ Sound ◦ Video

Multi-modal QA (both input and output) ◦ E.g. visual, voice modalities

What is in the answer?

The class of object sought by the question:

Entity: event, color, animal, plant,. . .

Description, Explanation & Justification : definition, manner, reason,. . . (“How, why …”)

Human: group, individual,. . . (“Who …”)

Location: city, country, mountain,. . . ( “Where …”)

Numeric: count, distance, size,. . . (“How many how far, how long …”)

Temporal: date, time, …(from “When …”)

Abbreviation

Long answers ◦ Definition/justification based.

Short answers ◦ Phrases.

◦ Named entities, numbers, aggregate, yes/no.

Relevance: The level in which the answer addresses users information needs.

Correctness: The level in which the answer is factually correct.

Conciseness: The answer should not contain irrelevant information.

Completeness: The answer should be complete.

Simplicity: The answer should be easy to interpret.

Justification: Sufficient context should be provided to support the data consumer in the determination of the query correctness.

Right: The answer is correct and complete.

Inexact: The answer is incomplete or incorrect.

Unsupported: The answer does not have an appropriate evidence/justification.

Wrong: The answer is not appropriate for the question.

What is in the QA system?

Simple Extraction: Direct extraction of snippets from the original document(s) / data records.

Combination: Combines excerpts from multiple sentences, documents / multiple data records, databases.

Summarization: Synthesis from large texts / data collections.

Operational/functional: Depends on the application of functional operators.

Reasoning: Depends on the application of an inference process over the original data.

Semantic Tractability (Popescu et al., 2003): Lexical and syntactic conditions for soundness and completeness.

Semantic Resolvability (Freitas et al., 2014): Vocabulary mapping types between the query and the answer.

Answer Locality (Webber et al., 2002): Whether answer fragments are distributed across different document fragments / documents or datasets/dataset records.

Derivability (Webber et al., 2002): Dependent if the answer is explicit or implicit. Level of reasoning dependency.

Semantic Complexity: Level of ambiguity and discourse/data heterogeneity.

Data pre-processing: Pre-processes the database data (includes indexing, data cleaning, feature extraction).

Question Analysis: Performs syntactic analysis and detects/extracts the core features of the question (NER, answer type, etc).

Data Matching: Matches terms in the question to entities in the data.

Query Construction: Generates structured query candidates considering the question-data mappings and the syntactic constraints in the query and in the database.

Scoring: Data matching and the query construction components output several candidates that need to be scored and ranked according to certain criteria.

Answer Retrieval & Extraction: Executes the query and extracts the natural language answer from the result set.

QA over Linked Data

(Case Studies)

Aqualog & PowerAqua (Lopez et al., 2006) ◦ Querying the Semantic Web

ORAKEL & Pythia (Cimiano et al., 2007; Unger & Cimiano, 2011) ◦ Ontology-specific question answering

TBSL (Unger et al., 2012) ◦ Template-based question answering

Kwiatowski et al. 2013 ◦ Scaling Semantic Parsers with On-the-fly Ontology

Matching

Treo (Freitas et al. 2011, 2014) ◦ Schema-agnostic querying using distributional

semantics

IBM Watson (Ferrucci et al., 2010) ◦ Large-scale evidence-based model for QA

QuestIO & Freya (Damljanovic et al. 2010)

QAKIS (Cabrio et al. 2012)

Yahya et al., 2013

Aqualog & PowerAqua (Lopez et al. 2006)

Key contributions: ◦ Pioneer work on the QA over Semantic Web

data. ◦ Semantic similarity mapping.

Terminological Matching: ◦ WordNet-based ◦ Ontology-based ◦ String similarity ◦ Sense-based similarity matcher

Evaluation: QALD (2011). Extends the AquaLog system.

Two words are strongly similar if any of the following holds: ◦ 1. They have a synset in common (e.g. “human” and

“person”) ◦ 2. A word is a hypernym/hyponym in the taxonomy of

the other word. ◦ 3. If there exists an allowable “is-a” path connecting a

synset associated with each word. ◦ 4. If any of the previous cases is true and the

definition (gloss) of one of the synsets of the word (or its direct hypernyms/hyponyms) includes the other word as one of its synonyms, we said that they are highly similar.

Lopez et al. 2006

ORAKEL (Cimiano et al, 2007) & Pythia

(Unger & Cimiano, 2011)

Key contributions: ◦ Using ontologies to interpret user questions

◦ Relies on a deep linguistic analysis that returns semantic representations aligned to the ontology vocabulary and structure

Evaluation: Geobase

Ontology-based QA: ◦ Ontologies play a central role in interpreting user questions

◦ Output is a meaning representation that is aligned to the ontology underlying the dataset that is queried

◦ ontological knowledge is used for drawing inferences, e.g. for resolving ambiguities

Grammar-based QA: ◦ Rely on linguistic grammars that assign a syntactic and

semantic representation to lexical units

◦ Advantage: can deal with questions of arbitrary complexity

◦ Drawback: brittleness (fail if question cannot be parsed because expressions or constructs are not covered by the grammar)

Ontology-independent entries ◦ mostly function words

quantifiers (some, every, two)

wh-words (who, when, where, which, how many)

negation (not)

◦ manually specified and re-usable for all domains

Ontology-specific entries ◦ content words and phrases corresponding to

concepts and properties in the ontology

◦ automatically generated from an ontology lexicon

Aim: capture rich and structured linguistic information about how ontology elements are lexicalized in a particular language

lemon (Lexicon Model for Ontologies)

http://lemon-model.net ◦ meta-model for describing ontology lexica with

◦ declarative (abstracting from specific syntactic and semantic theories)

◦ separation of lexicon and ontology

Semantics by reference: ◦ The meaning of lexical entries is specified by

pointing to elements in the ontology.

Example:

Which cities have more than three universities?

SELECT DISTINCT ?x WHERE {

?x rdf:type dbo:City .

?y rdf:type dbo:University .

?y dbo:city ?x .

GROUP BY ?y

HAVING (COUNT(?y) > 3) 70

TBSL (Unger et al., 2012)

Key contributions: ◦ Constructs a query template that directly

mirrors the linguistic structure of the question

◦ Instantiates the template by matching natural language expressions with ontology concepts

Evaluation: QALD 2012

In order to understand a user question, we need to understand:

The words (dataset-specific) Abraham Lincoln → res:Abraham Lincoln

died in → dbo:deathPlace

The semantic structure (dataset-independent)

who → SELECT ?x WHERE { … }

the most N → ORDER BY DESC(COUNT(?N)) LIMIT 1

more than i N → HAVING COUNT(?N) > i

Goal: An approach that combines both an analysis of the semantic structure and a mapping of words to URIs.

Two-step approach: ◦ 1. Template generation

Parse question to produce a SPARQL template that directly mirrors the structure of the question, including filters and aggregation operations.

◦ 2. Template instantiation

Instantiate SPARQL template by matching natural language expressions with ontology concepts using statistical entity identification and predicate detection.

SPARQL template:

?y rdf:type ?c .

?y ?p ?x .

ORDER BY DESC(COUNT(?y))

OFFSET 0 LIMIT 1

?c CLASS [films]

?p PROPERTY [produced]

Instantiations:

?c = <http://dbpedia.org/ontology/Film>

?p = <http://dbpedia.org/ontology/producer>

1. Natural language question is tagged with part-of-speech information.

2. Based on POS tags, grammar entries are built on the fly.

◦ Grammar entries are pairs of:

tree structures (Lexicalized Tree Adjoining Grammar)

semantic representations (ext. Discourse Representation Structures)

3. These lexical entries, together with domain-independent lexical entries, are used for parsing the question (cf. Pythia).

4. The resulting semantic representation is translated into a SPARQL template.

Domain-independent: who, the most

Domain-dependent: produced/VBD, films/NNS

SPARQL template 1: SELECT DISTINCT ?x WHERE {

?x ?p ?y .

?y rdf:type ?c .

ORDER BY DESC(COUNT(?y)) LIMIT 1 ?c CLASS [films]

?p PROPERTY [produced]

SPARQL template 2:

?x ?p ?y .

ORDER BY DESC(COUNT(?y)) LIMIT 1 ?p PROPERTY [films]

1. For resources and classes, a generic approach to entity detection is applied: ◦ Identify synonyms of the label using WordNet. ◦ Retrieve entities with a label similar to the slot label based

on string similarities (trigram, Levenshtein and substring similarity).

2. For property labels, the label is additionally

compared to natural language expressions stored in the BOA pattern library.

3. The highest ranking entities are returned as candidates for filling the query slots.

?c CLASS [films]

<http://dbpedia.org/ontology/Film>

<http://dbpedia.org/ontology/FilmFestival> ... ?p PROPERTY [produced]

<http://dbpedia.org/ontology/producer>

<http://dbpedia.org/property/producer>

<http:// dbpedia.org/ontology/wineProduced>

1. Every entity receives a score considering string similarity and prominence.

2. The score of a query is then computed as the average of the scores of the entities used to fill its slots.

3. In addition, type checks are performed: ◦ For all triples ?x rdf:type <class>, all query triples ?x p e

and e p ?x are checked w.r.t. whether domain/range of p is consistent with <class>.

4. Of the remaining queries, the one with highest score that returns a result is chosen.

?x <http://dbpedia.org/ontology/producer> ?y .

?y rdf:type <http://dbpedia.org/ontology/Film> .

ORDER BY DESC(COUNT(?y)) LIMIT 1

Score: 0.76

?x <http://dbpedia.org/ontology/producer> ?y .

?y rdf:type <http://dbpedia.org/ontology/FilmFestival>.

ORDER BY DESC(COUNT(?y)) LIMIT 1

Score: 0.60

The created template structure does not always coincide with how the data is actually modelled.

Considering all possibilities of how the data could be modelled leads to a big amount of templates (and even more queries) for one question.

Kwiatowski et al., 2013 Scaling Semantic Parsers with On-the-fly

Ontology Matching

Recent approaches view interpretation as a machine

translation problem (translating natural language

questions into meaning representations or SPARQL

queries).

Example:

Construct all possible interpretations and learn a model to

score and rank them (from either question-query pairs or question-answer pairs).

QA over Freebase.

1. datatset-independent probabilistic CCG

parsing: ◦ mapping sentences to underspecified meaning

representations (containing generic logical constants not

yet aligned to any ontology/dataset schema)

◦ one grammar for all domains (with domain-independent

entries as well as generic entries built on the basis of

E.g. ◦ city: N lambda x.city(x)

◦ visit: S\NP/NP lambda x y exists e . visit(x,y,e)

2. ontology matching: ◦ structural matching (transformations of the meaning representations)

◦ Collapsing, e.g. public(x) and library(x) and of(x,NewYork,e) ->

PublicLibraryOfNewYork

◦ Expansion, e.g. discover(x,y,e) -> discover(x,e) and discover'(y,e)

◦ Constant matching (replacing all generic constants with constants

from the ontology)

This leads to a lot of possible interpretations.

learn function that ranks derivations, then prune and pick

the highest ranked one

Estimate a linear model for scoring derivations (including

all parsing and matching decisions) from question-answer

Weighted features include: ◦ parse features (e.g. pairings of words with categories)

◦ structural features (e.g. types of constants, number of domain-

independent constants) --> allows adaptation to knowledge base

◦ lexical features (e.g. similarity of NL string and ontology constant

based on stem and synonyms)

◦ knowledge base features (e.g. violation of domain/range restrictions)

Weights are learned so they support separation of

derivations that yield correct answers from those that don't

Treo (Freitas et al. 2011, 2014)

Key contributions: ◦ Distributional semantic relatedness matching

model.

◦ Distributional model for QA.

Terminological Matching: ◦ Explicit Semantic Analysis (ESA)

◦ String similarity + node cardinality

Evaluation: QALD (2011)

Treo (Irish): Direction

Structured Representation

Inference

• Most semantic models have dealt with particular types of constructions, and have been carried out under very simplifying assumptions, in true lab conditions.

• If these idealizations are removed it is not clear at all that modern semantics can give a full account of all but the simplest models/statements.

Sahlgren, 2013

Formal World

Real World

Baroni et al. 2013

“Words occurring in similar (linguistic) contexts are semantically related.”

If we can equate meaning with context, we can simply record the contexts in which a word occurs in a collection of texts (a corpus).

This can then be used as a surrogate of its semantic representation.

husband

spouse

(number of times that the words occur in c1)

husband

spouse

Query Planner

Large-scale

unstructured data

Database

Query Analysis Query Query Features

Query Plan

Query Planner

Wikipedia

Query Analysis Query Query Features

Query Plan

The vector space is

segmented by the

instances

Instance search ◦ Proper nouns

◦ String similarity + node cardinality

Class (unary predicate) search ◦ Nouns, adjectives and adverbs

◦ String similarity + Distributional semantic relatedness

Property (binary predicate) search ◦ Nouns, adjectives, verbs and adverbs

◦ Distributional semantic relatedness

Navigation

Extensional expansion ◦ Expands the instances associated with a class.

Operator application ◦ Aggregations, conditionals, ordering, position

Disjunction & Conjunction

Disambiguation dialog (instance, predicate)

Minimize the impact of Ambiguity, Vagueness, Synonymy. Address the simplest matchings first (heuristics).

Semantic Relatedness as a primitive operation.

Distributional semantics as commonsense knowledge.

Lightweight syntactic constraints

Transform natural language queries into triple patterns.

“Who is the daughter of Bill Clinton married to?”

Step 1: POS Tagging ◦ Who/WP ◦ is/VBZ ◦ the/DT ◦ daughter/NN ◦ of/IN ◦ Bill/NNP ◦ Clinton/NNP ◦ married/VBN ◦ to/TO ◦ ?/.

Step 2: Core Entity Recognition ◦ Rules-based: POS Tag + TF/IDF

Who is the daughter of Bill Clinton married to? (PROBABLY AN INSTANCE)

Step 3: Determine answer type ◦ Rules-based.

Who is the daughter of Bill Clinton married to? (PERSON)

Step 4: Dependency parsing ◦ dep(married-8, Who-1) ◦ auxpass(married-8, is-2) ◦ det(daughter-4, the-3) ◦ nsubjpass(married-8, daughter-4) ◦ prep(daughter-4, of-5) ◦ nn(Clinton-7, Bill-6) ◦ pobj(of-5, Clinton-7) ◦ root(ROOT-0, married-8) ◦ xcomp(married-8, to-9)

Step 5: Determine Partial Ordered Dependency Structure (PODS) ◦ Rules based.

Remove stop words.

Merge words into entities.

Reorder structure from core entity position.

Bill Clinton daughter married to

(INSTANCE)

Person

ANSWER

QUESTION FOCUS

Step 5: Determine Partial Ordered Dependency Structure (PODS) ◦ Rules based.

Remove stop words.

Merge words into entities.

Reorder structure from core entity position.

Bill Clinton daughter married to

(INSTANCE)

Person

(PREDICATE) (PREDICATE) Query Features

Map query features into a query plan.

A query plan contains a sequence of:

◦ Search operations.

◦ Navigation operations.

(INSTANCE) (PREDICATE) (PREDICATE) Query Features

(1) INSTANCE SEARCH (Bill Clinton)

(2) DISAMBIGUATE ENTITY TYPE

(3) GENERATE ENTITY FACETS

(4) p1 <- SEARCH RELATED PREDICATE (Bill Clintion, daughter)

(5) e1 <- GET ASSOCIATED ENTITIES (Bill Clintion, p1)

(6) p2 <- SEARCH RELATED PREDICATE (e1, married to)

(7) e2 <- GET ASSOCIATED ENTITIES (e1, p2)

(8) POST PROCESS (Bill Clintion, e1, p1, e2, p2)

Query Plan

Bill Clinton daughter married to Person

:Bill_Clinton

:Bill_Clinton :Chelsea_Clinton :child

:Baptists :religion

:Yale_Law_School

:almaMater

(PIVOT ENTITY)

(ASSOCIATED

TRIPLES)

:Baptists :religion

:Yale_Law_School

:almaMater

sem_rel(daughter,child)=0.054

sem_rel(daughter,child)=0.004

sem_rel(daughter,alma mater)=0.001

Computation of a measure of “semantic proximity” between two terms.

Allows a semantic approximate matching between query terms and dataset terms.

It supports a commonsense reasoning-like behavior based on the knowledge embedded in the corpus.

(PIVOT ENTITY)

:Mark_Mezvinsky :spouse

What is the highest mountain?

(CLASS) (OPERATOR) Query Features

mountain - highest

Mountain highest

:Mountain :typeOf

(PIVOT ENTITY)

Mountain highest

:Mountain :Everest

:typeOf

(PIVOT ENTITY)

:K2 :typeOf

Mountain highest

:Mountain :Everest :typeOf

(PIVOT ENTITY)

:K2 :typeOf

:elevation

:location

:deathPlaceOf

Mountain highest

:Mountain :Everest :typeOf

(PIVOT ENTITY)

:K2 :typeOf

:elevation

8848 m

8611 m

Mountain highest

:Mountain :Everest

:typeOf

(PIVOT ENTITY)

:K2 :typeOf

:elevation

8848 m

8611 m

TOP_MOST

Semantic approximation in databases (as in any IR system): semantic best-effort.

Need some level of user disambiguation, refinement and feedback.

As we move in the direction of semantic systems we should expect the need for principled dialog mechanisms (like in human communication).

Pull the the user interaction back into the system.

IBM Watson (Ferrucci et al., 2010)

Key contributions: ◦ Evidence-based QA system.

◦ Complex and high performance QA Pipeline.

Uses more than 50 scoring components that produce scores which range from probabilities and counts to categorical features.

◦ Major cultural impact:

Before: QA as AI vision, academic exercise.

After: QA as an attainable software architecture in the short term.

Evaluation: Jeopardy! Challenge

“Rap” Sheet

This archaic term for a mischievous or annoying

child can also mean a rogue or scamp.

Rapscallion

Can be more challenging from a question analysis perspective

Higher specificity from an Information Retrieval perpective

Question analysis: includes shallow and deep parsing, extraction of logical forms, semantic role labelling, coreference resolution, relations extraction, named entity recognition, among others.

Question decomposition: decomposition of the question into separate phrases, which will generate constraints that need to be satisfied by evidence from the data.

Ferrucci et al. 2010

Question

Question & Topic Analysis

Question Decomposition

“Rap” Sheet

This archaic term for a mischievous or annoying

child can also mean a rogue or scamp.

This archaic term for a mischievous or

annoying child.

This term can also mean a rogue or

scamp.

Rapscallion

Hypothesis generation:

◦ Primary search Document and passage retrieval

SPARQL queries are used over triple stores.

◦ Candidate answer generation (maximizing recall). Information extraction techniques are applied to the

search results to generate candidate answers.

Soft filtering: Application of lightweight (less resource intensive) scoring algorithms to a larger set of initial candidates to prune the list of candidates before the more intensive scoring components.

Question

Primary

Search

Candidate

Answer

Generation

Hypothesis Generation

Answer

Sources

Hypothesis and Evidence Scoring

Hypothesis and evidence scoring:

◦ Supporting evidence retrieval Seeks additional evidence for each candidate answer from

the data sources while the deep evidence scoring step determines the degree of certainty that the retrieved evidence supports the candidate answers.

◦ Deep evidence scoring Scores are then combined into an overall evidence profile

which groups individual features into aggregate evidence dimensions.

Answer

Scoring

Question

Evidence

Sources

Primary

Search

Candidate

Answer

Generation

Answer

Sources

Evidence

Retrieval

Evidence

Scoring

Answer merging: is a step that merges answer candidates (hypotheses) with different surface forms but with related content, combining their scores.

Ranking and confidence estimation: ranks the hypotheses and estimate their confidence based on the scores, using machine learning approaches over a training set. Multiple trained models cover different question types.

Farrell, 2011

Answer

Scoring

Models

Answer &

Confidence

Question

Evidence

Sources

Models

Models Primary

Search

Candidate

Answer

Generation

Final Confidence Merging & Ranking

Synthesis

Answer

Sources

Evidence

Retrieval

Evidence

Scoring

Learned Models

help combine and

weigh the Evidence

Question 100s Possible

Answers

1000’s of

Pieces of Evidence

Multiple

Interpretations

100,000’s scores from many simultaneous

Text Analysis Algorithms 100s sources

Final Confidence Merging & Ranking

Synthesis

Answer &

Confidence

Ferrucci et al. 2010,

Farrell, 2011

UIMA for interoperability

UIMA-AS for scale-out and speed

Evaluation of QA over Linked Data

Test Collection ◦ Questions

◦ Datasets

◦ Answers (Gold-standard)

Evaluation Measures

Measures how complete is the answer set.

The fraction of relevant instances that are retrieved.

Which are the Jovian planets in the Solar System? ◦ Returned Answers:

Mercury

Jupiter

Saturn

Gold-standard:

– Jupiter

– Saturn

– Neptune

– Uranus

Measures how accurate is the answer set.

The fraction of retrieved instances that are relevant.

Which are the Jovian planets in the Solar System? ◦ Returned Answers:

Mercury

Jupiter

Saturn

Gold-standard:

– Jupiter

– Saturn

– Neptune

– Uranus

Measures the ranking quality.

The Reciprocal-Rank (1/r) of a query can be defined as the rank r at which a system returns the first relevant result.

Which are the Jovian planets in the Solar System?

Returned Answers:

– Mercury

– Jupiter

– Saturn

Gold-standard:

– Jupiter

– Saturn

– Neptune

– Uranus

Query execution time

Indexing time

Index size

Dataset adaptation effort (Indexing time)

Semantic enrichment/disambiguation ◦ # of operations/time

Question Answering over Linked Data (QALD-CLEF)

INEX Linked Data Track

BioASQ

SemSearch

QALD is a series of evaluation campaigns on question answering over linked data. ◦ QALD-1 (ESWC 2011) ◦ QALD-2 as part of the workshop

(Interacting with Linked Data (ESWC 2012))

◦ QALD-3 (CLEF 2013) ◦ QALD-4 (CLEF 2014)

It is aimed at all kinds of systems that mediate between a user, expressing his or her information need in natural language, and semantic data.

QALD-4 is part of the Question Answering track at CLEF 2014:

http://nlp.uned.es/clef-qa/

Tasks: ◦ 1. Multilingual question answering over DBpedia

◦ 2. Biomedical question answering on interlinked data

◦ 3. Hybrid question answering

Given a natural language question or keywords, either retrieve the correct answer(s) from a given RDF repository, or provide a SPARQL query that retrieves these answer(s).

◦ Dataset: DBpedia 3.9 (with multilingual labels)

◦ Questions: 200 training + 50 test

◦ Seven languages:

English, Spanish, German, Italian, French, Dutch, Romanian

<question id = "36" answertype = "resource"

aggregation = "false"

onlydbo = "true" >

Through which countries does the Yenisei river flow? Durch welche Länder fließt der Yenisei? ¿Por qué países fluye el río Yenisei? ...

PREFIX res: <http://dbpedia.org/resource/>

PREFIX dbo: <http://dbpedia.org/ontology/>

SELECT DISTINCT ?uri WHERE {

res:Yenisei_River dbo:country ?uri .

Datasets: SIDER, Diseasome, Drugbank

Questions: 25 training + 25 test

require integration of information from different datasets

Example: What is the side effects of drugs used for Tuberculosis?

disease:1154 diseasome:possibleDrug ?v2 .

?v2 a drugbank:drugs .

?v3 owl:sameAs ?v2 .

?v3 sider:sideEffect ?x .

Dataset: DBpedia 3.9 (with English abstracts)

Questions: 25 training + 10 test

require both structured data and free text from the abstract to be answered

Example: Give me the currencies of all G8 countries.

SELECT DISTINCT ?uri WHERE {

?x text:"member of" text:"G8" .

?x dbo:currency ?uri .

Focuses on the combination of textual and structured data.

Datasets:

◦ English Wikipedia (MediaWiki XML Format)

◦ DBpedia 3.8 & YAGO2 (RDF)

◦ Links among the Wikipedia, DBpedia 3.8, and YAGO2 URI's.

Tasks:

◦ Ad-hoc Task: return a ranked list of results in response to a search topic that is formulated as a keyword query (144 search topics).

◦ Jeopardy Task: Investigate retrieval techniques over a set of natural-language Jeopardy clues (105 search topics – 74 (2012) + 31 (2013)).

https://inex.mmci.uni-saarland.de/tracks/lod/

Focuses on entity search over Linked Datasets. Datasets: ◦ Sample of Linked Data crawled from publicly available

sources (based on the Billion Triple Challenge 2009).

Tasks: ◦ Entity Search: Queries that refer to one particular

entity. Tiny sample of Yahoo! Search Query. ◦ List Search: The goal of this track is select objects that

match particular criteria. These queries have been hand-written by the organizing committee.

http://semsearch.yahoo.com/datasets.php#

List Search queries: ◦ republics of the former Yugoslavia ◦ ten ancient Greek city ◦ kingdoms of Cyprus ◦ the four of the companions of the prophet ◦ Japanese-born players who have played in MLB where

the British monarch is also head of state ◦ nations where Portuguese is an official language ◦ bishops who sat in the House of Lords ◦ Apollo astronauts who walked on the Moon

Entity Search queries: ◦ 1978 cj5 jeep

◦ employment agencies w. 14th street

◦ nyc zip code

◦ waterville Maine

◦ LOS ANGELES CALIFORNIA

◦ ibm

◦ KARL BENZ

◦ MIT

Balog & Neumayer, A Test Collection for Entity Search in DBpedia (2013).

Datasets: ◦ PubMed documents

Tasks: ◦ 1a: Large-Scale Online Biomedical Semantic

Indexing

Automatic annotation of PubMed documents.

Training data is provided.

◦ 1b: Introductory Biomedical Semantic QA

300 questions and related material (concepts, triples and golden answers).

Metrics, Statistics, Tests - Tetsuya Sakai (IR) ◦ http://www.promise-noe.eu/documents/10156/26e7f254-

1feb-4169-9204-1c53cc1fd2d7

Building test Collections (IR Evaluation - Ian Soboroff) ◦ http://www.promise-noe.eu/documents/10156/951b6dfb-

a404-46ce-b3bd-4bbe6b290bfd

Do-it-yourself (DIY): Core Resources

DBpedia ◦ http://dbpedia.org/

YAGO ◦ http://www.mpi-inf.mpg.de/yago-naga/yago/

Freebase ◦ http://www.freebase.com/

Wikipedia dumps ◦ http://dumps.wikimedia.org/

ConceptNet ◦ http:// conceptnet5.media.mit.edu/

Common Crawl ◦ http://commoncrawl.org/

Where to use: ◦ As a commonsense KB or as a data source

High domain coverage: ◦ ~95% of Jeopardy! Answers. ◦ ~98% of TREC answers.

Wikipedia is entity-centric. Curated link structure. Complementary tools: ◦ Wikipedia Miner.

Where to use: ◦ Construction of distributional semantic models. ◦ As a commonsense KB

WordNet ◦ http://wordnet.princeton.edu/

Wiktionary ◦ http://www.wiktionary.org/ ◦ API: https://www.mediawiki.org/wiki/API:Main_page

FrameNet ◦ https://framenet.icsi.berkeley.edu/fndrupal/

VerbNet ◦ http://verbs.colorado.edu/~mpalmer/projects/verbnet.html

English lexicon for DBpedia 3.8 (in the lemon format) ◦ http://lemon-model.net/lexica/dbpedia_en/

PATTY (collection of semantically-typed relational patterns) ◦ http://www.mpi-inf.mpg.de/yago-naga/patty/

BabelNet ◦ http://babelnet.org/

Where to use: ◦ Query expansion ◦ Semantic similarity ◦ Semantic relatedness ◦ Word sense disambiguation

Lucene & Solr ◦ http://lucene.apache.org/

Terrier ◦ http://terrier.org/

Where to use: ◦ Answer Retrieval

◦ Scoring

◦ Query-Data matching

GATE (General Architecture for Text Engineering) ◦ http://gate.ac.uk/

NLTK (Natural Language Toolkit) ◦ http://nltk.org/

Stanford NLP ◦ http://www-nlp.stanford.edu/software/index.shtml

LingPipe ◦ http://alias-i.com/lingpipe/index.html

Where to use: ◦ Question Analysis

MALT ◦ http://www.maltparser.org/ ◦ Languages (pre-trained): English, French, Swedish

Stanford parser ◦ http://nlp.stanford.edu/software/lex-parser.shtml ◦ Languages: English, German, Chinese, and others

CHAOS ◦ http://art.uniroma2.it/external/chaosproject/ ◦ Languages: English, Italian

C&C Parser ◦ http://svn.ask.it.usyd.edu.au/trac/candc

Where to Use: ◦ Question Analysis

NERD (Named Entity Recognition and Disambiguation) ◦ http://nerd.eurecom.fr/

Stanford Named Entity Recognizer ◦ http://nlp.stanford.edu/software/CRF-NER.shtml

FOX (Federated Knowledge Extraction Framework) ◦ http://fox.aksw.org

DBpedia Spotlight ◦ http://spotlight.dbpedia.org

Where to use: ◦ Question Analysis ◦ Query-Data Matching

Wikipedia Miner ◦ http://wikipedia-miner.cms.waikato.ac.nz/

WS4J (Java API for several semantic relatedness algorithms) ◦ https://code.google.com/p/ws4j/

SecondString (string matching) ◦ http://secondstring.sourceforge.net

EasyESA (distributional semantics framework) ◦ http://easy-esa.org

S-space (distributional semantics framework) ◦ https://github.com/fozziethebeat/S-Space

Where to use: ◦ Query-Data matching ◦ Semantic relatedness & similiarity ◦ Word Sense Disambiguation

DIRT ◦ Paraphrase Collection:

http://aclweb.org/aclwiki/index.php?title

◦ DIRT_Paraphrase_Collection

http://demo.patrickpantel.com/demos/lexsem/paraphrase.htm

PPDB (The Paraphrase Database) ◦ http://www.cis.upenn.edu/~ccb/ppdb/

Where to use: ◦ Query-Data matching

Apache UIMA ◦ http://uima.apache.org/

Open Advancement of Question Answering Systems (OAQA) ◦ http://oaqa.github.io/

OKBQA ◦ http://www.okbqa.org/documentation

https://github.com/okbqa

Where to use: ◦ Components integration

Trends

Querying distributed linked data Integration of structured and unstructured data

User interaction and context mechanisms Integration of reasoning (deductive, inductive,

counterfactual, abductive ...) on QA approaches and test collections

Measuring confidence and answer uncertainty

Multilinguality Machine Learning

Reproducibility and resource integration in QA research

Linked/Big Data demand new principled semantic approaches to cope with the scale and heterogeneity of data.

Part of the Semantic Web/AI vision can be addressed today with a multi-disciplinary perspective:

◦ Linked Data, IR and NLP

The multidiscipinarity of the QA problem can show what semantic computing have achieved and can be transported to other information system types.

Challenges are moving from the construction of basic QA systems to more sophisticated semantic functionalities.

Very active research area.

[1] Kaufmann & Bernstein, How Useful are Natural Language Interfaces to the Semantic Web for Casual End-users?, 2007

[2] Chin-Yew Lin, Question Answering.

[3] Farah Benamara, Question Answering Systems: State of the Art and Future Directions.

[4] Yahya et al Robust Question Answering over the Web of Linked Data, CIKM, 2013.

[5] Freitas et al., Querying Heterogeneous Datasets on the Linked Data Web: Challenges, Approaches and Trends, 2012.

[6] Freitas et al., Answering Natural Language Queries over Linked Data Graphs: A Distributional Semantics Approach,, 2014.

[7] Freitas et al., Querying Heterogeneous Datasets on the Linked Data Web: Challenges, Approaches and Trends, 2012.

[8] Freitas & Curry, Natural Language Queries over Heterogeneous Linked Data Graphs: A Distributional-Compositional Semantics Approach, IUI, 2014.

[9] Cimiano et al., Towards portable natural language interfaces to knowledge bases, 2008.

[10] Lopez et al., PowerAqua: fishing the semantic web, 2006.

[11] Damljanovic et al., Natural Language Interfaces to Ontologies: Combining Syntactic Analysis and Ontology-based Lookup through the User Interaction, 2010

[12] Unger et al. Template-based Question Answering over RDF Data, 2012.

[13] Cabrio et al., QAKiS: an Open Domain QA System based on Relational Patterns, 2012.

[14] How Useful Are Natural Language Interfaces to the Semantic Web for Casual End-Users?, 2007.

[15] Popescu et al.,Towards a theory of natural language interfaces to databases., 2003.

[16] Farrel, IBM Watson A Brief Overview and Thoughts for Healthcare Education and Performance Improvement .

[17] Freitas et al. On the Semantic Mapping of Schema-agnostic Queries: A Preliminary Study, NLIWoD, 2014.

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