Personal Knowledge Graphs
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The document discusses personal knowledge graphs (PKGs), which are structured knowledge bases containing information about entities that are personally relevant to a user. PKGs differ from general knowledge graphs in that they focus on a user's specific interests and activities. The document outlines several challenges in developing PKGs, including how to represent entities and relationships, how to populate and link information from external sources to the PKG, and how to maintain the PKG over time as new information becomes available. It argues that giving users control over their own PKG data could enable more personalized conversational assistants and applications.
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Authority of assertion in repository contributions to the PID graph / George ...
The principles surrounding Linked Open Data and their implementation within digital libraries are well understood. Such implementations may be challenging, but successes are now well documented and continue to demonstrate the benefits of disseminating and enriching existing metadata with improved semantics and relational associations. Often facilitated in machine-readability enhancements to metadata by harnessing serializations of the Resource Description Framework (RDF) and its reliance of URIs, these LOD approaches have ensured digital libraries, and similar GLAMR initiatives elsewhere, contribute to the growing knowledge graphs associated with the wider semantic web by declaring statements of fact about web entities. Within open scholarly ecosystems a growing use of persistent identifiers (PIDs) to define and link scholarly entities has emerged, e.g., DOIs, ORCIDs, etc. The requirement for greater URI persistence has been motivated by several developments within the scholarly space; suffice to state that, when combined with appropriate structured data, PIDs can support improvements to resource discovery, as well as facilitate contributions to the ‘PID graph’ – a scholarly data graph describing and declaring associative relations between scholarly entities.
While the increased adoption of PIDs has the potential to transform scholarship, ensuring that these PIDs are used appropriately, encoded correctly within metadata, and that all relevant relational associations between scholarly entities are declared presents challenges. This is especially true within open scholarly repositories, from where many contributions to the PID graph will be made but – unlike many LOD contexts – from where the authority to assert specific relations may not always exist. Such declarations need to demonstrate reliability and provenance and are central to the interlinking of heterogeneous textual objects, datasets, software, research instruments, equipment, and the related PIDs these items may generate, such as for people, organizations, or other abstract entities.
This paper will explore the issues that arise when levels of authority to assert are lacking or are uncertain, and review results from a related study exploring the ‘PID literacy’ of scholars...
Paper presented at the Metadata & Discovery Group Conference & RDA Day (6th - 8th Sept 2023 at IET Austin Court, Birmingham)
An Introduction to NOSQL, Graph Databases and Neo4j
Neo4j is a graph database that stores data in nodes and relationships. It allows for efficient querying of connected data through graph traversals. Key aspects include nodes that can contain properties, relationships that connect nodes and also contain properties, and the ability to navigate the graph through traversals. Neo4j provides APIs for common graph operations like creating and removing nodes/relationships, running traversals, and managing transactions. It is well suited for domains that involve connected, semi-structured data like social networks.
Building a Knowledge Graph with Spark and NLP: How We Recommend Novel Drugs t...
It is widely known that the discovery, development, and commercialization of new classes of drugs can take 10-15 years and greater than $5 billion in R&D investment only to see less than 5% of the drugs make it to market.
AstraZeneca is a global, innovation-driven biopharmaceutical business that focuses on the discovery, development, and commercialization of prescription medicines for some of the world’s most serious diseases. Our scientists have been able to improve our success rate over the past 5 years by moving to a data-driven approach (the “5R”) to help develop better drugs faster, choose the right treatment for a patient and run safer clinical trials.
However, our scientists are still unable to make these decisions with all of the available scientific information at their fingertips. Data is sparse across our company as well as external public databases, every new technology requires a different data processing pipeline and new data comes at an increasing pace. It is often repeated that a new scientific paper appears every 30 seconds, which makes it impossible for any individual expert to keep up-to-date with the pace of scientific discovery.
To help our scientists integrate all of this information and make targeted decisions, we have used Spark on Azure Databricks to build a knowledge graph of biological insights and facts. The graph powers a recommendation system which enables any AZ scientist to generate novel target hypotheses, for any disease, leveraging all of our data.
In this talk, I will describe the applications of our knowledge graph and focus on the Spark pipelines we built to quickly assemble and create projections of the graph from 100s of sources. I will also describe the NLP pipelines we have built – leveraging spacy, bioBERT or snorkel – to reliably extract meaningful relations between entities and add them to our knowledge graph.
Ngsp
This document discusses challenges with the current scientific publishing system and proposes a vision for next generation scientific publishing (NGSP). Some key problems include retractions due to misconduct, lack of reproducibility, and non-reusable data and methods. NGSP would feature transparent and computable data and methods, open annotation of narratives and objects, and no restrictions on text mining or remixing. It would move information more quickly and allow verification through an open, service-oriented system without walled gardens. Taking NGSP forward will require collaboration across stakeholders in research communications.
Human Machine Collaboration for Improved Analytical Processes
Human and machine collaboration can produce better outcomes than either could achieve alone. An analytical process can be deconstructed into tasks that are better suited for humans or machines. The interface between human and machine is important to design intuitively. An example framework was presented for collaborative data exploration, with phases for data preparation and exploration. Requirements and examples were provided for different task types like category aggregation, binning, clustering, filtering and graph analysis. The key is assigning each step in the process to the agent - human or machine - best equipped to handle it.
What Is GDS and Neo4j’s GDS Library
Speakers: Alicia Frame, Senior Data Scientist and Amy Hodler, Director Graph Analytics and AI Programs, Neo4j
Data Communities - reusable data in and outside your organization.
Description
Data is a critical both to facilitate an organization and as a product. How can you make that data more usable for both internal and external stakeholders? There are a myriad of recommendations, advice, and strictures about what data providers should do to facilitate data (re)use. It can be overwhelming. Based on recent empirical work (analyzing data reuse proxies at scale, understanding data sensemaking and looking at how researchers search for data), I talk about what practices are a good place to start for helping others to reuse your data. I put this in the context of the notion data communities that organizations can use to help foster the use of data both within your organization and externally.
Managing 'Big Data' in the social sciences: the contribution of an analytico-...
This document discusses managing large datasets in the social sciences. It describes how the UK Data Service curates and provides access to large survey and census data. It explores how classification schemes could help organize and provide subject access to these growing datasets. A pilot project classified datasets using the Universal Decimal Classification scheme and found it efficient and helped visualize subject categories. Overall, carefully chosen knowledge organization tools can help provide multidimensional subject access needed to analyze complex datasets.
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Applications for Social Networking Strategies in an Agency Context
Applications for Social Networking Strategies in an Agency Context
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Authority of assertion in repository contributions to the PID graph / George ...
Authority of assertion in repository contributions to the PID graph / George ...
An Introduction to NOSQL, Graph Databases and Neo4j
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Human Machine Collaboration for Improved Analytical Processes
Human Machine Collaboration for Improved Analytical Processes
Data Communities - reusable data in and outside your organization.
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Towards Filling the Gap in Conversational Search: From Passage Retrieval to C...
Talk given at the 32nd ACM International Conference on Information and Knowledge Management (CIKM '23) in October 2023
Conversational AI from an Information Retrieval Perspective: Remaining Challe...
Talk given at the 2nd International Conference on Design of Experimental Search & Information REtrieval Systems (DESIRES ’21) in September 2021.
What Does Conversational Information Access Exactly Mean and How to Evaluate It?
This talk discusses a set of specific tasks and scenarios related to information access within the vast space that is casually referred to as conversational AI. While most of these problems have been identified in the literature for quite some time now, progress has been limited. Apart from the inherently challenging nature of these problems, the lack of progress, in large part, can be attributed to the shortage of appropriate evaluation methodology and resources. This talk presents some recent work towards filling this gap.
In one line of research, we investigate the presentation of tabular search results in a conversational setting. Instead of generating a static summary of a result table, we complement brief summaries with clues that invite further exploration, thereby taking advantage of the conversational paradigm. One of the main contributions of this study is the development of a test collection using crowdsourcing.
Another line of work focuses on large-scale evaluation of conversational recommender systems via simulated users. Building on the well-established agenda-based simulation framework from dialogue systems research, we develop interaction and preference models specific to the item recommendation scenario. For evaluation, we compare three existing conversational movie recommender systems with both real and simulated users, and observe high correlation between the two means of evaluation.
This talk has been given at the CIIR talk series at the University of Massachusetts Amherst in Jan 2021 as well as at the IR seminar series at the University of Glasgow in March 2021.
Entities for Augmented Intelligence
Keynote given at the 23rd International Conference on Theory and Practice of Digital Libraries (TPDL '19)
Table Retrieval and Generation
Keynote talk given at the SIGIR’18 workshop on Data Search (DATA:SEARCH’18) in Ann Arbor, Michigan, USA, July 2018
Entity Search: The Last Decade and the Next
Keynote talk given at the 10th Russian Summer School in Information Retrieval (RuSSIR ’16), Saratov, Russia, August 2016.
Note: part of the work is under still review; those slides are not yet included.
Overview of the TREC 2016 Open Search track: Academic Search Edition
The document provides an overview of the TREC 2016 Open Search track, which focused on academic search. It discusses the track organization, methodology using a living labs approach, three academic search engine sites used (CiteSeerX, SSOAR, and Microsoft Academic Search), results from rounds 1 and 2, and key issues and contributions. The track aimed to enable meaningful IR research using real users and data, allowing participants to experiment by replacing components in a live search system and evaluating performance.
Overview of the Living Labs for IR Evaluation (LL4IR) CLEF Lab
This document provides an overview of the Living Labs for IR Evaluation (LL4IR) CLEF Lab. The key ideas are to make information retrieval evaluation more realistic by testing new retrieval methods with real users on live sites, and making interaction data available for method development. An API orchestrates data exchange between live sites and experimental systems. The methodology involves making query, document, and click data available to participants, who generate rankings through the API. Rankings are interleaved with the production system for test queries. Participants receive feedback on clicks. The ultimate measure is wins against the production system over time. The living labs platform is open source and has been used for product search use cases.
Entity Linking
Entity linking involves recognizing entity mentions in text and linking them to entries in a knowledge base. It includes three main steps: mention detection to identify linkable phrases, candidate selection to generate potential entities for each mention, and disambiguation to select the best entity for each mention based on context. Disambiguation approaches consider local compatibility of mentions and entities as well as global coherence across all entity linking decisions. Collective approaches aim to jointly optimize all entity annotations for a document.
Evaluation Initiatives for Entity-oriented Search
This document discusses evaluation initiatives for entity-oriented search tasks. It describes several shared tasks and evaluation campaigns that have been held at conferences like TREC, CLEF, and INEX to provide standardized test collections, gold standard annotations, and evaluation metrics for tasks like ad-hoc entity retrieval from knowledge bases. Examples of test collections created for entity retrieval include datasets using Wikipedia, DBpedia, and a web crawl. The document also discusses related entity finding, entity linking, and understanding keyword queries by associating terms with entities.
Entity Retrieval (tutorial organized by Radialpoint in Montreal)
This is Part II of the tutorial "Entity Linking and Retrieval for Semantic Search" given at a tutorial organized by Radialpoint (together with E. Meij and D. Odijk).
Previous versions of the tutorial were given at WWW'13, SIGIR'13, and WSDM'14. The current version contains an overhaul of the type-aware ranking part.
For the complete tutorial material (including slides for the other parts) visit http://ejmeij.github.io/entity-linking-and-retrieval-tutorial/
Entity Retrieval (WSDM 2014 tutorial)
This is Part II of the tutorial "Entity Linking and Retrieval for Semantic Search" given at WSDM 2014 (together with E. Meij and D. Odijk). For the complete tutorial material (including slides for the other parts) visit http://ejmeij.github.io/entity-linking-and-retrieval-tutorial/
Time-aware Evaluation of Cumulative Citation Recommendation Systems
This document proposes a framework for temporally evaluating cumulative citation recommendation (CCR) systems on streaming data. It involves:
1) Slicing time into uniform or non-uniform intervals and measuring relevance within each slice.
2) Aggregating slice-based relevance scores according to slice importance weights based on relevance burstiness.
3) Applying the framework to CCR systems from TREC 2012 shows that considering temporal effects provides additional insights beyond atemporal evaluation and favors some systems over others. The framework could also be applied to other time-aware retrieval tasks.
Entity Retrieval (SIGIR 2013 tutorial)
This is Part II of the tutorial "Entity Linking and Retrieval" given at SIGIR 2013 (together with E. Meij and D. Odijk). For the complete tutorial material (including slides for the other parts) visit http://ejmeij.github.io/entity-linking-and-retrieval-tutorial/
Multi-step Classification Approaches to Cumulative Citation Recommendation
This document summarizes two multi-step classification approaches to the task of cumulative citation recommendation (CCR) at the TREC 2012 KBA track. The approaches involve classifying documents first by whether they mention a target entity, then by relevance, and finally centrality. Features used include document, entity, document-entity, and temporal information. Evaluation shows differentiating relevant from central documents is difficult. Later work reframes CCR as a learning-to-rank problem, which outperforms the classification approaches using the same features.
Entity Retrieval (WWW 2013 tutorial)
This document provides an overview of entity retrieval tasks and methods. It discusses what entities are and different entity retrieval tasks such as ad-hoc retrieval and question answering. It describes representing entities with ready-made descriptions or indirectly through documents. Probabilistic models are presented for ranking entities with and without explicit representations using language models, generative models, and graph-based models. Evaluation initiatives and future directions are also outlined.
Semistructured Data Seach
The document describes the PROMISE Winter School 2013, which aimed to give participants a grounding in information retrieval and databases. The school was a week-long event in Bressanone, Italy in February 2013 consisting of lectures from experts in the field, and was intended for PhD students, Masters students, and senior researchers. The document contains metadata tags providing keywords and a description of the school.
Collection Ranking and Selection for Federated Entity Search
Presented at the 19th International Symposium on String Processing and Information Retrieval (SPIRE 2012).
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Personal Knowledge Graphs
- 1. PERSONAL KNOWLEDGE GRAPHS Krisztian Balog University of Stavanger @krisztianbalog Dagstuhl Seminar on Conversa>onal Search | Dagstuhl, Germany, November 2019
- 2. KNOWLEDGE GRAPHS ARE POWERFUL ASSETS FOR A BROAD RANGE OF SEARCH, RECOMMENDATION, AND MINING SCENARIOS
- 4. What is the capital of Norway?
- 5. What is its popula>on?
- 6. KNOWLEDGE GRAPHS ARE POWERFUL ASSETS FOR A BROAD RANGE OF SEARCH, RECOMMENDATION, AND MINING SCENARIOS They tend to focus on prominent, globally important en@@es This rules out many en@@es we interact with on a daily basis!
- 7. MOTIVATIONAL SCENARIO I would like to get some new strings for my guitar AIOK, would that be your electric guitar or the acoustic one? The electric one. AIAlright. I can repeat your Amazon order of 3 months ago, or you can go by a music store on Elm street on the way to your dentist appointment this afternoon.
- 8. MOTIVATIONAL SCENARIO #2 AISince you're running a half marathon at Hackney in May, may I suggest you undertake a 10k run this weekend? Yes, that sounds like a good idea. Any suggestions for a not too popular route that I haven't done before? AISure thing. I'll upload some routes to the running app on your phone. Cheers mate!
- 9. IT IS DIFFICULT TO IMAGINE A TRULY PERSONAL CONVERSATIONAL ASSISTANT WITHOUT IT HAVING ACCESS TO STRUCTURED PERSONAL INFORMATION
- 10. PERSONAL KNOWLEDGE GRAPHS A personal knowledge graph (PKG) is a resource of structured information about entities that are of personal interest to the user Key differences from general KGs: • Entities of personal interest to the user • Distinctive shape (“spiderweb” layout) • Links between a PKG and external sources are inherent to its nature
- 11. ASSOCIATED PROBLEMS • Knowledge representation • Semantic annotation of text • Population and maintenance • Integration with external sources
- 12. KNOWLEDGE REPRESENTATION Task: representing entities and their properties • KGs are organized according to a knowledge model (schema) Peculiarities/challenges • Entities need to be (directly/indirectly) connected to the user • Not all attributes have to be filled; the focus is on personal relevance • Information about entities can be very sparse • Some entities may not have any digital presence • Relations can be highly temporal in nature RQ1 How should en@@es and their proper@es and rela@ons be represented, considering the vast but sparse set of possible predicates and their short-lived nature?
- 13. SEMANTIC ANNOTATION OF TEXT Task: annotating text with respect to a knowledge repository (commonly known as entity linking) • Usually involves mention detection, entity disambiguation, and NIL- detection Challenges • Entities might have little to no digital presence • Entities are not necessarily proper nouns • Linking, NIL-detection, and KG population are intertwined RQ2a How can en@ty linking be performed against a personal knowledge graph, where structured en@ty informa@on to rely on is poten@ally absent? When should en@ty linking be performed against a personal knowledge graph as opposed to a general-purpose KG? RQ2b
- 14. POPULATION AND MAINTENANCE Task: extending a KG from external sources (KB acceleration/ population) or via internal inferencing • Verification of facts in the KG Challenges • Single curator => More automation is desired than for KGs, but the user should still be in control • The first mention of an entity should trigger population • Properties may be inferred from the context RQ3 How can personal knowledge graphs be automa@cally populated and reliably maintained?
- 15. INTEGRATION WITH EXTERNAL SOURCES Task: recognizing the same entity across multiple data sources • Also known as: object resolution, record linkage Challenges • One-to-many, as opposed to one-to-one linkage • Continuous process, not a one-off effort • Two-way synchronization would be desired • Conflicting facts or relations need resolving by the user RQ4 How should external knowledge sources con@nuously be integrated with, in a two-way process, poten@ally involving the user?
- 16. THERE IS MORE… Evaluation • This would require an environment where users can interact with a PKG Implementation • Storage (cloud vs. device), security, privacy, access control, ... Utilization • From specific apps (calendar, health/wellbeing app, etc.) to a truly personal assistant
- 17. TAKING CONTROL OF OUR DATA Present Alternative service #1 service #2 service #3 service #1 service #2 service #3 Different service providers each have some (possibly overlapping) portion of the person’s PKG The person has full control over her PKG and may grant access to different service providers to specific parts of the PKG