The SemSets model for ad-hoc semantic list search

Ciglan, Marek; Nørvåg, Kjetil; Hluchý, Ladislav

doi:10.1145/2187836.2187855

Cited by 33 publications

(41 citation statements)

References 16 publications

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“…Other interpretations, such as those that cannot be answered by the knowledge base, should be less favored, everything else beging equal. The optimization is subject to the following constraints which both forbid nonsensical interpretations (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12), and downweigh empty interpretations of a query (13, 14):…”

Section: P(t)mentioning

confidence: 99%

Robust question answering over the web of linked data

Yahya

Berberich

Elbassuoni

et al. 2013

Proceedings of the 22nd ACM International Conference on Conference on Information &Amp; Knowledge Management - CIKM '13

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Knowledge bases and the Web of Linked Data have become important assets for search, recommendation, and analytics. Natural-language questions are a user-friendly mode of tapping this wealth of knowledge and data. However, question answering technology does not work robustly in this setting as questions have to be translated into structured queries and users have to be careful in phrasing their questions. This paper advocates a new approach that allows questions to be partially translated into relaxed queries, covering the essential but not necessarily all aspects of the user's input. To compensate for the omissions, we exploit textual sources associated with entities and relational facts. Our system translates user questions into an extended form of structured SPARQL queries, with text predicates attached to triple patterns. Our solution is based on a novel optimization model, cast into an integer linear program, for joint decomposition and disambiguation of the user question. We demonstrate the quality of our methods through experiments with the QALD benchmark.

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Section: P(t)mentioning

confidence: 99%

Robust question answering over the web of linked data

Yahya

Berberich

Elbassuoni

et al. 2013

Proceedings of the 22nd ACM International Conference on Conference on Information &Amp; Knowledge Management - CIKM '13

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“…For example, Leo Kant is a jobseeker registered with us, "Leo_Kant" becomes an entity with attribute values (jobseeker ID 80108, first name "Leo", last name "Kant", salutation "Mr", login "lkant", etc.) From Leo"s profile documents (resume and cover letter) that are either in HTML or MS Word file format, we have to transform these raw content into semantic metadata in resource description framework (RDF) format; RDF [46,12,24] includes entities, events and facts with entities scores [2,11,14,32,40,48]. In this Leo Kant"s documents, there are 2 countries "India" occurring 6 times and "Canada" appearing 4 times, many IT skills are detected "ms sql server" and "visual basic", etc.…”

Section: Related Workmentioning

confidence: 99%

Semantic Search on Applicant Tracking System

Ha¹,

Rahman²

2017

International Journal of Advanced Research in Computer and Comm

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Web search engines often federate many user queries to relevant structured databases. For example, a recruitment-related query might be federated to a jobseekers-and-employers database containing their resumes and skills. The relevant structured data items are then returned to the user along with web search results. Though each structured database is searched in isolation, the search often produces empty / incomplete results as the database may not contain the required information to answer the query. Starting from our Applicant Tracking System (ATS), we have 16 development databases of over 650,000 profile documents of resumes / cover letters / skills. There are on average 238 keywords per document. In fact, per minute there can be up to 200,000 transactions within all these databases. Our existing traditional database search technique (by full-text keyword PostgreSQL search) can be frozen or taking very long to respond unless if we cut off / only search from top profiles, we will not have the search results ready by thirty seconds, but this cut-off limitation returned incorrect results; for example for a query "Jet fuel Thermal Oxidation" to request information about job seekers whose resumes contain skills in Oil and Gas industry, in the top ten results there was a conflict in relevance ranking. In order to research a more suitable full-text keyword search technique better than the existing database search, we considered employment of semantic search models. Our semantic search technique has 88% -91.22% accuracy with very much quicker queries that can help users to make a search of 4 keywords of skills completed from 1 second to 28 seconds. Furthermore, the semantic search engine becomes very strong that users can search by entering a whole text paragraph. We designed a combination of semantic search that look for web pages per search and database search.

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“…We additionally report Mean Average Precision (MAP) [41] when evaluating against binary relevance judgments using the REWQ-ClueWeb data. The values are computed with the TREC Evaluation tool 6 and reported in the following as the arithmetic mean over all queries.…”

Section: Evaluation Metricsmentioning

confidence: 99%

Ranking Entities for Web Queries Through Text and Knowledge

Schuhmacher

Dietz

Ponzetto

2015

Proceedings of the 24th ACM International on Conference on Information and Knowledge Management

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When humans explain complex topics, they naturally talk about involved entities, such as people, locations, or events. In this paper, we aim at automating this process by retrieving and ranking entities that are relevant to understand free-text web-style queries like Argentine British relations, which typically demand a set of heterogeneous entities with no specific target type like, for instance, Falklands_-War or Margaret_Thatcher, as answer. Standard approaches to entity retrieval rely purely on features from the knowledge base. We approach the problem from the opposite direction, namely by analyzing web documents that are found to be query-relevant. Our approach hinges on entity linking technology that identifies entity mentions and links them to a knowledge base like Wikipedia. We use a learning-to-rank approach and study different features that use documents, entity mentions, and knowledge base entities -thus bridging document and entity retrieval. Since established benchmarks for this problem do not exist, we use TREC test collections for document ranking and collect custom relevance judgments for entities. Experiments on TREC Robust04 and TREC Web13/14 data show that: i) single entity features, like the frequency of occurrence within the top-ranked documents, or the query retrieval score against a knowledge base, perform generally well; ii) the best overall performance is achieved when combining different features that relate an entity to the query, its document mentions, and its knowledge base representation.

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The SemSets model for ad-hoc semantic list search

Cited by 33 publications

References 16 publications

Robust question answering over the web of linked data

Robust question answering over the web of linked data

Semantic Search on Applicant Tracking System

Ranking Entities for Web Queries Through Text and Knowledge

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