Question Answering over Linked Data: Challenges, Approaches & Trends (Tutorial @ ESWC 2014)

Question Answering over Linked Data

Challenges, Approaches & Trends

André Freitas and Christina Unger

Tutorial @ ESWC 2014

Understand the changes on the database landscape in the direction of more heterogeneous data scenarios, especially the Semantic Web.

Understand how Question Answering (QA) fits into this new scenario.

Give the fundamental pointers for you to develop your own QA system from the state-of-the-art.

Focus on coverage.

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Goal of this Talk

Motivation & Context Big Data, Linked Data & QA Challenges for QA over LD 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

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Outline

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Motivation & Context

Humans are built-in with natural language communication capabilities.

Very natural way for humans to communicate information needs.

The archetypal AI system.

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Why Question Answering?

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◦ ...

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What is Question Answering?

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What is Question Answering?

QA System

Knowledge Bases

Question: Who is the daughter of Bill

Clinton married to?

Answer: Marc Mezvinsky

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

techniques.

QA as a ‘Semantic Pentathlon’

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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.

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QA vs IR

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

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QA vs Database Querying

Keyword search:◦ Simple information needs.◦ Predictable search behavior.◦ Vocabulary redundancy (large document

collections, Web). Structured queries:

◦ Demand for absolute precision/recall guarantees.◦ Small & centralized schemas.◦ More data volume/less semantic heterogeneity.

QA:◦ Specification of complex information needs.◦ Heterogeneous and schema-less data.◦ More automated semantic interpretation.

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When to use?

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QA: Vision

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QA: Reality (FB Graph Search)

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QA: Reality (Watson)

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QA: Reality (Siri)

QA: Reality (Google Knowledge Graph)

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

QA supports the specification of more complex information needs.

QA, Information Retrieval and Databases are complementary perspectives.

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To Summarize

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Big Data, Linked Data

& QA

Big Data: More complete data-based picture of the world.

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Big Data

Heterogeneous, complex and large-scale databases. Very-large and dynamic “schemas”.

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From Rigid Schemas to Schema-less

10s-100s attributes

1,000s-1,000,000s attributescirca 2000

circa 2013

Multiple perspectives (conceptualizations) of the reality. Ambiguity, vagueness, inconsistency.

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Multiple Interpretations

Franklin et al. (2005): From Databases to Dataspaces.

Helland (2011): If You Have Too Much Data, then “Good Enough” Is Good Enough.

Fundamental trends:◦ Co-existence of heterogeneous data.◦ Semantic best-effort behavior.◦ Pay-as-you go data integration.◦ Co-existing query/search services.

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Big Data & Dataspaces

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Trend 1: Data size

Gantz & Reinsel, The Digital Universe in 2020 (2012).

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Trend 2: Connectedness

Eifrem, A NOSQL Overview And The Benefits Of Graph Database (2009).

Decentralization of content generation.

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Trend 3

Eifrem, A NOSQL Overview And The Benefits Of Graph Database (2009)

Volume Velocity Variety

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Big Data

- The most interesting but usually neglected dimension.

The Semantic Web Vision: Software which is able to understand meaning (intelligent, flexible).

QA as a way to support this vision.

QA & Semantic Web

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From which university did the wife of Barack Obama graduate?

With Linked Data we are still in the DB world

QA & Linked Data

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With Linked Data we are still in the DB world

(but slightly worse)

QA & Linked Data

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Addresses practical problems of data accessibility in a data heterogeneity scenario.

A fundamental part of the original Semantic Web vision.

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QA & Linked Data

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Challenges for QA over Linked Data

From questions to answers

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Example: What is the currency of the Czech Republic?

SELECT DISTINCT ?uri WHERE { res:Czech_Republic dbo:currency ?uri . }

Main challenges:

The gap between natural language and linked data

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

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

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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

...

Mapping natural language expressions to vocabulary elements

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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)}

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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?

SELECT DISTINCT ?uri WHERE { res:Bruce_Lee dbo:child ?c . ?c dbo:child ?uri .}

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


Who produced the most films?

SELECT DISTINCT ?uri WHERE { ?x rdf:type dbo:Film . ?x dbo:producer ?uri .}ORDER BY DESC(COUNT(?x))OFFSET 0 LIMIT 1

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

Example:

Meaning variation: Ambiguity

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

Meaning variation: Semantically light expressions

Which museum has the most paintings?

?museum dbo:exhibits ?painting .

Which country has the most caves?

?cave dbo:location ?country .

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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).

Multilinguality

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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 quality and data heterogeneity

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Data is distributed among a large collection of interconnected datasets.

Distributed and linked datasets

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

SELECT DISTINCT ?xWHERE { disease:1154 diseasome:possibleDrug ?d1. ?d1 a drugbank:drugs . ?d1 owl:sameAs ?d2. ?d2 sider:sideEffect ?x.}

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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).

Performance and scalability

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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).

Summary: Challenges

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The Anatomy of a QA System

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Natural Language Interfaces (NLI) ◦ Input: Natural language queries◦ Output: Either processed or unprocessed answers

Processed: Direct answers (QA). Unprocessed: Database records, text snippets,

documents.

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NLI & QAs

NLI

QA

Categorization of questions and answers. Important for:

◦ Understanding the challenges before attacking the problem.

◦ Scoping the QA system.

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

of the Art and Future Directions.

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Basic Concepts & Taxonomy

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 …”.

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Terminology: Question Phrase

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 Taj Mahal?”

LIST: “Give me all cities in Germany.”

SUPERLATIVE: “What is the highest mountain?”

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

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Terminology: Question Type

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?”51

Terminology: Question Type

The class of object sought by the question: Abbreviation 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 …”)

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Terminology: Answer Type

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)

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Terminology: Question Focus & Topic

Structure level:◦ Structured data (databases).◦ Semi-structured data (e.g. XML).◦ Unstructured data.

Data source:◦ Single dataset (centralized).◦ Enumerated list of multiple, distributed datasets.◦ Web-scale.

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Terminology: Data Source Type

Domain Scope:◦ Open Domain◦ Domain specific

Data Type:◦ Text◦ Image◦ Sound◦ Video

Multi-modal QA

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Terminology: Domain Type

Long answers◦ Definition/justification based.

Short answers ◦ Phrases.

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

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Terminology: Answer Format

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.

Answer Quality Criteria

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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.

Answer Assessment

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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.

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Answer Processing

Semantic Tractability (Popescu et al., 2003): Vocabulary overlap/distance between the query and the answer.

Answer Locality (Webber et al., 2003): Whether answer fragments are distributed across different document fragments / documents or datasets/dataset records.

Derivability (Webber et al, 2003): Dependent if the answer is explicit or implicit. Level of reasoning dependency.

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

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Complexity of the QA Task

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High-level Components

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 Constuction: 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.

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Main Components

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QA over Linked

Data(Case Studies)

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

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

Aqualog & PowerAqua (Lopez et al. 2006)◦ Querying on a Semantic Web scale

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

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QA Systems (Case Studies)

QuestIO & Freya (Damljanovic et al. 2010) QAKIS (Cabrio et al. 2012) Yahya et al., 2013

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QA Systems (Not covered)

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ORAKEL (Cimiano et al, 2007) & Pythia

(Unger & Cimiano, 2011)Ontology-specific question answering

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

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ORAKEL (Cimiano et al. 2007) &Pythia (Unger & Cimiano 2011)

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ORAKEL (Cimiano et al. 2007) &Pythia (Unger & Cimiano 2011)

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)

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Grammars 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

Ontology Lexicon

lemon (Lexicon Model for Ontologies)http://lemon-model.net

◦ meta-model for describing ontology lexica with RDF

◦ declarative (abstracting from specific syntactic and semantic theories)

◦ separation of lexicon and ontology

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

pointing to elements in the ontology.

Example:

Ontology Lexicon

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Architecture

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Which cities have more than three universities?

Example

SELECT DISTINCT ?x WHERE { ?x rdf:type dbo:City . ?y rdf:type dbo:University . ?y dbo:city ?x .}GROUP BY ?yHAVING (COUNT(?y) > 3)

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

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

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TBSL (Unger et al., 2012)

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

Motivation

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 1more than i N → HAVING COUNT(?N) > i

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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.

Template-based QA

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Example: Who produced the most films?

SPARQL template:

SELECT DISTINCT ?x WHERE { ?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>

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Architecture

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Step 1: Template generationLinguistic processing

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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.

Step 1: Template generationLinguistic processing

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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:

SELECT DISTINCT ?x WHERE { ?x ?p ?y .}ORDER BY DESC(COUNT(?y)) LIMIT 1

?p PROPERTY [films]

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Step 2: Template instantiationEntity identification and predicate detection

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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.

Step 2: Template instantiationEntity identification and predicate detection

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?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>

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Step 3: Query ranking and selection

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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.

Step 3: Query ranking and selection

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SELECT DISTINCT ?x WHERE { ?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

SELECT DISTINCT ?x WHERE { ?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

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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.

Main drawback

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Aqualog & PowerAqua (Lopez et al.

2006)Querying on a Semantic Web scale

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.

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PowerAqua (Lopez et al. 2006)

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WordNet

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.

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Sense-based similarity matcher

Lopez et al. 2006

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PowerAqua (Lopez et al. 2006)

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

distributional semantics

Key contributions: ◦ Distributional semantic relatedness matching

model.◦ Compositional-distributional model for QA.

Terminological Matching: ◦ Explicit Semantic Analysis (ESA)◦ String similarity + node cardinality

Evaluation: QALD (2011)

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Treo (Freitas et al. 2011, 2014)

Treo (Irish): Direction

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Simple Queries (Video)

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More Complex Queries (Video)

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Vocabulary Search (Video)

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Treo Answers Jeopardy Queries (Video)

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Semantics for a Complex World• 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

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Baroni et al. 2013

Distributional Semantics“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.

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Distributional Semantic Model

c1

child

husbandspouse

cn

c2

function (number of times that the words occur in c1)

0.7

0.5

Commonsense is here

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Semantic Relatedness

θ

c1

child

husbandspouse

cn

c2

Works as a semantic ranking function

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Approach Overview

Query Planner

Ƭ-Space

Large-scale unstructured data

Commonsense knowledge

Database

Distributional semantics

Core semantic approximation &

composition operations

Query AnalysisQuery Query Features

Query Plan

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Approach Overview

Query Planner

Ƭ-Space

Wikipedia

Commonsense knowledge

RDF

Explicit Semantic Analysis

Core semantic approximation &

composition operations

Query AnalysisQuery Query Features

Query Plan

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Ƭ-Space

e

p

r

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Ƭ-Space The vector space is segmented by the instances

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Core OperationsQuery

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Core Operations

Search & Composition Operations

Query

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Search and Composition Operations 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)

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Core Principles

Minimize the impact of Ambiguity, Vagueness, Synonymy.

Address the simplest matchings first (heuristics).

Semantic Relatedness as a primitive operation.

Distributional semantics as commonsense knowledge.

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Transform natural language queries into triple patterns.

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

Query Pre-Processing (Question Analysis)

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


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

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


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Step 3: Determine answer type◦ Rules-based.

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


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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)


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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 TYPE

QUESTION FOCUS

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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

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Map query features into a query plan. A query plan contains a sequence of:

◦ Search operations.◦ Navigation operations.

Query Planning

(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

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Core Entity Search

Bill Clinton daughter married to Person

:Bill_Clinton

Query:

Linked Data:

Entity Search

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Distributional Semantic Search


:Bill_Clinton

Query:

Linked Data:

:Chelsea_Clinton

:child

:Baptists:religion

:Yale_Law_School

:almaMater

...(PIVOT ENTITY)

(ASSOCIATED TRIPLES)

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:Bill_Clinton

Query:

Linked Data:

:Chelsea_Clinton

:child

: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

Which properties are semantically related to ‘daughter’?

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:Bill_Clinton

Query:

Linked Data:

:Chelsea_Clinton

:child

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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.

Distributional Semantic Relatedness

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Use distributional semantics to semantically match query terms to predicates and classes.

Distributional principle: Words that co-occur together tend to have related meaning.◦ Allows the creation of a comprehensive semantic model

from unstructured text.◦ Based on statistical patterns over large amounts of text.◦ No human annotations.

Distributional semantics can be used to compute a semantic relatedness measure between two words.


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:Bill_Clinton

Query:

Linked Data:

:Chelsea_Clinton

:child

(PIVOT ENTITY)

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:Bill_Clinton

Query:

Linked Data:

:Chelsea_Clinton

:child:Mark_Mezvinsky

:spouse

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Architecture

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Index Structure

134

Results

135

Post-Processing

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What is the highest mountain?

Second Query Example

(CLASS) (OPERATOR) Query Features

mountain - highest PODS

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Entity Search

Mountain highest

:Mountain

Query:

Linked Data:

:typeOf

(PIVOT ENTITY)

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Extensional Expansion

Mountain highest

:Mountain

Query:

Linked Data:

:Everest

:typeOf

(PIVOT ENTITY)

:K2:typeOf

...

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Distributional Semantic Matching

Mountain highest

:Mountain

Query:

Linked Data:

:Everest:typeOf

(PIVOT ENTITY)

:K2:typeOf

...

:elevation

:location...

:deathPlaceOf

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Get all numerical values

Mountain highest

:Mountain

Query:

Linked Data:

:Everest:typeOf

(PIVOT ENTITY)

:K2:typeOf

...

:elevation

:elevation

8848 m

8611 m

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Apply operator functional definition

Mountain highest

:Mountain

Query:

Linked Data:

:Everest

:typeOf

(PIVOT ENTITY)

:K2:typeOf

...

:elevation

:elevation

8848 m

8611 m

SORT

TOP_MOST

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Results

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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.

From Exact to Approximate

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From Exact to Approximate

145

User Feedback

146

147

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

QA

Key contributions: ◦ Evidence-based QA system.◦ Complex and high performance QA Pipeline.◦ The system 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

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IBM Watson

Lexical Answer Type

Ferrucci et al. 2010149

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.

Query processing

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Query processing


Question

Question & Topic Analysis

QuestionDecomposition

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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.

Hypothesis Generation

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Hypothesis Generation


Question

PrimarySearch

CandidateAnswer

Generation

Hypothesis

Generation

Answer Sources



HypothesisGeneration

Hypothesis and Evidence Scoring

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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.

Evidence Scoring

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Evidence Scoring


Answer Scoring

Question

Evidence Sources

PrimarySearch

CandidateAnswer

Generation

Hypothesis

Generation


Answer Sources



EvidenceRetrieval

Deep Evidence Scoring



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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.

Answer Generation

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Answer Generation Ferrucci et al. 2010Farrell, 2011

Answer Scoring

Models

Answer & Confidence

Question

Evidence Sources

Models

Models

Models

Models

ModelsPrimarySearch

CandidateAnswer

Generation

Hypothesis

Generation


Final Confidence Merging & Ranking

Synthesis

Answer Sources



EvidenceRetrieval

Deep Evidence Scoring



Learned Modelshelp combine and

weigh the Evidence

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www.medbiq.org/sites/default/files/presentations/2011/Farrell.pp

Architecture

Question100s Possible

Answers

1000’s of Pieces of Evidence

Multiple Interpretations

100,000’s scores from many simultaneous Text Analysis Algorithms100s sources



Final Confidence Merging & Ranking

SynthesisQuestion & Topic Analysis




Answer & Confidence

Ferrucci et al. 2010, Farrell, 2011

158 www.medbiq.org/sites/default/files/presentations/2011/Farrell.pp

UIMA for interoperability

UIMA-AS for scale-out and speed

Evidence Profile


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Evaluation of QA over Linked Data

Test Collection◦ Questions◦ Datasets◦ Answers (Gold-standard)

Evaluation Measures

Evaluation Campaigns

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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

Recall

Gold-standard:– Jupiter– Saturn– Neptune– Uranus

Recall = 2/4 = 0.5

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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

Precision


Recall = 2/3 = 0.67

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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.

Mean Reciprocal Rank (MRR)

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

– Mercury– Jupiter– Saturn


rr = 1/2 = 0.5

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Query execution time Indexing time Index size Dataset adaptation effort (Indexing time) Semantic enrichment/disambiguation

◦ # of operations/time

Additional measures

Question Answering over Linked Data (QALD-CLEF)

INEX Linked Data Track BioASQ SemSearch

Test Collections

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QALDhttp://www.sc.cit-ec.uni-bielefeld.de/qald/

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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

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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

QALD-4

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Task: 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

Task 1: Multilingual question answering

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Example

<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 .}

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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?

Task 2: Biomedical question answering on interlinked data

SELECT DISTINCT ?x WHERE { disease:1154

diseasome:possibleDrug ?v2 . ?v2 a drugbank:drugs . ?v3 owl:sameAs ?v2 . ?v3 sider:sideEffect ?x .}

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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.

Task 3: Hybrid question answering

SELECT DISTINCT ?uri WHERE { ?x text:"member of" text:"G8" . ?x dbo:currency ?uri .}

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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)).

INEX Linked Data Track (2013)

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

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

INEX Linked Data Track (2013)

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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.

SemSearch Challenge

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

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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

SemSearch Challenge

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Entity Search queries:◦ 1978 cj5 jeep ◦ employment agencies w. 14th street ◦ nyc zip code ◦ waterville Maine ◦ LOS ANGELES CALIFORNIA ◦ ibm ◦ KARL BENZ ◦ MIT

SemSearch Challenge

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Baseline

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

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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).

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Metrics, Statistics, Tests - Tetsuya Sakai (IR)◦ http://www.promise-noe.eu/documents/10156/26e7f254-1fe

b-4169-9204-1c53cc1fd2d7

Building test Collections (IR Evaluation - Ian Soboroff)◦ http://www.promise-noe.eu/documents/10156/951b6dfb-a40

4-46ce-b3bd-4bbe6b290bfd

Going Deeper

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http://www.promise-noe.eu/documents/10156/26e7f254-1feb-4169-9204-1c53cc1fd2d7

http://www.promise-noe.eu/documents/10156/26e7f254-1feb-4169-9204-1c53cc1fd2d7

http://www.promise-noe.eu/documents/10156/951b6dfb-a404-46ce-b3bd-4bbe6b290bfd

http://www.promise-noe.eu/documents/10156/951b6dfb-a404-46ce-b3bd-4bbe6b290bfd

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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

Datasets

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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

Wikipedia

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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

Lexical Resources

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

Terrier◦ http://terrier.org/

Where to use:◦ Answer Retrieval◦ Scoring◦ Query-Data matching

Indexing & Search Engines

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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

Text Processing Tools

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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

Parsers

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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

Named Entity Recognition/Linking

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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 semantic relatedness)◦ http://treo.deri.ie/easyESA

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

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

String similarity and semantic relatedness

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DIRT◦ Paraphrase Collection:

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

Demo: 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

Text Entailment

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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

NLP Pipelines

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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 in QA research

Research Topics/Opportunities

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Big/Linked 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 serve 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.

Take-away Message

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Unger, C., Freitas, A., Cimiano, P., Introduction to Question Answering for Linked Data, Reasoning Web Summer School, 2014.

Introductory Reference

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[1] Eifrem, A NOSQL Overview And The Benefits Of Graph Database, 2009

[2] Idehen, Understanding Linked Data via EAV Model, 2010.

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

[4] Chin-Yew Lin, Question Answering.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

References

204

Question Answering over Linked Data: Challenges, Approaches & Trends (Tutorial @ ESWC 2014)

Education

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