Functional Inferences Over Heterogeneous Data

Research output: Chapter in Book/Report/Conference proceedingConference contribution


The increasing availability of knowledge bases (KBs) on the web has opened up the possibility of improved inference in automated query answering (QyA) systems. We have developed a rich inference framework (RIF) that responds to queries where no suitable answer is readily contained in any available data source, by applying functional inferences over heterogeneous data from the web. Our technique combines heuristics, logic and statistical methods to infer novel answers to queries. It also determines what facts are needed for inference, searches for them, and then integrates these diverse facts and their formalisms into a local query-specific inference tree. We explain the internal representation of RIF, the grammar and inference methods for expressing queries and the algorithm for inference. We also show how RIF estimates confidence in its answers, given the various forms of uncertainty faced by the framework.
Original languageEnglish
Title of host publicationWeb Reasoning and Rule Systems
Subtitle of host publicationInternational Conference on Web Reasoning and Rule Systems (RR 2016)
Place of PublicationAberdeen, United Kingdom
PublisherSpringer International Publishing
Number of pages8
ISBN (Electronic)978-3-319-45276-0
ISBN (Print)978-3-319-45275-3
Publication statusPublished - 26 Aug 2016
EventWeb Reasoning and Rule Systems - 10th International Conference - Aberdeen, United Kingdom
Duration: 9 Sep 201611 Sep 2016

Publication series

NameLecture Notes in Computer Science (LNCS)
PublisherSpringer International Publishing
ISSN (Print)0302-9743


ConferenceWeb Reasoning and Rule Systems - 10th International Conference
Abbreviated titleRR 2016
Country/TerritoryUnited Kingdom
Internet address


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