{"content":[{"text":"Angeles) cannot be succinctly axiomatised in RDFS/OWL for interpretation by \n the query answering system. Instead, we argue that the RDF descriptions of \n resources can be used to compute an inductive, generic notion of distance. \n In this paper, we thus advocate a relaxed form of RDF query-answering where \n the query should be interpreted as specifying the ideal criteria for answers, such \n that other relevant (but non-crisp) scored answers are returned. This is similar to \n top-k query-answering for Information Retrieval engines: a paradigm that works \n well in highly-heterogeneous, incomplete scenarios, including Web search. \n We ﬁrst present an industrial use-case from the European Aeronautic De- \n fence and Space Company (EADS) that requires matching witness observations \n against crisp knowledge integrated from various law-enforcement and intelligence \n agencies (§ 2). Next, we provide a survey of literature that relates to the needs \n of EADS’ use-case and to query relaxation (§ 3). We then propose a generic \n framework for building a relaxed RDF query engine (§ 4); we currently focus on \n entity-lookup queries using similarities of RDF terms. Subsequently, we discuss \n a generic technique for extracting distance/similarity scores between resources \n based on their structured descriptions (§ 5). We then outline an early prototype \n for relaxing queries—that represent witness observations—against a dataset of \n vehicle descriptions, testing diﬀerent similarity measures (§ 6). We conclude that \n our own results are too preliminary for deployment, but argue that RDF query \n relaxation (or more generally “fuzzy querying”) is an important, timely research \n topic not only for EADS’ use-case, but may unlock various potential and diverse \n Semantic Web applications involving vague/uncertain user requirements.","bbox":{"left":0.220204,"top":0.147049,"width":0.5651170000000001,"height":0.34851999999999994}},{"text":"2","bbox":{"left":0.220204,"top":0.51934,"width":0.010977999999999988,"height":0.018733}},{"text":"Use-case overview","bbox":{"left":0.253159,"top":0.51934,"width":0.173292,"height":0.018733}},{"text":"Our use-case arises from an on-going research project at the European Aeronau- \n tic Defence and Space Company (EADS): a large European aerospace, defence \n and military contractor. EADS Innovation Works is the corporate research and \n technology department of EADS that explores areas of mobility, security and \n environment. One of the team’s key interests relates to civilian security, and en- \n abling increased agency collaboration through use of intelligent systems. EADS \n has been working on the development of systems for intelligence analysis to aid \n post-crime police investigations [30], where analysts need to process raw infor- \n mation, determine valuable evidence, and identify the entities involved and their \n relationships. Investigations often rely on human observations, including police \n reports, or statements from victims, witnesses and informers. \n Such data are the result of subjective assessment and often carry inherent \n vagueness and uncertainty. Human observations provide an estimate of the entity \n observed, described in natural language, and may be imprecise (i.e., stating that \n a suspect was 1.77 m tall when (s)he was 1.79 m tall) or vague (i.e., “between \n 1.7 m – 1.85 m”, or “average height”, etc.). Previous work [28,29] analysed issues \n with using human intelligence data (HUMINT), and presented methods to align \n data in diﬀerent formats (numeric, textual and ranges). Herein, we view such \n observations as structured fuzzy queries to be executed over crisp data.","bbox":{"left":0.220204,"top":0.553225,"width":0.5650850000000001,"height":0.2878370000000001}}],"images_bbox":[],"images_bbox_not_text_overlap":[]}