When Simple is (more than) Good Enough: Effective Semantic Search with (almost) no Semantics

Neumayer, Robert; Balog, Krisztian; Nørvåg, Kjetil

doi:10.1007/978-3-642-28997-2_59

Cited by 19 publications

(22 citation statements)

References 5 publications

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“…Note that this number is two magnitudes larger than what was considered in prior work (6 in [17] and 11 at most in [12,13]). We employ a heuristic to identify title fields; following [16], attributes names ending in "label", "name," or "title" are considered to hold title values. For each entity, we store a content field, collapsing all its predicates.…”

Section: Methodsmentioning

confidence: 99%

“…Specifically, we use the following language modeling based methods: LM: the standard query likelihood approach [23]; MLM-tc: the Mixture of Language Models [17], with two fields: title and content. Following [16] we set the title weight to 0.2 and the content weight to 0.8; MLM-all: the Mixture of Language Models [17], where all fields are considered with equal weight; PRMS: the Probabilistic Retrieval Model for Semistructured Data. The difference to MLM-all is that field weights are determined dynamically for each query term [13].…”

Section: Baseline Methodsmentioning

confidence: 99%

“…We also use BM25: with standard parameter settings (k1 = 1.2, b = 0.8) [20]; BM25F-tc: the fielded extension of BM25 [20], we consider title and content fields, the title weight is set to 0.2 and the content weight to 0.8 [16]; BM25F-all: all fields are considered with equal weight. We use the same b value for all fields in the fielded variant BM25F, analogous to [18].…”

Section: Baseline Methodsmentioning

confidence: 99%

See 2 more Smart Citations

A test collection for entity search in DBpedia

Balog

Neumayer

2013

Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval

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We develop and make publicly available an entity search test collection based on the DBpedia knowledge base. This includes a large number of queries and corresponding relevance judgments from previous benchmarking campaigns, covering a broad range of information needs, ranging from short keyword queries to natural language questions. Further, we present baseline results for this collection with a set of retrieval models based on language modeling and BM25. Finally, we perform an initial analysis to shed light on certain characteristics that make this data set particularly challenging.

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Section: Methodsmentioning

confidence: 99%

Section: Baseline Methodsmentioning

confidence: 99%

Section: Baseline Methodsmentioning

confidence: 99%

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A test collection for entity search in DBpedia

Balog

Neumayer

2013

Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval

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“…If the above solutions cannot provide us a link to ontology information, for providing attribute and relationship labels we resort to a practical fall-back, based on the observation that often property URI values do convey a hint of the semantics. That is, for finding labels of CS properties we shorten URIs (e.g., http://purl.org/goodrelations/v1#offers becomes offers), by removing the ontology prefix (e.g., http://purl.org/goodrelations/v1#) or simply using the part after the last slash, as suggested by [11].…”

Section: Step3: Human-friendly Labelsmentioning

confidence: 99%

Advances in Large-Scale RDF Data Management

Boncz

Erling

Pham

2014

Linked Open Data -- Creating Knowledge Out of Interlinked Data

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Abstract.One of the prime goals of the LOD2 project is improving the performance and scalability of RDF storage solutions so that the increasing amount of Linked Open Data (LOD) can be efficiently managed. Virtuoso has been chosen as the basic RDF store for the LOD2 project, and during the project it has been significantly improved by incorporating advanced relational database techniques from MonetDB and Vectorwise, turning it into a compressed column store with vectored execution. This has reduced the performance gap ("RDF tax") between Virtuoso's SQL and SPARQL query performance in a way that still respects the "schema-last" nature of RDF. However, by lacking schema information, RDF database systems such as Virtuoso still cannot use advanced relational storage optimizations such as table partitioning or clustered indexes and have to execute SPARQL queries with many selfjoins to a triple table, which leads to more join effort than needed in SQL systems. In this chapter, we first discuss the new column store techniques applied to Virtuoso, the enhancements in its cluster parallel version, and show its performance using the popular BSBM benchmark at the unsurpassed scale of 150 billion triples. We finally describe ongoing work in deriving an "emergent" relational schema from RDF data, which can help to close the performance gap between relational-based and RDF-based storage solutions. General ObjectivesOne of the objectives of the LOD2 EU project is to boost the performance and the scalability of RDF storage solutions so that it can, efficiently manage huge datasets of Linked Open Data (LOD). However, it has been noted that given similar data management tasks, relational database technology significantly outperformed RDF data stores. One controlled scenario in which the two technologies can be compared is the BSBM benchmark [2], which exists equivalent relational and RDF variants. As illustrated in Fig. 1, while the SQL systems can process by up to 40-175K QMpH, the Triple stores can only reach 1-10K QMpH, showing a factor of 15-40 of performances difference.

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“…Alternatively, it can be used encode query-independent evidence based on document length, authority, popularity, link structure, etc. [21,22,32].…”

Section: Retrieval Frameworkmentioning

confidence: 99%

Semistructured Data Search

Balog

2014

Lecture Notes in Computer Science

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Abstract. This paper presents a selection of methods for searching in heterogeneous data collections where some amount of structure is available. We start with a general retrieval framework, based on generative probabilistic modeling, for ranking unstructured document representations. Then, we consider structure at two different levels: documents and queries. For documents, the internal structure is captured through the use of multiple document fields, and various approaches to setting field weights are discussed. For queries, the focus is on effectively utilizing additional input data that the user might provide along with the keyword query, such as target categories or example documents. We place a particular emphasis on methods that are robust with respect to the availability of structured data and are able to deal with inconsistent or incomplete information.

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When Simple is (more than) Good Enough: Effective Semantic Search with (almost) no Semantics

Cited by 19 publications

References 5 publications

A test collection for entity search in DBpedia

A test collection for entity search in DBpedia

Advances in Large-Scale RDF Data Management

Semistructured Data Search

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