Improving semantic search using query log analysis
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Improving Semantic Search Using Query Log Analysis. Khadija Elbedweihy, Stuart N . Wrigley and Fabio Ciravegna OAK Research Group, Department of Computer Science, University of Sheffield, UK. Outline. Introduction Semantic Query Logs Analysis - Query-Concepts Model

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Improving Semantic Search Using Query Log Analysis

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Improving semantic search using query log analysis

Improving Semantic Search UsingQuery Log Analysis

Khadija Elbedweihy, Stuart N. Wrigley and Fabio Ciravegna

OAK Research Group,

Department of Computer Science,

University of Sheffield, UK


Improving semantic search using query log analysis

Outline

Introduction

Semantic Query Logs Analysis

- Query-Concepts Model

- Concepts-Predicates Model

- Instance-Types Model

Results Augmentation

Data Visualisation


Introduction

Introduction


Improving semantic search using query log analysis

Motivation

See our paper from this morning’s IWEST 2012 workshop

  • Little work on results returned (answers) and presentation style.

    • Users want direct answers augmented with more information for richer experience1

    • Users want more user-friendly and attractive results presentation format1

    • Semantic query logs: logs of queries issued to repositories containing RDF data.


Improving semantic search using query log analysis

Related Work

Semantic query logs analysis:

Moller et al. identified patterns of Linked Data usage with respect to different types of agents.

Arias et al. analysed the structure of the SPARQL queries to identify most frequent language elements.

Luczak-Rösch et al. analysed query logs to detect errors and weaknesses in LD ontologies and support their maintenance.


Improving semantic search using query log analysis

Related Work (cont’d)

How our work is different:

Analyze semantic query logs to produce models capturing different patterns of information needs on Linked Data:

  • Concepts used together in a query: query-concepts model

  • Predicate used with a concept: concept-predicates model

  • Concepts used as types of a LD entity: instance-types model

    The models make use of the “collaborative knowledge” inherent in the logs to enhance the search process.


Semantic query log analysis

Semantic query log analysis


Improving semantic search using query log analysis

Extraction

Extract SPARQL query

Query logs entries follow the Combined Log Format (CLF):

SELECT DISTINCT ?genre, ?instrument WHERE

{

<…dbpedia.org…/Ringo_Starr> ?rel <…dbpedia.org/…/The_Beatles>.

<…dbpedia.org…/Ringo_Starr> dbpedia:genre ?genre.

<…dbpedia.org…/Ringo_Starr> dbpedia:instrument ?instrument.

}


Improving semantic search using query log analysis

Analysis

query

type

http://dbpedia.org/resource/Ringo_Starr

type

http://dbpedia.org/ontology/MusicalArtist

SELECT DISTINCT ?genre, ?instrument WHERE

{

<…dbpedia.org…/Ringo_Starr> ?rel <…dbpedia.org/…/The_Beatles>.

<…dbpedia.org…/Ringo_Starr> dbpedia:genre ?genre.

<…dbpedia.org…/Ringo_Starr> dbpedia:instrument ?instrument.

}

For each bound resource (subject or object) ->

query endpoint for the type of the resource


Improving semantic search using query log analysis

Query-Concepts Model

SELECT DISTINCT ?genre, ?instrument WHERE

{ <…dbpedia.org…/Ringo_Starr> ?rel<…dbpedia.org/…/The_Beatles>.

<…dbpedia.org…/Ringo_Starr> dbpedia:instrument ?instrument. }

1) Retrieve types of resources in the query:

Ringo_Starrtype dbpedia-owl:MusicalArtist, umbel:MusicalPerformer

The_Beatlestype dbpedia-owl:Band, schema:MusicGroup

2) Increment the co-occurrence of each concept in the first list with each concept in the second:

MusicalArtist Band MusicalPerformer MusicGroup

MusicalArtist MusicGroupMusicalPerformer Band


Improving semantic search using query log analysis

Concept-Predicates Model

SELECT DISTINCT ?genre, ?instrument WHERE

{ <…dbpedia.org…/Ringo_Starr> ?rel<…dbpedia.org/…/The_Beatles>.

<…dbpedia.org…/Ringo_Starr> dbpedia:genre?genre.

<…dbpedia.org…/Ringo_Starr> dbpedia:instrument?instrument. }

1) Retrieve types of resources used as subjects in the query:

Ringo_Starrtype dbpedia-owl:MusicalArtist, umbel:MusicalPerformer

2) Identifyboundpredicates (dbpedia:genre, dbpedia:instrument)

3) Increment the co-occurrence of each type with the predicate used in the same triple pattern:

MusicalPerformer genreMusicalPerformer instrument

MusicalArtist genre MusicalArtist instrument


Improving semantic search using query log analysis

Instance-Types Model

SELECT DISTINCT ?genre, ?instrument WHERE

{ <…dbpedia.org…/Ringo_Starr> ?rel<…dbpedia.org/…/The_Beatles>.

<…dbpedia.org…/Ringo_Starr> dbpedia:instrument ?instrument. }

1) Retrieve types of resources in the query:

Ringo_Starrtype dbpedia-owl:MusicalArtist, umbel:MusicalPerformer

The_Beatlestype dbpedia-owl:Band, schema:MusicGroup

2) Increment the co-occurrence of concepts found as types for the same instance:

MusicalArtist MusicalPerformer

Band MusicGroup


Result augmentation

Result Augmentation


Improving semantic search using query log analysis

Dataset

Two sets of DBpedia query logs made available at the USEWOD2011 and USEWOD2012 workshops.

The logs contained around 5 million queries issued to DBpedia over a time period spanning almost 2 years


Improving semantic search using query log analysis

Results Enhancement

2. See our paper from this morning’s IWEST 2012 workshop

Google, Yahoo!, Bing, etc. enhance searchresults using structureddata

FalconS and VisiNav return extra information together with each entity in the answers (e.g. type, label)

Evaluation of Semantic Search showed that augmenting answers with extra information provides a richer user experience2.


Improving semantic search using query log analysis

FalconS Results

Query: `population of New York city’

Information chosen depend on manually (randomly) predefined set.


Improving semantic search using query log analysis

Motivation for proposed approach

3. Luczak-Rösch et al. ; Elbedweihy et al.

Utilizing query logs as a source of collaborative knowledge able to capture implicit associations between Linked Data entities and properties.

Use this to select which information to show the user.

Two recent studies3 analyzed semantic query logs and observed that a class of entities is usually queried with similar relations and concepts.


Improving semantic search using query log analysis

Two Related Types of Result Augmentation

  • Additional result-related information.

    • More details about each result item

    • Provides better understanding of the answer.

  • Additional query-related information.

    • More results related to the query entities

    • Assists users in discovering useful findings (serendipity)


Improving semantic search using query log analysis

Return additional result-relatedinformation

Steps

  • For each result item, find types of instance.

  • Most frequently queried predicates associated with them are extracted from the concept-predicates model.

  • Generate queries with each pair (instance, predicate).

    e.g. (<…dbpedia.org…/Ringo_Starr> , genre)

  • Show aggregated results to the user.


Improving semantic search using query log analysis

Return additional result-related information

MusicalArtist-> genre, associatedBand, occupation, instrument, birthDate, birthPlace, hometown, prop:yearsActive, foaf:surname, prop:associatedActs, …

Query: “Who played drums for the Beatles?”

Result: Ringo Starr

➔ Pop music, Rock music (genre)

➔ Keyboard, Drum, Acousticguitar(instrument)

➔ The Beatles, Plastic Ono Band, Rory Storm,(assoc.Band)


Improving semantic search using query log analysis

Return additional query-related information

Steps

Extract all concepts from query.

For any instances, find their types.

For each query concept, find most frequently occurring concepts from the query-concepts model.

For each related concept, query for instances that have relation with the originating instance.

Show aggregated results to the user.


Improving semantic search using query log analysis

Return additional query-related information

City-> Book, Person, Country, Organisation, SportsTeam, MusicGroup, Film, RadioStation, River, University, SoccerPlayer, Hospital, ...

Query: “Where is the University of Sheffield located?”

Result: Sheffield, UK

➔ Nick Clegg, Clive Betts, David Blunkett (Person)

➔ Sheffield United F.C., Sheffield Wednesday (SportsTeam)

➔ Hallam FM, Real Radio, BBC Radio Sheffield(RadioStn.)

➔ Jessop Hosp., Northern General, Royal Hallamshire(Hospital)

➔ Uni.of Sheffield, Sheffield Hallam Uni. (University)


Visualisation

Visualisation


Improving semantic search using query log analysis

Data Visualization

View-based interfaces (e.g. Semantic Crystal and Smeagol) support users in query formulation by showing the underlying data and connections.

Helpful for users, especially those unfamiliar with the search domain.

Try to bridge the gap between user terms and tool terms (habitability problem)

Facing challenge to visualize large datasets without cluttering the view and affecting user experience.


Improving semantic search using query log analysis

Data Visualization: Proposed approach

Visualizing large datasets (especially heterogeneous ones) is a challenge.

To overcome this, we need to select and visualize specific parts of the data.

Exploit collaborative knowledge in query logs to derive selection of concepts and predicates added to user’s subgraph of interest.


Improving semantic search using query log analysis

Data Visualization: Proposed approach

Steps

  • User enters NL query

  • Return best-attempt results

  • Identify query instances and find their types

  • For each type:

    • Extract most queried predicates associated with it from concept-predicates model.

    • Extract most queried concepts associated with it from query-concepts model.

  • Add these to the user’s query graph (see next slide)


Improving semantic search using query log analysis

Example

Best-attempt results

Result-Related information

➔ depiction:

Query: “What is the capital of Egypt?”

Answer: Cairo

➔ latitude: 30.058056

➔ longitude: 31.228889

➔ population: 6758581

➔ area: 453000000

➔ time zone: Eastern European Time

➔ subdivision: Governorates of Egypt

➔ page: http://www.cairo.gov.eg/default.aspx

➔ nickname: The City of a Thousand Minarets, Capital of the

Arab World


Improving semantic search using query log analysis

Example

Query-Related information

Query: “What is the capital of Egypt?”

Answer: Cairo

➔ Cairo Uni., Ain Shams Uni., German Uni., British Uni. (University)

➔ IttihadEl Shorta, El Shams Club, AlNasr Egypt (SportsTeam)

➔ Orascom Telecom, HSBC Bank, EgyptAir, Olympic Grp (Organisation)

➔ Nile River (River)

➔ Al Azhar Park (Park)

➔ Hani Shaker, Sherine, Umm Kulthum, Am Diab (MusicalArtist)

➔ Nile TV, AL Nile, Al-Baghdadia TV (BroadCaster)

➔ Egyptian Museum, Museum of Islamic Art (Museum)


Improving semantic search using query log analysis

Data Visualization: Proposed approach

Most queried predicates with “Country”

Most queried concepts with “Country”

Query instance

Step 5: Add concepts and predicates to user’s query graph


Improving semantic search using query log analysis

Questions

Thank You

Questions?


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