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 Tutorial @ Knowledge Graph Conference 2021 ( 2021-04-09T13:20:50+02:00 by Julia Holze)

2021-04-09T13:20:50+02:00 by Julia Holze

On May 4, 2021 we will organize a tutorial at the Knowledge Graph Conference (KGC) 2021. Read more about "DBpedia Tutorial @ Knowledge Graph Conference 2021"

DBpedia @ Google Summer of Code program 2021 ( 2021-03-15T09:41:22+01:00 by Julia Holze)

2021-03-15T09:41:22+01:00 by Julia Holze

DBpedia, one of InfAI’s community projects, will participate in the Google Summer of Code (GSoC) program for the 10th time. The GsoC program has the goal to bring students from all over the globe into open source software development. Read more about "DBpedia @ Google Summer of Code program 2021"

DBpedia’s New Website ( 2021-01-28T12:42:40+01:00 by Julia Holze)

2021-01-28T12:42:40+01:00 by Julia Holze

We are proud to announce the completion of the new DBpedia website. Read more about "DBpedia’s New Website"

SANSA 0.7.1 (Semantic Analytics Stack) Released ( 2020-01-17T09:52:41+01:00 by Prof. Dr. Jens Lehmann)

2020-01-17T09:52:41+01:00 by Prof. Dr. Jens Lehmann

We are happy to announce SANSA 0.7.1 – the seventh release of the Scalable Semantic Analytics Stack. SANSA employs distributed computing via Apache Spark and Flink in order to allow scalable machine learning, inference and querying capabilities for large knowledge graphs. Read more about "SANSA 0.7.1 (Semantic Analytics Stack) Released"

More Complete Resultset Retrieval from Large Heterogeneous RDF Sources ( 2019-12-05T15:46:09+01:00 Andre Valdestilhas)

2019-12-05T15:46:09+01:00 Andre Valdestilhas

Over recent years, the Web of Data has grown significantly. Various interfaces such as LOD Stats, LOD Laundromat and SPARQL endpoints provide access to hundreds of thousands of RDF datasets, representing billions of facts. Read more about "More Complete Resultset Retrieval from Large Heterogeneous RDF Sources"