DEQA: Deep Web Extraction for Question Answering

Despite decades of effort, intelligent object search remains elusive. Neither search engine nor semantic web technologies alone have managed to provide usable systems for simple questions such as “Find me a flat with a garden and more than two bedrooms near a supermarket.” We introduce DEQA, a conceptual framework that achieves this elusive goal through combining state-of-the-art semantic technologies with effective data extraction. To that end, we apply DEQA to the UK real estate domain and show that it can answer the majority of such questions correctly. DEQA achieves this by mapping natural language questions to SPARQL patterns. These patterns are then evaluated on an RDF database of current real estate offers. The offers are obtained using OXPATH, a state-of-the-art data extraction system, on the major agencies in the Oxford area and linked through LIMES to background knowledge such as the location of supermarkets.

Demo

AutoSPARQL prototype user interface: http://autosparql-tbsl.dl-learner.org

General Approach

DEQA provides a conceptual framework for enhancing classic information retrieval and search techniques using recent advances in web extraction, data integration and question answering. The overall approach is illustrated in the figure above: Given a particular domain, such as real estate, the first step consists of identifying relevant websites and extracting data from those. This previously tedious task can now be reduced to the rapid creation of OXPath wrappers. In DEQA, data integration is performed through a triple store using a common base ontology. Hence, the first phase may be a combination of the extraction of unstructured and structured data. For instance, websites may already expose data as RDFa, which can then be transformed to the target schema, e.g.using R2R, if necessary. This basic RDF data is enriched, e.g. via linking, schema enrichment, geo-coding or post-processing steps on the extracted data. This is particularly interesting, since the LOD cloud contains a wealth of information across different domains which allows users to formulate queries in a more natural way (e.g., using landmarks rather than postcodes or coordinates). For instance, in our analysis of the real estate domain, over 100k triples for 2,400 properties were extracted and enriched by over 100k links to the LOD cloud. Finally, question answering or semantic search systems can be deployed on top of the created knowledge. One of the most promising research areas in question answering in the past years is the conversion of natural language to SPARQL queries, which allows a direct deployment of such systems on top of a triple store. Finally, DEQA first attempts to convert a natural language query to SPARQL, yet can fall back to standard information retrieval, where this fails.

Use Case: Application to the Real Estate Domain

he domain-specific implementation of the conceptual framework, which we used for the real estate domain, is depicted in the figure above. It covers the above described steps by employing state-of-the-art tools in the respective areas, OXPath for data extraction to RDF, LIMES for linking to the linked data cloud, and TBSL for translating natural language questions to SPARQL queries. Below are the configuration files necessary to set up the system and a pointer to a user interface for testing it:

Members

DIADEM

  • Dr. Tim Furche, http://furche.net
  • Dr. Giovanni Grasso, http://www.giovannigrasso.it/
  • Dr. Christian Schallhart, http://www.cs.ox.ac.uk/people/christian.schallhart/
  • Dr. Andrew Sellers, http://www.cs.ox.ac.uk/people/andrew.sellers/
  • David Liu

CITEC

  • Dr. Christina Unger, http://www.sc.cit-ec.uni-bielefeld.de/people/cunger/

News

DBpedia @ Google Summer of Code – GSoC 2017 ( 2017-03-13T11:12:50+01:00 Christopher Schulz)

2017-03-13T11:12:50+01:00 Christopher Schulz

DBpedia, one of InfAI’s community projects, will be part of the 5th Google Summer of Code program. The GsoC has the goal to bring students from all over the globe into open source software development. Read more about "DBpedia @ Google Summer of Code – GSoC 2017"

New GERBIL release v1.2.5 – Benchmarking entity annotation systems ( 2017-03-10T11:49:51+01:00 by Ricardo Usbeck)

2017-03-10T11:49:51+01:00 by Ricardo Usbeck

Dear all, the Smart Data Management competence center at AKSW is happy to announce GERBIL 1.2.5. Read more about "New GERBIL release v1.2.5 – Benchmarking entity annotation systems"

DBpedia Open Text Extraction Challenge – TextExt ( 2017-03-09T12:15:57+01:00 Christopher Schulz)

2017-03-09T12:15:57+01:00 Christopher Schulz

DBpedia, a community project affiliated with the Institute for Applied Informatics (InfAI) e.V., extract structured information from Wikipedia & Wikidata. Now DBpedia started the DBpedia Open Text Extraction Challenge – TextExt. Read more about "DBpedia Open Text Extraction Challenge – TextExt"

The USPTO Linked Patent Dataset release ( 2017-02-24T17:18:51+01:00 by Mofeed Hassan)

2017-02-24T17:18:51+01:00 by Mofeed Hassan

Dear all, We are happy to announce USPTO Linked Patent Dataset release. Patents are widely used to protect intellectual property and a measure of innovation output. Read more about "The USPTO Linked Patent Dataset release"

Two accepted papers in ESWC 2017 ( 2017-02-22T17:43:38+01:00 by Dr. Mohamed Ahmed Sherif)

2017-02-22T17:43:38+01:00 by Dr. Mohamed Ahmed Sherif

Hello Community! We are very pleased to announce the acceptance of two papers in ESWC 2017 research track. The ESWC 2017 is to be held in Portoroz, Slovenia from 28th of May to the 1st of June. Read more about "Two accepted papers in ESWC 2017"