WP8: User Centred Applications

Enrico Motta, Marta Sabou, Vanessa Lopez, Laurian Gridinoc, Lucia Specia

Knowledge Media InstituteThe Open UniversityMilton Keynes, UK

WP8 Goals and Tasks

• Objective:– To provide and evaluate concrete applications of OK to

support user tasks on the Web, such as knowledge retrieval and ontology-assisted browsing.

• Tasks:– T8.1. Semantic Browsing

• Evolve Magpie so that it does not rely on design time ontology selection

– T8.2. Ontology Based Query Answering• Evolve AquaLog towards domain independent QA

– Evaluating the value of OK Technology• Compare standard and OK-enabled versions of both systems

Outline

• Vision:– “Open” is core to novel Semantic Web applications– Novel technical challenges arise

• Building novel applications within OpenKnowledge:– New methods:

• Dynamic ontology mapping

– Providing more semantic data:• Folksonomy enrichment

The SW gets BIGGER

Lee, J., Goodwin, R. (2004) The Semantic Webscape: a View of the Semantic Web. IBM Research Report.

The Semantic Web registered a 300% growth in 2004 alone, thus outpacing the growth of the Web itself.

Access Gateways exist

Example1: AquaLog

1. NL Question

2. Linguistic interpretation

3. Ontology based interpretation

4. Answer

Limited to the domain and data provided by a single ontology

Example1: AquaLog

Cross domain QA: Selects and combines relevant information from multiple ontologies:• automatically locate ontologies • map user terminology to ontologies• integrate info from different ontologies (mapping)

PowerAqua: QA on the 'open' Semantic Web

Example2: Magpie

NL QuestionOntology concepts

Instances highlighted according to their type

Example2: Magpie

Limited to the domain and data provided by a single ontology

PowerMagpie: Semantic browsing on the 'open' SW

Open semantic browsing: Dynamically selects and combines relevant information from multiple ontologies:• automatically locate ontologies • integrate info from different ontologies (mapping)

New Tools are OPEN

• … with respect to the topic domain– Instead of deciding the domain at design time– Let the user decide the domain of interest at run-time– Thus: Lower the cost of user participation

• … with respect to the explored data– Instead of “hard-wiring” one knowledge sources at design

time - smart databases– Dynamically select and make use of multiple,

heterogeneous knowledge sources:• Online available ontologies/semantic data• Non-semantic data, e.g., folksonomies

– Thus: Lower the cost of data integration

Key Paradigm Shift

Invited talks and papers:

Motta, E., Sabou, M. "Next Generation Semantic Web Applications". ASWC’06. Motta, E., Sabou, M. "Language Technologies and the Evolution of the Semantic Web". LREC’06

Source of Intelligence:

• Early Semantic Web tools:• A function of sophisticated, task-centric problem solving

• New Tools: •A side-effect of size and heterogeneity (Collective Intelligence)

What is needed?

Dynamic Ontology Selection

Ontology Modularization

Dynamic Ontology Mapping

Current work focuses on

user-mediated ontology selection

Current work assumes user involvement

Current work:• design-time mapping of complete ontologies• assumptions on the domain and structure of the ontologies

Outline

• Vision:– “Open” is core to novel Semantic Web applications– Novel technical challenges arise

• Building novel applications within OpenKnowledge:– New methods:

• Dynamic ontology matching

– Providing more semantic data:• Folksonomy enrichment

Achievements – at a glance

• Ontology Matching– Two dynamic ontology matching algorithms

• Run-time matching of knowledge structures• No assumptions on domain, structure etc.

– Core to our tools and to the OK infrastructure– Defined, implemented, documented, partially tested

• PowerMap – part of PowerAqua• MatchMiner - matching by using the Semantic Web as

background knowledge

• Acquiring semantic data– A Hybrid Algorithm for learning relations from text– Semantic enrichment of folksonomies by exploring online

ontologies

PowerMap: core of PowerAqua

Keywords

OntologyTriples

1. Ontology identification•Syntactic mapping

2. Extracting (clusters of) triples•Semantic mapping

3. Filtering triples

PowerMap

–Lopez, V., Sabou, M., Motta, E. "Mapping the Real Semantic Web on the Fly". ISWC’06.–Reported in deliverables D3.1. and D4.1.

• rely on online ontologies (Semantic Web) to derive mappings• ontologies are dynamically discovered and combined• does not require any a priori knowledge about the domain• returns semantic relations as mappings

A Brel

Semantic Web

MatchMiner

•M. Sabou, M. d’Aquin, E. Motta, “Using the Semantic Web as Background Knowledge in OntologyMapping", Ontology Mapping Workshop, ISWC’06. – Best Paper Award•Reported in Deliverable D4.1.

Evaluation: 1600 mappings, two teamsAverage precision: 70% (comparable/better than standard)

(derived from 180 different ontologies)

Matching AGROVOC (16k terms) and NALT(41k terms)

Large Scale Evaluation

M. Sabou, M. d’Aquin, W.R. van Hage, E. Motta, “Improving Ontology Matching by Dynamically Exploring Online Knowledge", submitted for review, 2007.

Semantic Folksonomy Enrichment

Tags

{camera, digital, photograph} {damage, flooding, hurricane, katrina, Louisiana} Clusters

digital

cameraphotographtakenWith

Ontology

NLP/Clustering

Find and combine Online ontologies

L.Specia, E. Motta, "Integrating Folksonomies with the Semantic Web", submitted for review, 2007.

Examples

Examples

Summary

• The growing SW allows opening up applications– With respect to their domain– And the exploited data sources

• Novel (dynamic) methods are required for:– Ontology selection, matching and modularization

• Dynamic and approximate ontology matching:– Is core to both our applications and the OK framework– We provided two novel algorithms for this topic

• Folksonomy enrichment– Is a way to get more data for our tools– We provided an algorithm based on ontology matching

Next Steps

• Finalize the prototypes:– PowerAqua (M18)

• Integrate PowerMap within PowerAqua• Make use of the semantically enriched folksonomies

– Semantic Browser (M18)• Combine ontology selection, matching and modularization

techniques

• Evaluate our applications (M24, M36):– When based on mainstream SW technology– Extended to take advantage of the OK infrastructure

Thank you!

Vision Papers

• Motta, E., Sabou, M. (2006). "Next Generation Semantic Web Applications". ASWC.

• Motta, E., Sabou, M. (2006). "Language Technologies and the Evolution of the Semantic Web". LREC 2006

• Motta, E. (2006). "Knowledge Publishing and Access on the Semantic Web: A Socio-Technological Analysis". IEEE Intelligent Systems, Vol.21, 3, (88-90).

• V. Lopez, E. Motta and V. Uren (2006) “PowerAqua: Fishing the Semantic Web”, ESWC’06.

Ontology Matching

• Lopez, V., Sabou, M., Motta, E. (2006). "Mapping the real semantic web on the fly". ISWC.

• Sabou, M., D'Aquin, M., Motta, E. (2006). "Using the semantic web as background knowledge for ontology mapping". ISWC 2006 Workshop on Ontology Mapping.

• M. Sabou, M. d’Aquin, W.R. van Hage (2007), E. Motta, “Improving Ontology Matching by Dynamically Exploring Online Knowledge", submitted for review.

Relation Learning/Folksonomy Enrichment

• L. Specia, E. Motta (2006): “A hybrid approach for relation extraction aimed to semantic annotations”. 7th Flexible Query Answering Systems (FQAS).

• L. Specia, E. Motta (2006): “A hybrid approach for extracting semantic relations from texts”. Workshop on Ontology Learning and Population (OLP2)

• L.Specia, E. Motta (2007), "Integrating Folksonomies with the Semantic Web", submitted for review, 2007

Related NeOn papers

• Ontology Selection– Sabou, M., Lopez, V., Motta, E. (2006). "Ontology

Selection for the Real Semantic Web: How to Cover the Queen’s Birthday Dinner?". EKAW 2006

• Ontology Modularization– D'Aquin, M., Sabou, M., Motta, E. (2006).

"Modularization: A key for the dynamic selection of relevant knowledge components". ISWC 2006 Workshop on Ontology Modularization