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.
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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
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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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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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From the QA expert perspective
◦QA depends on mastering different semantic computing techniques.
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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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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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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.
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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.
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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).
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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
...
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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?
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?
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:
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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.
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).
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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.
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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.
}
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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).
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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).
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The Anatomy of a QA System
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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.
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Natural Language Interfaces (NLI)
◦Input: Natural language queries
◦Output:
QA: Direct answers.
NLI: Database records, text snippets, documents, data visualizations.
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NLI
QA

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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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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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?”
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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?”
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Usually:
◦Rules + Part-of-Speech Tags + Regular Expressions
... goes a long way!
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Structure level:
◦Structured data.
◦Semi-structured data.
◦Unstructured data.
Data source distribution:
◦Single dataset (centralized).
◦Enumerated list of multiple, distributed datasets.
◦Web-scale.
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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
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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
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Long answers
◦Definition/justification based.
Short answers
◦Phrases.
◦Named entities, numbers, aggregate, yes/no.
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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.
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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.
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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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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.
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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.
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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
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QuestIO & Freya (Damljanovic et al. 2010)
QAKIS (Cabrio et al. 2012)
Yahya et al., 2013
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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.
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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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Lopez et al. 2006

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

◦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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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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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 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:
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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)
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◦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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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
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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.
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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>
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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.
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Domain-independent: who, the most
Domain-dependent: produced/VBD, films/NNS
SPARQL template 1:
?x ?p ?y .
?y rdf:type ?c .
}
ORDER BY DESC(COUNT(?y)) LIMIT 1
?c CLASS [films]
SPARQL template 2:
?x ?p ?y .
}
?p PROPERTY [films]
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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.
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?c CLASS [films]
<http://dbpedia.org/ontology/Film>
<http://dbpedia.org/ontology/FilmFestival>
...
<http://dbpedia.org/ontology/producer>
<http://dbpedia.org/property/producer>
<http:// dbpedia.org/ontology/wineProduced>
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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.
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?x <http://dbpedia.org/ontology/producer> ?y .
?y rdf:type <http://dbpedia.org/ontology/Film> .
}
Score: 0.76
?x <http://dbpedia.org/ontology/producer> ?y .
?y rdf:type <http://dbpedia.org/ontology/FilmFestival>.
}
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.
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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.
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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 POS)
E.g.
◦city: N lambda x.city(x)
◦visit: SNP/NP lambda x y exists e . visit(x,y,e)
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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
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Estimate a linear model for scoring derivations (including all parsing and matching decisions) from question-answer pairs
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
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Treo (Freitas et al. 2011, 2014)

◦Distributional semantic relatedness matching model.
◦Distributional model for QA.
Terminological Matching:
◦Explicit Semantic Analysis (ESA)
◦String similarity + node cardinality
Evaluation: QALD (2011)
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Data
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
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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.
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c1
child
husband
spouse
cn
c2 (number of times that the words occur in c1)
0.7
0.5
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θ
c1
child
husband
spouse
cn
c2
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Query Planner
Ƭ
Large-scale
unstructured data
Database
Query Query Analysis Query Features
Query Plan
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Query Planner
Ƭ
Wikipedia
RDF
Query Query Analysis Query Features
Query Plan
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The vector space is segmented by the instances
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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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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
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Transform natural language queries into triple patterns.
“Who is the daughter of Bill Clinton married to?”
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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.
(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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Bill Clinton
daughter
married to
Person
:Bill_Clinton
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Bill Clinton
daughter
married to
Person
:Bill_Clinton
:Chelsea_Clinton
:child
:Baptists
:religion
:Yale_Law_School
:almaMater
...
(PIVOT ENTITY)
(ASSOCIATED TRIPLES)
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Bill Clinton
daughter
married to
Person
:Bill_Clinton
: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
122

Bill Clinton
daughter
married to
Person
:Bill_Clinton
:Chelsea_Clinton
:child
123

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.
124

Bill Clinton
daughter
married to
Person
:Bill_Clinton
:Chelsea_Clinton
:child
(PIVOT ENTITY)
126

Bill Clinton
daughter
married to
Person
:Bill_Clinton
:Chelsea_Clinton
:child
:Mark_Mezvinsky
:spouse
127

What is the highest mountain?
(CLASS)
(OPERATOR)
Query Features
mountain - highest
PODS
134

Mountain
highest
:Mountain
:typeOf
(PIVOT ENTITY)
135

Mountain
highest
:Mountain
:Everest
:typeOf
(PIVOT ENTITY)
:K2
:typeOf
136

Mountain
highest
:Mountain
:Everest
:typeOf
(PIVOT ENTITY)
:K2
:typeOf
:elevation
:location
:deathPlaceOf
137

Mountain
highest
:Mountain
:Everest
:typeOf
(PIVOT ENTITY)
:K2
:typeOf
:elevation
:elevation
8848 m
8611 m
138

Mountain
highest
:Mountain
:Everest
:typeOf
(PIVOT ENTITY)
:K2
:typeOf
:elevation
:elevation
8848 m
8611 m
SORT
TOP_MOST
139

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.
141

144
IBM Watson (Ferrucci et al., 2010)

◦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
145

146 “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.
148

Ferrucci et al. 2010
Question
Question & Topic Analysis
Question Decomposition
149

150 “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.
151

Question
Primary
Search
Candidate
Answer
Generation
Hypothesis Generation
Answer Sources
Question
Decomposition
Hypothesis
Generation
Hypothesis and Evidence Scoring
152

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.
153

Answer Scoring
Question
Evidence Sources
Primary Search
Candidate
Answer
Generation
Hypothesis
Generation
Answer Sources
Question
Decomposition
Evidence
Retrieval
Deep Evidence Scoring
Hypothesis
Generation
154

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.
155

Ferrucci et al. 2010 Farrell, 2011
Answer Scoring
Models
Answer & Confidence
Question
Evidence Sources
Models
Models
Models
Models
Models
Primary
Search
Candidate Answer Generation
Hypothesis
Generation
Final Confidence Merging & Ranking
Synthesis
Answer Sources
Question
Decomposition
Evidence Retrieval
Deep Evidence Scoring
Hypothesis
Generation
Learned Models help combine and weigh the Evidence
156

Question
100s Possible Answers
1000’s of
Pieces of Evidence
Multiple Interpretations
100,000’s scores from many simultaneous Text Analysis Algorithms
100s sources
Hypothesis
Generation
Final Confidence Merging & Ranking
Synthesis
Question
Decomposition
Hypothesis Generation
Answer & Confidence
Ferrucci et al. 2010, Farrell, 2011
157
UIMA for interoperability
UIMA-AS for scale-out and speed

159
Evaluation of QA over Linked Data

Test Collection
◦Questions
◦Datasets
◦Answers (Gold-standard)
Evaluation Measures
160

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
161

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
162

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
163

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
167

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.
169

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
170

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
171

<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/>
res:Yenisei_River dbo:country ?uri .
}
172

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 .
}
173

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.
?x text:"member of" text:"G8" .
?x dbo:currency ?uri .
}
174

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/
180

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#
182

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
183

Entity Search queries:
◦1978 cj5 jeep
◦employment agencies w. 14th street
◦nyc zip code
◦waterville Maine
◦LOS ANGELES CALIFORNIA
◦ibm
◦KARL BENZ
◦MIT
184

Balog & Neumayer, A Test Collection for Entity Search in DBpedia (2013).
185

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).
186

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
187

188
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
189

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
190

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
191

Lucene & Solr
◦http://lucene.apache.org/
Terrier
◦http://terrier.org/
Where to use:
◦Answer Retrieval
◦Scoring
◦Query-Data matching
192

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
193

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:
194

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:
◦Query-Data Matching
195

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:
◦Semantic relatedness & similiarity
◦Word Sense Disambiguation
196

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:
197

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
198

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
200

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.
201

[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.
202

Question Answering over Linked Data (Reasoning Web Summer School)

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